Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite Monitoring of Natural Reforestation Efforts in China's Drylands

Summary Desertification in Northern China degrades air quality in China's eastern cities by causing frequent dust storms. To stop desert expansion, China's government initiated the Three-North Shelterbelt Program, a large-scale reforestation project. Many issues with the project have been raised, from the choice of ill-adapted species to planting methods. Recently, the government implemented “natural reforestation”—closing former pastures to let vegetation regrow naturally. Unfortunately, it has been difficult to estimate the large-scale success of natural reforestation because measuring arid ecosystem productivity is a challenge for optical remote sensing. Here, we use satellite data to monitor vegetation water content and photosynthetic activity, thereby quantifying changes in vegetation biomass and productivity in Northern China. These satellite data corroborate official reforestation data. Our results show that vegetation activity is strongly correlated with both natural and traditional active reforestation, indicating opportunities for new natural reforestation techniques combined with satellite monitoring in other semi-arid regions.
Developing Inference Frameworks for Random Forests Using Bag of Little Bootstraps & Related Methods

In this thesis we seek to develop statistical inference frameworks for random forests, a widely-used machine learning method. We first explore the possibility of using ideas from the Bag of Little Bootstraps (BLB) to develop a modified implementation of the random forest algorithm which could potentially produce error estimates for random forest predictions. We then explore other approaches for attempting to obtain accurate prediction intervals for new observations. We delve into the specifics of constructing such prediction intervals, first employing naive percentile intervals and assessing their performance. We then propose an alternative method for constructing prediction intervals, based on examining the response variability within the nodes of decision trees. We thoroughly define this method and characterize its performance.
Coordinated Traffic Signal Control Via Bayesian Optimization for Hierarchical Conditional Spaces

We study the problem of coordination control of multiple traffic signals to mitigate traffic congestion. The parameters we optimize are the coordination pattern and offsets. A coordination pattern indicates which traffic signals are coordinated. Offsets show how traffic signals can be coordinated. We aim at finding the optimal combination of the coordination pattern and offsets. In this paper, we treat it as an optimization problem whose search space is a conditional space with hierarchical relationships; the coordination pattern determines the controllability of the offsets. Then, we tackle the problem by proposing a novel method built upon Bayesian Optimization, called BACH. Experiments demonstrate that BACH successfully optimizes coordination control of traffic signals and BACH outperforms various state-of-the-art approaches in the literature of traffic signal control and Bayesian Optimization in terms of best parameters found by these methods with a fixed budget.
Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach

Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.
Deliverable no.: D3.6 Title of Deliverable: Code Generation from Untimed Specifications

This deliverable reports on the progress of the work within W3.6: code generation from untimed specifications. We describe the P2J compiler which compiles a PROMELA model to an equivalent Java application. Keyword list: untimed specification, code generation, PROMELA, Java, P2J ICT-FP7-STREP-214755 / QUASIMODO Page 2 of 13 Public
Joint experimental and theoretical investigation of the propensity of peptoids as drug carriers

Transport across the membrane is one of the key obstacles drug molecules must overcome to effectively function in the cell. Potential drugs should therefore be designed taking into account these specific membrane-transport properties. Here we investigate, both experimentally and by simulation, one promising class of molecules. Peptoids are structural analogs of the amino acids that make up the proteins of the cell. They constitute a very promising class of drug carriers because they are known to cross several biological barriers and, unlike proteins, they are unaffected by proteases. All-atom molecular dynamics simulations demonstrate that even short peptoid molecules exist in a rapidly fluctuating conformational ensemble, which differentially presents hydrophobic and hydrophilic molecular surface area to its environment, making these molecules well suited candidates for tunable membrane transport. In vivo studies showed that the synthesized peptoid Fluo-{6,6,6,6,6,6}-NH2 efficiently translocates into the cells and accumulates preferably in the cytosol. Key-Words: drug carrier, peptoid, membrane transport, specific accumulation, cytosol, molecular surface area, allatom simulation
White Box – Yellow Box – Blakely’s Red Gum Grassy Woodland and Derived Native Grassland in the NSW North Coast, New England Tableland, Nandewar, Brigalow Belt South, Sydney Basin, South Eastern Highlands and NSW South Western Slopes Bioregions

