Miracle-dz/UniFAR-FTUs huggingface dataset:
Climatic changes on Szczecin Seashore and their impact on river flows

Abstract An analysis has been made of the variability of temperature and precipitation at stations at Szczecin, Świnoujście, Resko and Kołobrzeg, as well as a study of river flows in the Ina and Rega Rivers in Goleniów, Resko and Trzebiatów, for the years 1955–2014. Data was provided by IMGW-PIB. Correlation and regression methods have been used in this work. Studies have shown that the average annual, spring and summer air temperature on Szczecin Seashore is rising. Precipitation totals remain basically unchanged. They grow a little in July in Świnoujście and in March in Szczecin. No significant changes in river flows occurred in the course of the examined sixty-year period. Flows in the Ina River rose slightly in February and they diminished in the Rega River in October in Resko and in May in Trzebiatów. Long-term variability of river flows is more strongly affected by the variability of precipitation totals than by the variability of air temperature. The variability of flows correlates most strongly with the variability of precipitation totals in March, April and November, while the influence of the preceding month’s precipitation on river flows is evident in all the year.
the Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation.

Serverless platforms facilitate transparent resource elasticity and ﬁne-grained billing, making them an attractive choice for data analytics. We ﬁnd that while server-centric analytics frameworks typically optimize for job completion time (JCT), resource utilization and isolation via inter-job scheduling policies, serverless analytics requires optimizing for JCT and cost of execution instead, introducing a new scheduling problem. We present Caerus, a task scheduler for serverless analytics frameworks that employs a ﬁne-grained N IMBLE scheduling algorithm to solve this problem. N IMBLE efﬁciently pipelines task executions within a job, minimizing execution cost while being Pareto-optimal between cost and JCT for arbitrary analytics jobs. To this end, N IMBLE models a wide range of execution parameters — pipelineable and non-piplineable data dependencies, data generation, consumption and processing rates, etc. — to determine the ideal task launch times. Our evaluation results show that in practice, Caerus is able to achieve both optimal cost and JCT for queries across a wide range of analytics workloads.
The User as Network

The user has become central to the way technology is conceptualized, designed, and studied in sociotechnical research and human-computer interaction; recently, non-users have also become productive foci of scholarly analysis. This paper argues that a focus on individualized users and non-users is incomplete, and conflates multiple modes of complex relation among people, institutions, and technologies. Rather than the use/non-use conception, I argue for conceptualizing users as networks: as constellations of power relations and institutional entanglements, mediated through technologies. To illustrate, I offer a case study of Nexafed, a tamperproof formulation of pseudoephedrine. The market for Nexafed seems nonexistent in traditional use/non-use terms, but when we construe the user more broadly — as a network of interpersonal, legal, and institutional relationships, consisting of multiple modes of relation between people and technology — not only does the drug’s market make sense, but we also understand how new motivations (social shame, mistrust, robbery, gossip) can act as salient drivers of technology use. The Nexafed case illustrates the utility of a networked perspective to develop more nuanced theoretical understandings of use and non-use in sociotechnical relations, beyond the direct human-technology interface.
Way‐Finder: guided tours through complex walkthrough models

The exploration of complex walkthrough models is often a difficult task due to the presence of densely occluded regions which pose a serious challenge to online navigation. In this paper we address the problem of algorithmic generation of exploration paths for complex walkthrough models. We present a characterization of suitable properties for camera paths and we discuss an efficient algorithm for computing them with little or no user intervention. Our approach is based on identifying the free‐space structure of the scene (represented by a cell and portal graph) and an entropy‐based measure of the relevance of a view‐point. This metric is key for deciding which cells have to be visited and for computing critical way‐points inside each cell. Several results on different model categories are presented and discussed.
Using machine learning to find cloud‐base height: a didactic challenge

The research herein presents retrieved mete-orological data, including precipitation and temperature, from the weekly newspaper ‘ Semanario Sóller ’ in Mallorca. These values were recorded at the end of the nineteenth century, before the start of official measurements. The collected data portray the high variability of the weather at this time, showing slightly warmer and wet conditions, compared with measurements taken 100 years later. Even so, the 10-year sample is not long enough, nor are possible instrumental and exposure differences known to sufficient detail, to reach any conclusions regarding changes to the climate.
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences

In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs). Synthesizing realistic 3D mesh animation sequences is a challenging and important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore can produce unrealistic animation. Deep architectures that operate directly on mesh sequences remain unexplored, due to the following major barriers: meshes with irregular triangles, sequences containing rich temporal information and flexible deformations. To address these, we utilize convolutional neural networks defined on triangular meshes along with a shape deformation representation to extract useful features, followed by LSTM cells that iteratively process the features. To allow completion of a missing mesh sequence from given endpoints, we propose a new weight-shared bidirectional structure. The bidirectional generation loss also helps mitigate error accumulation over iterations. Benefiting from all these technical advances, our approach outperforms existing methods in sequence prediction and completion both qualitatively and quantitatively. Moreover, this network can also generate follow-up frames conditioned on initial shapes and improve the accuracy as more bootstrap models are provided, which other works in the geometry processing domain cannot achieve.
Woodhead Behavior and the Pricing of Residential Mortgages

Mortgage terminations arise because borrowers exercise options. This paper investigates the apparently irrational behavior of those borrowers who do not terminate their mortgages even when the exercise value of the option is deeply in the money. We develop an option-based empirical model to analyze this phenomenon -- the behavior of irrational "woodheads. " Of course we do not observe "woodheads" explicitly in any body of data. Instead, we analyze the correlates of unobserved heterogeneity within a large sample of mortgage holders. We develop an error correction maximum likelihood (ECML) estimator using martingale transforms to estimate the competing risks of mortgage prepayment and default, recognizing unobserved heterogeneity which is due in part to the behavior of "woodheads. " The extended model is clearly superior to alternatives on statistical grounds. We then analyze the economic implications of this more powerful model. We analyze the predictions of the model for the valuation and pricing of mortgage pools and mortgage-backed securities. Based upon an extensive Monte Carlo simulation, we find that the ECML model yields prices for seasoned mortgage pools that deviate by 0.3 to almost 3.0 percent from more primitive estimates. The results indicate the empirical importance of heterogeneity and the implications of non-optimizing behavior for the valuation and pricing of mortgages and mortgage-backed securities.
A survey of security in wireless sensor networks

Wireless sensor networks are spatially distributed sensor nodes that keeps the track of the physical or environmental conditions like sound, stress waves, temperature of surrounding etc, and also send the sensed data back to the sink or base station. WSN are used in many applications like military areas, disaster management in remote areas, in building smart cities etc. Therefore security is an important aspect in WSN. These networks can be prone to various disastrous attacks or hackers that has the motive to disrupt the entire network. In this article, various aspects of security in Wireless Sensor Networks has been observed like secure routing protocols, security at the node level in the network, cryptography etc.
Caching Support for Skyline Query Processing with Partially Ordered Domains

Existing methods have addressed the issue of handling each individual skyline query performed on data sets with partially ordered domains. However, it is still very challenging to process such queries for on-line applications with low response time. In this paper, we introduce a cache-based framework, called CSS, for further reducing the query processing time to support high-responsive applications. Skyline queries that were previously processed with user preferences similar to those of the current query contribute useful candidate result points. Hence, the answered queries are cached with both their results and user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of selected queries with compatible user preferences. We introduce a similarity measure that establishes the level of similarity between the user preferences of a new query and a cached query; hence the system can start with the most similar candidates. Furthermore, if a new query is only partially answerable from the cache, then the query processor utilizes the partial result sets and performs less expensive constraint skyline queries guided by violated preferences. Furthermore, we introduce two access methods for cached queries indexed by their user preferences to only access a set of relevant cached queries for similarity measures. Extensive experiments are presented to demonstrate the performance and utility of our novel approach.
1 Peer-to-Peer Data Mining , Privacy Issues , and Games

In recent years, privacy-preserving data mining has been studied extensively, due to the wide increase of sensitive information on the internet. A number of algorithms and procedures have been designed, some of which are yet to be implemented, but a few of them are actually employed in the form of software systems to preserve the privacy of users, and the content in peer-to-peer networks. Privacy issues are becoming widely recognized when using peer-topeer networks. In this paper, we provide a review of the privacy-preserving data mining techniques used in order to overcome privacy issues. We discuss methods of sanitization, data distortion, data hiding, cryptography and the data mining algorithm KDEC. Further discussion involves data transfer using proxy techniques, creating social communities among peerto-peer users forming trusted peers. These techniques have shown to administer the issue of preserving data however show lack of scalability and performance. We design a framework to perform a comparison study on the techniques shown above and present the results with some recommendations of how we think the issues could be unraveled.
Combining Shape Analysis and Texture Pattern for Palmprint Recognition

