nils-herrmann/clean_scirepeval huggingface dataset:
A new dynamic control strategy for power transmission congestion management using series compensation

The growth of electricity market due to increase in demand and infrastructure made the power system more complex. Managing the transmission congestion is one of the main challenges faced by the utilities. To relieve from the bottlenecks, Flexible AC Transmission Systems (FACTS) and Distributed FACTS (D FACTS) devices can be used in controlling the transmission line power flows. The real power flow control is realized by varying the transmission line impedance. The power flow in transmission line should satisfy inequality constraints to maintain the system in normal state. To achieve this, an algorithm is developed to control the FACTS/D FACTS devices connected to all the transmission lines of n bus system. The significant changes required in line impedance which will be deployed by FACTS devices are decided by the algorithm. In this paper, a 5 bus system and a 14 bus system with FACTS devices in all the transmission lines is considered. The transmission lines of the 5 and 14 bus systems are made to be overloaded in different combinations by choosing appropriate loading conditions. The control algorithm is tested on all the overloaded conditions to overcome the congestion. The FACTS devices controlled by the algorithm removes the overloading effect and improve the reliability of the network.
Semi supervised structured output prediction by local linear regression and sub gradient descent
Venue: ArXiv

We propose a novel semi supervised structured output prediction method based on local linear regression in this paper. The existing semi supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction. To solve this problem, we propose to learn the missing structured outputs and local predictors for neighborhoods of different data points jointly. Using the local linear regression strategy, in the neighborhood of each data point, we propose to learn a local linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization problem is solved by sub gradient descent algorithms. We conduct experiments over two benchmark data sets, and the results show the advantages of the proposed method.
Learning Linear Regression Models over Factorized Joins
Venue: SIGMOD Conference

We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end to end solution to learning over joins. We propose a new paradigm for computing batch gradient descent that exploits the factorized computation and representation of the training datasets, a rewriting of the regression objective function that decouples the computation of cofactors of model parameters from their convergence, and the commutativity of cofactor computation with relational union and projection. We introduce three flavors of this approach: F/FDB computes the cofactors in one pass over the materialized factorized join; Favoids this materialization and intermixes cofactor and join computation; F/SQL expresses this mixture as one SQL query. Our approach has the complexity of join factorization, which can be exponentially lower than of standard joins. Experiments with commercial, public, and synthetic datasets show that it outperforms MADlib, Python StatsModels, and R, by up to three orders of magnitude.
Accelerated Sparse Linear Regression via Random Projection
Venue: AAAI

In this paper, we present an accelerated numerical method based on random projection for sparse linear regression. Previous studies have shown that under appropriate conditions, gradient based methods enjoy a geometric convergence rate when applied to this problem. However, the time complexity of evaluating the gradient is as large as O(nd) where n is the number of data points and d is the dimensionality, making those methods inefficient for large scale and high dimensional dataset. To address this limitation, we first utilize random projection to find a rank k approximator for the data matrix, and reduce the cost of gradient evaluation to O(nk dk) a significant improvement when k is much smaller than d and n. Then, we solve the sparse linear regression problem via a proximal gradient method with a homotopy strategy to generate sparse intermediate solutions. Theoretical analysis shows that our method also achieves a global geometric convergence rate, and moreover the sparsity of all the intermediate solutions are well bounded over the iterations. Finally, we conduct experiments to demonstrate the efficiency of the proposed method.
Robust estimation of parameters in nonlinear ordinary differential equation models
Venue: J. Syst. Sci. Complex.

Ordinary differential equation (ODE) models are widely used to model dynamic processes in many scientific fields. Parameter estimation is usually a challenging problem, especially in nonlinear ODE models. The most popular method, nonlinear least square estimation, is shown to be strongly sensitive to outliers. In this paper, robust estimation of parameters using M estimators is proposed, and their asymptotic properties are obtained under some regular conditions. The authors also provide a method to adjust Huber parameter automatically according to the observations. Moreover, a method is presented to estimate the initial values of parameters and state variables. The efficiency and robustness are well balanced in Huber estimators, which is demonstrated via numerical simulations and chlorides data analysis.
Parameter Estimation Using Divide and Conquer Methods for Differential Equation Models
Venue: Journal of biometrics biostatistics

