Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformational leadership model construction service industry in Jakarta during the covid-19 pandemic era

This study aims to analyze a transformational leadership model in the construction service industry in Jakarta during the Covid-19 pandemic using the dimensions of individual consideration, intellectual stimulation, inspirational motivation and idealized influence. The analysis technique uses the Structural Equetion Model (SEM) with SmartPLS. The total sample is 95 heads of construction service companies in DKI Jakarta. Data were collected through distributing questionnaires. The results of the analysis show that transformational leadership in the construction service industry during the pandemic is at a high level. The results of testing the transformational leadership model show that each dimension is significant in composing transformational leadership. The four significant dimensions recommended are idealized influence, intellectual stimulation, individual consideration and inspirational motivation. Among the four dimensions, idealized influence and individual consideration are the dominant dimensions, this shows that during the Covid-19 pandemic, the influence of charismatic leadership role models became the main characteristics for companies that managed to survive, at the same time paying attention to each individual employee is indispensable. The practice of transformational leadership in companies is manifested in the transformation of information processes, new technology, and adaptation of techniques, communication, and data management utilizing digital media. © 2021
Decentralized Robust Voltage Control of Islanded AC Microgrids: An LMI-Based <tex>$H_{\infty}$</tex> Approach

In this paper, a decentralized control method is presented for the islanded inverter-interfaced microgrids with robust performance approach. The system is consisted of two Distributed Generations (DG) linked to their loads by inverter, RL filter and a step up transformer. The objective here is to guarantee the stability of the system and its desired performance under large load perturbation and Plug-and-Play (PnP) of the DGs. To achieve that aim, a Linear Matrix Inequality (LMI) state-feedback control technique with H guaranteed cost is used for each DG. Contributions of the designed control system are: 1) the control design is entirely decentralized; 2) the stability and the desired performance of the overall system is guaranteed; 3) the control system is completely robust against PnP functionality of the DGs; 4) the controller provides robustness against large load perturbation. The ability of the presented control system with regard to voltage tracking, load changes and topology changes is evaluated on several case studies based on simulation in SimPowerSystems Toolbox in MATLAB. Keywords—decentralized control, islanded microgrid, plugand-play (PnP), linear matrix inequality (LMI)
The Effect of Role Ambiguity on the Job Satisfaction and Intention to Leave

It is stated that role ambiguity had negative impacts on employees’ attitudes and behaviors by increasing job stress of employees. It was seen that studies which are about role ambiguity and job satisfaction have different results. Based on the different results of role ambiguity on job satisfaction, the aim of study is to test the impact of role ambiguity on job satisfaction by controlling the common method variance problem. For this purpose, data were collected in three weeks from 227 participants working in the public sector with one-week intervals by temporal separation. After common method analysis, regression and correlation analyses were performed to test hypotheses. Because of research conducted in a large-scale public institution, it was found that role ambiguity has a positive and significant effect on job satisfaction. The relationship of role ambiguity and intention to leave was also investigated to test this positive and significant effect. Role ambiguity has a significant and negative impact on intention to leave. The findings were argued, in the context of social loafing and social compensation theory.
The effect of different factors on intention to apply total quality management system in petroleum and liquefied gas enterprises in Vietnam: The role of the leader’s personality

This study was conducted based on Davis's extended technology acceptance model (TAM) (1989) and survey data of 486 petroleum and liquefied gas enterprises in Vietnam on their intention to apply using a total quality management (TQM) system. Research results show that factors such as (1) Perception of the effectiveness of TQM; (2) Perception of the effectiveness of the convenience in using TQM; and (3) Perception of the effectiveness of TQM compared to the old system which has a positive impact on the intention to apply the total quality management system in petroleum and liquefied petroleum gas enterprises in Vietnam. Besides, the perception of decision-makers on issues related to quality management in these enterprises also has a direct influence on the perception of the TQM usability and indirect effect of the intention to use TQM application in these enterprises.
A Systematic Literature Review on Dynamic Business Processes

Organization's business processes are changing constantly due to a dynamic environment. Accordingly, all these changes need to be implemented into dynamic business processes, which at the runtime will ensure the required dynamicity of business. However these processes are not sufficient investigate what prevents their effective use. Thus far, there does not exist the generally accepted understanding of the dynamic business process (DBP), including its requirements, and measures of dynamicity. Therefore, this paper presents a systematic literature review of conference and journal articles on the topic of DBP. The review has been undertaken to define DBP, identify the DBP requirements and analyse proposed solutions for their implementation. In addition, this paper identifies gaps in current research in order to appropriately identify topical research activities in the field of DBP.
A Survey on IOT Based Smart Greenhouse

