Miracle-dz/UniFAR-FTUs huggingface dataset:
Protocol design and performance analysis for mobile ad hoc networks

The future wireless communication system will be heterogeneous, broadband, and inter-operational, and mobile ad hoc networks (MANETs) will be an important component. However, the salient characteristics make the design of MANETs face serious challenges. This book attempts to introduce the design, modeling and the performance analysis of various types of MANET protocols related to dynamic mobility and scalability in the network layer, and the minimization of the data collisions in the MAC layer. The first focus is on the network protocols to reduce the influence of host mobility. A novel location service protocol based on k-hop cluster is proposed to provide mobility management for large scale ad hoc networks. On the other hand, a broadcasting relay approach is proposed for effectively rebroadcast packets in an MANET. The second focus is to improve the performance of IEEE 802.11 MAC in a multi-hop environment with large interference range. This book would be useful for the scholars and research students working in the field of MANETs. The analysis methodologies applied in this book are helpful to understand and formulate the characteristics of MANETs.
Generation and Analysis of Gridded Visibility Data in the Arctic

With the accelerated warming of the arctic and the gradual opening of the Arctic passages, more and more attention has been paid to assessing the risk of the navigation environment in the Arctic. Due to the scarcity of visibility data in the Arctic, this study proposes a model for referring visibility based on a back propagation (BP) neural network. The reliability of the model is validated and the gridded atmospheric visibility data in the Arctic from 2009 to 2018 was obtained. At the same time, this study analyzed the spatial and temporal features of visibility in the Arctic. The results show that the mean relative error is less than 20% under the different sample forms and it is more accurate to infer the visibility in a specific month using the multiple-year data of that month as training samples. Furthermore, the amount of sample data has a positive effect on the accuracy of inferred visibility, but the effect decreases with data quantity increasing. Visibility changes quickly in the south of 80° N in August, but slowly in the north in that time. At the same time, visibility in July and August is lower than that in other months but higher in March and May.
Green Storm-water Infrastructure Strategy Generation and Assessment Tool For Site Scale and Urban Planning

Green Stormwater Infrastructure (GSI), which includes elements like green roofs, bioretentions, pervious pavement and cisterns, has proven to be a cost effective alternative that can achieve a wider range of benefits in stormwater management compared to the single-purpose grey/traditional infrastructure (Combined Sewer System or Municipal Separate Storm and Sewer System). However, the implementation of GSI as a replacement or support system for grey infrastructure faces some barriers preventing its adoption by planners/designers and decision makers, in the private as well as in the public sector. While understanding and quantifying the full costs and the benefits of GSI plans are critical for sound decision making and to facilitate its adoption, planners/designers face challenges pertaining to hydrologic calculations (including sizing of Green Stormwater Infrastructure Elements (GSIEs), cost estimation (life cycle costs) and benefit assessment). This thesis presents a proof of concept tool (prototype) that is developed to help planners/designers and different stakeholders involved in conducting a comprehensive GSI planning process. The tool generates GSI alternative solutions (combination and size of GSIEs) that meet the user's financial and hydrologic objectives. These alternatives are generated to maximize benefits, reduce costs and account for design specifications and multi-functionality of GSI. The prototype has three modules:· An Interface Module (including Input and Output Interfaces)· A Process Module for hydrology, cost and benefit calculation and an optimization model for strategy generation. · A Database Module for storing collected data that are used for different process module calculations. The prototype, which is developed in Ms. Excel, was successfully tested for functionality and usability on a residential development site in Pittsburgh, PA. The prototype required minimal input from the user (site specific data) and generated strategies that double the net annual benefits compared to the plan proposed by the developer. The tool also allowed generation of strategy scenarios that responded to multiple user objectives.
Agrammatism as a variable phenomenon

Abstract With a group of 11 Dutch agrammatic speakers, the studies on production and comprehension of word order by Saffran et al. (1980) and Schwartz et al. (1980) were replicated. We found a pattern of errors that was qualitatively the same as observed by these authors. However, the absolute number of errors was much lower: as a group our agrammatics performed highly above chance. In a second study, we asked seven patients to order various types of sentences in which the position of the verb varied. Again the level of performance was highly above chance. There was no effect of sentence type with respect to verb position (VSO or SOV). Another effect of sentence type, however, was apparent. If the position of the verb could be determined by processing a cue that was within the clause to be ordered, the sentences elicited few errors. Sentences with out-of-clause cues were much more difficult. It seems that there is variability, both between patients and between sentences. A series of possible accounts of t.
A Preliminary Investigation on the Role of Brittle Fracture in the Kinematics of the 2014 San Leo Landslide

