Assets Data Research Articles

Assessing computational reproducibility in Behavior Research Methods

Psychological science has thrived thanks to new methods and innovative practices. Journals, including Behavior Research Methods (BRM), continue to support the dissemination and evaluation of research assets including data, software/hardware, statistical code, and databases of stimuli. However, such research assets rarely allow for computational reproducibility, meaning they are difficult to reuse. Therefore, in this preregistered report, we explore how BRM’s authors and BRM structures shape the landscape of functional research assets. Our broad research questions concern: (1) How quickly methods and analytical techniques reported in BRM can be used and developed further by other scientists; (2) Whether functionality has improved following changes to BRM journal policy in support of computational reproducibility; (3) Whether we can disentangle such policy changes from changes in reproducibility over time. We randomly sampled equal numbers of papers (N = 204) published in BRM before and after the implementation of policy changes. Pairs of researchers recorded how long it took to ensure assets (data, software/hardware, statistical code, and materials) were fully operational. They also coded the completeness and reusability of the assets. While improvements were observed in all measures, only changes to completeness were altered significantly following the policy changes (d = .37). The effects varied between different types of research assets, with data sets from surveys/experiments showing the largest improvements in completeness and reusability. Perhaps more importantly, changes to policy do appear to have improved the life span of research products by reducing natural decline. We conclude with a discussion of how, in the future, research and policy might better support computational reproducibility within and beyond psychological science.

Cybersecurity Regulations for Protection and Safeguarding Digital Assets (Data) in Today’s Worlds

Cybersecurity Regulations for Protection and Safeguarding Digital Assets (Data) in Today’s Worlds

Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts

With the emergence of foundation models (FMs) that are trained on large amounts of data at scale and adaptable to a wide range of downstream applications, AI is experiencing a paradigm revolution. BERT, T5, ChatGPT, GPT-3, Codex, DALL-E, Whisper, and CLIP are now the foundation for new applications ranging from computer vision to protein sequence study and from speech recognition to coding. Earlier models had a reputation of starting from scratch with each new challenge. The capacity to experiment with, examine, and comprehend the capabilities and potentials of next-generation FMs is critical to undertaking this research and guiding its path. Nevertheless, these models are currently inaccessible as the resources required to train these models are highly concentrated in industry, and even the assets (data, code) required to replicate their training are frequently not released due to their demand in the real-time industry. At the moment, only large tech companies such as OpenAI, Google, Facebook, and Baidu can afford to construct FMs. We attempt to analyze and examine the main capabilities, key implementations, technological fundamentals, and socially constructed possible consequences of these models inside this research. Despite the expected widely publicized use of FMs, we still lack a comprehensive knowledge of how they operate, why they underperform, and what they are even capable of because of their emerging global qualities. To deal with these problems, we believe that much critical research on FMs would necessitate extensive multidisciplinary collaboration, given their essentially social and technical structure. Throughout the investigation, we will also have to deal with the problem of misrepresentation created by these systems. If FMs live up to their promise, AI might see far wider commercial use. As researchers studying the ramifications on society, we believe FMs will lead the way in massive changes. They are closely managed for the time being, so we should have time to comprehend their implications before they become a major concern.

Portfolio Optimization for Five Influencing Companies of Technology Industry in the Hong Kong Stock Market

Nowadays, as a such big number of countries pay more attention to the scientific research strength, The importance of high and new technology is more and more recognized, the proportion in the securities market is increasing, technology stocks in the eyes of investors. This paper selects 5 Chinese technology growth stocks in Hang Seng Index as the research object and establish the dataset which contains the monthly time series of price in Hang Seng index. Fama experiment is used to well diversify the idiosyncratic risk and make sure that the portfolio is affected by the system risk. Through the mean-standard deviation plot, it can check the independence of the data we collect and check the normality. The portfolio can be divided into two situations: one includes risk-free assets and non risk-free assets. Finally, the performances of the portfolios are accessed including return volatility and weight of assets. The results show that, first, based on the Fama experiment, at least 5 stocks must be chosen to deal with idiosyncratic risk. Second, according to the mean-standard deviation plot,all the assets data are i.i.d. Third, the result show that both maximum return portfolios have the high return, and the portfolio contains risk-free assets is higher than portfolios without risk-free assets in the maximum return portfolio, but lower in the maximum Sharpe ratio portfolio. The findings may be useful to stimulate investors pay attention to invest in the technology stocks and create efficient portfolio. Meanwhile, this paper can suggest people how to rationally allocate stocks from multiple dimensions, build an effective combination, and try to obtain higher returns as far as possible in the condition of low risk.

