nils-herrmann/clean_scirepeval huggingface dataset:
Neuromorphic Processors with Memristive Synapses: Synaptic Interface and Architectural Exploration

Due to their nonvolatile nature, excellent scalability, and high density, memristive nanodevices provide a promising solution for low-cost on-chip storage. Integrating memristor-based synaptic crossbars into digital neuromorphic processors (DNPs) may facilitate efficient realization of brain-inspired computing. This article investigates architectural design exploration of DNPs with memristive synapses by proposing two synapse readout schemes. The key design tradeoffs involving different analog-to-digital conversions and memory accessing styles are thoroughly investigated. A novel storage strategy optimized for feedforward neural networks is proposed in this work, which greatly reduces the energy and area cost of the memristor array and its peripherals.
Spatial indexing and analytics on Hadoop

Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Hadoop is one such open-source framework that is enjoying widespread adoption. In this paper, we detail an approach to indexing and performing key analytics on spatial data that is persisted in HDFS. Our technique differs from other approaches in that it combines spatial indexing, data load balancing, and data clustering in order to optimize performance across the cluster. In addition, our index supports efficient, random-access queries without requiring a MapReduce job; neither a full table scan, nor any MapReduce overhead is incurred when searching. This facilitates large numbers of concurrent query executions. We will also demonstrate how indexing and clustering positively impacts the performance of range and k-NN queries on large real-world datasets. The performance analysis will enable a number of interesting observations to be made on the behavior of spatial indexes and spatial queries in this distributed processing environment.
VALUE CREATION IN E-BUSINESS

We explore the theoretical foundations of value creation in e-business by examining how 59 American and European e-businesses that have recently become publicly traded corporations create value. We observe that in e-business new value can be created by the ways in which transactions are enabled. Grounded in the rich data obtained from case study analyses and in the received theory in entrepreneurship and strategic management, we develop a model of the sources of value creation. The model suggests that the value creation potential of e-businesses hinges on four interdependent dimensions, namely: efficiency, complementarities, lock-in, and novelty. Our findings suggest that no single entrepreneurship or strategic management theory can fully explain the value creation potential of e-business. Rather, an integration of the received theoretical perspectives on value creation is needed. To enable such an integration, we offer the business model construct as a unit of analysis for future research on value creation in e-business. A business model depicts the design of transaction content, structure, and governance so as to create value through the exploitation of business opportunities. We propose that a firm’s business model is an important locus of innovation and a crucial source of value creation for the firm and its suppliers, partners, and customers.
Key management systems for sensor networks in the context of the Internet of Things

If a wireless sensor network (WSN) is to be completely integrated into the Internet as part of the Internet of Things (IoT), it is necessary to consider various security challenges, such as the creation of a secure channel between an Internet host and a sensor node. In order to create such a channel, it is necessary to provide key management mechanisms that allow two remote devices to negotiate certain security credentials (e.g. secret keys) that will be used to protect the information flow. In this paper we will analyse not only the applicability of existing mechanisms such as public key cryptography and pre-shared keys for sensor nodes in the IoT context, but also the applicability of those link-layer oriented key management systems (KMS) whose original purpose is to provide shared keys for sensor nodes belonging to the same WSN.
Increasing Users' Confidence in Uncertain Data by Aggregating Data from Multiple Sources

We often base our decisions on uncertain data - for instance, when consulting the weather forecast before deciding what to wear. Due to their uncertainty, such forecasts can differ by provider. To make an informed decision, many people compare several forecasts, which is a time-consuming and cumbersome task. To facilitate comparison, we identified three aggregation mechanisms for forecasts: manual comparison and two mechanisms of computational aggregation. In a survey, we compared the mechanisms using different representations. We then developed a weather application to evaluate the most promising candidates in a real-world study. Our results show that aggregation increases users' confidence in uncertain data, independent of the type of representation. Further, we find that for daily events, users prefer to use computationally aggregated forecasts. However, for high-stakes events, they prefer manual comparison. We discuss how our findings inform the design of improved interfaces for comparison of uncertain data, including non-weather purposes.
A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims

Healthcare is an integral component in people's lives, especially for the rising elderly population. Medicare is one such healthcare program that provides for the needs of the elderly. It is imperative that these healthcare programs are affordable, but this is not always the case. Out of the many possible factors for the rising cost of healthcare, claims fraud is a major contributor, but its impact can be lessened through effective fraud detection. We propose a general outlier detection model, based on Bayesian inference, using probabilistic programming. Our model provides probability distributions rather than just point values, as with most common outlier detection methods. Credible intervals are also generated to further enhance confidence that the detected outliers should in fact be considered outliers. Two case studies are presented demonstrating our model's effectiveness in detecting outliers. The first case study uses temperature data in order to provide a clear comparison of several outlier detection techniques. The second case study uses a Medicare dataset to showcase our proposed outlier detection model. Our results show that the successful detection of outliers, which indicate possible fraudulent activities, can provide effective and meaningful results for further investigation within medical specialties or by using real-world, medical provider fraud investigation cases.
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.
An energy budget for signaling in the grey matter of the brain.

Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with <or=15% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons. Functional magnetic resonance imaging signals are likely to be dominated by changes in energy usage associated with synaptic currents and action potential propagation.
Fast and Energy-Efficient CNN Inference on IoT Devices

Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped internet of things (IoT) devices permeate into every aspect of modern life, it is increasingly important to run CNN inference, a computationally intensive application, on resource constrained devices. We present a technique for fast and energy-efficient CNN inference on mobile SoC platforms, which are projected to be a major player in the IoT space. We propose techniques for efficient parallelization of CNN inference targeting mobile GPUs, and explore the underlying tradeoffs. Experiments with running Squeezenet on three different mobile devices confirm the effectiveness of our approach. For further study, please refer to the project repository available on our GitHub page: https://github.com/mtmd/Mobile
An intelligent SDN framework for 5G heterogeneous networks

In fifth-generation (5G) mobile networks, a major challenge is to effectively improve system capacity and meet dynamic service demands. One promising technology to solve this problem is heterogeneous networks (HetNets), which involve a large number of densified low power nodes (LPNs). This article proposes a software defined network (SDN) based intelligent model that can efficiently manage the heterogeneous infrastructure and resources. In particular, we first review the latest SDN standards and discuss the possible extensions. We then discuss the advantages of SDN in meeting the dynamic nature of services and requirements in 5G HetNets. Finally, we develop a variety of schemes to improve traffic control, subscriber management, and resource allocation. Performance analysis shows that our proposed system is reliable, scalable, and implementable.
Concepts and Guidelines of Feature Modeling for Product Line Software Engineering

Product line software engineering (PLSE) is an emerging software engineering paradigm, which guides organizations toward the development of products from core assets rather than the development of products one by one from scratch. In order to develop highly reusable core assets, PLSE must have the ability to exploit commonality and manage variability among products from a domain perspective. Feature modeling is one of the most popular domain analysis techniques, which analyzes commonality and variability in a domain to develop highly reusable core assets for a product line. Various attempts have been made to extend and apply it to the development of software product lines. However, feature modeling can be difficult and time-consuming without a precise understanding of the goals of feature modeling and the aid of practical guidelines. In this paper, we clarify the concept of features and the goals of feature modeling, and provide practical guidelines for successful product line software engineering. The authors have extensively used feature modeling in several industrial product line projects and the guidelines described in this paper are based on these experiences.
Experiences in Malware Binary Deobfuscation

Malware authors employ a myriad of evasion techniques to impede automated reverse engineering and static analysis efforts. The most popular technologies include ‘code obfuscators’ that serve to rewrite the original binary code to an equivalent form that provides identical functionality while defeating signature-based detection systems. These systems significantly complicate static analysis, making it challenging to uncover the malware intent and the full spectrum of embedded capabilities. While code obfuscation techniques are commonly integrated into contemporary commodity packers, from the perspective of a reverse engineer, deobfuscation is often a necessary step that must be conducted independently after unpacking the malware binary. In this paper, we describe a set of techniques for automatically unrolling the impact of code obfuscators with the objective of completely recovering the original malware logic. We have implemented a set of generic debofuscation rules as a plug-in for the popular IDA Pro disassembler. We use sophisticated obfuscation strategies employed by two infamous malware instances from 2009, Conficker C and Hydraq (the binary associated with the Aurora attack) as case studies. In both instances our deobfuscator enabled a complete decompilation of the underlying code logic. This work was instrumental in the comprehensive reverse engineering of the heavily obfuscated P2P protocol embedded in the Conficker worm. The plug-in is integrated with the HexRays decompiler to provide a complete reverse engineering of malware binaries from binary form to C code and is available for free download on the SRI malware threat center website:
Second-order Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image. In the past, however, second-order statistics computed from handcrafted features, e.g., covariances, have proven highly effective in diverse recognition tasks. In this paper, we introduce a novel class of CNNs that exploit second-order statistics. To this end, we design a series of new layers that (i) extract a covariance matrix from convolutional activations, (ii) compute a parametric, second-order transformation of a matrix, and (iii) perform a parametric vectorization of a matrix. These operations can be assembled to form a Covariance Descriptor Unit (CDU), which replaces the fully-connected layers of standard CNNs. Our experiments demonstrate the benefits of our new architecture, which outperform the first-order CNNs, while relying on up to 90% fewer parameters.
Nonlinear Dynamic Modeling for High Performance Control of a Quadrotor

In this paper, we present a detailed dynamic and aerodynamic model of a quadrotor that can be used for path planning and control design of high performance, complex and aggressive manoeuvres without the need for iterative learning techniques. The accepted nonlinear dynamic quadrotor model is based on a thrust and torque model with constant thrust and torque coefficients derived from static thrust tests. Such a model is no longer valid when the vehicle undertakes dynamic manoeuvres that involve significant displacement velocities. We address this by proposing an implicit thrust model that incorporates the induced momentum effects associated with changing airflow through the rotor. The proposed model uses power as input to the system. To complete the model, we propose a hybrid dynamic model to account for the switching between different vortex ring states of the rotor.
Principles for allocation of scarce medical interventions

