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With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https://github.com/lyotvincent/PBGI.

I study the effects of language commonality (i.e., sharing a native language) on information production in financial markets. Using a hand-collected data set on the prevalent dialects for 2,091 cities (counties) in China, I identify the effects of language commonality separately from those of shared hometown and geographic proximity. In in-sample tests, language commonality between analysts and CEOs increases the return of trading on analysts’ recommendations by 5.5%. The results mainly stem from less intelligible dialects. Broadly speaking, language commonality can alleviate communication frictions when nonnative languages are used in professional settings. This paper was accepted by Gustavo Manso, finance.

Attribute-based keyword search (ABKS) has a broad developing prospect in providing search service for users and realizing fine-grained access control over ciphertext in the background of cloud computing. However, two open problems prevent further development and application of ABKS. Firstly, most of ABKS schemes suffer from inside keyword guessing attack (KGA) inherently, which is a serious security threat. Secondly, the existing ABKS schemes only focus on single or conjunctive keyword search, these inflexible retrieval modes may lead to efficiency loss caused by inaccurate positioning of user's interest and greatly reduce user search experience. In this paper, we introduce a semi-trusted server and build a dual server model. Based on the dual server model and our proposed techniques, we are the first to put forward an attribute-based multi-keyword ranked search scheme against inside keyword guessing attack (ABKRS-KGA) to solve the mentioned two problems simultaneously. We provide strict security definitions for two types of adversaries and we are the first to prove that the construction is adaptively secure against both chosen-keyword attack (CKA) and KGA. Finally, all-side simulation with real-world data set is implemented for the proposed scheme, and the simulation results show that the efficiency of our scheme is acceptable.

This paper describes the impression/display data and corresponding user, creator, and music content card data from NetEase Cloud Music. This data set is collectively supplied by the Revenue Management and Pricing (RMP) section of INFORMS and NetEase Cloud Music to support data-driven research in operations management. The data contain more than 57 million impressions/displays of music content cards recommended to a random sample of [Formula: see text] users from November 1, 2019 to November 30, 2019. For each impression, the data provide the corresponding user activities, such as clicks, likes, and follows. Moreover, the data set also contains information on each user, each content creator, and each content in the impression sample.

The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can either be used as a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published dataset with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at: https://github.com/wfondrie/ppx

The real motive behind the Realtime Blood Bank Data Repository is to streamline and mechanize the way toward looking for blood if there should be an occurrence of crisis and keep up the records of blood givers, beneficiaries, blood gift projects and blood stocks in the bank. As of now, people, in general, can just think about the blood donation event occasions through traditional media means like radio, paper or TV commercials. There is no data with respect to the blood gift programs accessible on any of the gateway. With the manual archive, there are issues in dealing with the records. There is no concentrated data set of volunteer benefactors. Along these lines, it turns out to be truly dreary for an individual to look through blood if there should arise an occurrence of crisis. This task plans to mechanize the blood and contribute to the board framework in a blood donation centre to improve the record executives effectiveness because of the developed size of records of information.

The inventor figures succeeded in contributing to people's lives and making people's lives developed with the findings of the inventor figures, but there are still many people who do not know and know the inventor figures, people only know the objects found by the inventor figures but do not know who the characters find the objects. that thing. The introduction of the inventor character is one of the reference materials that can be used to introduce the inventor character. In this day and age, android-based inventor character recognition applications are very efficient to use. In making a character recognition application, strings can be implemented for the word search process. String can be interpreted as an approach how to find the pattern of arrangement of string characters in other strings or part of the body of text. String has several algorithms, one of which is the Sequential Search algorithm where this algorithm is a very simple search algorithm that is carried out by comparing data one by one from a predetermined data set (Array) until the data is completed (found) or there is no match (not found). ). This inventor character recognition application is designed by utilizing the setudio android software. So that the application is easier to use and can be used independently anywhere and anytime.

Purpose This study aims to investigate and compare mobile and desktop user search behaviours of the 1300K site, a Korean shopping search engine, by using transaction log analysis. Design/methodology/approach Transaction logs of 1300K site were collected over a three months’ period, from 1 January to 31 March 2018. The data set of this study consists of 1,149,690 desktop queries, 2,346,938 mobile queries, 2,481,747 desktop browsing activities and 2,550,309 mobile browsing activities. This study quantitatively analyses transaction log of 1300K site. Findings The results of this study show that mobile usage is higher than desktop usage: there are more mobile sessions than desktop sessions and the number of mobile queries is more than double of desktop queries. Overall, mobile query search behaviours are more simple, targeted and focused than desktop query search behaviours. Also, mobile browsing behaviours are more simple and passive than desktop browsing behaviours. However, mobile click behaviours are more active than desktop click behaviours. Originality/value To the best of the authors’ knowledge, this study appears to be the first of its type in Korea that compared search behaviours of a large number of users on desktop computers and mobile phones. To identify various characteristics of user search behaviours, this study analyses users’ directory browsing behaviour and click behaviour as well as query search behaviour. The results of this study can be implemented to address the effective improvement and development of search services and interfaces for different devices.

The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.

Bioprinting technology is here to revolutionize the healthcare sector by providing customized solutions to the medicine industry. A review of the bioprinting patent documents provides insight into the development of this technology. A patent search was conducted using open-access patent databases and the raw data were refined by manually checking and eliminating irrelevant patents followed by hand-coding with master keywords. Graphics created from the data set facilitate the generation of patent intelligence on current bioprinting technologies in terms of prominent patent-filing countries and institutions, territorial biases in the areas of technology, and applications in the drug discovery process.

