nils-herrmann/clean_scirepeval huggingface dataset:
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems.
Constructing Deterministic Finite-State Automata in Recurrent Neural Networks

Recurrent neural networks that are <italic>trained</italic> to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidel discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can <italic>construct</italic> second-order recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, that is, the constructed network correctly classifies strings of <italic>arbitrary length</italic>. The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with <italic>n</italic> state and <italic>m</italic>input alphabet symbols, the constructive algorithm generates a “programmed” neural network with <italic>O</italic>(<italic>n</italic>) neurons and <italic>O</italic>(<italic>mn</italic>) weights. We compare our algorithm to other methods proposed in the literature.
A portfolio approach to technical debt management

Technical debt describes the effect of immature software artifacts on software maintenance - the potential of extra effort required in future as if paying interest for the incurred debt. The uncertainty of interest payment further complicates the problem of what debt should be incurred or repaid and when. To help software managers make informed decisions, a portfolio approach is proposed in this paper. The approach leverages the portfolio management theory in the finance domain to determine the optimal collection of technical debt items that should be incurred or held. We expect this approach could provide a new perspective for technical debt management.
Distributed Vector Representations of Words in the Sigma Cognitive Architecture

Recently reported results with distributed-vector word representations in natural language processing make them appealing for incorporation into a general cognitive architecture like Sigma. This paper describes a new algorithm for learning such word representations from large, shallow information resources, and how this algorithm can be implemented via small modifications to Sigma. The effectiveness and speed of the algorithm are evaluated via a comparison of an external simulation of it with state-of-the-art algorithms. The results from more limited experiments with Sigma are also promising, but more work is required for it to reach the effectiveness and speed of the simulation.
On the efficient representation and execution of deep acoustic models

In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values. The proposed quantization scheme leads to significant memory savings and enables the use of optimized hardware instructions for integer arithmetic, thus significantly reducing the cost of inference. Finally, we propose a ‘quantization aware’ training process that applies the proposed scheme during network training and find that it allows us to recover most of the loss in accuracy introduced by quantization. We validate the proposed techniques by applying them to a long short-term memory-based acoustic model on an open-ended large vocabulary speech recognition task.
A self-tracking augmented reality system

We present a color-video-based augmented reality (AR) system that is designed to be self-tracking, that is, it requires no separate tracking subsystem. Rather, tracking is performed strictly from the video images acquired through the lens of the camera also used to view the real world. The methods for tracking are rooted in prior research in photogrammetry and computer vision. This approach to tracking for AR systems enables a variety of new applications in assembly guidance that are not feasible with current AR technology. Our initial pplication is in aircraft manufacturing. We outline our approaches to feature detection, correspondence, pose determination, and system calibration. The results obtained thus far are summarized along with the problems we encountered.
Making sense of Cronbach's alpha

Medical educators attempt to create reliable and valid tests and questionnaires in order to enhance the accuracy of their assessment and evaluations. Validity and reliability are two fundamental elements in the evaluation of a measurement instrument. Instruments can be conventional knowledge, skill or attitude tests, clinical simulations or survey questionnaires. Instruments can measure concepts, psychomotor skills or affective values. Validity is concerned with the extent to which an instrument measures what it is intended to measure. Reliability is concerned with the ability of an instrument to measure consistently.1 It should be noted that the reliability of an instrument is closely associated with its validity. An instrument cannot be valid unless it is reliable. However, the reliability of an instrument does not depend on its validity.2 It is possible to objectively measure the reliability of an instrument and in this paper we explain the meaning of Cronbach’s alpha, the most widely used objective measure of reliability. Calculating alpha has become common practice in medical education research when multiple-item measures of a concept or construct are employed. This is because it is easier to use in comparison to other estimates (e.g. testretest reliability estimates)3 as it only requires one test administration. However, in spite of the widespread use of alpha in the literature the meaning, proper use and interpretation of alpha is not clearly understood. 2, 4, 5 We feel it is important, therefore, to further explain the underlying assumptions behind alpha in order to promote its more effective use. It should be emphasised that the purpose of this brief overview is just to focus on Cronbach’s alpha as an index of reliability. Alternative methods of measuring reliability based on other psychometric methods, such as generalisability theory or item-response theory, can be used for monitoring and improving the quality of OSCE examinations 6-10, but will not be discussed here.
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

