nils-herrmann/clean_scirepeval huggingface dataset:
The Struggle for District-based Health Information Systems in South Africa

This article describes the initial period (1994–2001) of an ongoing action research project to develop health information systems to support district management in South Africa. The reconstruction of the health sector in postapartheid South Africa striving for equity in health service delivery and building of a decentralized structure based on health districts. In terms of information systems (IS) development, this reform process translates into standardization of health data in ways that inscribe the goals of the new South Africa by enhancing local control and integration of information handling. We describe our approach to action research and use concepts from actor-network and structuration theories in analyzing the case material. In the detailed description and analysis of the process of IS development provided, we focus on the need to balance standardization and local  exibility (localization); standardization is thus seen as bottom-up alignment of an array of heterogeneous actors. Building on a social system model of information systems, we conceptualize the IS design strategy developed and used as the cultivation of processes whereby these actors are translating and aligning their interests. We develop a modular hierarchy of global and local datasets as a framework within which the tensions between standardization and localization may be understood and addressed. Finally, we discuss the possible relevance of the results of the research in other countries.
Towards Autonomous Vehicular Clouds - A Position Paper (Invited Paper)

The dawn of the 21 century has seen a growing interest in vehicular networking and its myriad potential applications. The initial view of practitioners and researchers was that radio-equipped vehicles could keep the drivers informed about potential safety risks and increase their awareness of road conditions. The view then expanded to include access to the Internet and associated services. This position paper proposes and promotes a novel and more comprehensive vision namely, that advances in vehicular networks, embedded devices, and cloud computing will enable the formation of autonomous clouds of vehicular computing, communication, sensing, power and physical resources. Hence, we coin the term, Autonomous Vehicular Clouds (AVCs). A key features distinguishing AVCs from conventional cloud computing is that mobile AVC resources can be pooled dynamically to serve authorized users and to enable autonomy in real-time service sharing and management on terrestrial, aerial, or aquatic pathways or theatres of operations. In addition to general-purpose AVCs, we also envision the emergence of specialized AVCs such as mobile analytics laboratories. Furthermore, we envision that the integration of AVCs with ubiquitous smart infrastructures including intelligent transportation systems, smart cities, and smart electric power grids, will have an enormous societal impact enabling ubiquitous utility cyber-physical services at the right place, right time, and with right-sized resources.
The Ins and Outs of the Probabilistic Model Checker MRMC

The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing timeand reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tool’s current status and its implementation details.
Design And Application Guide For High Speed MOSFET Gate Drive Circuits By

The main purpose of this paper is to demonstrate a systematic approach to design high performance gate drive circuits for high speed switching applications. It is an informative collection of topics offering a “one-stop-shopping” to solve the most common design challenges. Thus it should be of interest to power electronics engineers at all levels of experience. The most popular circuit solutions and their performance are analyzed, including the effect of parasitic components, transient and extreme operating conditions. The discussion builds from simple to more complex problems starting with an overview of MOSFET technology and switching operation. Design procedure for ground referenced and high side gate drive circuits, AC coupled and transformer isolated solutions are described in great details. A special chapter deals with the gate drive requirements of the MOSFETs in synchronous rectifier applications. Several, step-by-step numerical design examples complement the paper.
Implicit theories about willpower predict self-regulation and grades in everyday life.

Laboratory research shows that when people believe that willpower is an abundant (rather than highly limited) resource they exhibit better self-control after demanding tasks. However, some have questioned whether this "nonlimited" theory leads to squandering of resources and worse outcomes in everyday life when demands on self-regulation are high. To examine this, we conducted a longitudinal study, assessing students' theories about willpower and tracking their self-regulation and academic performance. As hypothesized, a nonlimited theory predicted better self-regulation (better time management and less procrastination, unhealthy eating, and impulsive spending) for students who faced high self-regulatory demands. Moreover, among students taking a heavy course load, those with a nonlimited theory earned higher grades, which was mediated by less procrastination. These findings contradict the idea that a limited theory helps people allocate their resources more effectively; instead, it is people with the nonlimited theory who self-regulate well in the face of high demands.
The Quest to Replace Passwords : a Framework for Comparative Evaluation of Web Authentication Schemes