Background: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the Clinical Registry, AdminisTrative Data and Electronic Medical Records (CREATE) database, which includes linked data from 4 population databases: Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics. Methods: We used deterministic data linkage to link SCM electronic medical record data to APPROACH clinical registry data using patient identifier variables. The APPROACH-SCM data set was subsequently linked to DAD and NACRS to obtain inpatient and outpatient cohort data. We further validated the quality of the linkage, where applicable, in these databases by comparing against the Alberta Health Insurance Care Plan registry database. Results: We achieved 99.96% linkage across these 4 databases. Currently, there are 30,984 patients with 35,753 catheterizations in the CREATE database. The inpatient cohort contained 65.75% (20,373/30,984) of the patient sample, whereas the outpatient cohort contained 29.78% (9226/30,984). The infrastructure and the process to update and expand the database has been established. Conclusions: CREATE is intended to serve as a database for supporting big data analytics activities surrounding cardiac precision health. The CREATE database will be managed by the Centre for Health Informatics at the University of Calgary, and housed in a secure high-performance computing environment. Résumé: Contexte: Les initiatives en matière de médecine de précision et les systèmes de santé apprenants ont besoin de bases de données riches et exactes sur les caractéristiques des patients. Nous présentons ici la base de données CREATE (Clinical Registry, Administrative Data and Electronic Medical Records), qui regroupe les données couplées de quatre bases de données populationnelles : le registre clinique national APPROACH (Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease), le système de gestion des dossiers médicaux électroniques SCM (Sunrise Clinical Manager, utilisé à l’échelle municipale), la Base de données sur les congés des patients (BDCP), et le Système national d’information sur les soins ambulatoires (SNISA). Notre objectif est d’offrir une base de données portant précisément sur les maladies cardiovasculaires, afin de soutenir les activités en santé de précision nécessitant l’analyse de mégadonnées. Méthodologie: Nous avons utilisé une méthode de couplage déterministe pour apparier les données du système SCM à celles du registre APPROACH à l’aide de variables d’identification des patients. L’ensemble de données SCM-APPROACH a ensuite été couplé aux données de la BDCP et du SNISA, afin d’obtenir les données des cohortes des patients hospitalisés et des patients ambulatoires. Lorsque c’était possible, nous avons en outre validé la qualité du couplage en comparant les données à celles de la base de données du Régime d’assurance maladie de l’Alberta. Résultats: Nous avons obtenu un taux de couplage de 99,96 % pour les quatre bases de données. À l’heure actuelle, la base de données CREATE compte 30 984 patients ayant subi 35 753 cathétérismes. La cohorte des patients hospitalisés représente 65,75 % (20 373/30 984) de l’échantillon, tandis que la cohorte des patients ambulatoires représente 29,78 % (9226/30 984). L’infrastructure et le processus de mise à jour et d’expansion de la base de données ont été définis. Conclusions: La base de données CREATE est destinée à soutenir les activités d’analyse de mégadonnées nécessaires à la santé cardiaque de précision. Elle sera gérée par le Centre for Health Informatics de l’Université de Calgary et hébergée dans un environnement informatique à haut rendement sécurisé.

Tourism is one of the leading industries across the globe. Tourists need to get information about the places to visit in their preferred destination. However, it is quite tedious to get individual information about these places. Thus, the proposed work aims in developing a system that analyses users likes and the preferred time period of the user to visit a particular location in the preferred place to form a trained data set and furnishes the results which showcase the path of a user can take to explore a specific city based on the trained data set. The proposed system adopts a haversine algorithm and traveling salesman problem in conjunction with a text refinement framework for extracting the information on recreational sites from media contents, social sites, and other web contents to form a rich corpus of data based on which the user is suggested appropriate places to visit. It is inferred from the experiment that the proposed approach has better time efficiency with changing user preferences, where the observed time is 1.33 ms with 16 user preferences.

This large-scale study used a survey to collect data on K–12 world language programming in Massachusetts public schools. Building off work by Pufahl and Rhodes (2011), this study focused on world language programs, program types, and languages offered, as well as student exclusion, program leadership, and the Seal of Biliteracy. The study resulted in 188 districts represented in the final data set, which was analyzed using descriptive statistics. Results indicate comparable findings to national data in terms of program and language offerings, with shifts in program type. Data further revealed patterns of student exclusion and trends in offering the Seal of Biliteracy. Although programs are most often led by a world language specialist, school principals are more likely responsible for teacher evaluation. Survey data were collected at the beginning of school closures due to COVID-19, capturing a picture of K–12 world language education and a point of comparison for future research.

The problems of privacy and security is becoming a major challenge when it comes to the distributed systems, federated machine learning system especially when data are been transmitted or learned on a network , this necessitated the reasons for this research work which is all about wireless federated machine learning process using a Raspberry Pi. The Raspberry Pi 4 is a single hardware board with built in Linux operating system. We used data set of names from nine (9) different languages and then develop a training model using recurrent neural network to train this names compare to the names in the existing language like French, Scottish to predict if the names are from any of this language, this is done wirelessly with the Wi-Fi network in a federated machine learning environment for experimental setup with PySft’s that is installed in the python environment. The system was able to predict that name from which the language it originate from, the methodology that is implore in the research work is the Rapid Application Development (RAD). The benefits of this system are to ensure privacy, reduces the computing power, ensure real time learning and most importantly it is cost effective.

The Bioconductor project (Nat. Methods 2015, 12 (2), 115-121) has shown that the R statistical environment is a highly valuable tool for genomics data analysis, but with respect to proteomics, we are still missing low-level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag (J. Proteome Res. 2018, 17 (8), 2908-2914) has provided the proof-of-principle how access to mass spectrometry raw files can be realized by wrapping a vendor-provided advanced programming interface (API) for the purpose of metadata analysis and visualization. Our novel package rawrr now provides complete, OS-independent access to all spectral data logged in Thermo Fisher Scientific raw files. In this technical note, we present implementation details and describe the main functionalities provided by the rawrr package. In addition, we report two use cases inspired by real-world research tasks that demonstrate the application of the package. The raw data used for demonstration purposes was deposited as MassIVE data set MSV000086542. Availability: https://github.com/fgcz/rawrr.