of Conservation Assessment White Box – Yellow Box – Blakely’s Red Gum Grassy Woodland and Derived Native Grassland was found to be eligible for listing as Critically Endangered under Criteria A3 and D3. The main reasons for this Ecological Community being eligible are that it has undergone a very large historical reduction in geographic distribution (since approximately 1750) and has experienced disruption of biotic processes of relative severity >90% over more than 90% of its distribution since 1750.
A Load-Balanced Video Multicast Routing System in Software-Defined Networks

IP multicast in traditional networks, dictates high-end routers and incurs high administrative overhead, which is no longer suitable for deployment due to its complicated operations. In this thesis, we implement a multicast routing system based on SDN framework. For computing the multicast routes, we study the problem of establishing multipath multicast routing for streaming videos in Software-Defined Networks (SDNs). The objectives of the considered problem are robustness, load balance, adaptiveness, and SDN compatibility. We formulate the multicast routing problem into a mathematical optimization problem and propose a min-max link load multicast routing algorithm, called Robust Multipath Multicast Routing (RMMR). We further design a heuristic algorithm to obtain the multicast trees efficiently. We implement our proposed algorithm in our multicast routing system on OpenFlow controller. We conduct the experiments in real testbed and Mininet emulator to demonstrate the practicality, performance and scalability. In the experiment of real testbed, we measure the response time of our system: (i) all operations of flow-entries insertion are completed in less than 5 milliseconds, (ii) the detection time of all clients are no more than 0.15 second. In the experiments in Mininet emulator, we compare our algorithms with IP multicast. The results show the merits of our algorithms over the IP multicast, e.g., we observe: (i) frame loss rate reduction between 19% and 95%, (ii) video quality improvement between 4 dB and 15 dB, (iii) sink throughput increase between 25% and 66%, and (iv) maximal link utilization reduction between 15% and 50%. We also show the tradeoff between optimality and running time of the two proposed algorithms: one of them is more suitable for smaller and more static networks, and the other one is more suitable for larger and more dynamic networks.
Spectral embedding of graphs

These lecture notes introduce the spectral embedding of graphs, where each node is represented by a vector of low dimension using the spectral decomposition of the Laplacian matrix. This embedding can in turn be used to apply classical learning techniques, either semi-supervised (with labels attached to some nodes) or unsupervised (like ranking or clustering nodes). We shall see that the spectral embedding of graphs is closely related to random walks in the graph. The analogy with various ﬁelds of physics, like thermodynamics, mechanics and electricity, will also emerge naturally. These notes are mainly based on [1, 2, 3, 5]. We also refer the reader to [4] for an overview on spectral clustering.
On Using Application-Layer Middlebox Protocols for Peeking Behind NAT Gateways

—Typical port scanning approaches do not achieve a full coverage of all devices connected to the Internet as not all devices are directly reachable via a public (IPv4) address: due to IP address space exhaustion, ﬁrewalls, and many other reasons, an end-to-end connectivity is not achieved in today’s Internet anymore. Especially Network Address Translation (NAT) is widely deployed in practice and it has the side effect of “hiding” devices from being scanned. Some protocols, however, require end-to-end connectivity to function properly and hence several methods were developed in the past to enable crossing network borders. In this paper, we explore how an attacker can take advantage of such application-layer middlebox protocols to access devices located behind these gateways. More speciﬁcally, we investigate different methods for identifying such devices by using only legitimate protocol features. We categorize the available protocols into two classes: First, there are persistent protocols that are typically port-forwarding based. Such protocols are used to allow local network devices to open and forward external ports to them. Second, there are non-persistent protocols that are typically proxy-based to route packets between network edges, such as HTTP and SOCKS proxies. We perform a comprehensive, Internet-wide analysis to obtain an accurate overview of how prevalent and widespread such protocols are in practice. Our results indicate that hundreds of thousands of hosts are vulnerable for different types of attacks, e.g., we detect over 400,000 hosts that are likely vulnerable for attacks involving the UPnP IGD protocol. More worrisome, we ﬁnd empirical evidence that attackers are already actively exploiting such protocols in the wild to access devices located behind NAT gateways. Amongst other ﬁndings, we discover that at least 24% of all open Internet proxies are misconﬁgured to allow accessing hosts on non-routable addresses.
AI-assisted Network-slicing based Next-generation Wireless Communications

