Miracle-dz/UniFAR-FTUs huggingface dataset:
An UAV-based Content Delivery Architecture for Rural Areas and Future Smart Cities

Content delivery in vehicular environments can serve multiple purposes, such as safety, entertainment and news delivery that can be geographically relevant to vehicles traveling within a certain area. The traditional approach to address this problem, based on fixed networking infrastructure, suffers from two drawbacks: (i) the efficient delivery of large-sized contents to multiple moving receivers simultaneously can be hard to achieve; and (ii) most of the roads outside the main urban areas lack such fixed infrastructures due to economic reasons. In this paper, we tackle both these issues by proposing rapidly deployable wireless access infrastructures combining RaptorQ-protected content diffusion and Unmanned Aerial Vehicles (UAVs). We performed experiments using actual vehicles and UAVs
No . 02-43 Does Experience Matter ? Innovations and the Productivity of ICT in German Services

In this paper, it is argued that ICT investment is closely linked with complementary innovations and most productive in firms with innovative experience. In an analysis based on firm–level panel data covering the period 1994–99, system GMM estimates for an extended production function framework reveal significant productivity effects of ICT in the German service sector. Moreover, there is strong support for the hypothesis that the experience gained from past process innovations is a specific feature that makes ICT investment more productive. The results suggest that ICT may have been contributing to productivity differentials between firms.
A 2 GHz fractional-N digital PLL with 1b noise shaping ΔΣ TDC

A 2 GHz fractional-N digital PLL with a single delay cell, noise shaping ΔΣ TDC is implemented in a 0. 13µm CMOS. With a simple structure of Δ modulator followed by a charge pump integrator, a wide range TDC input is converted to ΔΣ modulated bit stream. The implemented TDC consumes 1 mA, and the DPLL shows the in-band phase noise of −107 dBc at 500 kHz offset.
A Framework for Secure Speech Recognition

We present an algorithm that enables privacy-preserving speech recognition transactions between multiple parties. We assume two commonplace scenarios. One being the case where one of two parties has private speech data to be transcribed and the other party has private models for speech recognition. And the other being that of one party having a speech model to be trained using private data of multiple other parties. In both of the above cases data privacy is desired from both the data and the model owners. In this paper we will show how such collaborations can be performed while ensuring no private data leaks using secure multiparty computations. In neither case will any party obtain information on other parties data. The protocols described herein can be used to construct rudimentary speech recognition systems and can be easily extended for arbitrary audio and speech processing.
Stock Market Prediction Using Twitter Sentiment Analysis

In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
On Visualization and Reconstruction from Non‐Uniform Point Sets using B‐splines

In this paper we present a novel framework for the visualization and reconstruction from non‐uniform point sets. We adopt a variational method for the reconstruction of 3D non‐uniform data to a uniform grid of chosen resolution. We will extend this reconstruction to an efficient multi‐resolution uniform representation of the underlying data. Our multi‐resolution representation includes a traditional bottom‐up approach and a novel top‐down hierarchy for adaptive hierarchical reconstruction. Using a hybrid regularization functional we can improve the reconstruction results. Finally, we discuss further application scenarios and show rendering results to emphasize the effectiveness and quality of our proposed framework. By means of qualitative results and error comparisons we demonstrate superiority of our method compared to competing methods.
A Unified Approach to Bed Load Transport Description Over a Wide Range of Flow Conditions via the Use of Conditional Data Treatment

Bed load transport is a highly nonlinear phenomenon. Numerous stress‐transport power relations, with exponents varying between 1.5 and 16, have been proposed to capture the entire range of solid discharge trends exhibited by experimental data. A physics‐based explanation of the variation in exponent values is provided here. The concept of time‐resolved local stream power is used to determine the above‐threshold energy available for mobilizing bed materials, giving rise to solid discharge estimates. The generated transport capacity records, analyzed by a long‐term averaging process, allow for the construction of bed load curves that resemble the trends frequently reported in prior experimental studies. Under such conditions, use of long‐term averaged bed shear stress and bed load transport rates provide practical, yet oversimplified accounts of the transport phenomenon. The limitation of this methodology is particularly evident in low‐to‐moderate transport rates, where the calculated bed shear stress and consequent bed load transport rates are underestimated compared to values based on active periods of sediment movement. As the degree of intermittency in bed load transport increases, so does the exponent to compensate for the inactive periods of bed mobility. Conditionally averaged stress‐transport data, based on the active periods of bed load transport alone, however, exhibit a constant trend, reasonably well represented by a 1.5 power formula across the entire transport range. This approach better reflects the prevailing cause and effect relation by properly accounting for varied transport timescales. Furthermore, the resulting transport trend signifies a nearly constant efficiency in entraining and transporting sediment particles.
Enhancing Network Robustness via Shielding

