Miracle-dz/UniFAR-FTUs huggingface dataset:
4PCDT: A Quantifiable Parameter-based Framework for Academic Software Project Management

Many authorities like Project Management Body of Knowledge (PMBOK) and Capability Maturity Model Integration for Development (CMMI-Dev) lend a hand to software development organizations in management of their crucial projects. Though this area needs focused research, such models are not dedicatedly available for the academic projects developed by students of computer science and engineering where software project development is considered as one of the criteria for the award of degree to the future professionals of the IT industry. With this motivation, we explored 4PTRB, 3PR and software project management practices, approaches as well processes framed and provided by PMBOK and CMMI-Dev. The main aim of this research is to introduce and propose a software project management framework for the academic domain. The proposed framework contains identification and description of 7 and 26 quantifiable parameters and sub-parameters respectively . The framework is called 4PCDT for People, Process, Product, Project, Complexity, Duration and Technology for the academic software projects. To validate the proposed framework, an online survey of 113 faculties was conducted to rank and weigh the quantifiable parameters. The results show that People, Process and Technology management parameters are top 3 ranked parameters. The robustness of the approach is further evident from the results of experimentation on 18 actual academic software projects of final year post graduate students of the IT domain.Not only the proposed work is first of its kind, but also it is bound to generate an excellent ripple effect in the research community. Keywords—Academic; CMMI-Dev;PMBOK; project management; software project; student
Breeding for adaptation to climate change: genomic selection for drought response in a white spruce multi‐site polycross test

With climate change, increasingly intense and frequent drought episodes will be affecting water availability for boreal tree species, prompting tree breeders and forest managers to consider adaptation to drought stress as a priority in their reforestation efforts. We used a 19‐year‐old polycross progeny test of the model conifer white spruce (Picea glauca) replicated on two sites affected by distinct drought episodes at different ages to estimate the genetic control and the potential for improvement of drought response in addition to conventional cumulative growth and wood quality traits. Drought response components were measured from dendrochronological signatures matching drought episodes in wood ring increment cores. We found that trees with more vigorous growth during their lifespan resisted better during the current year of a drought episode when the drought had more severe effects. Phenotypic data were also analyzed using genomic prediction (GBLUP) relying on the genomic relationship matrix of multi‐locus gene SNP marker information, and conventional analysis (ABLUP) based on validated pedigree information. The accuracy of predicted breeding values for drought response components was marginally lower than that for conventional traits and comparable between GBLUP and ABLUP. Genetic correlations were generally low and nonsignificant between drought response components and conventional traits, except for resistance which was positively correlated to tree height. Heritability estimates for the components of drought response were slightly lower than for conventional traits, but similar single‐trait genetic gains could be obtained. Multi‐trait genomic selection simulations indicated that it was possible to improve simultaneously for all traits on both sites while sacrificing little on gain in tree height. In a context of rapid climate change, our results suggest that with careful phenotypic assessment, drought response may be considered in multi‐trait improvement of white spruce, with accelerated screening of large numbers of candidates and selection at young age with genomic selection.
Network Temperature: A Novel Statistical Index for Networks Measurement and Management

Being able to monitor each packet path is critical for effective measurement and management of networks. However, such detailed monitoring can be very expensive especially for large-scale networks. To address such problem, inspired by thermodynamics, which uses the statistical characteristics of a large number of molecules’ motion but not each molecule’s trajectory for analysis, we propose the new concept of network temperature together with the notions of network-specific heat and network temperature gradient. Our approach does not only provide a statistical view of the current network state consisting of all the active packet paths at each time instant, but can be used to represent transitions among network states. Our network temperature-based methods have a broad applicability, such as to DDoS detection, dynamic node importance ranking, network stability and robustness evaluation, reliable packets routing, provenance compression assessment, and so on. Numerical and/or the experimental results show that our methods are effective.
Working mothers in East and West Germany: a cluster analysis using a three-stage approach

