nils-herrmann/clean_scirepeval huggingface dataset:
Sparse Additive Models

We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. SpAM is essentially a functional version of the grouped lasso of Yuan and Lin (2006). SpAM is also closely related to the COSSO model of Lin and Zhang (2006), but decouples smoothing and sparsity, enabling the use of arbitrary nonparametric smoothers. We give an analysis of the theoretical properties of sparse additive models, and present empirical results on synthetic and real data, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.
Marker tracking and HMD calibration for a video-based augmented reality conferencing system

CSCW; in this setting computers can provide the same type of collaborative information that people have in face-to-face interactions, such as communication by object manipulation, voice and gesture . Work on the DIVE project , GreenSpace  and other fully immersive multi-participant virtual environments has shown that collaborative work is indeed intuitive in such surroundings. However most current multi-user VR systems are fully immersive, separating the user from the real world and their traditional tools. As Grudin  points out, CSCW tools are generally rejected when they force users to change the way they work. This is because of the introduction of seams or discontinuities between the way people usually work and the way they are forced to work because of the computer interface. Ishii describes in detail the advantages of seamless CSCW interfaces . Obviously immersive VR interfaces introduce a huge discontinuity between the real and virtual worlds. An alternative approach is through Augmented Reality (AR), the overlaying of virtual objects onto the real world. In the past researchers have explored the use of AR approaches to support face-to-face collaboration. Projects such as Studierstube , Transvision [7], and AR2 Hockey [8] allow users can see each other as well as 3D virtual objects in the space between them. Users can interact with the real world at the same time as the virtual images, bringing the benefits of VR interfaces into the real world and facilitating very natural collaboration. In a previous paper we found that this meant that users collaborate better on a task in a face-to-face AR setting than for the same task in a Abstract We describe an augmented reality conferencing system which uses the overlay of virtual images on the real world. Remote collaborators are represented on Virtual Monitors which can be freely positioned about a user in space. Users can collaboratively view and interact with virtual objects using a shared virtual whiteboard. This is possible through precise virtual image registration using fast and accurate computer vision techniques and HMD calibration. We propose a method for tracking fiducial markers and a calibration method for optical see-through HMD based on the marker tracking.
A Measurement Study of the Content Security Policy on Real-World Applications

Content Security Policy (CSP) is a browser security mechanism that aims to protect websites from content injection attacks. To adopt CSP, website developers need to manually compile a list of allowed content sources. Nearly all websites require modifications to comply with CSP’s default behavior, which blocks inline scripts and the use of the eval() function. Alternatively, websites could adopt a policy that allows the use of this unsafe functionality, but this opens up potential attack vectors. In this paper, our measurements on a large corpus of web applications provide a key insight on the amount of efforts web developers required to adapt to CSP. Our results also identified errors in CSP policies that are set by website developers on their websites. To address these issues and make adoption of CSP easier and error free, we implemented UserCSP a tool as a Firefox extension. The UserCSP uses dynamic analysis to automatically infer CSP policies, facilitates testing, and gives savvy users the authority to enforce client-side policies on websites.
The learning brain: lessons for education: a précis.

This book highlights the importance of anchoring education in an evidence base derived from neuroscience. For far too long has the brain been neglected in discussions on education and often information about neuroscientific research is not easy to access. Our aim was to provide a source book that conveys the excitement of neuroscience research that is relevant to learning and education. This research has largely, but not exclusively, been carried out using neuroimaging methods in the past decade or so, ranging from investigations of brain structure and function in dyslexia and dyscalculia to investigations of the changes in the hippocampus of London taxi drivers. To speak to teachers who might not have scientific backgrounds, we have tried to use nontechnical language as far as possible and have provided an appendix illustrating the main methods and techniques currently used and a glossary, defining terms from Acetylcholine, Action Potentials and ADHD to White Matter, Word Form Area and Working Memory. We start with the idea that the brain has evolved to educate and to be educated, often instinctively and effortlessly. We believe that understanding the brain mechanisms that underlie learning and teaching could transform educational strategies and enable us to design educational programmes that optimize learning for people of all ages and of all needs. For this reason the first two-thirds of the book follows a developmental framework. The rest of the book focuses on learning in the brain at all ages. There is a vast amount brain research of direct relevance to education practice and policy. And yet neuroscience has had little impact on education. This might in part be due to a lack of interaction between educators and brain scientists. This in turn might be because of difficulties of translating the neuroscience knowledge of how learning takes place in the brain into information of value to teachers. It is here where we try to fill a gap. Interdisciplinary dialogue needs a mediator to prevent one or other discipline dominating, and, notwithstanding John Bruer’s remarks that it is cognitive psychology that ‘bridges the gap’ between neuroscience and education (Bruer, 1997), we feel that now is the time to explore the implications of brain science itself for education.
Knowledge sharing and social media: Altruism, perceived online attachment motivation, and perceived online relationship commitment