Cirebon Regency Library is one of the libraries that stands on Jl. Sunan Drajat No.9 Source. Where the library has improved its services through the provision of facilities and rooms that are very adequate. With the increase in book collections in the library, it would be great if you use a data mining system that functions to determine the data set as a process that can help in finding the books desired by library customers. The association rule technique using a priori algorithm can be used as a reference in finding out simultaneous borrowing in one transaction that occurs on one item. After determining that one item, testing is carried out whether it can meet the support standard which can be used as a minimum requirement then a rule is made in accordance with the confidance drinking standard, since 2020 the Library System in Cirebon Regency has used localhost which is an integrated library system in providing services to customers. One of the services provided is information that is needed centrally so that it can support decision makers what books are available for borrowing, what are the conditions for becoming a member, who is already a member. By using this system, we can see a pattern of borrowed books based on recommendations from librarians and can make arrangements in book storage in accordance with the patterns of information in book storage

The Canadian Community Health Survey (CCHS) is one of the world's largest ongoing cross-sectional population health surveys, with over 130,000 respondents every two years or over 1.1 million respondents since its inception in 2001. While the survey remains relatively consistent over the years, there are differences between cycles that pose a challenge to analyze the survey over time.A program package called cchsflow was developed to transform and harmonize CCHS variables to consistent formats across multiple survey cycles. An open science approach was used to maintain transparency, reproducibility and collaboration.The cchsflow R package uses CCHS survey data between 2001 and 2014. Worksheets were created that identify variables, their names in previous cycles, their category structure, and their final variable names. These worksheets were then used to recode variables in each CCHS cycle into consistently named and labelled variables. Following, survey cycles can be combined. The package was then added as a GitHub repository to encourage collaboration with other researchers.The cchsflow package has been added to the Comprehensive R Archive Network (CRAN) and contains support for over 160 CCHS variables, generating a combined data set of over 1 million respondents. By implementing open science practices, cchsflow aims to minimize the amount of time needed to clean and prepare data for the many CCHS users across Canada.RéSUMé: CONTEXTE: L’Enquête sur la santé dans les collectivités canadiennes (ESCC) est l’une des plus grandes enquêtes transversales sur la santé de la population, avec plus de 130 000 sondés tous les deux ans et plus de 1,1 million de sondés depuis son début en 2001. Tant que l’enquête reste relativement cohérent, il y a des différences entre des cycles qui posent une challenge majeure pour analyser l’enquête au fil du temps. INTERVENTION: Un paquet de programme appelé cchsflow a été développé pour transformer et harmoniser les variables CCHS aux formats cohérents à travers plusieurs cycles de sondage. Une approche de science ouverte était utilisée pour maintenir la transparence, la reproductibilité et la collaboration. RéSULTATS: Le paquet cchsflow R développé utilisait les données d’enquête de l’ESCC entre 2001 et 2014. Les feuilles de calcul ont été créées pour identifier des variables, leurs noms dans des cycles précédents, leurs structures de catégories et leurs noms de variables finales. Ces feuilles de calcul ont ensuite été utilisées pour recoder les variables dans chaque cycle de l’ESCC pour générer les ensembles de données harmonisés qui peuvent être combiner dans un ensemble de données constamment étiqueté pour l’analyse. Le paquet a ensuite été ajouté comme un entrepôt de GitHub pour encourager la collaboration avec les autres chercheurs. IMPLICATION: Le paquet cchsflow a été ajouté au Comprehensive R Archive Network (CRAN) et contient un appui pour plus de 160 variables de l’ESCC, générant un ensemble de données de plus d’un million de sondés. En exécutant les pratiques de sciences ouvertes, cchsflow vise à minimiser le temps requis pour nettoyer et préparer les données pour les plusieurs utilisateurs du CCHS à travers le Canada.

The aim of this paper is to present the Report of innovative restoration techniques, technologies and materials used in conservation in the context of natural sciences and heritage science of the RUINS Project. First, the specifics of preservation and maintenance of ruins are briefly described. Subsequently, the context of natural sciences and the new scientific discipline of heritage science, including the link to innovation, are indicated. The paper then presents the structure of the report and an example of one record representing one specific technique. Record structure is ready to be transferred to versatile data set so it could be converted into an updatable online database in the future.

Purpose The impact of cyberattacks all over the world has been increasing at a constant rate every year. Performing exploratory analysis helps organizations to identify, manage and safeguard the information that could be vulnerable to cyber-attacks. It encourages to the creation of a plan for security controls that can help to protect data and keep constant tabs on threats and monitor their organization’s networks for any breaches. Design/methodology/approach The purpose of this experimental study is to state the use of data science in analyzing data and to provide a more detailed view of the most common cybersecurity attacks, what are the most accessed logical ports, visible patterns, as well as the trends and occurrence of attacks. The data to be processed has been obtained by aggregating data provided by a company’s technology department, which includes network flow data produced by nine different types of attacks within every day user activities. This could be insightful for many companies to measure the damage caused by these breaches but also gives a foundation for future comparisons and serves as a basis for proactive measures within industry and organizations. Findings The most common cybersecurity attacks, most accessed logical ports and their visible patterns were found in the acquired data set. The strategies, which attackers have used with respect to time, type of attacks, specific ports, IP addresses and their relationships have been determined. The statistical hypothesis was also performed to check whether attackers were confined to perform random attacks or to any specific machines with some pattern. Originality/value Policies can be suggested such that if an attack is conducted on a specific machine, which can be prevented by identifying the machine, ports and duration of the attacks on which the attacker is targeting and to formulate such policies that the organization should follow to tackle these targeted attacks in the future.