Purpose The second Frankfurt Career Study was conducted in 2017 in East and West Germany to analyze the impact of motherhood on female professional advancement in the specific national context of Germany. In addition, this study aims to present a unique perspective of the similarities and dissimilarities between the Western and Eastern parts of the country. Design/methodology/approach The research is presented as a three-stage statistical approach based on quantitative data generated from a survey conducted among 2,130 working mothers. In the first step, the authors performed a multiple correspondence analysis to explore the relationships between important categorical variables. Using the object scores obtained in the first step, we then ran a hierarchical cluster analysis, followed by the third and last step: using the k-means clustering method to partition the survey respondents into groups. Findings The authors found that working mothers in Germany are distributed according to four clusters mainly described by demographics and orientation toward work. East Germany has been found as a more egalitarian context than West Germany with respect to family system arrangements. However, the upper bound of the sample in West Germany presented an atypical female breadwinner model in high-performance households. Originality/value The authors want to contribute to previous investigations on the topic by providing a more comprehensive view of the phenomenon, especially comparing the two different family systems and social norms from the Eastern and Western parts of the country. The authors ask whether and how career perspectives and female labor supply are influenced by drivers such as work–family conflict determinants, working mothers demographics, partner support and employer support.
Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International Multi-Center Study

Autism spectrum disorder (ASD) is a neurodevelopmental condition diagnosed in approximately 2% of children. Reliance on the emergence of clinically observable behavioral patterns only delays the mean age of diagnosis to approximately 4 years. However, neural pathways critical to language and social functions develop during infancy, and current diagnostic protocols miss the age when therapy would be most effective. We developed non-invasive ASD biomarkers using mass spectrometry analyses of elemental metabolism in single hair strands, coupled with machine learning. We undertook a national prospective study in Japan, where hair samples were collected at 1 month and clinical diagnosis was undertaken at 4 years. Next, we analyzed a national sample of Swedish twins and, in our third study, participants from a specialist ASD center in the US. In a blinded analysis, a predictive algorithm detected ASD risk as early as 1 month with 96.4% sensitivity, 75.4% specificity, and 81.4% accuracy (n = 486; 175 cases). These findings emphasize that the dynamics in elemental metabolism are systemically dysregulated in autism, and these signatures can be detected and leveraged in hair samples to predict the emergence of ASD as early as 1 month of age.
Mooring Pattern Optimization using Genetic Algorithms

1. Abstract This paper presents the development of a Genetic Algorithm (GA) to solve the problem of the mooring pattern of floating units used in oil exploitation operations. The distribution of mooring lines is one of the factors that directly influence the displacements (offsets) suffered by floating units when subjected to environmental conditions such as winds, waves and currents. Thus, the GA seeks an optimum distribution of the mooring lines whose final goal is to minimize the units' displacements. The computation of the floating unit's static equilibrium position is accomplished by applying the catenary equilibrium equation to each mooring line in order to obtain the out-of-balance forces on the unit, and by using an iterative process to compute the final unit equilibrium position. The objective function consists of the sum of the squares of the floating unit's displacements for each set of environmental conditions. The developed GA as well as a representative example and some conclusions are also presented.
Estimating the Effectiveness of Pulmonary Rehabilitation in Patients with Chronic Obstructive Lung Disease

Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible airflow limitation with inflammatory response of the lung and airways. COPD is a preventable and treatable disease with some significant extra-pulmonary effects that may contribute to the severity and progress of the disease. Pulmonary Rehabilitation is multidisciplinary intervention helps in improvement of symptoms, decrease of breathlessness, and improves the quality of life. The aim of this study is to estimate and evaluate the effectiveness of PR program in COPD patients. In this study regarding Pulmonary Function Test, there was no statistical significant difference in FEV1 pre and post Rehabilitation, but there was statistical significant difference in (FEV1%F, FVC and FEF) pre and post Rehabilitation. Regarding arterial blood gases there was statistical significant difference in ( PH, Pao2 and O2 mood) ,but there was no statistical significant difference in (Paco2 and Hco3) pre and post Rehabilitation . Regarding 6MWT there was statistical significant difference pre and post Rehabilitation and finally regarding mMRC dysnea scale there was statistical significant difference pre and post Rehabilitation.
Society of Exploration Geophysicists