We consider shielding critical links to enhance the robustness of a network, in which shielded links are resilient to failures. We first study the problem of increasing network connectivity by shielding links that belong to small cuts of a network, which improves the network reliability under random link failures. We then focus on the problem of shielding links to guarantee network connectivity under geographical and general failure models. We develop a mixed integer linear program (MILP) to obtain the minimum cost shielding to guarantee the connectivity of a single source–destination pair under a general failure model, and exploit geometric properties to decompose the shielding problem under a geographical failure model. We extend our MILP formulation to guarantee the connectivity of the entire network, and use Benders decomposition to significantly reduce the running time. We also apply simulated annealing to obtain near-optimal solutions in much shorter time. Finally, we extend the algorithms to guarantee partial network connectivity, and observe significant reduction in the shielding cost, especially when the geographical failure region is small.
Global Positioning System measurements of strain accumulation and slip transfer through the restraining bend along the Dead Sea fault system in Lebanon

An edited version of this paper was published in Geophysical Journal International by Blackwell Publishing. Blackwell Publishing retains the copyright to this paper (Copyright 2007). See also: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-246X.2006. 03328.x; http://atlas.geo. cornell.edu/deadsea/publications/Gomez2007_GJI. htm
Leveraging the Potential of Personality Traits for Digital Health Interventions: A Literature Review on Digital Markers for Conscientiousness and Neuroticism

Digital health interventions (DHIs) are designed to help individuals manage their disease, such as asthma, diabetes, or major depression. While there is a broad body of literature on how to design evidence-based DHIs with respect to behavioral theories, behavior change techniques or various design features, targeting personality traits has been neglected so far in DHI designs, although there is evidence of their impact on health. In particular, conscientiousness, which is related to therapy adherence, and neuroticism, which impacts long-term health of chronic patients, are two personality traits with an impact on health. Sensing these traits via digital markers from online and smartphone data sources and providing corresponding personality change interventions, i.e. to increase conscientiousness and to reduce neuroticism, may be an important active and generic ingredient for various DHIs. As a first step towards this novel class of personality change DHIs, we conducted a systematic literature review on relevant digital markers related to conscientiousness and neuroticism. Overall, 344 articles were reviewed and 21 were selected for further analysis. We found various digital markers for conscientiousness and neuroticism and discuss them with respect to future work, i.e. the design and evaluation of personality change DHIs.
Methodologies : ORM and NIAM Revisited : DOGMA in detail

Formal ontologies can be seen as mathematical objects that form the range of a classical "Tarskian" semantics interpretation mapping of first-order language constructs that could represent situations, functions or procedures related to a given domain. Some design methods and techniques such as view integration that were originally developed for large databases, where the "data models" and their semantics typically are limited to a particular application, could be relevant for this purpose, and we analyze parallelisms and fundamental differences between databases and ontologies. In particular the ORM (ObjectRole Modeling) method, or rather its precursor NIAM, with its rigorous distinction and handling of so-called lexical and non-lexical knowledge proved to be an interesting candidate to help identify and clarify a number of these issues, and a number of examples are given. We report on research within STARLab’s DOGMA Project that indicates how such methods may or must be adapted to be usable in the context of ontologies, and how they may then help to define ontology updates, the role of domain constraints, and future tools that assist in e.g. the alignment of ontologies.
University of Groningen Reinforcement Learning in the Game of Othello : Learning Against a Fixed Opponent and Learning from Self-Play

This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learn to play the game of Othello. The three strategies that are compared are: Learning by self-play, learning from playing against a fixed opponent, and learning from playing against a fixed opponent while learning from the opponent’s moves as well. These issues are considered for the algorithms Q-learning, Sarsa and TD-learning. These three reinforcement learning algorithms are combined with multi-layer perceptrons and trained and tested against three fixed opponents. It is found that the best strategy of learning differs per algorithm. Q-learning and Sarsa perform best when trained against the fixed opponent they are also tested against, whereas TD-learning performs best when trained through self-play. Surprisingly, Q-learning and Sarsa outperform TD-learning against the stronger fixed opponents, when all methods use their best strategy. Learning from the opponent’s moves as well leads to worse results compared to learning only from the learning agent’s own moves.
Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs

—Reinforcement learning algorithms enable an agent to optimize its behavior from interacting with a speciﬁc environment. Although some very successful applications of reinforcement learning algorithms have been developed, it is still an open research question how to scale up to large dynamic environments. In this paper we will study the use of reinforcement learning on the popular arcade video game Ms. Pac-Man. In order to let Ms. Pac-Man quickly learn, we designed particular smart feature extraction algorithms that produce higher-order inputs from the game-state. These inputs are then given to a neural network that is trained using Q-learning. We constructed higher-order features which are relative to the action of Ms. Pac-Man. These relative inputs are then given to a single neural network which sequentially propagates the action-relative inputs to obtain the different Q-values of different actions. The experimental results show that this approach allows the use of only 7 input units in the neural network, while still quickly obtaining very good playing behavior. Furthermore, the experiments show that our approach enables Ms. Pac-Man to successfully transfer its learned policy to a different maze on which it was not trained before.
Sorting Methods for Small

We present and compare four eecient quadratic, comparison-based algorithms for small arrays and (for three of them) almost sorted inputs. In addition to the well-known insertion sort and selection sort, we analyze 2-insertion sort (a variation of insertion sort) and stacksort (based on selection sort). We show that the new algorithms perform fewer comparisons on average than their original versions. The theoretical analysis is connrmed by experimental data, which include promising results concerning the use of 2-insertion sort in conjunction with quicksort. Resumo Esta monograaa apresenta e compara quatro algoritmos quadrrticos baseados em com-paraaaes que sso eecientes para o tratamento de vetores pequenos e (no caso de trrs dos mmtodos) para entradas quase ordenadas. Allm dos conhecidos insertion sort e selection sort, tambm sso analisados os algoritmos 2-insertion sort (uma variaaao do insertion sort) e stacksort (baseado no selection sort). Demonstra-se que os novos algoritmos propostos executam menos comparaaaes em mmdia que as respectivas versses originais. A annlise teerica connrmada por dados experimentais, que incluem resultados promissores sobre o uso do algoritmo 2-insertion sort com o quicksort.
PREDA tutorial

PREDA (Position RElated Data Analysis) tool is a novel R package for integrative analyses of functional genomics data. PREDA implements a procedure to analyze the relationships between data and physical genomic coordinates along chromosomes with the final aim of identifying chromosomal regions with likely relevant functional role. The procedure for position related data analysis is highly flexible and can be applied on data obtained with different technologies. In principle, it can analyze different types of quantitative functional genomics data, e.g., gene expression, copy number, methylation levels. In particular, the underlying algorithm so far has been successfully adopted for the analysis of gene expression and copy number data obtained with various microarray platforms on different organisms. This tool can also integrate analysis results from different types of functional genomics data (e.g. integrated analysis of copy number and gene expression).
A Finitely Axiomatized Formalization of Predicate Calculus with Equality

We present a formalization of first-order predicate calculus with equality which, unlike traditional systems with axiom schemata or substitution rules, is finitely axiomatized in the sense that each step in a formal proof admits only finitely many choices. This formalization is primarily based on the inference rule of condensed detachment of C. A. Meredith. The usual primitive notions of free variable and proper substitution are absent, making it easy to verify proofs in a machine-oriented application. Completeness results are presented. The example of Zermelo-Fraenkel set theory is shown to be finitely axiomatized under the formalization. The relationship with resolution-based theorem provers is briefly discussed. A closely related axiomatization of traditional predicate calculus is shown to be complete in a strong metamathematical sense.
Design of Driver-Assist Systems Under Probabilistic Safety Specifications Near Stop Signs

In this paper, we consider the problem of designing in-vehicle driver-assist systems that warn or override the driver to prevent collisions with a guaranteed probability. The probabilistic nature of the problem naturally arises from many sources of uncertainty, among which the behavior of the surrounding vehicles and the response of the driver to on-board warnings. We formulate this problem as a control problem for uncertain systems under probabilistic safety specifications and leverage the structure of the application domain to reach computationally efficient implementations. Simulations using a naturalistic data set show that the empirical probability of safety is always within 5% of the theoretical value in the case of direct driver override. In the case of on-board warnings, the empirical value is more conservative due primarily to drivers decelerating more strongly than requested. However, the empirical value is greater than or equal to the theoretical value, demonstrating a clear safety benefit.
Development and Testing of a Decision Tree for the Forecasting of Sea Fog Along the Georgia and South Carolina Coast