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, etc. Such requirements pose new challenges to architecture design, network management, and resource orchestration in next-generation wireless networks. Starting from illustrating these challenges, this paper targets at providing an understanding of the overall architecture of next-generation wireless networks and three speciﬁc research directions under this overall architecture. First, we introduce a network-slicing based architecture and explain why and where artiﬁcial intelligence (AI) should be adopted into this architecture. Next, the motivation, research challenges, existing works
Automated Scoring of Levels of Integrative Complexity Using Machine Learning and Natural Language Processing

- Conceptual/Integrative complexity (IC) is a construct used in political psychology and clinical psychology to gauge an individual’s ability to consider different perspectives on a particular issue and subsequently form a conclusion that draws from the said perspectives. Presently IC is scored from text manually which is time-intensive, laborious and expensive. For a rater to be qualified to score IC, it is standard that he/she go through a rigorous training program. Consequently, there is a demand for automating the scoring, which could significantly reduce the time, expense and cognitive resources. Any algorithm that could achieve the above with a reasonable accuracy could assist in researchers who are interested in broadening the horizon for IC research. Furthermore, such a development could also assist in the design of intervention systems for reducing the potential for aggression, systems for recruitment processes and even training personnel for improving group complexity in the corporate world. In this study we used machine learning and natural language techniques to predict IC levels from text. We developed an intelligent feature called Semantic Paragraph Coherence for the prediction of IC levels in text. We achieved over 83% accuracy in a three way classification.
The Consequences of Media Mulitasking for Youth: A Review

The increasing prevalence of media multitasking among youth has raised concerns regarding its negative effects on youths’ functioning. Although the number of empirical studies on the consequences of media multitasking for youth has grown rapidly, there has been no attempt to integrate theory with the results of these studies. This review integrates available findings on the relationship between media multitasking and three domains of youths’ functioning: cognitive control, academic performance, and socioemotional functioning. Three databases (PsycINFO, ERIC, and CMMC) were searched to identify relevant studies, resulting in 8448 citations. Fifty-six studies met the inclusion criteria: nine studies on cognitive control, 43 on academic performance, and four on socioemotional functioning. Overall, the findings indicate a small to moderate negative relationship between media multitasking and the three domains of youths’ functioning. However, evidence regarding the causal direction of this relationship is lacking. Based on the included studies, we identify several research gaps and present five main directions for future research: examining causality, establishing more targeted theories, improving media multitasking measurements, focusing on individual and contextual differences, and including representative samples. THIS CHAPTER IS PUBLISHED AS: van der Schuur, W. A. , Baumgartner, S. E. , Sumter, S. R. , & Valkenburg, P. M. (2015). The consequences of media multitasking for youth: A review. Computers in Human Behavior, 53, 204-215. doi:10.1016/j.chb. 2015.06. 035
Mathematical modelling as a virtual learning environment for teacher education programs

