Miracle-dz/UniFAR-FTUs huggingface dataset:
Change management to support institutional amendment of higher education based on Kotter's model

Change Management needs to be implemented for all academic communities of Telkom Jakarta Academy, due to institutional changes to be Institute level. This paper aims to support successful change management for shareholders and stakeholders of Telkom Jakarta Academy and Telkom Education Foundation to implement The Jakarta Telecommunications Institute of Technology based on measurement results of five indicators studied (implementation team, Telkom Education Foundations, individuals, leaders, and implementation readiness). The method conducted in this paper is Action Research, where the deep interviews with some chairmen of Telkom Education Foundation are conducted, questionnaire for Akademi Telkom's stakeholder based on Kotter's model guidance in managing Change Management. The results of this reseacrh indicate that there are differences between the five research indicators compared (the implementation team, Telkom Education Foundations, individuals, leaders, and implementation readiness).
Digital twin of an urban-integrated hydroponic farm

Abstract This paper presents the development process of a digital twin of a unique hydroponic underground farm in London, Growing Underground (GU). Growing 12x more per unit area than traditional greenhouse farming in the UK, the farm also consumes 4x more energy per unit area. Key to the ongoing operational success of this farm and similar enterprises is finding ways to minimize the energy use while maximizing crop growth by maintaining optimal growing conditions. As such, it belongs to the class of Controlled Environment Agriculture, where indoor environments are carefully controlled to maximize crop growth by using artificial lighting and smart heating, ventilation, and air conditioning systems. We tracked changing environmental conditions and crop growth across 89 different variables, through a wireless sensor network and unstructured manual records, and combined all the data into a database. We show how the digital twin can provide enhanced outputs for a bespoke site like GU, by creating inferred data fields, and show the limitations of data collection in a commercial environment. For example, we find that lighting is the dominant environmental factor for temperature and thus crop growth in this farm, and that the effects of external temperature and ventilation are confounded. We combine information learned from historical data interpretation to create a bespoke temperature forecasting model (root mean squared error < 1.3°C), using a dynamic linear model with a data-centric lighting component. Finally, we present how the forecasting model can be integrated into the digital twin to provide feedback to the farmers for decision-making assistance.
Exploiting Smart Contract Bytecode for Classification on Ethereum

Due to the increase in smart contracts in Ethereum, a need for proper classiﬁcation has emerged. Although the smart contracts are accessible due to the open nature of the Blockchain, readability is still an issue with respect to the smart contract bytecode. We propose an automated approach for classifying smart contracts that utilize popular text classiﬁcation methods on the opcode translation of the smart contract bytecode in order to overcome this limitation. Our experiments indicate that the decision-tree-based techniques like Random Forest and Xgboost outmatch the traditional classiﬁcation tools like Na¨ıve Bayes, Logistic Regression, and SVM once the opcode input is presented as n-gram tf-idf vectors.
Towards Developing Trusted Smart Contracts in Simulink

Blockchain has emerged as a subject of intense interest in research fields and beyond. One of key enabling technologies is smart contracts. Smart contracts bring transparency, simplicity, and efficiency to blockchain applications. Several languages and tools have been developed for smart contracts over the years but few are easily usable for domain experts in control systems as most of them are Simulink users and learning a new language and tool could be a challenge for them. In this paper, we propose a trusted smart contract development approach in Simulink Stateflow for blockchain applications. The approach introduces a design environment and generates evidences of trust for smart contracts via formal verification, simulation, automated test generation, and test execution. Finally, the formally verified Stateflow models is automatically synthesized into Solidity for deployment on blockchain platforms. This approach will not only bring the assurance of smart contracts to the next level, but also will make the blockchain technology accessible to the control experts, which could inspire broader blockchain applications in industry. We implemented the framework as a toolbox in Simulink, which has been used by GE Research. We will demonstrate the capabilities and effectiveness of the toolbox using a real trans-active energy example to show that valid issues are identified along the development cycle.
Hard-coded Censorship in Open Source Mastodon Clients - How Free is Open Source?