This paper presents a novel learning approach for Face Recognition by introducing Optimal Local Basis. Optimal local bases are a set of basis derived by reinforcement learning to represent the face space locally. The reinforcement signal is designed to be correlated to the recognition accuracy. The optimal local bases are derived then by finding the most discriminant features for different parts of the face space, which represents either different individuals or different expressions, orientations, poses, illuminations, and other variants of the same individual. Therefore, unlike most of the existing approaches that solve the recognition problem by using a single basis for all individuals, our proposed method benefits from local information by incorporating different bases for its decision. We also introduce a novel classification scheme that uses reinforcement signal to build a similarity measure in a non-metric space. Experiments on AR, PIE, ORL and YALE databases indicate that the proposed method facilitates robust face recognition under pose, illumination and expression variations. The performance of our method is compared with that of Eigenface, Fisherface, Subclass Discriminant Analysis, and Random Subspace LDA methods as well.
The relationship between total quality management practices and their effects on firm performance

Recent research on total quality management (TQM) has examined the relationships between the practices of quality management and various levels of organizational performance. These studies have produced mixed results, probably because of the nature of the research designs used such as measuring TQM or performance as a single construct. Based on a comprehensive literature review, this study identifies the relationships among TQM practices and examines the direct and indirect effects of these practices on various performance levels. A proposed research model and hypotheses are tested by using cross-sectional mail survey data collected from firms operating in the US. The test of the structural model supports the proposed hypotheses. The implications of the findings for researchers and practitioners are discussed and further research directions are offered.
3 D City Modeling from High-resolution Satellite Images

High-resolution satellite images having a footprint smaller than 5 m are becoming increasingly available. A large number of the high resolution satellite sensors acquire imagery in a pushbroom mode with CCD linear array technology. The need for accurate 3D object reconstruction requires a sophisticated camera model, being able to deal with such sensor geometry. The software package SAT-PP (Satellite Image Precision Processing), a full suite for the precision processing of linear array satellite sensors, has been recently developed at the Institute of Geodesy and Photogrammetry, ETH Zurich. The software can accommodate images from IKONOS, QuickBird, SPOT-5 HRS/HRG and will include sensors of similar type in the future. High-resolution satellite images capture very detailed information of the terrain. In particular, images such as IKONOS and Quickbird are at the resolution close to that of small-scale aerial photographs. Important applications could be the updating of urban/suburban databases, such as road network, building reconstruction, 3D city modeling and other GIS related applications. For these purposes, the SAT-PP software includes a module which can be used for feature collection. The functions include monoplotting with existing or automatically derived DTMs / DSMs, monoscopic measurement from stereo pairs or multiple images in semi-automatic mode and stereoscopic collection of objects in manual mode. Moreover, in combination with the software package CyberCity Modeler, 3D city models can be generated. The potential of SAT-PP and CyberCity Modeler to extract DSMs and buildings, and to produce 3D city models has been tested with IKONOS and Quickbird stereo images. In this paper the software packages are briefly described and the results achieved are reported and discussed.
Operational and topological optimization of multi-carrier energy systems

This paper presents an approach for the combined optimization of energy systems including multiple energy carriers such as electricity, natural gas, and district heat. Power flow and conversion between the different energy infrastructures is described as multi-input multi-output coupling, what enables simple analysis and optimization of the flows. While previous work deals with operational optimization (multi-carrier optimal dispatch and power flow), this paper focuses on optimization of the couplings between the different systems
Visual navigation of a mobile robot with laser-based collision avoidance

In this paper, we propose and validate a framework for visual navigation with collision avoidance for a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such path, the robot is able to avoid obstacles which were not present during teaching, and which are sensed by an on-board range scanner. Our control scheme guarantees that obstacle avoidance and navigation are achieved simultaneously. In fact, in the presence of obstacles, the camera pan angle is actuated to maintain scene visibility while the robot circumnavigates the obstacle. The risk of collision and the eventual avoiding behaviour are determined using a tentacle-based approach. The framework can also deal with unavoidable obstacles, which make the robot decelerate and eventually stop. Simulated and real experiments show that with our method, the vehicle can navigate along a visual path while avoiding collisions.
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

Studying the social dynamics of a city on a large scale has traditionally been a challenging endeavor, often requiring long hours of observation and interviews, usually resulting in only a partial depiction of reality. To address this difficulty, we introduce a clustering model and research methodology for studying the structure and composition of a city on a large scale based on the social media its residents generate. We apply this new methodology to data from approximately 18 million check-ins collected from users of a location-based online social network. Unlike the boundaries of traditional municipal organizational units such as neighborhoods, which do not always reflect the character of life in these areas, our clusters, which we call Livehoods, are representations of the dynamic areas that comprise the city. We take a qualitative approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly characterized areas of the city and the forces that shape them.
A framework for the automated analysis of subcellular patterns in human protein atlas images.