In systems biology, a key topic is the elucidation of the dynamic behavior of biological processes that are made up of complex biochemical networks. Statistical modeling is an important to capture the dynamics of biochemical networks such as metabolic networks, signal transduction pathways, and gene regulatory networks. These biochemical models have a set of parameters that represent the physical properties of the systems, such as kinetic constants and reaction rates. In general, the development of these models requires two steps: model structure construction and parameter estimation. The models are often constructed with time derivative expressions, such as ordinary differential equations (ODEs) to describe the change of certain quantities of interest over time  The model parameters are then estimated by simulating the actual processes obtained from experimental analyses [3 5] However, because the differential equation model has many uncertain parameters and limited measurement data, parameter estimation is a major bottleneck in the development of useful biochemical models [6,7]
Fe N Doped Carbon Capsules with Outstanding Electrochemical Performance and Stability for the Oxygen Reduction Reaction in Both Acid and Alkaline Conditions.
Venue: ACS nano

High surface area N doped mesoporous carbon capsules with iron traces exhibit outstanding electrocatalytic activity for the oxygen reduction reaction in both alkaline and acidic media. In alkaline conditions, they exhibit more positive onset (0.94 V vs RHE) and half wave potentials (0.83 V vs RHE) than commercial Pt/C, while in acidic media the onset potential is comparable to that of commercial Pt/C with a peroxide yield lower than 10% The Fe N doped carbon catalyst combines high catalytic activity with remarkable performance stability (3500 cycles between 0.6 and 1.0 V vs RHE) which stems from the fact that iron is coordinated to nitrogen. Additionally, the newly developed electrocatalyst is unaffected by the methanol crossover effect in both acid and basic media, contrary to commercial Pt/C. The excellent catalytic behavior of the Fe N doped carbon, even in the more relevant acid medium, is attributable to the combination of chemical functions (N pyridinic, N quaternary, and Fe N coordination sites) and structural properties (large surface area, open mesoporous structure, and short diffusion paths) which guarantees a large number of highly active and fully accessible catalytic sites and rapid mass transfer kinetics. Thus, this catalyst represents an important step forward toward replacing Pt catalysts with cheaper alternatives. In this regard, an alkaline anion exchange membrane fuel cell was assembled with Fe N doped mesoporous carbon capsules as the cathode catalyst to provide current and power densities matching those of a commercial Pt/C, which indicates the practical applicability of the Fe N carbon catalyst.
Synthesis of nanoporous structured iron carbide/Fe N carbon composites for efficient oxygen reduction reaction in Zn air batteries

Large scale industrial level applications of fuel cells and metal air batteries have called for the development of highly efficient and low cost oxygen reduction electrodes. Here we report the effective and simple preparation of iron carbide embedded Fe N doped carbon (Fe3C/Fe N/C) composites using an iron phenanthroline (Fe Phen) complex and dicyandiamide (DCA) as the precursors that are subsequently heat treated. The optimal catalyst pyrolyzed at 800 degC (Fe Phen N 800) exhibits superior oxygen reduction activity with onset and half wave potentials of 0.99 and 0.86 V in 0.1 M KOH, respectively, which are higher than those of Pt/C (onset and half wave potentials of 0.98 and 0.84 V) at the same catalyst loading. Moreover, the obtained Fe Phen N 800 displays comparable activity to Pt/C in 0.1 M HClO4 solution. Notably, the well developed Fe Phen N 800 catalyst shows much higher long term stability and better methanol tolerance than Pt/C. The results suggest that our catalyst is one of the most promising candidates to replace Pt catalysts toward oxygen reduction. Strikingly, a primary Zn air battery using Fe Phen N 800 as the air cathode catalyst delivers higher voltages and gravimetric energy densities than those of a Pt/C based system at the discharge current densities of 10 and 25 mA cm 2, thus demonstrating the potential applications of our catalyst for energy conversion devices.
Deep Learning with Differential Privacy
Venue: CCS

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Federated Learning: Strategies for Improving Communication Efficiency
Venue: ArXiv

Federated Learning is a machine learning setting where the goal is to train a high quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections. We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client side updates are aggregated to compute a new global model. The typical clients in this setting are mobile phones, and communication efficiency is of the utmost importance. In this paper, we propose two ways to reduce the uplink communication costs: structured updates, where we directly learn an update from a restricted space parametrized using a smaller number of variables, e.g. either low rank or a random mask; and sketched updates, where we learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling before sending it to the server. Experiments on both convolutional and recurrent networks show that the proposed methods can reduce the communication cost by two orders of magnitude.
Navigation of autonomous tractor for orchards and plantations using a laser range finder: Automatic control of trailer position with tractor