Social media, such as Facebook and Twitter, have become extremely popular. Facebook, for example, has more than a billion registered users and thousands of millions of units of information are shared every day, including short phrases, articles, photos, and audio and video clips. However, only a tiny proportion of these sharing units trigger any type of knowledge exchange that is ultimately beneficial to the users. This study draws on the theory of belonging and the intrinsic motivation of altruism to explore the factors contributing to knowledge sharing behavior. Using a survey of 299 high school students applying for university after the release of the public examination results, we find that perceived online attachment motivation (β=0.31, p<0.001) and perceived online relationship commitment (β=0.49, p<0.001) have positive, direct, and significant effects on online knowledge sharing (R 0.568). Moreover, when introduced into the model, altruism has a direct and significant effect on online knowledge sharing (β=0.46, p<0.001) and the total variance explained by the extended model increases to 64.9 percent. The implications of the findings are discussed.
Energy-efficient acceleration of big data analytics applications using FPGAs

A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization. Considering the time dedicated to create such hardware implementations, an analysis that estimates how much benefit we gain in terms of speed and energy efficiency, through offloading various functions to hardware would be necessary. This work analyzes data mining and machine learning algorithms, which are utilized extensively in big data applications in a heterogeneous CPU+FPGA platform. We select and offload the computational intensive kernels to the hardware accelerator to achieve the highest speed-up and best energy-efficiency. We use the latest Xilinx Zynq boards for implementation and result analysis. We also perform a first order comprehensive analysis of communication and computation overheads to understand how the speedup of each application contributes to its overall execution in an end-to-end Hadoop MapReduce environment. Moreover, we study how other system parameters such as the choice of CPU (big vs little) and the number of mapper slots affect the performance and power-efficiency benefits of hardware acceleration. The results show that a kernel speedup of upto χ 321.5 with hardware+software co-design can be achieved. This results in χ2.72 speedup, 2.13χ power reduction, and 15.21χ energy efficiency improvement (EDP) in an end-to-end Hadoop MapReduce environment.
Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).
Synthesized Classifiers for Zero-Shot Learning

Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main idea is to align the semantic space that is derived from external information to the model space that concerns itself with recognizing visual features. To this end, we introduce a set of "phantom" object classes whose coordinates live in both the semantic space and the model space. Serving as bases in a dictionary, they can be optimized from labeled data such that the synthesized real object classifiers achieve optimal discriminative performance. We demonstrate superior accuracy of our approach over the state of the art on four benchmark datasets for zero-shot learning, including the full ImageNet Fall 2011 dataset with more than 20,000 unseen classes.
Recent Development of Open-Source Speech Recognition Engine Julius

It executes real-time speech recognition of a 60k-word dictation task on low-spec PCs with small footprint, and even on embedded devices. Julius supports standard language models such as statistical N-gram model and rule-based grammars, as well as Hidden Markov Model (HMM) as an acoustic model. One can build a speech recognition system of his own purpose, or can integrate the speech recognition capability to a variety of applications using Julius. This article describes an overview of Julius, major features and specifications, and summarizes the developments conducted in the recent years.
Learning shape placements by example

We present a method to learn and propagate shape placements in 2D polygonal scenes from a few examples provided by a user. The placement of a shape is modeled as an oriented bounding box. Simple geometric relationships between this bounding box and nearby scene polygons define a feature set for the placement. The feature sets of all example placements are then used to learn a probabilistic model over all possible placements and scenes. With this model, we can generate a new set of placements with similar geometric relationships in any given scene. We introduce extensions that enable propagation and generation of shapes in 3D scenes, as well as the application of a learned modeling session to large scenes without additional user interaction. These concepts allow us to generate complex scenes with thousands of objects with relatively little user interaction.
OpenCL caffe: Accelerating and enabling a cross platform machine learning framework

Deep neural networks (DNN) achieved significant breakthrough in vision recognition in 2012 and quickly became the leading machine learning algorithm in Big Data based large scale object recognition applications. The successful deployment of DNN based applications pose challenges for a cross platform software framework that enable multiple user scenarios, including offline model training on HPC clusters and online recognition in embedded environments. Existing DNN frameworks are mostly focused on a closed format CUDA implementations, which is limiting of deploy breadth of DNN hardware systems.
 This paper presents OpenCL™ caffe, which targets in transforming the popular CUDA based framework caffe  into open standard OpenCL backend. The goal is to enable a heterogeneous platform compatible DNN framework and achieve competitive performance based on OpenCL tool chain. Due to DNN models' high complexity, we use a two-phase strategy. First we introduce the OpenCL porting strategies that guarantee algorithm convergence; then we analyze OpenCL's performance bottlenecks in DNN domain and propose a few optimization techniques including batched manner data layout and multiple command queues to better map the problem size into existing BLAS library, improve hardware resources utilization and boost OpenCL runtime efficiency.
 We verify OpenCL caffe's successful offline training and online recognition on both server-end and consumer-end GPUs. Experimental results show that the phase-two's optimized OpenCL caffe achieved a 4.5x speedup without modifying BLAS library. The user can directly run mainstream DNN models and achieves the best performance for a specific processors by choosing the optimal batch number depending on H/W properties and input data size.
Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap.