The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a protein's function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection

We propose a new method for fusing a LIDAR point cloud and camera-captured images in the deep convolutional neural network (CNN). The proposed method constructs a new layer called non-homogeneous pooling layer to transform features between bird view map and front view map. The sparse LIDAR point cloud is used to construct the mapping between the two maps. The pooling layer allows efficient fusion of the bird view and front view features at any stage of the network. This is favorable for the 3D-object detection using camera-LIDAR fusion in autonomous driving scenarios. A corresponding deep CNN is designed and tested on the KITTI[1] bird view object detection dataset, which produces 3D bounding boxes from the bird view map. The fusion method shows particular benefit for detection of pedestrians in the bird view compared to other fusion-based object detection networks.
Challenges in middleware solutions for the internet of things

The Internet of Things (IoT) aims to interconnect our everyday life items. It provides them with information processing capabilities to enable computers to sense, integrate, present, and react to all aspects of the physical world. This move from “interconnected computers” to “interconnected things” requires simplifying the development of new applications and services by supporting interoperability among heterogeneous devices; so that the programmer can focus on the development of applications enabled by the infrastructure of IoT. Middleware is a software layer interposed between the infrastructure and the applications using it  that basically aims to support important requirements for these applications. This paper surveys existing middleware designed for IoT and focuses on various technical challenges in this domain.
A fast Iterative Shrinkage-Thresholding Algorithm with application to wavelet-based image deblurring

We consider the class of Iterative Shrinkage-Thresholding Algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods is attractive due to its simplicity, however, they are also known to converge quite slowly. In this paper we present a Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) which preserves the computational simplicity of ISTA, but with a global rate of convergence which is proven to be significantly better, both theoretically and practically. Initial promising numerical results for wavelet-based image deblurring demonstrate the capabilities of FISTA.
Conformal Tapered Substrate Integrated Waveguide Leaky-Wave Antenna

We demonstrate the capability to conform a substrate integrated waveguide leaky-wave antenna (SIW LWA) along an arbitrarily curved line by suitably tapering the leaky mode along the antenna length. In particular, it is shown that by means of locally adjusting the pointing angle of the radiated wave, a coherent plane-wave front in the far-field region can be obtained. Combined with the capability to taper the leakage rate along the antenna, this allows designing antennas with high illumination efficiency, which provides higher directivity when compared with previous conformal LWAs such as the half-mode microstrip LWAs (HMLWAs). These concepts have been validated with both measured and simulated results involving the proposed conformal SIW LWA as well as three different configurations of HMLWA: conformal tapered HMLWA, conformal nontapered HMLWA and conventional rectilinear HMLWA. The antennas have been designed to operate at 15 GHz, and in the case of the SIW LWA an analysis of its frequency scanning response has been performed.
Thinking in Terms of Design Decisions When Developing Maturity Models

To measure dedicated aspects of “maturity”, a range of maturity models have been developed in the field of information systems by practitioners and academics over the past years. Despite its broad proliferation, the concept has not escaped criticism. Unnecessary bureaucracy, poor theoretical foundation, and the impression of a falsified certainty to achieve success are a few examples. As there is a significant lack of knowledge on how to design theoretically sound and widely accepted maturity models, in this paper, the author opens the discussion on design decisions when developing these models. Based on analogy and informed arguments, the author synthesizes a generic but adjuvant framework that consists of five common design steps and eighteen decision parameters that help practitioners as well as researchers in the development of maturity models.  10.4018/978-1-4666-1589-2.ch010
BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could significantly improve the efficiency and lower the energy consumption at runtime, which enables the application of state-of-the-art deep learning models on low power devices. BMXNet is an open-source BNN library based on MXNet, which supports both XNOR-Networks and Quantized Neural Networks. The developed BNN layers can be seamlessly applied with other standard library components and work in both GPU and CPU mode. BMXNet is maintained and developed by the multimedia research group at Hasso Plattner Institute and released under Apache license. Extensive experiments validate the efficiency and effectiveness of our implementation. The BMXNet library, several sample projects, and a collection of pre-trained binary deep models are available for download at https://github.com/hpi-xnor.
HyPer4: Using P4 to Virtualize the Programmable Data Plane

Through virtualization, single physical data planes can logically support multiple networking contexts. We propose HyPer4 as a portable virtualization solution. HyPer4 provides a general purpose program, written in the P4 dataplane programming language, that may be dynamically configured to adopt behavior that is functionally equivalent to other P4 programs. HyPer4 extends, through software, the following features to diverse P4-capable devices: the ability to logically store multiple programs and either run them in parallel (network slicing) or as hot-swappable snapshots; and virtual networking between programs (supporting program composition or multi-tenant service interaction). HyPer4 permits modifying the set of programs, as well as the virtual network connecting them, at runtime, without disrupting currently active programs. We show that realistic ASICs-based hardware would be capable of running HyPer4 today.