The autonomous control of a tractor trailer system in orchards and plantations has frequently been observed for the transportation, loading and unloading of products between plants and trees. The objective of this research was to develop a control algorithm for a single sensor tractor trailer navigation system for navigating within a row plantation and travelling between plots. A control scheme for stopping the tractor trailer for various in field tasks, such as product loading/unloading using a laser range finder (LRF) is presented. The LRF was used to navigate a full size autonomous agricultural tractor equipped with a two wheeled trailer. For ease of operation in narrow rows, a sliding hitch bar (SHB) was developed to control the trailer by adjusting the position of the hitch point between the tractor and trailer. Compared to the tractor trailer system driven by human, the control system could navigate the tractor trailer with the RMS differences of 0.275 m (SD: 0.009 m) 0.373 m (SD: 0.030 m) and 0.518 m (SD: 0.022 m) for wide curve tight curve and U turn experimental paths, respectively. The SHB unit also supported the navigation system with wider turn for the trailer than conventional single hitch point about 0.383 m (7.66% of the 5 m path width, SD: 0.028 m) 0.762 m (15.23% of 5 m path width, SD: 0.010 m) and 1.094 m (21.88% of 5 m path width, SD: 0.037 m) for wide curve tight curve and U turn experimental paths, respectively. The control system also stopped the tractor trailer at the specified landmarks. The results show that the navigation of the tractor trailer was demonstrated with satisfactory accuracy and that the trailer position was controlled by the SHB with a wider turn in the experimental paths. Therefore, this laser based landmark navigation system, and the SHB unit, can be adopted for different applications of autonomous tractor trailer systems with controlled trailer positioning.
Alfabetismo transmedia. Estrategias de aprendizaje informal y competencias mediaticas en la nueva ecologia de la comunicacion

espanolTras una breve descripcion de las mutaciones de la ecologia de medios en las ultimas dos decadas, se hace un recorrido por las distintas formas de alfabetismo para detenerse en las concepciones mas novedosas, derivadas de los usos emergentes de las Tecnologias de la Informacion y la Comunicacion (TIC) En este contexto se propone el concepto de alfabetismo transmedia, entendido no solo como un nuevo conjunto de competencias, sino tambien como una vision diferente de las relaciones entre sujetos, TIC e instituciones educativas. EnglishAfter a brief description of the mutations of media ecology in the last two decades, we review the different forms of literacy highlighting the newest conceptions, derived from emerging uses of information and communications technologies (ICTs) In this context, the concept of transmedia literacy is proposed, understood not only as a new set of skills, but also as a different view of relations between subjects, ICTs and educational institutions.
Systems Approaches to Managing Change: A Practical Guide

This compilation of readings is a practical guide for practitioners involved with managing complex and uncertain situations. At a time when the media continually tells of a world facing many crises, it provides an opportunity to gain understanding of robust approaches to managing complex and ever changing situations more effectively. The Reader is used as a teaching resource on the new Open University postgraduate course Thinking strategically: system tools for managing change and it provides an introduction to five systems approaches: System dynamics (SD) developed originally in the late 1950s by Jay Wright Forrester, Viable systems method (VSM) developed originally in the late 1960s by Stafford Beer, Strategic options development and analysis (SODA: with cognitive mapping) developed originally in the 1960s by Colin Eden, Soft systems methodology (SSM) developed originally in the 1970s by Peter Checkland, Critical systems heuristics (CSH) developed originally in the early 1980s by Werner Ulrich.
Managing change in the delivery of complex projects: Configuration management, asset information and 'big data'

As we enter an era of 'big data' asset information is becoming a deliverable of complex projects. Prior research suggests digital technologies enable rapid, flexible forms of project organizing. This research analyses practices of managing change in Airbus, CERN and Crossrail, through desk based review, interviews, visits and a cross case workshop. These organizations deliver complex projects, rely on digital technologies to manage large data sets; and use configuration management, a systems engineering approach with mid 20th century origins, to establish and maintain integrity. In them, configuration management has become more, rather than less, important. Asset information is structured, with change managed through digital systems, using relatively hierarchical, asynchronous and sequential processes. The paper contributes by uncovering limits to flexibility in complex projects where integrity is important. Challenges of managing change are discussed, considering the evolving nature of configuration management; potential use of analytics on complex projects; and implications for research and practice.
Managing change: how Egypt's military leadership shaped the transformation