Collaborator finding systems are a special type of expert finding models. There is a long-lasting challenge for research in the collaborator recommending research area, which is the lack of the structured dataset to be used by the researchers. We introduce two datasets to fill this gap. The first dataset is prepared for designing a consistent, collaborator finding system. The next one, called a co-author finding model, models an academic social network as a table that contains different relations between the pair of users. Both of them provide an opportunity for introducing potential collaborators to each other. These two models have been extracted from ResearchGate (RG) data set and are available publicly. RG dataset has been collected from Jan. 2019 to April 2019 and includes raw data of 3980 RG users. The dataset consists of almost complete information about users. In the preprocessing phase, the well-known Elmo was used for analyzing textual data. We call this as ResearchGate dataset for Recommending Systems (RGRS). For assessing the validity of data, we analyze each layer of data separately, and the results are reported. After preparing data and evaluating the collaborator finding models, we have done some assessments on RGRS. Some of these assessments are co-author, following-follower, and question answering relations. The outcomes indicate that it is the best relation in propagating knowledge in the network. To the best of our knowledge, there is no processed and analyzed dataset of this size., Comment: J. of Data, Inf. and Manag. (2021)

Since its emergence, mobile applications market has been attracting the attention of all kinds of businesses due to the lucrative opportunities apps offer and the market's low barriers to entry. Yet, in this crowded space, only a small portion of apps can survive. Using a unique data set of 979 newly released applications, acquired from a leading mobile analytics company and enriched with publicly available data, the authors shed light on the factors associated with app downloads during an app's first year of existence. Results from time-varying-parameter models estimated separately for free and paid apps reveal that gaining traction with users shortly after release seems critical and that app platform owners can be very influential in these early days. However, as apps mature, affecting the number of downloads becomes increasingly more difficult. The findings add new insights to the growing literature on apps and provide practical implications for their developers., NA

With the increasing pervasive computation, “Activity Recognition” has become a vast and popular field of research. In the field of automated Activity Recognition, we use multiple sensors in wearable/portable devices in order to recognize the human activities such as standing still, sitting, relaxing, laying, walking, climbing stairs, knee bending cycling jogging etc. The main purpose of this paper is to discuss the field of Activity Recognition for patients and old- age persons or any person in general. This research paper can also be used for telemedicine purposes. Besides, different machine learning algorithm will be applied to achieve Activity Recognition rather precisely. Microsoft Azure ML Studio and a bench marking data set are used for creation as well as evaluation of Machine Learning Model. In addition, a Web Service for Activity Recognition is also developed by using Microsoft Azure ML Studio in order to help the developer and researcher while working on Activity Recognition

Social media users have the proclivity to write majority of the data for under resourced languages in code-mixed format. Code-mixing is defined as mixing of two or more languages in a single sentence. Research in code-mixed text helps apprehend security threats, prevalent on social media platforms. In such instances, language identification is an imperative task of code-mixed text. The focus of this paper is to carry out a word-level language identification (WLLI) of Malayalam-English code-mixed data, from social media platforms like YouTube. This study was centered around BERT, a transformer model, along with its variants - CamemBERT, DistilBERT - for intuitive perception of the language at the word-level. The propounded approach entails tagging Malayalam-English code-mixed data set with six labels: Malayalam (mal), English (eng), acronyms (acr), universal (univ), mixed (mix) and undefined (undef). Newly developed corpus of Malayalam-English was deployed for appraisal of the effectiveness of state-of-the-art models like BERT. Evaluation of the proffered approach, accomplished with other code-mixed language such as Hindi-English, notched a 9% increase in the F1-score.

Mobile fuel delivery (MFD) uses a fueling truck to fill up personal and commercial fleet vehicles while they are parked overnight. This study used a sample data set provided by a San Francisco Bay Area company to explore the potential impacts on vehicle miles traveled (VMT), carbon dioxide (CO2) emissions, and traffic congestion. An analysis of vehicle travel associated with gas station trips was conducted to establish a basis for comparison. Future scenarios comparing the potential impacts of scaled-up MFD services in 2030 were also developed. The study concluded that MFD services compared favorably to gas stations in relation to environmental and traffic benefits in the longer term, even though personal fueling trips tended to generate low VMT. Benefits stemmed from efficiencies achieved by fueling multiple vehicles per delivery trip, replacing car share vehicle fueling trips, and removing trips from the network during peak hours. This analysis estimated that total annual CO2 emissions associated with fuel delivery operations in the Bay Area were 76 metric tons, which is less than a typical gas station with 97 metric tons. Under assumptions of declining demand for gasoline and significantly fewer gas stations, and with highly efficient optimized operations, mobile delivery could gain up to 5% market share for gas and not add additional VMT over the business as usual scenario.

The COVID-19 pandemic has dramatically changed the shape of conventional recruitment processes over the past few months Remote working where it is technically possible has become a de facto standard all over the world For software development, Freelancing platforms, as virtually unlimited resources of globally distributed talents, pose a great opportunity but also plenty of risks This study proposes an automated model for recruiting remote specialists in a framework meant to reduce the time-to-market for software products The results are optimal in relation to the project’s cost, team’s productivity and overall product success The research methodology along with the system used to collect data and the relevance of the data set are described © 2020, Bucharest University of Economic Studies All rights reserved

In this paper, we analyze how the physical layout of cities affects innovation by influencing the organization of knowledge exchange. We exploit a novel data set covering all census block groups in the contiguous United States with information on innovation outcomes, street infrastructure, as well as population and workforce characteristics. To deal with concerns of omitted variable bias, we apply commuting zone fixed effects and construct instruments based on historic city planning. The results suggest that variation in street network density may explain regional innovation differentials beyond the traditional location externalities found in the literature.