A classification and regression tree analysis for sea fog has been developed using 648 low-visibility (<4.8 km) coastal fog events from 1998–2014 along the South Carolina and Georgia coastline. Correlations between these coastal fog events and relevant oceanic and atmospheric parameters determined the range in these parameters that were most favorable for predicting sea fog formation. Parameters examined during coastal fog events from 1998–2014 included sea surface temperature (SST), air temperature, dewpoint temperature, maximum wind speed, average wind speed, wind direction, inversion strength, and inversion height. The most favorable range in SST for sea fog formation was 10.6–23.9°C. The most favorable gaps between air temperature and SST, dewpoint temperature and SST, and dewpoint temperature and air temperature were found to be –1.7– 2.2°C, 0°C, and 0–2.2°C, respectively. The most favorable range in maximum wind speed was 11.1–20.4 km h-1, and the most favorable wind directions were parallel to the coast or SST isopleths. The most favorable range in inversion height was 70.6–617.2 m, and the most favorable inversion strength was anything >6°C. Utilizing these eight predictors, a forecasting decision tree was created and beta tested during the 2016/2017 sea fog season. The decision tree successfully predicted sea fog on 17 of the 18 dates that it occurred (94%) and successfully predicted a lack of sea fog for 189 of the 194 days where sea fog did not occur (97%). Two of the six incorrect predictions appear to have extenuating circumstances. ABSTRACT (Manuscript received 11 December 2017; review completed 16 April 2018)
International Journal of Sensor Networks and Data Communications

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms of computational, memory, bandwidth, and battery capability. Multimedia transmission over WSN requires certain Qos guarantees such as huge amount of bandwidth, strict delay and lower loss ratio, which makes transmitting multimedia content over it, is a challenging problem. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an energy aware framework for transmitting multimedia content over WSN (ECWMSN) is presented, where packet and path scheduling were introduced, so that It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type to drop less important packets in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority is routed through suitable path with suitable Qos taking into consideration the network lifetime. Simulation results show that ECWMSN optimizes video quality and prolongs network lifetime.
An analysis of land use and carbon emission in Hefei city based on carrying relationship

The relationship between land use types and carbon emission was established based on the carbon emission calculation in Hefei. The purpose of this research was to reveal the profound impact of land use change on carbon emission including emission intensity, the contribution rate and carbon sink increase or decrease. The results indicate: (1) from 1995 to 2012, Hefei’s net carbon emission increased from 2.76 million tons to 15.37 million tons with the energy consumption being the largest emission source and the energy consumption’s ratio was increasing year by year; (2) the carbon absorption intensity of various land use types in the terrestrial ecosystem remained stable. The cultivated land is the most important carbon sink land use type. The carbon emission intensity of urban industrial and mining land was much higher than other types of construction land; (3) the obvious contribution of land use types to carbon emission in Hefei was cultivated land (negative), urban industrial and mining land (positive) and rural residential areas (positive); (4) during the course of land use change, the conversion of cultivated land to urban industrial and mining land was the main cause of carbon sink loss. In conclusion, controlling energy consumption is an effective means of decreasing carbon emission. For land management, strictly controlling the extension of urban industrial and mining land, especially reducing the occupation of cultivated land, is the key to the increase of carbon sink and reduction of carbon emission.
Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps

Seasonal snowpacks in marginal snow environments are typically warm and nearly isothermal, exhibiting high inter‐ and intra‐annual variability. Measurements of snow depth and snow water equivalent were made across a small subalpine catchment in the Australian Alps over two snow seasons in order to investigate the extent and implications of snowpack spatial variability in this marginal setting. The distribution and dynamics of the snowpack were found to be influenced by upwind terrain, vegetation, solar radiation, and slope. The role of upwind vegetation was quantified using a novel parameter based on gridded vegetation height. The elevation range of the catchment was relatively modest (185 m), and elevation impacted distribution but not dynamics. Two characteristic features of marginal snowpack behaviour are presented. Firstly, the evolution of the snowpack is described in terms of a relatively unstable accumulation state and a highly stable ablation state, as revealed by temporal variations in the mean and standard deviation of snow water equivalent. Secondly, the validity of partitioning the snow season into distinct accumulation and ablation phases is shown to be compromised in such a setting. Snow at the most marginal locations may undergo complete melt several times during a season and, even where snow cover is more persistent, ablation processes begin to have an effect on the distribution of the snowpack early in the season. Our results are consistent with previous research showing that individual point measurements are unable to fully represent the variability in the snowpack across a catchment, and we show that recognising and addressing this variability are particularly important for studies in marginal snow environments.