This article analyses hard-coded domain blocking in open source software, using the GPL3-licensed Mastodon client Tusky as a case example. First, the question of whether such action is censorship is analysed. Second, the licensing compliance of such action is examined using the applicable open-source software and distribution licenses. Domain blocking is found to be censorship in the literal deﬁnition of the word, as well as possibly against some the used Google distribution licenses — though some ambiguity remains, which calls for clariﬁcations in the agreement terms. GPL allows for functionalities that limit the use of the software, as long as end-users are free to edit the source code and use a version of the application without such limitations. Such software is still open source, but no longer free (as in freedom). A multi-disciplinary ethical examination of domain blocking will be needed to ascertain whether such censorship is ethical, as all censorship is not necessarily wrong.
FabNet: an Automatic Hyperledger Fabric Network Wizard (short paper)

Hyperledger Fabric is continually evolving technology, thanks to its active community providing new features and functionalities. However, it still lacks a user-friendly interface that makes its adoption immediate and straightforward. In particular, one of the main barriers for non-expert users is the comprehension and creation of a network. In this paper, we present FabNet, a user-friendly wizard reducing the eﬀort for the conﬁguration and deployment of the Fabric blockchain network.
What Can Crowd Computing Do for the Next Generation of AI Systems?

The unprecedented rise in the adoption of artiﬁcial intelligence techniques and automation in many contexts is concomitant with shortcomings of such technology with respect to robustness, interpretability, usability, and trustworthiness. Crowd computing offers a viable means to leverage human intelligence at scale for data creation, enrichment, and interpretation, demonstrating a great potential to improve the performance of AI systems and increase the adoption of AI in general. Existing research and practice has mainly focused on leveraging crowd computing for training data creation. However, this perspective is rather limiting in terms of how AI can fully beneﬁt from crowd computing. In this vision paper, we identify opportunities in crowd computing to propel better AI technology, and argue that to make such progress, fundamental problems need to be tackled from both computation and interaction standpoints. We discuss important research questions in both these themes, with an aim to shed light on the research needed to pave a future where humans and AI can work together seamlessly, while beneﬁting from each other.
Methane variability measured across Russia during TROICA expeditions

Abstract To study the atmospheric constituents, including methane, in the boundary layer over Russia, surveys along the Trans-Siberian railway were carried out between 1996–2004 in the framework of the TROICA project. Statistical analysis of the data highlighted the main features of methane distribution and variability over the latitudinal belt along the railway. The lowest variability is found in Eastern Siberia and in the Far East (a mean of 1830 ppb), with the difference between expeditions and seasons less than 50 ppb. These regions appear to have negligible local methane sources and seem to represent a regional background. The highest methane concentrations are found over West Siberia in summer (a mean of 1895–2050 ppb). West Siberia has the highest signal non-uniformity and pronounced seasonality, and wetlands are discussed as the main methane source. In emission areas, the diurnal methane cycle is characterized by a morning concentration maximum which corresponds to accumulation under temperature inversions at night, the maximum accumulation (75 ppb) was observed in West Siberia in summer.
Congenital malformations : a report of a study of series of consecutive births in 24 centres

A report is presented of a study of births in 24 centers in 16 countries with respect to the occurrence and type of ciongenital malformations found in still born and liveborn infants. In all the outcomes of 421781 pregnancies were investigated (416695 single births 5022 sets of twins 63 sets of triplets and 1 set of quadruplets). The frequencies of malformations of specific types or of groups of malformations are considered with particular reference to geographical variations and associations with consanquinity of parents. The evidence relating to clinical and etiological heterogeneity is considered as well as that of the genetic contribution to congenital malformations and perinatal mortality. The data are presented in considerable detail in tables and in addition there is available on request to the authors a 400 page companion booklet of basic tables for each center. Among the findings of particular interest are the following. The large contribution of neural tube defects to fetal wastage in most countries and the significant correlations of frequencies of these defects over the 24 recording centers are noted. There is un unexplained correlation in frequency between neural tube defects and dizygous twinning. There is a marked association between consanquinity of parents with increases stillbirth rates and frequency of early death of an infant these frequencies being highest where parents are most closely related. There has also been demonstrated that malformations known to be due to the expression of a single recessive gene mutations are ignored consanquinity of parents is demonstrably associated in these data with neutral tube defect frequencies only. A number of interesting observations either novel or confirmatoryof views derived from different approaches emerge in respect of specific groups of malformations. This is so particularly with respect to harelip and cleft palate malformations of the gut malformations of the urogential tract and multiple malformations occurring in the same child. The findings concerning twin births are of interest in light of the relative contributions of monozygous and dizygous pairs to the total variance of twinning frequencies in different centers. Estimates are made of the effects of monozygosity on infant survival and on the occurrence of malformations. (authors modified)
ANXIETAS DITINJAU DARI HOPE PADA PASIEN PENDERITA PENYAKIT DIABETES DI KLINIK DIABETES