Bug assignment (BA), the process of ranking developers according to their potential ability to fix a given bug, is an important software-engineering task. BA usually requires the development of an expertise profile for each developer, and formulation of a similarity metric to estimate the relevance of developers to the bug. This needs us to answer the following question: ‘what is the information value of various contributions of developers in BA research?’ We address this question by making the following contributions. (i) We enhance the expertise metric of our prior work, vocabulary and time-based BA, to consider information regarding various sources of expertise with different importance. We show that this can improve the effectiveness of bug-assignment process. (ii) Using this ‘Multisource’ expertise metric, we investigate the information value of different pieces of information in open-source repositories for BA. We show that in addition to bug-fixing contributions, other technical and even social contributions of developers within the version-control system are useful information for BA. (iii) We provide a curated, up-to-date data set including technical information of 13 popular open-source projects in Github. To the best of our knowledge, this is the most comprehensive data set, currently available for bug-assignment research.

Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue trucks present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.

This study presented a secondary analysis of the National Assessment of Educational Progress (NAEP) dataset. The paper examined the impact of eighth grade students’ specific digital product creation on their Technology & Engineering Literacy ICT scores. In order to gain a better understanding of the impact of computer use technology achievement of eighth-grade students, this study used a quantitative descriptive research design to analyze secondary data extracted from the 2018 NAEP data set. The findings include (1) using computers to create, edit, or organize digital media both for school work and activities beyond school increased overall ICT scores. (2) The average score of students who used computers to create presentations in school increased while those who did this activity after school time saw a decrease in scores. (3) Students who reported that they create spreadsheets mostly saw a decrease in their average ICT score no matter the frequency or purpose for the activity. These findings may indicate that there is an ideal frequency for digital product creation in school, but that these types of activities may not be indicative of real-world use which is how students are assessed.

Web appli­ca­ti­on has be­en developed that will al­low clas­sifying, sa­ving and editing textual news from In­ter­net si­tes on­li­ne. The obtained results can be que­ued for further pro­ces­sing and use. The known met­hods of clas­si­fi­ca­ti­on of polythe­ma­tic textual in­for­ma­ti­on with the further pos­si­bi­lity of cho­osing the de­si­red one or the­ir com­bi­na­ti­on in order to most ef­fec­ti­vely me­et the estab­lis­hed customer req­ui­re­ments to se­lect in­for­ma­ti­on ac­cor­ding to va­ri­ous cri­te­ria, are analyzed. The most ef­fec­ti­ve met­hod for clas­si­fi­ca­ti­on of polythe­ma­tic textual news is de­ter­mi­ned. The method for polythematic textual news classification is defined; its work is designed for online mode of their coming with sequential analysis at the input of the textual data set. Web application architecture is designed for sequential classification of textual data online; its list of necessary functions that will provide storing, processing and viewing textual information obtained from the analysis of Internet sites or data required for its operation is substantiated. The structure of the database organization for the implementation of the web application, which will ensure reliable storage of classified information according to various criteria, as well as data for authorization and automation of user actions has been developed. The web application is implemented using the developer environment, the chosen programming language, implementation tools, and the designed client server architecture. The functionality of the web application is to process the relevant information, use the database for its storage, and perform various actions as well. For the web application to work effectively, there are various users whose demands for the service are available for a fee, which can be made directly on the resource. The web application functionality available to users is as follows: processing, storage, editing of textual news and the results of their classification, authorization and payment for additional functions.

This paper introduces transnational terrorist hostage event (TTHE) data set from 1978 to 2018. TTHE includes up to 50 variables for each of its 1,974 incidents, based on information in media sources. Four types of hostage incidents—kidnappings, barricade missions, skyjackings, and non-aerial hijackings—are recorded for a global sample. Select key variables include the number of hostages, targets of abductors’ demands, terrorist behavior in negotiations, amount of money ransom demands, other kinds of demands, duration of incident, and the outcome of negotiations. Our hostage data set also indicates location start and end, attack force size, victim numbers, casualties, weapons used, and logistical outcome. TTHE is tied to the International Terrorism: Attributes of Terrorist Events (ITERATE). We display interesting features of hostage incidents over time and an application of TTHE to identify the determinants of logistical and negotiation successes for hostage missions.

We still do not have a full understanding of how the different missions of academic organisations relate to their performance in knowledge sharing. This paper addresses this gap with a data set of ...

Personal informatics systems for the work environment can help improving workers' well-being and productivity. Using both self-reported data logged manually by the users and information automatically inferred from sensor measurements, such systems may track users' activities at work and help them reflect on their work habits through insightful data visualizations. They can further support interventions like, e.g., blocking distractions during work activities or suggest the user to take a break. The ability to automatically recognize when the user is engaged in a work activity or taking a break is thus a fundamental primitive such systems need to implement. In this paper, we explore the use of data collected from personal devices -- smartwatches, laptops, and smartphones -- to automatically recognize when users are working or taking breaks. We collect a data set of of continuous streams of sensor data captured from personal devices along with labels indicating whether a user is working or taking a break. We use multiple instruments to facilitate the collection of users' self-reported labels and discuss our experience with this approach. We analyse the available data -- 449 labelled activities of nine knowledge workers collected during a typical work week -- using machine learning techniques and show that user-independent models can achieve a (F1 score) of 94% for the identification of work activities and of 69% for breaks, outperforming baseline methods by 5-10 and 12-54 percentage points, respectively.