We propose an efficient method for principal line extraction from the palmprint and geometric framework for analyzing their shapes. This representation , along with the elastic Riemannian metric, seems natural for measuring principal line deformations and is robust to challenges such as orientation variation and re-parameterization due to pose variation and missing part, respectively. The palmprint texture is investigated using the fractal analysis; thus the resulting features are fused with the principal line features. This framework is shown to be promising from both empirical and theoretical perspectives. In terms of empirical evaluation, our results match or improve the state-of-the-art methods on three prominent palmprint datasets: PolyU, CASIA, and IIT-Delhi, each posing a different type of challenge. From a theoretical perspective, this framework allows fusing texture analysis and shape analysis.
Do Health Care Providers Use Online Patient Ratings to Improve the Quality of Care? Results From an Online-Based Cross-Sectional Study

Background Physician-rating websites have become a popular tool to create more transparency about the quality of health care providers. So far, it remains unknown whether online-based rating websites have the potential to contribute to a better standard of care. Objective Our goal was to examine which health care providers use online rating websites and for what purposes, and whether health care providers use online patient ratings to improve patient care. Methods We conducted an online-based cross-sectional study by surveying 2360 physicians and other health care providers (September 2015). In addition to descriptive statistics, we performed multilevel logistic regression models to ascertain the effects of providers’ demographics as well as report card-related variables on the likelihood that providers implement measures to improve patient care. Results Overall, more than half of the responding providers surveyed (54.66%, 1290/2360) used online ratings to derive measures to improve patient care (implemented measures: mean 3.06, SD 2.29). Ophthalmologists (68%, 40/59) and gynecologists (65.4%, 123/188) were most likely to implement any measures. The most widely implemented quality measures were related to communication with patients (28.77%, 679/2360), the appointment scheduling process (23.60%, 557/2360), and office workflow (21.23%, 501/2360). Scaled-survey results had a greater impact on deriving measures than narrative comments. Multilevel logistic regression models revealed medical specialty, the frequency of report card use, and the appraisal of the trustworthiness of scaled-survey ratings to be significantly associated predictors for implementing measures to improve patient care because of online ratings. Conclusions Our results suggest that online ratings displayed on physician-rating websites have an impact on patient care. Despite the limitations of our study and unintended consequences of physician-rating websites, they still may have the potential to improve patient care.
Optimum Body Biasing Technique in Domino Logic Gate Design for Low Power Applications

Domino CMOS logic circuit family finds a wide variety of applications in microprocessors, digital signal processors, and dynamic memory due to their high speed and low device count. These dynamic circuits are often favored in high performance designs because of the speed advantage offered over static CMOS logic circuits. In this paper, AND gates with different body biasing are compared taking power consumption and delay as parameters. The designs are tested in 32nm technology. The domino AND gate design with least power consumption, delay and power delay product is proposed.
Enhancing Mutual Understanding in Synchronous Computer-Mediated Communication by Training:

Ineffective use of text-based synchronous computer-mediated communication (CMC), that is, chats, may affect the quality of communicative exchange compared to effective use and to face-to-face (FtF) communication. Especially in groups making decisions in equivocal judgmental tasks, inappropriate use of the CMC medium often impairs performance. Users need high communication and media competencies to overcome the negative effects brought about by the technology. Without intervention, mutual understanding and satisfaction with the group process are reduced in computer-mediated decision groups. Training that helps participants adapt to the medium should provide them with the needed competencies.The authors found a complex pattern of process and outcome effects with the best performance scores in the FtF condition,performance scores in CMC with training approximating those of the FtF condition, and lowest performance scores in CMC without training.
The Ethnography of Infrastructure

This article asks methodological questions about studying infrastructure with some of the tools and perspectives of ethnography. Infrastructure is both relational and ecological—it means different things to different groups and it is part of the balance of action, tools, and the built environment, inseparable from them. It also is frequently mundane to the point of bore dom, involving things such as plugs, standards, and bureaucratic forms. Some of the difficul ties of studying infrastructure are how to scale up from traditional ethnographic sites, how to manage large quantities of data such as those produced by transaction logs, and how to understand the interplay of online and offline behavior. Some of the tricks of the trade involved in meeting these challenges include studying the design of infrastructure, under standing the paradoxes of infrastructure as both transparent and opaque, including invisible work in the ecological analysis, and pinpointing the epistemological status of indictors.
Filtering automated polling trafﬁc in network ﬂow data

—Detecting polling behaviour in a computer network has two important applications. First, the polling can be indicative of malware beaconing, where an undetected software virus sends regular communications to a controller. Second, the cause of the polling may not be malicious, since it may correspond to regular automated update requests permitted by the client; to build models of normal host behaviour for signature-free anomaly detection, this polling behaviour needs to be understood. This article presents a simple Fourier analysis technique for identifying regular polling, and focuses on the second application: modelling the normal behaviour of a host, using real data collected from the computer network of Imperial College London.
TRANSCOMP – XIV INTERNATIONAL CONFERENCE COMPUTER SYSTEMS AIDED SCIENCE, INDUSTRY AND TRANSP ORT