Design and Implementation of a Web-GIS for the management of road infrastructure in Zimbabwe

Road asset mapping has the potential of reducing: costs in keeping all assets data, time-consuming activities like retrieving asset attribute from large files, risks associated with losing all the data by using Geographic Information Systems (GIS). Traditional road data has been stored in the form of hard copy maps showing the different road infrastructure. The World Wide Web (WWW), has revolutionized the provision, dissemination, and data access to people in different geographical locations. Web-GIS based applications have gained popularity because their low cost, ease of use and availability to a large population – that is anyone with a web-browser. Through browsers, web-GIS based applications can display a map with useful information. The design and development of an interactive web-GIS based digital road infrastructure management tool not only allows users to visualize the road infrastructure content but also help in decision making. It makes use of open source GIS tools, PostgreSQL and PostGIS (to manage spatial and non-spatial data), Geoserver (to connect the database to the client mapping application) and Apache Tomcat (to build and deploy the application). The maps are published through Geoserver with their associated information using JavaScript libraries (Open Layers and Geoext). Further spatial analysis (attribute queries) can be done online. Results show that a web-GIS was developed that manages road asset infrastructure like road signs, bridges, animal grids, rest areas. A user can query precise assets they want to visualize for instance damaged bridges. HoweGIS:here is still need to further improve the application for instance allowing user to put complaints about damaged road assets. Thus, the development of the application will help decision makers as well as other users to utilize the information for the benefit of the country.

ASSET MANAGEMENT SYSTEM IN HIGHER EDUCATION INSTITUTION USING TDWI BIG DATA MATURITY MODEL

Asset management is the process of recording, collecting data, reporting and documenting assets at a specific time based on existing provisions, proper asset management can support operational activities especially in the maintenance, repair, and procurement of assets for the organization. This study aims to addition to acquiring additional assets data and calculating devaluation of assets, including tracking their location, collating data regarding their operation and maintenance status, with the aim of improving the overall utilization of the assets The methodology used in this research is TDWI Big Data Maturity, which can accommodate changes during the system design process, by taking samples based on the business process of asset management in one of the private institution. This study produced a web-based asset management information system that can assist in managing and tracking asset data and logistic items.

Notebook‐as‐a‐VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment

AbstractVirtual research environments (VREs) provide user‐centric support in the lifecycle of research activities, for example, discovering and accessing research assets or composing and executing application workflows. A typical VRE is often implemented as an integrated environment, including a catalog of research assets, a workflow management system, a data management framework, and tools for enabling user collaboration. In contrast, notebook environments like Jupyter allow researchers to rapidly prototype scientific code and share their experiments as online accessible notebooks. Jupyter can support several popular languages used by data scientists, such as Python, R, and Julia. However, such notebook environments do not have seamless support for running heavy computations on remote infrastructure or finding and accessing collaborative software code inside notebooks. This article investigates the gap between a notebook environment and a VRE and proposes an embedded VRE solution for the Jupyter environment called Notebook‐as‐a‐VRE (NaaVRE). The NaaVRE solution provides functional components via a component marketplace and allows users to create a customized VRE on top of the Jupyter environment. From the VRE, a user can search research assets (data, software, and algorithms), compose workflows, manage the lifecycle of an experiment, and share the results among users in the community. We demonstrate how such a solution can enhance a legacy workflow that uses Light Detection and Ranging (LiDAR) data from country‐wide airborne laser scanning surveys for deriving geospatial data products of ecosystem structure at high resolution over broad spatial extents. This enables users to scale out the processing of multi‐terabyte LiDAR point clouds for ecological applications to more data sources in a distributed cloud environment. Similar applications could be developed for workflows producing other essential biodiversity variables.

Combining maximum likelihood estimation and LSTM neural network to forecast reliability distributions: a study based on real data from the sugar-energy sector

The recent advances in reliability engineering and assets management allow industries to use advanced mathematical and statistical techniques to improve their maintenance strategies. Conversely, many of these methods requires the previous knowledge of the failure history for the assets, which are not always available. This study shows that is possible to support maintenance management decisions providing the distributions for assets with known and unknown failures history. The approach combines maximum likelihood estimation with a LSTM neural network to find and predict optimal reliability and failure distributions for data actual assets data and failures history. It is shown as an efficient and probably useful approach for practical business situations in the results section.