Internet-of-Things (IoT) devices and systems will be increasingly targeted by cybercriminals (including nation state-sponsored or affiliated threat actors) as they become an integral part of our connected society and ecosystem. However, the challenges in securing these devices and systems are compounded by the scale and diversity of deployment, the fast-paced cyber threat landscape, and many other factors. Thus, in this article, we design an approach using advanced deep learning to detect cyber attacks against IoT systems. Specifically, our approach integrates a set of long short-term memory (LSTM) modules into an ensemble of detectors. These modules are then merged using a decision tree to arrive at an aggregated output at the final stage. We evaluate the effectiveness of our approach using a real-world data set of Modbus network traffic and obtain an accuracy rate of over 99% in the detection of cyber attacks against IoT devices.

Purpose Job involvement can be linked with important work outcomes. One way for organizations to increase job involvement is to use machine learning technology to predict employees’ job involvement, so that their leaders of human resource (HR) management can take proactive measures or plan succession for preservation. This paper aims to develop a reliable job involvement prediction model using machine learning technique. Design/methodology/approach This study used the data set, which is available at International Business Machines (IBM) Watson Analytics in IBM community and applied a generalized linear model (GLM) including linear regression and binomial classification. This study essentially had two primary approaches. First, this paper intends to understand the role of variables in job involvement prediction modeling better. Second, the study seeks to evaluate the predictive performance of GLM including linear regression and binomial classification. Findings In these results, first, employees’ job involvement with a lot of individual factors can be predicted. Second, for each model, this model showed the outstanding predictive performance. Practical implications The pre-access and modeling methodology used in this paper can be viewed as a roadmap for the reader to follow the steps taken in this study and to apply procedures to identify the causes of many other HR management problems. Originality/value This paper is the first one to attempt to come up with the best-performing model for predicting job involvement based on a limited set of features including employees’ demographics using machine learning technique.

Since 2012, the National Center for Interprofessional Practice and Education has worked with over 70 sites implementing over 100 interprofessional education and collaborative practice (IPECP) programs in the United States (U.S.). Program leaders have contributed data and information to the National Center to inform an approach to advancing the science of interprofessional practice and education (IPE), called IPE Knowledge Generation. This paper describes how the evolution of IPE Knowledge Generation blends traditional research and evaluation approaches with the burgeoning field of health informatics and big data science. The goal of IPE Knowledge Generation is to promote collaboration and knowledge discovery among IPE program leaders who collect comparable, sharable data in an information exchange. This data collection then supports analysis and knowledge generation. To enable the approach, the National Center uses a structured process for guiding IPE program design and implementation in practice settings focused on learning and the Quadruple Aim outcomes while collecting the IPE core data set and the contribution of contemporary big data science.

Presentasi yaitu suatu kegiatan yang dilakukan sebagai penyampaian ilmu oleh pengajar untuk memberitahukan hasil penelitian serta gagasan. Presentasi sudah menjadi aktivitas yang dilakukan di hampir semua bidang pekerjaan sehari-hari.Untuk melakukan presentasi, dibutuhkan sebuahkomputer, mouse, dan keyboard sebagai alat bantu dan sarana untuk menjalankan slide presentasi. Selain dibutuhkan alat bantu presentasi, saat presentasi juga dibutuhkan seseorang operator untuk mengendalikan komputer saat presentasi berlangsung. Dengan melihat kondisi tersebut, dibutuhkan teknologi yang dapat mengintegrasikan berbagai macam fungsi alat bantu presentasi dan pengendali komputer saat presentasi. Deteksi telapak tangan pada penelitian ini memakai bahasa pemrograman python dan juga sebuah library OpenCV untuk proses inputan. Metode yang digunakan untuk mendeteksi gerakan telapak tangan menggunakan metode Haar Cascade Classifier. Metode Haar Cascade Classifier dapat digunakan untuk mendeteksi objek telapak tangan dan mampu mengontrol slide dengantelapak tangan dengan webcam secara real time. Dalam pendeteksian harus ditentukan centroid agar dapat diketahui jumlah frame untuk memindahkan slide serta jarak dari webcam ke objek juga dapat mempengaruhi dalam mengontrol presentasi. Namun, sistem ini memiliki banyak kelemahan dan harus diperbaiki. Sensitifitas pendeteksian yang tidak terlalu baik yang dapat dipengaruhi oleh beberapa faktor yaitu webcam yang kurang bagus, spesifikasi Personal Computer (PC) rendah, dataset yang meyebabkan false negative, serta pencahayaan ataupun pengaruh dari background.

The ongoing and increasing interest in HTAP (Hybrid Transactional and Analytical Processing) systems documents the intense interest from data owners in simultaneously running transactional and analytical workloads over the same data set. Much of the reported work on HTAP has arisen in the context of "greenfield" systems, answering the question "if we could design a system for HTAP from scratch, what would it look like?" While there is great merit in such an approach, and a lot of valuable technology has been developed with it, we found ourselves facing a different challenge: one in which there is a great deal of transactional data already existing in several transactional systems, heavily queried by an existing federated engine that does not "own" the transactional systems, supporting both new and legacy applications that demand transparent fast queries and transactions from this combination. This paper reports on our design and experiences with F1 Lightning, a system we built and deployed to meet this challenge. We describe our design decisions, some details of our implementation, and our experience with the system in production for some of Google's most demanding applications.

This article presents a general use corpus for Choctaw, an American indigenous language (ISO 639-2: cho, endonym: Chahta). The corpus contains audio, video, and text resources, with many texts also translated in English. The Oklahoma Choctaw and the Mississippi Choctaw variants of the language are represented in the corpus. The data set provides documentation support for this threatened language, and allows researchers and language teachers access to a diverse collection of resources.

In many countries, the quality of (large-scale quantitative) educational research is threatened by data challenges. In this article, we present an innovative data research project from the Netherlands in which many of the challenges that come forward in previous literature are addressed. The Netherlands Cohort Study on Education [in Dutch abbreviated as NCO (Nationaal Cohortonderzoek Onderwijs)] uses longitudinal register data on track placement of cohorts of pupils in primary and secondary education from Statistics Netherlands, which will be combined with school admin data on pupils’ performance and data from the major (inter)national surveys. NCO maps pupils’ pathways and performances through primary and secondary education and their trajectory into tertiary education. NCO so far covers more than 10 complete cohorts and is complemented with the newest data every year. These registers are made available at Statistics Netherlands. A major contribution of NCO is that data from school administrative systems and additional research projects are linked with administrative register data, thereby creating a unique data set that enriches not only research, but also policy and practice. This data brief elaborates on the possibilities of this database by exploring and following one of the cohorts over time.

Choosing a trusted cloud service provider (CSP) is a major challenge for cloud users (CUs) in the cloud environment, as many CSPs offer cloud services (CSs) with the same functionality. Trust evaluation of CSPs is often based on information from quality of service (QoS) monitoring and CUs’ feedback ratings. Despite the volume of feedback ratings received in trust management systems, the quality of feedback storage is very low, as many CUs do not send their feedback ratings when using CSs. Additionally, a percentage of existing feedback ratings may not be valid, since some malicious CUs send unfair feedback ratings to change the trust evaluation results. As these lead to poor data quality, the accuracy of trust evaluation results might be affected. To overcome these limitations, this paper proposes a new multi-level trust management framework, which completes previous frameworks by defining new components to improve the data quality of feedback storage. In our framework, new components were defined to solve the invalidity and sparse problems of feedback storage. Certainly, the trust assessment of CSP would be more accurate based on high-quality feedback ratings. The performance of the MLTM was evaluated using two different datasets based on a real Quality of Web Services dataset (QWS) and an artificial data set (Cloud-Armor), whose quality was reduced for the purpose of this study. Analytical values revealed that our proposed approach significantly outperformed other approaches even with the poor data quality of feedback storage.

In this paper, the roles of job requirements in Italian labour demand for the information technology sector are examined. For this purpose, a new data set collecting information on new hires in the sector is created by combining data from administrative and statistical sources. A conjoint analysis is used to estimate the partial utilities of job requirements in the labour matching. Moreover, using this approach a measure of importance for each job requirement is obtained. After estimating total utilities for alternative professional profiles, we check whether total utility and some features of the job vacancy affect the type of employment contract and occupation.

Speech Communication in a noisy environment is a difficult and challenging task. Many professionals work in noisy environments like aviation, constructions, or manufacturing, and find it difficult to communicate orally. Such noisy environments need an automated lip-reading system that could be helpful in communicating some instructions and commands. This paper proposes a novel lip-reading solution, which extracts the geometrical shape of lip movement from the video and predicts the words/sentences spoken. An Indian specific language data set is developed which consists of lip movement information captured from 50 persons. This includes students in the age group of 18 to 20 years and faculty in the age group of 25 to 40 years . All have spoken a paragraph of 58 words within 10 sentences in Hindi (Devanagari, spoken in India) language which was recorded under various conditions. The implementation consists of facial parts detection, along with Long short term memory’s. The proposed solution is able to predict the words spoken with 77% and 35% accuracy for data set of 3 and 10 words respectively. The sentences are predicted with 20% accuracy, which is encouraging.

Purpose This study aims to assist organizations to protect the privacy of their users and the security of the data that they store and process. Users may be the customers of the organization (people using the offered services) or the employees (users who operate the systems of the organization). To be more specific, this paper proposes a privacy impact assessment (PIA) method that explicitly takes into account the organizational characteristics and employs a list of well-defined metrics as input, demonstrating its applicability to two hospital information systems with different characteristics. Design/methodology/approach This paper presents a PIA method that employs metrics and takes into account the peculiarities and other characteristics of the organization. The applicability of the method has been demonstrated on two Hospital Information Systems with different characteristics. The aim is to assist the organizations to estimate the criticality of potential privacy breaches and, thus, to select the appropriate security measures for the protection of the data that they collect, process and store. Findings The results of the proposed PIA method highlight the criticality of each privacy principle for every data set maintained by the organization. The method employed for the calculation of the criticality level, takes into account the consequences that the organization may experience in case of a security or privacy violation incident on a specific data set, the weighting of each privacy principle and the unique characteristics of each organization. So, the results of the proposed PIA method offer a strong indication of the security measures and privacy enforcement mechanisms that the organization should adopt to effectively protect its data. Originality/value The novelty of the method is that it handles security and privacy requirements simultaneously, as it uses the results of risk analysis together with those of a PIA. A further novelty of the method is that it introduces metrics for the quantification of the requirements and also that it takes into account the specific characteristics of the organization.

The Internet has become open, public and widely used as a source of data transmission and exchanging messages between criminals, terrorists and those who have illegal motivations. Moreover, it can be used for exchanging important data between various military and financial institutions, or even ordinary citizens. One of the important means of exchanging information widely used on the Internet medium is the e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation, that prompts the researchers to work continuously to develop email analysis tool using the latest technologies to find digital evidence from email messages to assist the forensic expertise into to analyze email groups .This work presents a distinct technique for analyzing and classifying emails based on data processing and extraction, trimming, and refinement, clustering, then using the SWARM algorithm to improve the performance and then adapting support vector machine algorithm to classify these emails to obtain practical and accurate results. This framework, also proposes a hybrid English lexical Dictionary (SentiWordNet 3.0) for email forensic analysis, it contains all the sentiwords such as positive and negative and can deal with the Machine Learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system will be select available data which is Enron Data set. A high accuracy rate is 92% was obtained in best case. The experiment is conducted the Enron email dataset corpus (May 7, 2015 Version of the dataset).

Using a proprietary dataset from a provider of internal whistleblowing (WB) systems, we analyze nearly two million internal WB reports submitted to over one thousand publicly traded U.S. firms. We provide descriptive statistics, over time and across report types, on the amount and summary details of information provided, how extensively management reviews reports, the amount of time until reviews were completed, and the outcome of these reviews. Further, we examine the characteristics of firms with more actively used systems (i.e., a higher volume of reports, more information provided in reports, and reports that are more frequently reviewed by management). Finally, we show that internal WB report volume is associated with fewer and lower amounts of government fines and material lawsuits.

Our paper adds to a growing literature of doctoral training by studying factors that drive time-to-completion based on a new and unique data set from an international European graduate school. Whil...

Purpose The access to the rare originals of the early Spanish colonial imprints of the Philippines remains problematic. The reference librarians often are restricted to directing the students and scholars to the secondary resources that are available both in print and as a part of the digital assets within the North American academic libraries. This paper aims to focus on the select primary source editions including select Spanish language colonial imprints that are available electronically on the Web along the Open Access. These Web-based resources serve as the reference tools for the early history of the Philippines and Southeast Asia. As many of these publications are rare and extremely expensive for most libraries, the Open Access resources serve as an aid to building a virtual collection of these items. Design/methodology/approach The author had to create a data set of the early imprints of the Spanish Philippines using several bibliographic resources. The data set will be submitted as an Appendix for this research paper. The author did both qualitative and quantitative analysis of the data set along with the voyant-based digital humanities approach for topic modeling. Findings The goals of this paper were to not only survey the early Spanish printing of the Philippines but also provide the reader with a somewhat complete picture of how the printing began in the Spanish Philippines, what kind of the first books were printed and how one can access them given their rarity and fragility. The collection building paradigms are undergoing significant shifts, and the focus of many academic libraries is shifting toward providing access to these items. As these items high-value low-use items continue to be part of the Special Collections, the access to these is problematic. The virtual collections thus serve as a viable alternative that enables further research and access. While the creators of these works are long gone, the legacy of the Spanish colonial domination, printing and the religious orders in the Philippines remain alive through these works. Research limitations/implications As this is an introductory paper, the author focused on the critical editions rather than providing a comprehensive bibliographic landscape of the presses that produced these editions. He also did not take into consideration many pamphlets that were published in the same period. He also did not consider the Chinese language publications of the Islands. The Chinese had been block printing since medieval times (Little, 1996). In the context of the Spanish Philippines, the Chinese migration and trade have been studied in detail by Chia (2006), Bjork (1998) and Gebhardt (2017). The scope of this paper also was centered toward building a virtual collection of these rare books. Practical implications Rare books are often expensive and out-of-reach for many libraries; the virtual collection of the same along the Open Access model represents an alternative to collect and curate these collections. The stewardship of these collections also acquires a new meaning in the digital milieu. Social implications This research paper will allow scholars to see past the analog editions and help them focus on curating a virtual collection. The questions of electronic access are often ignored when it comes to visiting and using them in a controlled environment of the reading room in the Special Collections. The author argues that one way to enable access to these rare and expensive books is to provide access to their digital counterparts. These digital/virtual surrogates of the originals will facilitate further research. Originality/value The author could not find similar research on the publications of the early Spanish colony of the Philippines.

Widespread use of Internet also had the substantial impact on the increase of the online card transactions especially with the beginning of the last decade. Along with the increase of online transactions, the worldwide banking sector was forced to deal with or to encounter an unforeseen number of fraudulent activities, yet. Hence, rule-based systems were designed to mark the high-risk transactions and let the experts to confirm the fraudulent nature of such transactions. As a countermeasure, static nature of rule-based systems were exploited by the latest attacks to go undetected. Thus, researchers aimed at designing adaptive fraud detection systems utilizing mainly machine learning techniques with the very recent application of deep learning. However, they were focused on detecting fraudulent activities but, to the best of our knowledge, none of them delved into the better understanding the characteristics of fraudulent card transactions in order to produce more resilient models. Therefore, in this study, we built the biggest data set ever used in a research, consisting of 4B non-fraud and 245K fraud transactions contributed to by the 35 banks in Turkey. Consequently, we introduce and examine the performance of profile-based fraud detection models, namely card-type based model, transaction characteristics based model, and amount-based model. Also, we made temporal and spatial analysis on our data set to show the robustness of the proposed models against aging and zero-day attacks.

Electronic medical records can help people prevent diseases, improve cure rates, provide a significant basis for medical institutions and pharmaceutical companies, and provide legal evidence for medical negligence and medical disputes. However, the integrity and security problems of electronic medical data still intractable. In this paper, based on the ciphertext policy attribute-based encryption system and IPFS storage environment, combined with blockchain technology, we constructed an attribute-based encryption scheme for secure storage and efficient sharing of electronic medical records in IPFS storage environment. Our scheme is based on ciphertext policy attribute encryption, which effectively controls the access of electronic medical data without affecting efficient retrieval. Meanwhile, we store the encrypted electronic medical data in the decentralized InterPlanetary File System (IPFS), which not only ensures the security of the storage platform but also solves the problem of the single point of failure. Besides, we leverage the non-tamperable and traceable nature of blockchain technology to achieve secure storage and search for medical data. The security proof shows that our scheme achieves selective security for the choose keyword attacks. Performance analysis and real data set simulation experiments shows that our scheme is efficient and feasible.