The stability of high rock slopes is largely controlled by the location and orientation of geological features, such as faults, folds, joints, and bedding planes, which can induce structurally controlled slope instability. Under certain conditions, slope kinematics may vary with time, as propagation of existing fractures due to brittle failure may allow development of fully persistent release surfaces. In this paper, the progressive accumulation of brittle damage that occurred prior to and during the 2014 San Leo landslide (northern Italy) is investigated using a synthetic rock mass (SRM) approach. Mapping of brittle fractures, rock bridge failures, and major structures is undertaken using terrestrial laser scanning, photogrammetry, and high-resolution photography. Numerical analyses are conducted to investigate the role of intact rock fracturing on the evolution of kinematic freedom using the two-dimensional Finite-discrete element method (FDEM) code Elfen, and the three-dimensional lattice-spring scheme code Slope Model. Numerical analyses show that the gradual erosion of clay-rich material below the base of the plateau drives the brittle propagation of fractures within the rock mass, until a fully persistent, subvertical rupture surface form, causing toppling of fault-bounded rock columns. This study clearly highlights the potential role of intact rock fracturing on the slope kinematics, and the interaction between intact rock strength, structural geology, and slope morphology.
A Coordination Model for Network Computing Environments

Abstract Network computing environments (NCEs) are envisioned as collections of interactive and cooperating programs, tools, clients, and intelligent agents. The development of suitable coordination technology for NCEs is very important. In this article, we introduce a novel coordination model for NCEs. Our framework is basically a tuple-based coordination model, so our framework provides an anonymous and asynchronous coordination mechanism. Furthermore, our framework has the properties of high security and high flexibility and can be easily understood and maintained.
A new general fractional-order derivative with Rabotnov fractional-exponential kernel

In this article, a general fractional-order derivative of the Riemann-Liouville type with the non-singular kernel involving the Rabotnov fractional-exponential function is addressed for the first time. A new general fractional-order derivative model for the anomalous diffusion is discussed in detail. The general fractional-order derivative operator formula is as a novel and mathematical approach proposed to give the generalized presentation of the physical models in complex phenomena with power law.
SIMIT: Subjectively Interesting Motifs in Time Series

Numerical time series data are pervasive, originating from sources as diverse as wearable devices, medical equipment, to sensors in industrial plants. In many cases, time series contain interesting information in terms of subsequences that recur in approximate form, so-called motifs. Major open challenges in this area include how one can formalize the interestingness of such motifs and how the most interesting ones can be found. We introduce a novel approach that tackles these issues. We formalize the notion of such subsequence patterns in an intuitive manner and present an information-theoretic approach for quantifying their interestingness with respect to any prior expectation a user may have about the time series. The resulting interestingness measure is thus a subjective measure, enabling a user to find motifs that are truly interesting to them. Although finding the best motif appears computationally intractable, we develop relaxations and a branch-and-bound approach implemented in a constraint programming solver. As shown in experiments on synthetic data and two real-world datasets, this enables us to mine interesting patterns in small or mid-sized time series.
Awareness and Usage of E-Learning Materials among Saudi University Students

An investigation was conducted on the usage of e-learning tools among a sample of 350 Saudi university students, and their experience of usability. Data was collected from a nationwide survey using an instrument containing a self-constructed 12-item checklist. The results suggest a lack of awareness of e-learning tools persists, issues with usage, and common deficiencies in usability experiences. This may explain why some tools are under-utilised, and why learning management systems are inadequately exploited as technological aids in education. The study lends support to the need for improving software usability generally, and in the case of e-learning tools in particular if their usage is to be promoted.
Nonlinear Distortion in Wideband Radio Receivers and Analog-to-Digital Converters: Modeling and Digital Suppression