MTH5: An archive and exchangeable data format for magnetotelluric time series data

Magnetotellurics (MT) is a passive electromagnetic geophysical method that measures variations in subsurface electrical resistivity. MT data are collected in the time domain and processed in the frequency domain to produce estimates of a transfer function representing the Earth’s electrical structure. Unfortunately, the MT community lacks metadata and data standards for time series data. As the community grows and findability, accessibility, interoperability, and reuse of digital assets (FAIR) data principles are enforced by government and funding agencies, a standard is needed for time series data. Presented here is a hierarchical data format (MTH5) that is logically formatted to how MT data are collected. Open-source Python packages are also described to read, write, and manipulate MTH5 files. These include a package to deal with metadata (mt_metadata) based on standards developed by the Working Group for Magnetotelluric Data Handling and Software assembled by the Incorporated Research Institutions for Seismology (IRIS), and mth5: a package to interact with MTH5 files that uses mt_metadata. Example code and workflows are presented.

Fully Automated Algorithm for Light Pole Detection and Mapping in Rural Highway Environment Using Mobile Light Detection and Ranging Point Clouds

The widespread use of light detection and ranging (LiDAR) data provides a promising source for the automatic detection and inventory of roadside assets. One of the essential elements in roadside furniture is light poles. There is limited research on the mapping of light poles using point-cloud data on rural highways. In this environment, the placement of light poles within roadside clear zones often poses a safety concern, as they are related to an increased risk of collisions. Only a limited number of studies have explored the relationship between light poles and safety because of the time-consuming and labor-intensive practices of collecting light pole assets data using traditional manual methods. This paper proposes an automated approach to mapping the locations of light poles. First, the scanning vehicle trajectory is extracted, smoothed, and then used to segment the point-cloud data into smaller overlapped batches of data. Several filters are applied to extract pole-like objects from the data. The segments are combined back together, and a density-based clustering algorithm is used to group the remaining points into clusters. A geometric filter is finally applied to extract light poles. The model is tested on 28 km of data on three rural highways in Alberta, Canada. The proposed algorithm is found to be accurate relative to previous studies, with average precision, recall, and F1 scores exceeding 98% for the test segments. The proposed work can assist in the automation of light pole inventory and road safety audits by transportation agencies.

An empirical analysis of capital assets condition ratio in local governments: the case of Florida counties

PurposeThe main purpose of this paper is to examine the political, economic and institutional determinants of capital assets condition ratio in American local governments using government-wide financial statements.Design/methodology/approachBased on capital assets data from the period of 2011–2016 for the 66 Florida counties as reported on their government-wide financial statements, the authors use a panel two-way fixed effects estimation and a dynamic panel generalized method of moments estimation.FindingsThe authors find that social-economic factors, fiscal capacity and democratic voters explain the capital assets condition ratio in Florida county governments.Research limitations/implicationsThe major findings of this study may only apply to county government in one single state. It may raise the issue of the external validity of our research. It provides policy recommendations for local public officials to maintain and upgrade their capital assets.Originality/valueThe study utilizes a new approach of capital assets condition ratio to measure county government investment in capital assets based on the government-wide financial statements.

Combining Blockchains, Smart Contracts, and Complex Sensors Management Platform for Hyper-Connected SmartCities: An IoT Data Marketplace Use Case

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

Pengaruh Inflasi Indonesia Dan Bi Repo 7 Days Terhadap Kinerja Bank Devisa

This study aims to analyze the effect of inflation and BI 7-Day (Reverse) Repo Rate on Retrun On Assets(ROA). The data obtained were then entered into the Statistical Package Social Sciences (SPSS) program to carry out advanced statistical analysis. Data obtained from the financial statements of 10 banks listed on the Indonesia Stock Exchange (IDX) during the period 2015 to 2019. The analysis techniques used are classical assumption tests, hypothesis testing, and others. The results obtained from the simultaneous hypothesis test (f test) show that inflation and the BI 7-Day (Reverse) Repo Rate simultaneously have an effect on Retrun On Assets(ROA). The results of the partial test (t test) show that inflation data has a significant negative effect on Retrun On Assets(ROA) and BI 7-Day (Reverse) Repo Rate data has a significant positive effect on Retrun On Assets(ROA). The result of the coefficient of determination with adjusted R2 shows that the predictive ability of this research data is 98.8%, meaning that the independent variables, namely Inflation and BI 7-Day (Reverse) Repo Rate affect the dependent variable, namely Retrun On Assets(ROA). amounting to 98.8 %
 
 Keywords : Inflation BI 7-Day (Reverse) Repo Rate, Retrun On Assets(ROA)

Distributed Ledger Technology Review and Decentralized Applications Development Guidelines