 Memorywise-Effortless: users don’t have to remember any secrets  (Quasi-Memorywise-Effortless): one secret for everything  Scalable-for-Users: users can use it for many accounts without extra burden  Nothing-to-Carry: users don’t need to carry an additional physical token  (Quasi-Nothing-to-Carry): if the object is something they would carry everywhere (mobile phone)  Physically-Effortless: authentication process does not require physical effort  (Quasi-Physically-Effortless): limited to speaking  Easy-to-Learn: users can learn it and use it without too much trouble  Efficient-to-Use: The time to set up the verifier and make the authentication is reasonable  Infrequent-Errors: genuine users will usually succeed in the authentication process  Easy-Recovery-from-Loss: a user can authenticate if the token is lost or the secret forgotten
On Two Metaphors for Learning and the Dangers of Choosing Just One

The upshots of the former section can be put as follows: All our concepts and beliefs have their roots in a limited number of fundamental ideas that cross disciplinary boundaries and are carried from one domain to another by the language we use. One glance at the current discourse on learning should be enough to realize that nowadays educational research is caught between two metaphors that, in this article, will be called the acquisition metaphor and the participation metaphor. Both of these metaphors are simultaneously present in most recent texts, but while the acquisition metaphor is likely to be more prominent in older writings, more recent studies are often dominated by the participation metaphor.
Disconnected aging: Cerebral white matter integrity and age-related differences in cognition

Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging.
Analogy mapping for different learning style of learners in programming

Programming logic is often hard to understand for novice, but in the other side, programming is one of the most important subjects in computer science program. Analogy Mapping is a media which converts source code to analogies images using state machine in order to help students understanding programming logic easily. Therefore this paper reports our work about the effectivity of Analogy Mapping in learning process for basic programming subject students with different learning style.
SAT solving using FPGA-based heterogeneous computing

We present a heterogeneous computing solution to the Boolean satisfiability (SAT) problem. Our field-programmable gate array (FPGA)-based implementation for accelerating the common case computation within a SAT solver utilizes most of the FPGA resources and it seamlessly integrates with our software host. Algorithms and data structures were redesigned to maximize the strengths of customized computing and generalizable optimizations are proposed to maximize throughput, minimize communication latencies, and compact hardware memory. We are significantly faster than state-of-the-art SAT solvers in software and hardware.
Current mode class-D power amplifiers for high efficiency RF applications

We show that current mode class-D (CMCD) power amplifiers can achieve high efficiency at RF frequencies. In contrast with conventional voltage-mode class D amplifiers, the CMCD features "zero voltage switching" which eliminates the output capacitance discharge loss. Experimental CMCD amplifiers with 76.3% PAE at 290 mW output and 71.3% PAE at 870 mW output are demonstrated, using GaAs FETs at 900 MHz.
Lane detection using spline model

In this paper, a Catmull±Rom spline-based lane model which describes the perspective eect of parallel lines has been proposed for generic lane boundary. Since Catmull±Rom spline can form arbitrary shapes by dierent sets of control points, it can describe a wider range of lane structures compared with other lane models, i.e. straight and parabolic models. Moreover, the lane detection problem has been formulated here as the problem of determining the set of control points of lane model. The proposed algorithm ®rst detects the vanishing point (line) by using a Hough-like technique and then solves the lane detection problem by suggesting a maximum likelihood approach. Also, we have employed a multi-resolution strategy for rapidly achieving an accurate solution. This coarse-to-®ne matching oers us an acceptable solution at an aordable computational cost, and thus speeds up the process of lane detection. As a result, the proposed method is robust to noise, shadows, and illumination variations in the captured road images, and is also applicable to both the marked and the unmarked roads.
Resource Management for Device-to-Device Underlay Communication