Egypt's military has been the real winner of the country's political transformation. It was not only successful in preserving the overall power structure, which was challenged by young revolutionaries and Islamist opposition between 2011 and 2013. It also expanded its power within the political relevant elite. The article argues that the gradual approach chosen by the Generals in managing change as well as their ability to maintain a cohesive corporate structure and act therefore as a strong institutional player explain this outcome. However the military's dominance will hinder socio economic progress and makes the political order unsustainable over the long run.
Retailing Logistics And Fresh Food Packaging Managing Change In The Supply Chain

Thank you very much for reading retailing logistics and fresh food packaging managing change in the supply chain. As you may know, people have look hundreds times for their favorite readings like this retailing logistics and fresh food packaging managing change in the supply chain, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some infectious bugs inside their computer.
Psikologi konseling perkembangan dan penerapan konseling dalam psikologi

Konseling adalah sebuah interaksi antara seorang konselor dan konseli. Interaksi antara konselor dan konseli pada dasarnya merupakan interaksi antara konseli yaitu seorang individu atau kelompok yang sedang menghadapi masalah, yang mencari bantuan pihak ketiga (konselor) untuk membantu menyelesaikan masalahnya. Konseli berupaya mencari bantuan konselor ketika menghadapi masalah dan merasa tidak mampu menyelesaikan masalahnya sendiri, sehingga berupaya mendapatkan bantuan orang lain untuk menemukan alternatif penyelesaian atas masalah yang dihadapi. Ketika berhadapan dengan konselor, konseling membutuhkan nasihat, bimbingan, dan konsultasi dari konselor yang diharapkan memiliki posisi netral sehingga konselor dapat memberikan pendapat atau alternatif penyelesaian yang lebih objektif. Bagi konseli, orang lain yang berperan sebagai konselor ini diharapkan memiliki sikap dan cara pandang yang lebih dew as a dalam menyikapi persoalan, dan diharapkan tidak akan membocorkan masalah yang diceritakan atau dihadapi kepada orang lain.
What's Next for the LMS? From Walled Gardens to Open Ecosystems

Nationwide over 90% of educational institutions are running a learning management system. Yet there seems to be a restlessness with the current LMS design since it matches up poorly with newer teaching and learning models. Is there anything out there that truly differentiates itself from the pack? What do we see peeking over the horizon? In this session, the presenter will provide perspectives on the future of LMSs and what we might expect to see and utilize over the next several years. Spoiler alert: there is no big reveal! But terms like ecosystems and Lego approaches will be used generously. You may want to attend this session if for no other reason than to affirm your own (school's) knowledge and preparedness for the future. This presentation is based in part on the ELI Paper (Educause) called "Next Generation NGDLE (Next Generation Digital Learning Environment) which was developed in partnership with the Bill and Melinda Gates Foundation. The research explored the gaps between current learning management tools and a digital learning environment that could meet the changing needs of higher education. April 2015, Malcolm Brown, Joanne Dehoney, Nancy Millichap
Comparison of maintainability and flexibility on open source LMS
Venue: 2016 International Seminar on Application for Technology of Information and Communication (ISemantic)

This paper compares the Flexibility and Maintainability factors on open source Learning Management System (LMS) Moodle, Atutor, and ILIAS. This comparison is aimed to find out which LMS has the value maintainability and better flexibility in order to be used by agencies to consider implementation of LMS as well as useful for software developers to provide an overview of the capabilities of the source code for the maintenance source code and flexibility of the LMS. Comparison for Maintainability done by measuring the Maintainability Index value in each LMS with tools phpmterics. As for Flexibility, the comparison is done by measuring the value of Modularity and Simplicity. Measurement of modularity is done by looking at the values of the coupling and cohesion, while simplicity is measured by looking at the value of complexity. Measurement results show that Moodle has Maintainability Index of 89.85, Afferent Coupling of 0.54, Efferent Coupling of 1.81, Cohesion of 2.18, and Complexity of 17.26. ATutor has Maintainability Index of 72.95, Afferent Coupling of 0.95, Efferent Coupling of 2.63, Cohesion of 2.47, and Complexity of 16.84. ILIAS has Maintainability Index of 87.29, Afferent Coupling of 1.49, Efferent Coupling of 5.31, Cohesion of 3.45 and Complexity of 18.15. So it can be inferred that the Moodle source code have an easier maintenance opportunity as well as the modified source code compared to the ATutor and ILIAS.