The Distributed Ledger Technology (DLT) provides an infrastructure for developing decentralized applications with no central authority for registering, sharing, and synchronizing transactions on digital assets. In the last years, it has drawn high interest from the academic community, technology developers, and startups mostly by the advent of its most popular type, blockchain technology. In this paper, we provide a comprehensive overview of DLT analyzing the challenges, provided solutions or alternatives, and their usage for developing decentralized applications. We define a three-tier based architecture for DLT applications to systematically classify the technology solutions described in over 100 papers and startup initiatives. Protocol and Network Tier contains solutions for digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scalability and Interoperability Tier solutions address the scalability and interoperability issues with a focus on blockchain technology, where they manifest most often, slowing down its large-scale adoption. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design and implementation of traditional systems.

Effects of Joint Forest Management on Livelihood of Local Communities in Khyber Pakhtunkhwa, Pakistan

| This study measured the effects of Joint Forest Management (JFM) on livelihood of local communities in Khyber Pakhtunkhwa, Pakistan. Purposively three forest divisions had been selected namely Swat, Kalam and Siran of Malakand and Hazara forest regions. The study based on cross sectional data. Data were collected from 321 randomly selected households in three JFM (experimental group) and nearby three non-JFM (control group) villages, located in the same forest-ecological and socio-economic settings. The study analyzed the effects of JFM on local livelihood by using binary logistic regression. Odds ratio analysis of the social and natural assets data showed that the Joint Forest Management approach had significantly increased the social and natural assets of the participants. In terms of financial asset, odds ratio in favor of ‘NTFPs income’ and ‘employment opportunities’ for JFM sample households were 2.289, 2.501 times respectively more than non-JFM sample households. Trainings imparted by forest department significantly improved the skills, knowledge and leadership abilities of the JFM participants (human asset) as compared to non-participants but JFM did not contributed to resolve the health and educational problems of the participants. Regarding the physical asset indicators, data results of two groups showed no significant variation. It was concluded that overall livelihood assets condition of JFM participants was better as compared to non-participants. This study therefore suggested that physical and human assets of the JFM participants should be enhanced for further strengthening of JFM approach in Khyber Pakhtunkhwa.

Enhanced Approach of Risk Assessment for Insider Threats Detection in Smart Manufacturing

The smart manufacturing integrates Internet of Things (IoT), cloud systems, data analytics and machine learning for autonomous implementation particularly in the factory’s supply chain. The additional components from Information Technology (IT) gives convenience accessibility in spike of welcomed threats from inside or outside the boundary to do harmful events. The valuable information in smart manufacturing consists of intellectual property (IP), assets (hardware and software) data and human talent that lead to data theft and illegal activities due to its monetary values. However, little work is done on assessing risks invaluable information particularly threats from inside. In fact, studies on risk assessments are focusing on financial profit, supply chain production and IT assets. Therefore, an enhanced approach of risk assessment for insider threat detection (RAFITD) is introduced. The proposed approach contributes to a log monitoring of the log on and log out activities, based on the role of employee on a software dashboard. From the result, the log monitoring produces two scenarios, which is the ideal and non-ideal. For the ideal scenario, a balanced ratio between log on and log out is accomplished. Meanwhile, a non-ideal scenario performs an imbalance ratio. The impact of the study brings an insider threat detection for better analysis.

APLIKASI PENGELOLAAN ASET NAGARI BERBASIS WEB

Aset Nagari adalah barang milik nagari yang berasal dari kekayaan asli milik nagari, dibeli atau diperoleh atas beban Anggaran Pendapatan dan Belanja Nagari (APB Nagari) atau perolehan hak lainnya yang sah. Dalam pengelolaan aset permasalahan yang sering muncul biasanya pada sebuah instansi pemerintahan sering lalai akan pentingnya mengelolah data aset dengan baik dan benar, masih banyak ditemukan data aset yang belum terdata dengan baik, dan sering kali susah dalam pembuatan laporan tahunan. Hal ini pun terjadi pada nagari Sialang, pengelolahan data asetnya masih belum baik yakni di ketik di komputer kemudian di print lalu disimpan dalam sebuah arsip. Masalah selanjutnya belum adanya pengelompokan data aset berdasarkan jenis dan macamnya, agar dapat mempermudah dalam pencarian data aset. Oleh karena itu diperlukan suatu sistem pengelolaan aset berbasis web mengunakan bahasa pemograman php dan basis data mysql yang dapat membantu kenagarian Sialang dalam mengolah aset. Dengan system pengelolaan asset menggunakan bahasa pemrograman PHP. Pada sistem pengelolaan aset pada kenagarian Sialang, admin melakukan entri data aset. Data aset yang dientrikan dikelompokkan berdasar tahun. hal ini akan memudahkan dalam pembuatan laporan tahunan. Selanjutnya pada sistem pengelolaan aset user juga dapat mencatat status keadaan aset, seperti apakah ada aset yang rusak atau hilang dan perlu dinonaktifkan. Sehingga dengan adanya sistem pengelolaan aset ini pengelolaan aset pada kenagarian Sialang akan lebih mudah sepeti pembuatan laporan tahunan, penonaktifan aset yang rusak atau hilang, dan pencatatan pengurangan nilai aset pertahun. Nagari Asset is the property of the nagari wealth, purchased or obtained at the expense of nagari budget or the acquisition of other rights is legitimate. in asset management issues that often arise usually at a government agency often neglect the importance of managing the asset data properly, there are still many asset data is not recorded properly, and often difficult in making the annual report. It also occurs in the nagari Sialang, data management is still not good assets in the type of computer that is then printed and then stored in an archive. A further problem is not their grouping data based on the type and variety of assets, in order to facilitate the search for asset data. Therefore need a web-based asset management system using PHP programming language and MySQL database that can help nagari Sialang in processing assets. Web based asset management by using the PHP programming language can generate an asset management system in the nagari Sialang, In the asset management system in nagari Sialang, the user or officer performs an asset data entry. The assets data that is allocated are grouped by year. this will facilitate the making of the annual report. Furthermore, in the asset management system the user can also record the status of the assets, such as whether there are assets that are damaged or lost and need to be deactivated. So that with the existence of this asset management system, managing assets in nagari Sialang assets will be easier such as making annual reports, deactivating damaged or lost assets, and recording annual asset value reductions.

Ontology-Based Research Asset Management Model for Academic Environment

Data related to research assets are dispersed in various places and heterogeneous formats which may be structured or semi-structured in nature. This scattering of data causes a lot of repetition and inconsistencies. In this paper, we propose an Ontology-Based Research Asset Management Model (ORAM) useful for academic institutions. For this model, we created academic ontology. Data from different academic institutions is mapped to produce a single knowledge base. This mapping is performed by writing mapping rules. This unified knowledge base is integrated with web application to provide a single platform for decision-makers to retrieve information. The ORAM model is tested by developing a prototype with research assets data of various academic institutions available in structured and semi-structured formats. It is concluded that ontology plays a very important role in managing research assets effectively and efficiently.

PENGARUH NET INTEREST MARGIN DAN NON PERFORMING LOAN TERHADAP RETURN ON ASSETS

This study to determine the effect and analyze of the Net Interest Margin and Non Performing Loan partial influence on Return on Assets banking companies in Indonesia Stock Exchange. The approach taken in this research is quantitative and associative approach. The population in this research are all banking go public companies listed in Indonesia Stock Exchange which amounted to 43 companies. The sample in this research is 7 companies. The independent variables used in this study are Net Interest Margin and Non Performing Loan and dependent variable is Return on Assets Data collection techniques using documentation. The method of analysis used is the normality test, the classical assumption test, simple linear regression test, hypothesis test and coefficient of determination. Data processing in this study using Software SPSS (Statistic Package for the Social Scien) 25 for window. The results show that partially, Net Interest Margin has an effect on Return on Assets. While Non Performing Loan has no effect on Return on Assets. 
 Keywords : Net Interest Margin, Non Performing Loan, Return on Assets

Corporate governance, intellectual capital, and performance of indonesian public company

Two important issues for companies’ sustainability are corporate governance and intellectual capital. This research is intended to test the relation between corporate governance, intellectual capital and companies’ performance. This is a quantitative research with archival data. The data is taken from Financial Reports and Annual Reports of Indonesia Public Company during 2011-2016. Corporate governance is measure using proportion of female director and public ownership. Intellectual capital indicators are CEE (Capital Employed Efficiency) and ICE (Intellectual Capital Efficiency). Companies’ performance construct are Tobin’s Q and Return on Assets (ROA) Data then analysed using Partial Least Square. The empirical results is as follows. First, Corporate Governance has positive effect on Intellectual Capital. Second, there is negative effect of Corporate Governance on Performance. Third, Intellectual capital has no effect on firm’s performance. Fourth, Intellectual capital has no mediating effect to the the relation between corporate governance and company’s performance. This results are prove that Agency Theory is the better than Resources Based Theory to explain the Indonesia public companies condition.