The SEG Advanced Modeling Project (“SEAM”) is an important new initiative of the Society. There is a great need in academia and industry for advanced synthetic data sets based on realistic models of relevance to today’s geophysical challenges. Although computational capability continues to improve dramatically, the magnitude of the effort required is such that very few corporations can feasibly create these models and datasets independently. SEG is well positioned to take on the role of leading an industry-wide advanced modeling effort, as this is very much in alignment with the SEG mission to advance the science and technology of applied geophysics.
Managerial ownership and executive compensations: Interaction and impact on earnings management practices in an emerging economy

The study aimed to examine the relationship between executive compensations and earnings management. Also, it investigates whether managerial ownership influences that relation for the non-financial firms listed in Amman Stock Exchange (ASE) during the period 2010–2019. The study provides evidence that firms with a higher level of executive compensations are associated with a low level of earnings management practices. Results also show that the mitigating role of executive compensations is moderated in firms with managerial ownership, and executive compensations level in firms with managerial ownership is unlikely to be effective. In an attempt to maximize the personal interest, managers with sufficient ownership managed earnings in an opportunistic way to exploit the minority interest through taking advantage of the compensations contracts loopholes.
Guest Editorial Brain-Computer Interface: Today and Tomorrow

As the invited editor of this special issue on braincomputer interface (BCI), I am pleased to give a comment on the state-of-the-art with the introduction of recent advances made at the Chengdu BCI Group, University of Electronic Science and Technology of China (UESTC). The ability to “talk” with others, no matter using speech, face expression, writing, gesture, or soul induction if possible, is a crucial factor that makes our life more enjoyable. Also, communication is the groundwork of human existence, makes us available to understand each other, share happiness or sadness, and run the daily life. However, there is also a group around us who lost all or part of these evolutional outputs, no matter due to accident or pathology. How to repair or replace such an output is the main dream of the brain-computer interface. The study on BCI was initiated about 40 years ago, and is now becoming a hot topic in neuroscience. BCI or brain-machine interface (BMI) is usually considered as an effective pathway connecting the human brain (or some primate animals) to a computer which directly translates human intentions into sequences of control commands for an output device. Meanwhile, BCIs can also be taken as a novel and interesting tool of communication for normal persons. For example, in the field of multimedia, BCIs could possibly be utilized as an additional modality, such as imagine games in which BCIs are used for control. BCIs can be invasive or non-invasive. The invasive way may provide more detailed and comprehensive information about the brain mental events, thus augment the communication between the computer and the brain. But the involved technical and scientific problems are not solved completely; much more efforts are still on the way. The non-invasive mode is assumed being based on the scalp electroencephalogram (EEG), and the main remaining problems are clear, i.e. how to get efficient features of the underlying mental activities and classify these features accurately. The current efforts are focused on maturing the
GOVERNMENT POLICY OF THE CITY OF BANJARMASIN ON REDUCING THE USE OF PLASTIC BAGS (A CASE STUDY OF A GOVERNMENT POLICY OF THE CITY OF BANJARMASIN)

This study aims to determine the implementation of Banjarmasin city government policies in reducing the use of plastic bags in traditional markets in the city of Banjarmasin. This study uses a qualitative approach with a descriptive analysis method. As a key informant, the Head of the Sanitation and Waste Management Office of the Banjarmasin City Environmental Service has been interviewed to collect data. To complete the data, we also interviewed both sellers and buyers in the markets of Pandu and TelukDalam Banjarmasin City. The results showed that the city government had implemented the policy through a socialization program in communication and transmission. The socialization program has been implemented in almost all the markets owned by the Banjarmasin city of government. Through the socialization program, we were found that the implementation of the policy was clear and consistent. The Banjarmasin City Environmental Service apparatus is the human resource involved in implementing the policy. The other resources came from the Banjarmasin City Regional Revenue and Expenditure Budget in budget sources. In terms of disposition, policy implementers are committed to carrying out their duties properly and democratically. The existence of Standard Operating Procedures has guided the policy implementation in terms of bureaucratic structure. However, implementation constraints still exist, such as public response, including the general understanding of the policy's benefits to reduce the use of plastic bags.
Communicating Probability Information in Weather Forecasts: Findings and Recommendations from a Living Systematic Review of the Research Literature