Emerging wireless communications systems aim to flexible and efficient usage of radio spectrum in order to increase data rates. The ultimate goal in this field is a cognitive radio. It employs spectrum sensing in order to locate spatially and temporally vacant spectrum chunks that can be used for communications. In order to achieve that, flexible and reconfigurable transceivers are needed. A software-defined radio can provide these features by having a highly-integrated wideband transceiver with minimum analog components and mostly relying on digital signal processing. This is also desired from size, cost, and power consumption point of view. However, several challenges arise, from which dynamic range is one of the most important. This is especially true on receiver side where several signals can be received simultaneously through a single receiver chain. In extreme cases the weakest signal can be almost 100 dB weaker than the strongest one. Due to the limited dynamic range of the receiver, the strongest signals may cause nonlinear distortion which deteriorates spectrum sensing capabilities and also reception of the weakest signals. The nonlinearities are stemming from the analog receiver components and also from analog-to-digital converters (ADCs). This is a performance bottleneck in many wideband communications and also radar receivers. The dynamic range challenges are already encountered in current devices, such as in wideband multi-operator receiver scenarios in mobile networks, and the challenges will have even more essential role in the future. This thesis focuses on aforementioned receiver scenarios and contributes to modeling and digital suppression of nonlinear distortion. A behavioral model for direct-conversion receiver nonlinearities is derived and it jointly takes into account RF, mixer, and baseband nonlinearities together with I/Q imbalance. The model is then exploited in suppression of receiver nonlinearities. The considered method is based on adaptive digital post-processing and does not require any analog hardware modification. It is able
Cryptanalysis of some first round CAESAR candidates

AES _ CMCCv1, AVALANCHEv1, CLOCv1, and SILCv1 are four candidates of the first round of CAESAR. CLOCv1 is presented in FSE 2014 and SILCv1 is designed upon it with the aim of optimizing the hardware implementation cost. In this paper, structural weaknesses of these candidates are studied. We present distinguishing attacks against AES _ CMCCv1 with the complexity of two queries and the success probability of almost 1, and distinguishing attacks on CLOCv1 and SILCv1 with the complexity of O(2n/2) queries and the success probability of 0.63, in which n is bit length of message blocks. In addition, a forgery attack is presented against AVALANCHEv1 which requires only one query and has the success probability of 1. The attacks reveal weaknesses in the structure of these first round candidates and inaccuracy of their security claims.
The Choice of Initial Conditions and of Solution in the Problem of Efficiency Increase of Pipes Calibration on a Mandrel

This research work is devoted to the theoretical study of the pipe calibration on a mandrel. The aim of the study is to improve the precision of the calibrated pipes. As the paper shows, it is advisable to apply different methods of research depending on the purpose of the study of metal forming processes: mathematical, computer or physical simulation. Analytical review of existing mathematical models of the pipes calibration on a mandrel showed that the set of assumptions adopted in the mathematical modeling does not allow assessing the precision of the pipes during calibration. Therefore, finite-element method simulation package was used for this research. Research method and pipes precision index were developed on the basis of the computer simulation using Deform-3D package. The investigations have allowed us to get the dependence of the pipe precision on technological factors and to identify the root cause of reduced efficiency calibration – extrafocal deformation.
Predominantly Ferruginous Conditions in South China during the Marinoan Glaciation: Insight from REE Geochemistry of the Syn-glacial Dolostone from the Nantuo Formation in Guizhou Province, China

The Neoproterozoic Era witnessed two low-latitude glaciations, which exerted a fundamental influence on ocean–atmosphere redox conditions and biogeochemical cycling. Climate models and palaeobiological evidence support the belief that open waters provided oases for life that survived snowball Earth glaciations, yet independent geochemical evidence for marine redox conditions during the Marinoan glaciation remains scarce owing to the apparent lack of primary marine precipitates. In this study, we explore variability in rare earth elements (REEs) and trace metal concentrations in dolostone samples of the Cryogenian Nantuo Formation taken from a drill core in South China. Petrological evidence suggests that the dolostone in the Nantuo Formation was formed in near-shore waters. All the examined dolostone samples featured significant enrichment of manganese (345–10,890 ppm, average 3488 ppm) and middle rare earth elements (MREEs) (Bell Shape Index: 1.43–2.16, average 1.76) after being normalized to Post-Archean Australian Shale (PAAS). Most dolostone samples showed slight to no negative Ce anomalies (Ce*/Ce 0.53–1.30, average 0.95), as well as positive Eu anomalies (Eu*/Eu 1.77–3.28, average 1.95). This finding suggests that the dolostone samples were deposited from suboxic to iron-enriched and anoxic waters. Although total REE concentrations correlated positively with Th concentrations in dolostone samples, MREE-enriched PAAS-normalized patterns preclude the conclusion that REEs were largely introduced by terrestrial contamination. Rather, we interpret the correlation between REEs and Th as an indication that the former were transported by colloids and nanoparticles in meltwaters. Taken together, we propose that anoxic and ferruginous water columns dominated in South China during the Marinoan glaciation with a thin oxic/suboxic layer restricted to coastal waters. The extreme anoxic and ferruginous conditions prevailing in the Cryogenian would have provided a baseline for subsequent transient Ediacaran ocean oxygenation and life evolution.
Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm’s robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods—including the most recent CNN-based and generative adversarial network (GAN)-based ones—have only reached 25% AP in their best cases.
Rapid measurement of pasture evapotranspiration components using proximal sensors