This study aimed to determine the correlation between hope and anxiety. The subjects were patients of diabetic in Clinic Diabetes Dharma Medan. The sampling technique used for this study was total sampling with the number of respondents 126 people. The collection of data for this study used self reports method. Data were obtained from a scale for measuring hope and anxiety. The calculation was performed by means of testing requirements analysis (assumption) that consists of a test for normality and linearity. Data collected was analyzed with Pearson Product Moment through SPSS 18 for Windows. The results of data showed correlation coefficient r = -0,472, and p = 0.000 (p <0.05), indicating that there is a negative relationship between hope and anxiety. The results of this study indicated that the contribution (R2) of the hope variable on the anxiety was at 22.3 %, and 77.7 % was influenced by other factors not examined in this study. Based on the results it was concluded that there was a correlation between hope and anxiety.
From spatial to platial - the role and future of immersive technologies in the spatial sciences

Immersive technologies such as virtual and augmented reality have been part of the technology mindset in computer and geospatial sciences since early on. The promise of delivering realistic experiences to the human senses that are not bound by physical reality has inspired generations of scientists and entrepreneurs alike. However, the vision for immersive experiences has been in stark contrast to the technology that has yet to properly support that vision; the community has battled nuisances such as cybersickness, tethers, and display quality for the last few decades. With the "final wave" of immersive technologies, we are now able to fulfill a long-held promise and freely envision how immersive technologies change spatial sciences by creating embodied experiences for geospatial applications. These experiences are not restricted by time or place, nor are they limited to the physical world. This contribution envisions a future for spatial sciences that is enabled by immersive technologies discussing their potential and challenges.
Towards Immersive Comprehension of Software Systems Using Augmented Reality - An Empirical Evaluation

While traditionally, software comprehension relies on approaches like reading through the code or looking at charts on screens, which are 2D mediums, there have been some recent approaches that advocate exploring 3D approaches like Augmented or Virtual Reality (AR/VR) to have a richer experience towards understanding software and its internal relationships. However, there is a dearth of objective studies that compare such 3D representations with their traditional 2D counterparts in the context of software comprehension. In this paper, we present an evaluation study to quantitatively and qualitatively compare 2D and 3D software representations with respect to typical comprehension tasks. For the 3D medium, we utilize an AR-based approach for 3D visualizations of a software system (XRaSE), while the 2D medium comprises of textual IDEs and 2D graph representations. The study, which has been conducted using 20 professional developers, shows that for most comprehension tasks, the developers perform much better using the 3D representation, especially in terms of velocity and recollection, while also displaying reduced cognitive load and better engagement.
Position dependent response of Magnetic Nanoparticles using Magnetic Particle Imaging (MPI)

This paper presents the design and implementation of magnetic particle imaging (MPI) for 1D phantom imaging. Selection field setup, transmit and receive coil is designed and simulated in COMSOL Multiphysics®. Comprehensive analysis of the selection field and homogenous transmit field superposition is carried out in COMSOL. 1D phantom is scanned at different positions inside the field of view (FOV). Optimum signals are achieved at static field free point (FFP) and minimum signals are observed at the boundaries (zero velocity of FFP) of the FOV.
Integrated Intersection Management for Connected, Automated Vehicles, and Bicyclists

In recent years, significant progress in the development of connected and automated vehicles (CAVs) has been observed. In combination with new control strategies at intersection zones, CAVs have the potential to increase both safety and efficiency in urban traffic: Numerous studies that present and evaluate automated intersection management (AIM) show a significant reduction of vehicle delays. However, most of these studies assume a fully connected traffic environment and do not consider non-connected road users such as human-driven vehicles, pedestrians, and bicyclists. In this paper, novel strategies for integrating bicyclists into AIM are introduced and compared to two different fully actuated traffic signal controls. The presented strategies consist of a first-come, first-served strategy for vehicles in combination with traffic signals for bicyclists. Depending on the available information about approaching bicyclists, bicycle operations are included into the control in fixed cycles or on demand. A four-leg intersection is simulated using a microsimulation platform where all control strategies are implemented and tested. A set of scenarios considering different levels of vehicle and bicycle demand is evaluated. Results show that bicyclists can be included into AIM while guaranteeing a maximum bicyclist waiting time, and the level of service for vehicles can be significantly improved.
Model Predictive Torque Control of an Induction Motor with Discrete Space Vector Modulation

In order to improve the performance of model predictive torque control (MPTC) for induction motor (IM), this paper proposed the use of discrete space vector modulation (DSVM). In traditional MPTC, the application of one voltage vector is performed during the whole control cycle, which requires a small sampling time for proper operation. The application of the voltage vector can be performed in a discrete manner by using the DSVM to increase the sampling time required for the control. Nevertheless, DSVM increase considerable the computational burden of the controller, for this reason, this paper proposed a methodology to reduce the number of voltage vectors of DSVM evaluated in MPTC to keep computational burden feasible. Simulation results on a commercial IM are presented to prove the effectiveness of the proposed method.
Collaborated Closed Loops for Autonomous End-to-End Service Management in 5G