Probabilistic forecast information is rapidly spreading in the weather enterprise. Many scientists agree that this is a positive development, but incorporating probability information into risk communication can be challenging because communicators have little guidance about the most effective way to present it. This project endeavors to create such guidance by initiating a “living systematic review” of research studies that empirically examine the impact of risk messages that use probability information on protective action decision making, intentions, and behaviors. In this article, we explain how we began the review, map the current state of the literature, synthesize core findings, provide actionable recommendations to assist forecasters in risk communication, and introduce an online platform that scholars and forecasters can use to interact with the data from the review. We conclude with two key points from the review that necessitate emphasis: the research literature strongly suggests that (1) average people can make sense of and use probability information if consideration is given to information presentation and (2) assuming appropriate presentation, probability information generally improves decision quality.
Human Activity Recognition using Smartwatch and Smartphone: A Review on Methods, Applications, and Challenges

Recently, Human Activity Recognition (HAR) has been a popular research field due to wide spread of sensor devices. Embedded sensors in smartwatch and smartphone enabled applications to use sensors in activity recognition with challenges for example, support of elderly’s daily life . In the aim of recognizing and analyzing human activity many approaches have been implemented in researches. Most articles published on human activity recognition used a multi -sensors based methods where a number of sensors were tied on different positions on a human body which are not suitable for many users. Currently, a smartphone and smart watch device combine different types of sensors which present a new area for analysis of human attitude. This paper presents a review on methodologies applied to solve problems related to human activity recognition that use the equipped sensors in smartphone and smartwatch with the employ of Machine Learning and the advance of deep learning approaches. The literature is summarized from four aspects: sensors types, applications, Machine Learning (ML) and Deep Learning (DL) models, results and challenges.
PXR2020: Re-Seeing the Possibilities of Theatre in Virtual, Augmented, and Mixed Reality

Abstract:What new insights and opportunities does virtual reality (VR) offer to theatre and performance? In turn, what potential might theatre offer to VR creators? This potentially fruitful cross-pollination was precisely what prompted host organizations Single Thread Theatre Company and Electric Company Theatre to program the inaugural Performance and XR Symposium in October 2020, a conference designed to foster and explore the relationship between theatre and XR (a catch-all term for media created with virtual, augmented, and mixed reality). The experience of the conference was one of re-seeing the possibilities of performance in a new media form: discussions around access, spectatorship, and ethics—topics familiar to and well trodden by theatre artists and scholars—were reinvigorated in the context of virtual reality.
State of the Art of Power Saving in Clusters and Results from the EDF Case Study

Energy management for mobile devices has been traditionally a well studied topic during the last two decades, as these devices usually do not have a permanent connection to the power grid and thus solely rely on the limited battery charge. However, this trend has been mostly disregarded in the context of HPC systems as the main focus mainly relied on improving the performance at any cost. Therefore, energy costs for operating and cooling the equipment of current data centers have increased significantly up to a point where they are able to surpass the hardware acquisition costs. In this work we survey the current energy conservation efforts in distributed systems. Hence, we start our work with the introduction of the basic principles of power and energy management. This involves the distinguishing between the two often misleading terms power and energy management. Furthermore, we present several approaches on how to measure the power consumption at system and component level. Moreover, in order to get a deeper understanding on where the most of the power is spent within a server, we take a closer look at its components (i.e. CPU, RAM, etc.) and outline the currently available energy-saving mechanisms. Given that energy consumption strongly depends on the workload characteristics we also discuss the different types of workloads (i.e. mobile, commercial and scientific). Afterwards, we continue with the current approaches for energy savings in distributed systems by focusing on both node and cluster-level efforts. Finally, we finish this survey with an outline of some open challenges and the introduction of the EcoGrappe project. Thereby, we detail its objectives and present some of the work done at our project partner EDF.
ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.