This study was developed with 76 students enrolled in a teacher education program in Mathematics, which is part of the Brazilian Open University System, at the Universidade Federal de Ouro Preto. It was conducted during the second semester of 2017, in 5 (five) educational centres (polos) located in the states of Sao Paulo and Minas Gerais, in Brazil. This research was performed according to methodological procedures used in case studies, by covering the collection and data analysis. Methodological procedures used in this study enabled the critical and reflective interpretation of the results through the elaboration of categories that emerged from qualitative data collected during the development of the fieldwork. The main objective of this study was the proposition of mathematical modelling as a virtual learning environment (VLE) that favours the development of a critical view of the reality and the reflection of students in solving problems that they face daily, by developing modelling projects by applying technological tools available in the Moodle platform. Through these projects, students problematized, contextualized, and investigated problems they developed in their projects in the VLE. As well, they elaborated questions that aimed to seek, collect, select, organize, and handle the information that allowed them to critically reflect about the role of mathematics in society.
Inconsistency Measures for Disjunctive Logic Programs Under Answer Set Semantics

We address the issue of quantitatively assessing the severity of inconsistencies in disjunctive logic programs under the answer set semantics. Taking the non-monotonicity of answer set semantics into account brings new challenges that have to be addressed by reasonable accounts of inconsistency measures. We investigate the behaviour of inconsistency in logic programs by revisiting existing rationality postulates for inconsistency measurement and developing novel ones taking non-monotonicity into account. Further, we develop new measures for this setting and investigate their properties, in particular with respect to their compliance to these rationality postulates and their computational complexity.
Online-based Entrepreneurship Education - Its Role and Effects: a Randomised Controlled Trial about the Effects of an Online Entrepreneurship Programme Based on Role Models

The purpose of this paper is to analyse the effects of an online-based entrepreneurship programme A randomised controlled trial has been performed in which 580 randomly selected pupils (aged 14-15) have been randomly assigned to participate in online programmes that focus either on entrepreneurship or on environmental issues The analysis builds on responses collected before, directly after, and one year after the intervention The short-term results show that the programme focusing on entrepreneurship had a significantly positive influence on the participants entrepreneurial intentions, venture creation self-efficacy, entrepreneurial attitudes and perceived knowledge about entrepreneurship One year after the intervention, the differences between the groups were smaller, but the pupils in the experiment group still had, relative to the control group, significantly higher perceived entrepreneurial knowledge, and significantly more positive attitudes towards entrepreneurship The influence which the pupils prior experience with entrepreneurship had on the programmes effectiveness was limited This implies that the programme has a significant influence on the participants which goes beyond just increasing their familiarity with the topic The online programme did not, however, significantly influence the participants self-efficacy concerning enterprising competences An analysis of how the participants perceived that the educational focus in their normal education had changed, with regard to teaching focusing on creativity and value creation, demonstrated that this type of education can be an efficient way to foster enterprising self-efficacy
Personalized modeling of training load and physical capacity of an elite rower

There are many opportunities for using data science within the sports domain. One such opportunity is modeling the relationship between training load and an athlete’s training response. As this response is athlete specific, it is interesting to investigate this relationship on an individual level. Understanding this response may provide opportunities to optimize an athlete’s training plan and further improve athletic performance. Additionally, understanding this relationship may also aid in injury prevention. The idea of personalized models has been investigated in medicine, but is quite new in the sports domain. This thesis aims to investigate the application of personalized models within the sports domain. Specifically, we aim to build a personalized regression model, capable of predicting a specific athlete’s physical capacity based on historical training data. We constructed our model from the historical training data and physical capacity tests of an elite rower. This historical data was supplied in the form of a logbook, where several measures were logged daily. These measures described internal training load, external training, recovery and well-being. From this logbook, we derived features describing several aspects of training, with a focus on training load. Furthermore, we defined peak V O2 as the target measure of physical capacity. We used a feature-based approach, in which we translated daily logbook data into features corresponding to specific time windows. From this approach, we were able to obtain aggregated features and overcome the problem of unequal and inconsistent sampling rates. Hereafter, we applied the Least Absolute Shrinkage and Selection Operator (or LASSO) regression to select a subset of important features and fit a model. The resulting model was validated within a nested cross-validation procedure. The best cross-validated models only reached an R2 value of 0.30. Given the low amount and type of data available, the personalized models were not reliable enough for meaningful predictions. Future work should further explore under which conditions (such as more controlled data collection, higher frequency or more rigorous target variables) our modeling approach can yield insightful results.