: Now a days, as we know the demand of high quality products are increasing and people are more concern about food quality and security. Green house is use to improve the production of growing crops within sheltered structure covered by transparent material. It helps to control sunlight, humidity, soil moisture and temperature as per the need of plants. IOT smart farming solution is a system that is build for monitoring for the crop field with the help of sensors. This paper reviews the smart green house technologies and the IOT technologies for smart green house farming.
A Compact Control Unit for a Pneumatic Soft Colonoscope

Colorectal cancer is the third most common cause of cancer death worldwide [1]. A regular screening of the asymptomatic population can drastically reduce the cancer mortality. Colonoscopy remains the gold standard procedure, however, it causes pain, discomfort and it is expensive. The procedure is difficult and requires a long training for a colonoscopist to become proficient. A robotic soft colonoscope can provide an alternative solution to the current procedure because of the reduced pressure applied to the colonic wall that can potentially avoid pain and discomfort. The low production cost of soft devices can make the procedure cost effective. The control of a pneumatic system is usually implemented by using off-the shelf components. This typology of design is often bulky, may be noisy, and expensive. This work presents a wireless and compact hardware to control a soft pneumatic colonoscope [2]. The hardware includes a lowlevel PID pressure controllers for each pneumatic chamber, a Bluetooth communication module, and a high-level controller implemented in MATLAB® Simulink to easily test different locomotion strategies.
International Collaboration in the Field of Educational Research: A Delphi Study

(International) research collaboration (IRC) is considered one of the most important aspects of academic careers and, although it is often linked to co-authorship, investigations of academic perceptions and practices are rare. Reflections on factors affecting IRC, as well as effective technology for use in IRC are also now particularly vital, due to the outbreak of COVID-19. The aim of this three-round Delphi study, conducted prior to the pandemic, was to explore why and how academics conduct IRC in the field of Education. The results indicate that the benefits of IRC are more relevant than the barriers, and that networking with and learning from others are two of the most important reasons for conducting IRC. Four stages of IRC were identified, alongside a range of influencing factors, which could inform future project management training. Suggestions for institutional IRC policy are provided, as well as considerations for early career researchers.
Journal of Appled Horticulture, Volume 9, No 2 July-December

Capparis deciduas is an important constituent of desert ecosystem, however, due to population pressure on the land their stands are reducing at an alarming rate. Establishment through root suckers and seeds being very slow and cumbersome, remains the major bottleneck for their reestablishment in the area. Thus, realizing the constraint, a rapid and effi cient micropropagation protocol for multiple shoot regeneration employing shoot tip explant was developed. The protocol is more effi cient and reproducible than reported earlier. Maximum number of explants (100%) responded on Murashige and Skoog (MS) medium supplemented with 6-benzyl amino purine (BAP) 7 mg L-1 and naphthaline acetic acid (NAA) 0.1 mg L-1 while number of shoots per explant were maximum (8.5) on alone BAP 7 mg L-1. Regenerated shoots could be rooted best on MS medium supplemented with indole butyric acid (IBA) 1 mg L-1. Rooted shoots could be hardened and transplanted in to the fi eld.
Tools Impact on the Quality of Annotations for Chat Untangling

The quality of the annotated data directly influences in the success of supervised NLP models. However, creating annotated datasets is often time-consuming and expensive. Although the annotation tool takes an important role, we know little about how it influences annotation quality. We compare the quality of annotations for the task of chat-untangling made by non-experts annotators using two different tools. The first is SLATE, an existing command-line based tool, and the second is Parlay, a new tool we developed that integrates mouse interaction and visual links. Our experimental results indicate that, while both tools perform similarly in terms of annotation quality, Parlay offers a significantly better user experience.
TEACHING OF CREATIONISM: ANALYZING THE OPINIONS OF A SAMPLE OF BRAZILIANS

In order to assess the opinion of citizens in the region of Minas Gerais, Brazil, on the teaching of Creationism in schools at the expense of Evolutionism, it was proposed the question (in Likert scale format): “About the idea that science classes should only address Creationism, the idea that God created all living beings and they have not changed since then, you. ”. Religion and education were the variables used in the analysis and discussion of the responses. The variable religion proved to be significant. Thus, among the Gnostics, evangelicals that agreed with the statement outnumber Catholics. In contrast, atheists generally disagreed with the statement. Regarding education, it was observed that even in elementary school the tendency of the respondents is to agree with the question. This may be related to contact with the evolution theme that is approached from high school. However, respondents with higher education tended to disagree with the statement. These data are important when considering the recent growth in the number of evangelicals in Brazil, as there are concerns about possible interventions in the school curriculum, to encourage the teaching of Creationism in Science and Biology classes.
Evolutionary and Incremental Text Document Classifier using Deep Learning

Currently most of the generated data are stored in text format so text mining is believed to have a high commercial potential value. There are many sources for knowledge extraction, still unstructured text considered as the largest readily available source of knowledge. These data which is mostly available in digital format need to manage. Text document classification is basically a task to automatically categorize the text documents in some pre-defined classes. A lot of research work is already done in this domain and many classifiers have been developed including some ensemble classifiers. The classification accuracy of the classifier depends on many parameters such as preprocessing, features selection, data set, methodology etc. Any of these mentioned parameters can be significantly influential in increasing the accuracy of the classifier.
Adaptive Price-based Power Flow in next Generation Electric Power Systems

Demand load management in the context of gridconnected microgrids is the scope of this paper. This issue is formulated as a power flow problem between distributed power sources with the objective of grid operational cost minimization. Under the assumption of time-varying demand loads at individual microgrids, a distributed algorithm of interactions among microgrids is proposed for power sharing within the grid. Numerical results demonstrate the outperformance of the proposed load management scheme in comparison with no power sharing scheme in terms of the grid operational cost.
ANALYSIS OF SAUDI DEMAND FOR IMPORTED BROILER CHICKENS

The study was aimed to analyze the Saudi demand for imported broiler chickens from Brazil, France, and other countries. The ideal demand model for time series data on imported quantities and values were adjusted during the period 1990-2016. Since the model equations are nonlinear in transactions, Non-linear least squares in estimating the parameters of the model. The results showed that the model has achieved the condition of addition, and the equations are termed as homogeneous from the zero degrees, and it was found that all the price elasticity’s (own and cross both) and the elasticity of the expenditure agree with the economic theory. But it was noted that the demand for broiler chickens from France was highly elastic to the change in the price of Brazil and other countries. It was also noted that demand was found less elastic for total expenditure change for broiler chickens imported from other countries and that it was equally elasticity to change in the prices of Brazil and France chickens. The prediction of imported broiler chickens showed that there was a general trend expected to increase imports from Brazil and France.
A Comprehensive Review of Routing Protocols for Internet of Things

— With the dawn of Internet of Things (IoT), intelligent device implanted in things can be connected at anytime, anywhere, with anything and anyone by possibly utilizing any network and any service. IoT networks are self-organizing and decentralized in nature which results in dynamic changes in node’s position. Hence routing in IoT becomes crucial for successful delivery of the data. Further limited energy and processing capabilities of the connected things makes routing more challenging in IoT networks. This research work focuses on the study of some of the major routing algorithms designed for IoT network and their extensions. This review work also includes a comparison of these protocols on the basis of various performance metrics.
Learning Contextualized Knowledge Structures for Commonsense Reasoning

Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference. However, these methods rely on quality and contextualized knowledge structures (i.e., fact triples) that are retrieved at the pre-processing stage but overlook challenges caused by incompleteness of a KG, limited expressiveness of its relations, and retrieved facts irrelevant to the reasoning context. In this paper, we present a novel neural-symbolic model, named Hybrid Graph Network (HGN), which jointly generates feature representations for new triples (as a complement to existing edges in the KG), determines the relevance of the triples to the reasoning context, and learns graph module parameters for encoding the relational information. Our model learns a compact graph structure (comprising both extracted and generated edges) through filtering edges that are unhelpful to the reasoning process. We show marked improvement on three commonsense reasoning benchmarks and demonstrate the superiority of the learned graph structures with user studies.
Even more discouraged? The NEET generation at the age of Covid-19

This paper evaluates if and to what extent the risk of becoming Not in Employment, Education or Training (NEET) has worsened during the Covid-19 pandemic in Italy. The analysis is based on a unique dataset obtained from the merging of two sample surveys, the Italian Labor Force Survey and the Institutional Quality Index dataset. We find that the probability of being NEET significantly increased during the pandemic, but heterogeneously between age cohorts and geographical areas. The most affected categories have been young people (aged 25-34) and those living in North-West regions. Females are mostly affected compared to males, especially those experiencing motherhood and living in a Southern province. Investment in education reduces the NEET status, mainly for age-group 25-34 in the South. Participation in the civil society significantly reduces the probability to being NEET. Finally, active policies conducted at regional level are a further educational investment that protect from becoming NEET, although their effectiveness is not significant in the Southern regions. We provide novel evidence to inform policymakers and help building evidence-based policies, tailored on local needs.
Variationally Bayesian Medical Image Reconstruction Applied to X-ray Tomography (cid:63)

We present a Bayesian statistical approach to medical image reconstruction. Here, we obtain not a point estimate for the image of interest, but a posterior distribution that also quantiﬁes the reconstruction uncertainty. We approximate the posterior variationally. We illustrate the eﬀectiveness of our approach in numerical experiments in which we study variationally Bayesian X-ray tomography
One-sided RDMA-Conscious Extendible Hashing for Disaggregated Memory

Memory disaggregation is a promising technique in datacenters with the beneﬁt of improving resource utilization, failure isolation, and elasticity. Hashing indexes have been widely used to provide fast lookup services in distributed memory systems. However, traditional hashing indexes become inefﬁ-cient for disaggregated memory since the computing power in the memory pool is too weak to execute complex index requests. To provide efﬁcient indexing services in disaggregated memory scenarios, this paper proposes RACE hashing, a one-sided R DM A - C onscious E xtendible hashing index with lock-free remote concurrency control and efﬁcient remote resizing. RACE hashing enables all index operations to be efﬁciently executed by using only one-sided RDMA verbs without involving any compute resource in the memory pool. To support remote concurrent access with high performance, RACE hashing leverages a lock-free remote concurrency control scheme to enable different clients to concurrently operate the same hashing index in the memory pool in a lock-free manner. To resize the hash table with low overheads, RACE hashing leverages an extendible remote resizing scheme to reduce extra RDMA accesses caused by extendible resizing and allow concurrent request execution during resizing. Extensive experimental results demonstrate that RACE hashing outperforms state-of-the-art distributed in-memory hashing indexes by 1 . 4 − 13 . 7 × in YCSB hybrid workloads.
A STUDY: SENTIMENTAL ANALYSIS FOR ELECTION RESULTS BY USING TWITTER DATA

The entire world is changing at a breakneck pace, and technology is no exception. User-generated data is abundant on social networking platforms like Twitter. Users from all around the world offer their thoughts, opinions, ideas, and feelings about a variety of topics, including products, movies, and politics. Manual sentiment analysis is a time-consuming task. Opinion mining has recently gained popularity as a result of the large volume of opinionated data available on social networking sites such as Twitter. In this paper, we used a chronological approach to data collection, data pre-processing, emotional analysis, and machine learning analysis to forecast the outcome of the US 2020 presidential election using Twitter emotional analysis. We used a Random Forest classifier after completing a literature review and comparing all supervised ensemble machine learning algorithms to determine which one was the best. The proposed technique was tested on Twitter data, and it outperformed existing approaches.
Infrastructure for Development of Intelligent Learning Environments that Manage SCORM Content

: One of the major problems in continuous use of Intelligent Learning Environments (ILE) produced in academic and scientific projects is the difficulty in maintaining and actualizing the content, which is normally produced locally, have many external references or are copies of files such as PDF, DOC or images. These materials have problems to be reused in other environments since they have few resources to improve visual presentation and lack of traceability of what activities the student accessed. This paper presents an infrastructure to allow such environments to use content following the SCORM specification. The advantage of using SCORM content is the portability. Instructional material can be produced in specialized tools and can be use in any systems that are in compliance with the SCORM specification. As an ILE normally actuate promoting adaptation of educational content and pedagogical strategies according to student's individual needs, the infrastructure presented in this paper provides the means for SCORM packages to be customised for ILE environments. The infrastructure was named CELINE and was developed using components paradigm and Java technologies. The results of the development of three ILE that uses CELINE component are also discussed in this paper.
Performance Analysis of Decision Tree Classifier in Network Intrusion Detection System

: The speedy upward push in the use of the internet, communication, and social networking increased the network size and corresponding data. So, many strange attacks are being generated. This posed challenges for network security in order to detect intrusions accurately. An Intrusion Detection System (IDS) tool can be used to ensure confidentiality, integrity, and availability by continuously inspecting network traffic to prevent the network from intrusions. The Intrusion detection system is facing various challenges to reduce false alarm rates and achieve high accuracy in detection. The Machine Learning (ML) and Deep Learning (DL)-based IDS systems can be used to enhance the accuracy rate of detection of various attacks in the network traffic. By learning the patterns of network traffic, the ML and DL technique tools predict the normal and abnormal activities. Machine Learning is the method of self-learning from various experiences without human intervention. Machine Learning techniques can be used in IDS to reduce false alarm rates (FAR) and enhance accuracy in detection. In this paper, we investigated and experimented the use of a decision tree machine learning classifier in a network intrusion detection system. And also, we analyzed the performance of the decision tree classifier under various factors.