Allocation of very scarce medical interventions such as organs and vaccines is a persistent ethical challenge. We evaluate eight simple allocation principles that can be classified into four categories: treating people equally, favouring the worst-off, maximising total benefits, and promoting and rewarding social usefulness. No single principle is sufficient to incorporate all morally relevant considerations and therefore individual principles must be combined into multiprinciple allocation systems. We evaluate three systems: the United Network for Organ Sharing points systems, quality-adjusted life-years, and disability-adjusted life-years. We recommend an alternative system-the complete lives system-which prioritises younger people who have not yet lived a complete life, and also incorporates prognosis, save the most lives, lottery, and instrumental value principles.
Ecotourism : towards congruence between theory and practice

This paper and its successor examine the gap between ecotourism theory as revealed in the literature and ecotourism practice as indicated by its on-site application. A framework is suggested which, if implemented through appropriate management, can help to achieve a balance between conservation and development through the promotion of synergistic relationships between natural areas, local populations and tourism. The framework can also be used to assess the status of ecotourism at particular sites.
Land cover classification and change analysis of the Twin Cities ( Minnesota ) Metropolitan Area by multitemporal Landsat remote sensing

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities Metropolitan Area of Minnesota for 1986, 1991, 1998, and 2002. The overall seven-class classification accuracies averaged 94% for the four years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 80% to 90%. The maps showed that between 1986 and 2002 the amount of urban or developed land increased from 23.7% to 32.8% of the total area, while rural cover types of agriculture, forest and wetland decreased from 69.6% to 60.5%. The results quantify the land cover change patterns in the metropolitan area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions. D 2005 Elsevier Inc. All rights reserved.
Sparse surface reconstruction with adaptive partition of unity and radial basis functions

A new implicit surface fitting method for surface reconstruction from scattered point data is proposed. The method combines an adaptive partition of unity approximation with least-squares RBF fitting and is capable of generating a high quality surface reconstruction. Given a set of points scattered over a smooth surface, first a sparse set of overlapped local approximations is constructed. The partition of unity generated from these local approximants already gives a faithful surface reconstruction. The final reconstruction is obtained by adding compactly supported RBFs. The main feature of the developed approach consists of using various regularization schemes which lead to economical, yet accurate surface reconstruction.
Aging , Obsolescence and Organizational Innovation

• The order of authorship on this paper is random and contributions were equal. We would like to thank Ron Burt, Jim March and Mike Tushman for many helpful suggestions. Olav Sorenson provided particularly extensive comments on this paper. We would like to acknowledge the financial support of the University of Chicago, Graduate School of Business and a grant from the Kauffman Center for Entrepreneurial Leadership. Clarifying the relationship between organizational aging and innovation processes is an important step in understanding the dynamics of high-technology industries, as well as for resolving debates in organizational theory about the effects of aging on organizational functioning. We argue that aging has two seemingly contradictory consequences for organizational innovation. First, we believe that aging is associated with increases in firms' rates of innovation. Simultaneously, however, we argue that the difficulties of keeping pace with incessant external developments causes firms' innovative outputs to become obsolete relative to the most current environmental demands. These seemingly contradictory outcomes are intimately related and reflect inherent trade-offs in organizational learning and innovation processes. Multiple longitudinal analyses of the relationship between firm age and patenting behavior in the semiconductor and biotechnology industries lend support to these arguments.  In an increasingly knowledge-based economy, pinpointing the factors that shape the ability of organizations to produce influential ideas and innovations is a central issue for organizational studies. Among all organizational outputs, innovation is fundamental not only because of its direct impact on the viability of firms, but also because of its profound effects on the paths of social and economic change. In this paper, we focus on an ubiquitous organizational process-aging-and examine its multifaceted influence on organizational innovation. In so doing, we address an important unresolved issue in organizational theory, namely the nature of the relationship between aging and organizational behavior (Hannan 1998). Evidence clarifying the relationship between organizational aging and innovation promises to improve our understanding of the organizational dynamics of high-technology markets, and in particular the dynamics of technological leadership. For instance, consider the possibility that aging has uniformly positive consequences for innovative activity: on the foundation of accumulated experience, older firms innovate more frequently, and their innovations have greater significance than those of younger enterprises. In this scenario, technological change paradoxically may be associated with organizational stability, as incumbent organizations come to dominate the technological frontier and their preeminence only increases with their tenure. 1 Now consider the …
A design of axial-gap switched reluctance motor for in-wheel direct-drive EV

Switched reluctance motors (SR Motors) attract attention as motor that use no rare earth materials. And it is a candidate technology for electric vehicle application. In addition, axial-gap structure has possibility of effective utilization of in-wheel flat motor space. This paper mainly discusses the design and the characteristics of axial-gap SR motors. This study focuses on the volumetric constrains of in-wheel drive system. First, the results of comparing the axialgap SR motors specification to the radial-gap one at same volume are shown that utilize the available active volume. By the results, a new flat volume axial-gap SR motor for in-wheel direct-drive EV is designed. Finally, a new support link structure is proposed for the large axial direction electromagnetic force of axial-gap SR motor.