Background Evapotranspiration (ET) is the total amount of water released by a crop or pasture canopy in the form of Transpiration (T) and Evaporation (E). ET accounts for up to 96% of the water loss depending on the types of vegetation cover and climatic conditions (Wilcox et al., 2003). Transpiration is related to the productivity of crops and pasture; whereas, evaporation is the loss of water directly from soil surface. Estimating separately the components of ET in the field is challenging yet it is highly significant in terms of improving crop water use and irrigation efficiency, since a anywhere between 30 and 80% of the water flux can be associated with the allimportant evaporation component (Wilcox et al., 2003). Sap flow monitoring, using micro-lysimeters or isotopic analysis are the usual options for separately measuring transpiration and evaporation in plants but these are incompatible with in-situ field deployment. In this study a portable and convenient method for separately determining the evapotranspiration components has been developed. When coupled with widely-used active optical sensors, the device can be used to develop, in-situ, relationships between spectro-optical indices such as NDVI and evapotranspiration coefficients (Kc, Kcb and Ke) that characterize actual ET water loss relative to evaporative demand.
With All Deliberate Speed: The Reversal of Court-Ordered School Desegregation, 1970–20131

The retrenchment of court-ordered school desegregation has been more variable and incomplete than often acknowledged, challenging common accounts that blame changes in federal policy and legal precedent. This study supplements these accounts by examining local factors that influenced whether and when desegregation orders were dismissed between 1970 and 2013. After accounting for federal policy changes and districts’ variable success in desegregating schools, several ostensibly race-neutral organizational, financial, and political incentives appear to influence the survival of desegregation orders. Racial competition dynamics related to local racial composition also seem to play a role, as desegregation orders have been most vulnerable when and where black population shares surpass a tipping point of about 40%.
Policies for Online Target Date Assignment Problems: Competitive Analysis versus Expected Performance

Die selbständige und eigenhändige Anfertigung versichere ich an Eides statt. Acknowledgments Although my name is the only one appearing on the title page, several people have been directly and indirectly involved in the creation of this thesis. Martin Grötschel for supervising this thesis, for being an outstanding teacher and motivator, and for providing an excellent working environment at the Konrad-Zuse-Zentrum für Informationstechnik Berlin. Working at this institute in the last two years was very instructive. Specially , the Friday morning research sessions with Nicole Megow, Andreas Tuchscherer, and Dr. Tjark Vredeveld inspired me a lot. It was always a pleasure for me to work with them. A great portion of the results presented in this thesis were derived from these research sessions. for reading the preliminary versions, their professional advice, their helpful suggestions, and for answering any question I had. Special thanks to Andreas Tuchscherer for reading all chapters several times, for giving great suggestions to improve the presentation of this thesis, and for teaching me the programming language C++. Last, but not least, many thanks to my parents for always supporting and encouraging me throughout the years.
A computationally efficient and scalable approach for privacy preserving kNN classification

In the modern age, there is a great desire to mine users’ personal data from varied sources, to discover their behaviours. However, due to the growing awareness among the organizations regarding the privacy of user data and the strict privacy regulations of government, there is a growing resistance to share data directly with others. Encryption is used in the literature to achieve privacy preservation in data mining. Our technique is based on the application of Bloom filters on the sensitive data while still being able to perform collaborative data mining, in particular the kNN classification. In this work, we propose a parallel implementation on GPUs of the most time consuming part of the algorithm, i.e., the similarity computation of the Bloom filtered records based on the modified Jaccard metric and the classification of records. From our findings, we conclude that the proposed parallel implementation, apart from being cost effective, is highly scalable to accommodate huge data. The parallel implementation has an average speed up of 20 over serial implementation. Further, the speed up increases with increase in the size of the data set considered.