Plants are affected by complex genome×environment×management interactions which determine phenotypic plasticity as a result of the variability of genetic components. Whereas great advances have been made in the cost-efficient and high-throughput analyses of genetic information and non-invasive phenotyping, the large-scale analyses of the underlying physiological mechanisms lag behind. The external phenotype is determined by the sum of the complex interactions of metabolic pathways and intracellular regulatory networks that is reflected in an internal, physiological, and biochemical phenotype. These various scales of dynamic physiological responses need to be considered, and genotyping and external phenotyping should be linked to the physiology at the cellular and tissue level. A high-dimensional physiological phenotyping across scales is needed that integrates the precise characterization of the internal phenotype into high-throughput phenotyping of whole plants and canopies. By this means, complex traits can be broken down into individual components of physiological traits. Since the higher resolution of physiological phenotyping by 'wet chemistry' is inherently limited in throughput, high-throughput non-invasive phenotyping needs to be validated and verified across scales to be used as proxy for the underlying processes. Armed with this interdisciplinary and multidimensional phenomics approach, plant physiology, non-invasive phenotyping, and functional genomics will complement each other, ultimately enabling the in silico assessment of responses under defined environments with advanced crop models. This will allow generation of robust physiological predictors also for complex traits to bridge the knowledge gap between genotype and phenotype for applications in breeding, precision farming, and basic research.
Mnemosyne: lightweight persistent memory

New storage-class memory (SCM) technologies, such as phase-change memory, STT-RAM, and memristors, promise user-level access to non-volatile storage through regular memory instructions. These memory devices enable fast user-mode access to persistence, allowing regular in-memory data structures to survive system crashes.
 In this paper, we present Mnemosyne, a simple interface for programming with persistent memory. Mnemosyne addresses two challenges: how to create and manage such memory, and how to ensure consistency in the presence of failures. Without additional mechanisms, a system failure may leave data structures in SCM in an invalid state, crashing the program the next time it starts.
 In Mnemosyne, programmers declare global persistent data with the keyword "pstatic" or allocate it dynamically. Mnemosyne provides primitives for directly modifying persistent variables and supports consistent updates through a lightweight transaction mechanism. Compared to past work on disk-based persistent memory, Mnemosyne reduces latency to storage by writing data directly to memory at the granularity of an update rather than writing memory pages back to disk through the file system. In tests emulating the performance characteristics of forthcoming SCMs, we show that Mnemosyne can persist data as fast as 3 microseconds. Furthermore, it provides a 35 percent performance increase when applied in the OpenLDAP directory server. In microbenchmark studies we find that Mnemosyne can be up to 1400% faster than alternative persistence strategies, such as Berkeley DB or Boost serialization, that are designed for disks.
Google fusion tables: web-centered data management and collaboration

It has long been observed that database management systems focus on traditional business applications, and that few people use a database management system outside their workplace. Many have wondered what it will take to enable the use of data management technology by a broader class of users and for a much wider range of applications.
 Google Fusion Tables represents an initial answer to the question of how data management functionality that focused on enabling new users and applications would look in today's computing environment. This paper characterizes such users and applications and highlights the resulting principles, such as seamless Web integration, emphasis on ease of use, and incentives for data sharing, that underlie the design of Fusion Tables. We describe key novel features, such as the support for data acquisition, collaboration, visualization, and web-publishing.
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services

Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels. Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load. To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions. The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments. The proposed InterCloud environment supports scaling of applications across multiple vendor clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.
Complexity and Adaptivity in Supply Networks: Building Supply Network Theory Using a Complex Adaptive Systems Perspective

Supply networks are composed of large numbers of firms from multiple interrelated industries. Such networks are subject to shifting strategies and objectives within a dynamic environment. In recent years, when faced with a dynamic environment, several disciplines have adopted the Complex Adaptive System (CAS) perspective to gain insights into important issues within their domains of study. Research investigations in the field of supply networks have also begun examining the merits of complexity theory and the CAS perspective. In this article, we bring the applicability of complexity theory and CAS into sharper focus, highlighting its potential for integrating existing supply chain management (SCM) research into a structured body of knowledge while also providing a framework for generating, validating, and refining new theories relevant to real-world supply networks. We suggest several potential research questions to emphasize how a ∗We sincerely thank Professors Thomas Choi (Arizona State University), David Dilts (Vanderbilt University), and Kevin Dooley (Arizona State University) for their help, guidance, and support. †Corresponding author.