In 5G networks of high complexity, autonomous management of the end-to-end (E2E) services is desired. Closed-loop (CL) control is a future-proof means to enable this autonomy. In the multi-provider, multi-vendor, and multi-tenant environment, the autonomous service management demands collaborations of all providers and vendors. In this paper, the collaboration problem is formulated as cross-CL collaboration involving multiple CLs participating in the E2E service management. We present a cross-CL collaboration framework with key elements, e.g., APIs, governance models, intent and policies management. Then a representative use case of service assurance with edge clouds is described to exemplify how the framework is applied to implement cross-CL collaborations and answer key questions: WHEN collaboration is needed; and HOW collaboration is enabled.
Performance of Mixed Channel Cell-Free Systems

The pursuit of improving the performance of communications systems has, in recent years, led to the popularization of the concept of cell-free system. In this system, division of space in cells is abolished and base stations are replaced by low-cost access points serving all mobile terminals in the system. In the literature, the behavior of such systems has been studied considering the non line-of-sight (NLoS) condition and, more recently, LoS condition. In this work, we propose, for cell-free systems, the use of a mixed channel model, where the occurrence of both conditions is possible, depending on the distances of the links. Analyzes were performed based on the number of devices, the density of users and access points and the quality of the channel estimation. The results indicate that the densification of the system, by reducing the area, affects the three scenarios in different ways. The mixed scenario surpassed, in terms of achievable rates, the homogeneous scenario with line-of-sight.
The Bridge and Link Betweenness in the Networks

In the network science, there are many network measures and parameters to evaluate the centrality of the nodes in different aspects, such as degree, node betweeenness, closeness, eigenvector centrality, and so on. These measures and parameters can be utilized in crucial nodes determination. In many situations, we also need to evaluate the centrality of the links in the network. However, we only know two measures to describe the centrality of the link: the bridge link and the link betweenness. In this paper, we study the two measures of the links in the network and discover that the centrality of the link betweenness is much more robust and effective than the comprehensive centrality based on the bridge in the random networks, scale-free networks and empirical networks.
European Union Dataset and Annotation Tool for Real Time Automatic License Plate Detection and Blurring

Automatic license plate detection has always been a popular topic in intelligent transport systems. In recent years, many approached the problem using artificial intelligence and machine learning techniques. Sufficient amount of good quality data is critical for machine learning, however, most of the existing open source license plate datasets are either restrictive in nature or do not have enough number of images to allow training a robust license plate detection network. In this paper, the THI License Plate Dataset (TLPD) is presented. It has more than 17,000 vehicle images and 18,000 labelled license plates in it. It is one of the largest publicly available European Union license plate datasets. The images in the dataset capture license plates at different angles and distances in relation to the camera. They are also taken under different illumination and weather conditions, making the dataset suitable for training robust license plate detectors that could work in various scenarios. A fast labelling tool for single class labelling is also presented in this paper. This tool was used to label the license plates in TLPD. This paper also introduces two license plate detection networks trained with data from TLPD. The networks work robustly under various scenarios, achieving precision and recall value of 93% under day and night conditions and 87% under heavy snowing condition.
Data Analytics of Students' Profiles and Activities in a Full Online Learning Context

The use of a Learning Management System (LMS) in e-learning makes it easier for teachers to track and record student learning behavior. The right analytics of e-learning students can help teachers understand the student context and what learning experiences are most suitable for e-learning students to improve learning outcomes. However, e-learning teachers often experience difficulties in analyzing student data due to a large number of students who must be analyzed and limited data. To support research in this area, we conducted a descriptive analysis of a dataset containing student data from the Open and Distance Learning (ODL) that organizes e-learning. The dataset contains data on student demographic profiles and student activity or behavior during e-learning which is recorded in the LMS system at the Open University of Indonesia. In this initial study, the dataset contained information from 120 classes in 18 subjects with 4,741 students from 33 study programs with many logs on LMS 1,641,234 entries. This article presents an analytical description of the characteristics of students participating in e-learning using Exploratory data analytics (EDA) and machine learning approaches as the basis for predictive and prescriptive analytics of student learning outcomes based on a combination of demographic profile data and learning behavior. This study helps education practitioners in the first step of analytics data as the basis for developing e-Learning instructional designs that support the success of fully online students.