Coraz częściej w róŜnego rodzaju środkach transportu stosuje si ę napęd elektryczny oparty o niekonwencjonalne źródła energii. Jednym z przykładów takich źródeł jest ogniwo paliwowe typu PEM. Jako przeciwwa gę dla bezsprzecznych zalet tego typu ogniw cz ęsto podaje si ę, Ŝe nie są one zdolne przyjmowa ć duŜych zmian obciąŜenia. W niniejszym artykule przedstawiono analiz ę reakcji systemu zasilania elektrycznego opartego na ogniwie paliwowym typu PE M o mocy 6 kW, na dynamiczne zmiany obci ąŜenia w zakresie jego mocy znamionowej. Zaprezentowa proces modelowania podsystemu zasilania gazami reak cyjnymi oraz zawarto wybrane wyniki bada ń eksperymentalnych zaprojektowanego i zbudowanego systemu zasilania.
A GOTO-based Concept for Intuitive Robot Programming

This paper proposes a new concept to augment direct robot programming with sensor-based branching and looping. The objective is to derive a new robot pro gramming paradigm in order to enable non-programmers to use robots for different tasks. Inspired by the ancient programming style based on the GOTO-statement, this approach requires the user to deal with only two basic concepts: Intuitive Control Expressions and Spatial Labels. This simplicity yie lds a very intuitive robot programming interface. We introduce these new concepts and derive some statements about the power and the limitations of this approach.
DNS Of Falling Droplets In A Vertical Channel

This paper presents Direct Numerical Simulation (DNS) of the falling motion of single and multiple deformable drops in a vertical channel. A systematic study of the wall effect on the motion of single drop is performed for Eötvös number (0.5≤Eo≤5), Morton number (10−3≤M≤10-8), and confinement ratio CR = 2. Second, the gravity-driven motion of multiple drops and their interactions are studied in a periodic vertical channel for CR = 4. These simulations are performed using a multiple marker level-set methodology, integrated in a finite-volume framework on a collocated unstructured grid. Each droplet is described by a level-set function, which allows capturing multiple interfaces in the same control volume, avoiding the numerical merging of the droplets. Numerical algorithms for fluid motion and interface capturing have been developed in the context of the finite-volume and level-set methodology, surface tension is modeled by means of the continuous surface force approach, and the pressure-velocity coupling is solved using a fractional-step projection method. DNS of single drop shows that they migrate to the symmetry axis of the channel when the Reynolds number is low, following a monotonic approach or damped oscillations according to the dimensionless parameters. If Eötvös number increases, stronger oscillations around the symmetry axis are observed. Simulations of multiple drops show that the collision of two drops follows the drafting-kissing tumbling (DKT) phenomenon. Deformable drops do not collide with the wall, whereas DKT phenomenon in the droplet swarm leads to the formation of groups which move through the center of the channel.
The Public Distribution System in India: Policy Evolution and Program Delivery Trends

The targeted public distribution system (TPDS) is the largest safety net program in India, in terms of both government expenditures and number of beneficiary households.1 The program targets nearly 800 million people (WFP 2014), providing subsidized grain through a network of more than 500,000 fair price shops (FPSs) across the country. The distribution of subsidized cereals through the TPDS was and remains the centerpiece of India’s social protection system. Although India has expanded and diversified its social protection programs in the past decade, government spending continues to emphasize the TPDS. The program absorbs US$7 billion, which is almost 1 percent of India’s gross domestic product (GDP) (World Bank 2011). As figure 2.1 indicates, subsidies remain key, with food and fuel subsidies accounting for two-thirds of expenditure on social protection programs.2 In 2014–15, Rs 1,226.76 billion (US$18.9 billion) was spent on food subsidies, an increase from Rs 424.89 billion (US$6.5 billion) in 2009–10 (Ministry of Finance, various years, specifically for 2014–15).
MINIREVIEW Bioinformatics Resources for the Study of Gene Regulation in Bacteria (cid:1)

Genomics, which has been identiﬁed as the science of the century, is dramatically changing the historically weak relation-ship between experimental and theoretical biology. The addition to the Journal of Bacteriology of a section for computational biology marks a turning point in the history of this dialogue. This minireview is focused on the computational biology of gene regulation in bacteria, deﬁned as the extensive use of bioinformatics tools to increase our understanding of the regulation of gene expression.