Device-to-Device (D2D) communication is a technology component for LTE-A. The existing researches allow D2D as an underlay to the cellular network to increase the spectral efficiency. In this book, D2D communication underlaying cellular networks is studied. Some physical-layer techniques and cross-layer optimization methods on resource management and interference avoidance are proposed and discussed. WINNER II channel models is applied to be the signal and interference model and simulation results show that the performance of D2D link is closely related to the distance between D2D transmitter and receiver and that between interference source and the receiver. Besides, by power control, D2D SINR degrades, which will naturally contribute to low interference to cellular communication. A simple mode selection method of D2D communication is introduced. Based on path-loss (PL) mode selection criterion, D2D gives better performance than traditional cellular system. When D2D pair is farther away from the BS, a better results can be obtained. Game theory, which offers a wide variety of analytical tools to study the complex interactions of players and predict their choices, can be used for power and radio resource management in D2D communication. A reverse iterative combinatorial auction is formulated as a mechanism to allocate the spectrum resources for D2D communications with multiple user pairs sharing the same channel. In addition, a game theoretic approach is developed to implement joint scheduling, power control and channel allocation for D2D communication. Finally, joint power and spectrum resource allocation method is studied under consideration of battery lifetime, which is an important application of D2D communication on increasing user's energy efficiency. The simulation results show that all these methods have beneficial effects on improving the system performance.
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce a method to train Binarized Neural Networks (BNNs) neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. To validate the effectiveness of BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. Last but not least, we wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST BNN 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for training and running our BNNs is available on-line.
The Architecture and Evolution of CPU-GPU Systems for General Purpose Computing

GPU computing has emerged in recent years as a viable execution platform for throughput oriented applications or regions of code. GPUs started out as independent units for program execution but there are clear trends towards tight-knit CPU-GPU integration. In this work, we will examine existing research directions and future opportunities for chip integrated CPU-GPU systems. We first seek to understand state of the art GPU architectures and examine GPU design proposals to reduce performance loss caused by SIMT thread divergence. Next, we motivate the need of new CPU design directions for CPU-GPU systems by discussing our work in the area. We examine proposals as to how shared components such as lastlevel caches and memory controllers could be evolved to improve the performance of CPU-GPU systems. We then look at collaborative CPUGPU execution schemes. Lastly, we discuss future work directions and research opportunities for CPU-GPU systems.
Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.

Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Preand posttests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners’ insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.
Efficient and Reliable Data Recovery Technique in Cloud Computing

Cloud computing provides accessing of any kind of services dynamically over Internet on demand basis. One of the most significant service that is being provided is storage as a service. Cloud customer can store any amount of data into cloud storage results to huge amount of data at the datacenter. The data may get deleted by man-made disaster (either CSP or customer itself without their knowledge) or by natural disasters (either earth quakes or volcanoes) from the datacenters. Nowadays, data has been generated in large quantity that requires the data recovery services or techniques. Therefore there is a requirement for designing an efficient data recovery technique to recover the lost data. Many researchers have proposed different data recovery techniques but they lack in efficiency and reliability. In this paper, a multi-server system based on Enriched Genetic Algorithm to recover the lost data by using four cloud backup servers is discussed. To achieve reliability the proposed technique provides the flexibility for the user to collect information from any backup server when main cloud server loses its data and is unable to provide data to users.
Towards Linked Research Data: an Institutional Approach

For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment.
Neuronlike adaptive elements that can solve difficult learning control problems

It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.
A dual frequency OMT in the Ku band for TT&C applications

A mixed characterisation by means of the generalized admittance matrix and the generalized scattering matrices, obtained with mode matching is proposed to design dual-band orthomode transducer (OMT) components. Accurate and efficient fullwave analysis software based on this procedure has been developed. A dual frequency OMT in the Ku band with high performance has been fully designed with the developed software. The good agreement between the numerical and experimental results validates the design process.
An End-to-End Neural Network for Polyphonic Piano Music Transcription

We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network-based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yield the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications.