nils-herrmann/clean_scirepeval huggingface dataset:
Nonclassical Computation - A Dynamical Systems Perspective

In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-
Local subspace-based outlier detection using global neighbourhoods

Outlier detection in high-dimensional data is a challenging yet important task, as it has applications in, e.g., fraud detection and quality control. State-of-the-art density-based algorithms perform well because they 1) take the local neighbourhoods of data points into account and 2) consider feature subspaces. In highly complex and high-dimensional data, however, existing methods are likely to overlook important outliers because they do not explicitly take into account that the data is often a mixture distribution of multiple components. We therefore introduce GLOSS, an algorithm that performs local subspace outlier detection using global neighbourhoods. Experiments on synthetic data demonstrate that GLOSS more accurately detects local outliers in mixed data than its competitors. Moreover, experiments on real-world data show that our approach identifies relevant outliers overlooked by existing methods, confirming that one should keep an eye on the global perspective even when doing local outlier detection.
A Classification for Community Discovery Methods in Complex Networks

In the last years it has been found that many real-world networks show a so-called community structure organization. Much effort has been devoted in literature in order to develop methods and algorithms that can efficiently highlight this hidden structure of the network, traditionally operating a partition of the graph. Since the network representation can be very complex and can contain different variants in the traditional graph model, each algorithm present in literature focuses on some of these properties and establishes its own definition of community. Then it extracts communities according to this definition, able to reflect only some of the features of real communities. Aim of this survey is to provide a manual for the community discovery problem. Given a meta definition of what is a community in a social network, our aim is to organize the main categories of community discovery approaches basing on their own definition of community. Given a desired definition of community and the features of a problem (size of network, direction of edges, multidimensionality, and so on) we aim to provide the set of approaches a researcher might focus on.
SODA: A High-Performance DSP Architecture for Software-Defined Radio

Software-defined radio (SDR) belongs to an emerging class of applications with the processing requirements of a supercomputer but the power constraints of a mobile terminal. The authors developed the signal-processing on-demand architecture (SODA), a fully programmable architecture that supports SDR, by examining two widely differing protocols, W-CDMA and 802.11A. It meets power-performance requirements by separating control and data processing and by employing ultrawide SIMD execution
An analysis of the dynamic behavior of JavaScript programs

The JavaScript programming language is widely used for web programming and, increasingly, for general purpose computing. As such, improving the correctness, security and performance of JavaScript applications has been the driving force for research in type systems, static analysis and compiler techniques for this language. Many of these techniques aim to reign in some of the most dynamic features of the language, yet little seems to be known about how programmers actually utilize the language or these features. In this paper we perform an empirical study of the dynamic behavior of a corpus of widely-used JavaScript programs, and analyze how and why the dynamic features are used. We report on the degree of dynamism that is exhibited by these JavaScript programs and compare that with assumptions commonly made in the literature and accepted industry benchmark suites.
Determinants of satisfaction and continuance intention towards self-service technologies

Self-service technologies (SSTs) allow customers to offer their own service encounters via the interaction of electronic service interfaces or machines rather than by interacting with a firm’s service personnel. This paper aims to develop an integrated model designed to predict and explain an individual’s continuous use of SSTs based on the concepts of technology readiness (TR), technology acceptance model (TAM), and theory of planned behavior (TPB).  The participants are from 481 SST users as the sample finally. Structural equation modeling is applied to demonstrate the stability of the proposed model and the results of hypotheses testing.  This paper finds that consumers’ satisfaction significantly influences continuance intention, while the perceived usefulness, perceived ease of use, subjective norm (SN), and perceived behavioral control (PBC) simultaneously influence satisfaction. Optimism and innovativeness are also the significant motivators of satisfaction. However, TR’s inhibitors (discomfort and insecurity) have no significantly negative influence on continuance intention towards adopting SST services.  Different from previous SSTs studies, this paper suggests that SN and PBC play critical factors in users’ adoption at SSTs encounters. However, additionally empirical evidences should be discussed why the inhibitors (discomfort and insecurity) of TR have no effect.  This paper proposes a comprehensive model to synthesize the essence of TR, TAM, and TPB for explaining customers’ continuous intention of SSTs.
Technology in spiritual formation: an exploratory study of computer mediated religious communications

In this paper, we report findings from a study of American Christian ministers' uses of technologies in religious practices. We focus on the use of technologies for spiritual purposes as opposed to pragmatic and logistical, but report on all. We present results about the uses of technologies in three aspects of religious work: religious study and reflection, church services, and pastoral care. We end by examining how the collaborative religious uses of technologies cross and blend work and personal life.
A First Look at a Telepresence System with Room-Sized Real-Time 3 D Capture and Life-Sized Tracked Display Wall

This paper provides a first look at a telepresence system offering room-sized, fully dynamic real-time 3D scene capture and continuous-viewpoint head-tracked display on a life-sized tiled display wall. The system is an expansion of a previous system, based an array of commodity depth sensors. We describe adjustments and improvements made to camera calibration, sensor data processing, data merger, rendering, and display, as required to scale the earlier system to room-sized.
Binarization-free OCR for historical documents using LSTM networks

A primary preprocessing block of almost any typical OCR system is binarization, through which it is intended to remove unwanted part of the input image, and only keep a binarized and cleaned-up version for further processing. The binarization step does not, however, always perform perfectly, and it can happen that binarization artifacts result in important information loss, by for instance breaking or deforming character shapes. In historical documents, due to a more dominant presence of noise and other sources of degradations, the performance of binarization methods usually deteriorates; as a result the performance of the recognition pipeline is hindered by such preprocessing phases. In this paper, we propose to skip the binarization step by directly training a 1D Long Short Term Memory (LSTM) network on gray-level text lines. We collect a large set of historical Fraktur documents, from publicly available online sources, and form train and test sets for performing experiments on both gray-level and binarized text lines. In order to observe the impact of resolution, the experiments are carried out on two identical sets of low and high resolutions. Overall, using gray-level text lines, the 1D LSTM network can reach 24% and 19% lower error rates on the low- and high-resolution sets, respectively, compared to the case of using binarization in the recognition pipeline.
Let ’ s keep it simple : Using simple architectures to outperform deeper architectures

In recent years, all winning architectures that achieved state of the art results have been very deep and had parameters ranging from tens to hundreds of millions. While an optimal depth has yet to be discovered, it is known that these deep architectures are far from being optimal. Very deep and heavy architecture such as VGGNet, GoogleNet, ResNet and the likes, are very demanding in terms of hardware requirements, and so their practical use has become very limited and costly. The computational and memory overhead caused by such architectures has a negative effect on the expansion of methods and applications utilizing deep architectures. In order to overcome this issues, some thinned architectures such as Squeezenet are proposed that are computationally lightweight and useful for embedded systems. However their usage is also hindered by the low accuracy they provide. While deep architectures do provide good accuracy, we empirically show that a wellcrafted yet simple and reasonably deep architecture can equally perform. This allows for more practical uses, especially in embed systems, or systems with computational and memory limitations. In this work, we present a very simple fully convolutional network with 13 layers that outperforms almost all deeper architectures to date such as ResNet, GoogleNet,WRN, etc with 2 to 25 times fewer number of parameters, and rarely when it does not supersede an architecture, it performs on par. We achieved state of the art results and very close to it on datasets such as CIFAR10/100, MNIST and SVHN with simple or no data-augmentation.
Federated Learning for Keyword Spotting

We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the “Hey Snips” wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is a reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is being open sourced with the aim of fostering further transparent research in the application of federated learning to speech data.
On Secure Bulletin Boards for E-Voting

Vote collection together with storage of collected votes is the first phase of practically any electronic voting (e-voting) protocol. This functionality is provided by a bulletin board system. Many research papers in e-voting require the existence of a secure bulletin board, but there are only a few concrete systems. In the literature it is common to assume that bulletin board is a centralized trusted party, but in recent works the importance of a distributed fault-tolerant bulletin board has been raised. In this thesis, we propose a formal model for analysis of security and functionality of a bulletin board system motivated by the security requirements Culnane and Schneider introduced in Computer Security Foundations Symposium 2014. We consider a secure bulletin board as a robust public transaction ledger presented by Garay et al. in Eurocrypt 2015 that additionally provides receipts for successful postings. More precisely, we introduce two properties: (Confirmable) Persistence and Confirmable Liveness. We study a bulletin board system proposed by Culnane and Schneider in our model, and show that their protocol does not achieve Confirmable Liveness if there exist corrupted item collection peers, but achieves Confirmable Persistence for < N/3 corrupted item collection peers using only our trivial threshold signature scheme, otherwise the bound is < N/4. Motivated by the security analysis of Culnane-Schneider bulletin board system, we propose a fully secure bulletin board system and prove that it tolerates < N/3 corrupted item collection peers for Confirmable Persistence and < N/2 corrupted item collection peers for Confirmable Liveness. This thesis is based on a submitted paper ”A Cryptographic Approach to Bulletin Boards” with co-authors Aggelos Kiayas, Helger Lipmaa, Janno Siim and Thomas Zacharias.
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2– 10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.
Battery-Draining-Denial-of-Service Attack on Bluetooth Devices

We extend the Xen virtual machine monitor with the ability to host a hundred of virtual machines on a single physical node. Similarly to a demand paging of virtual memory, we page out idle virtual machines making them available on demand. Paging is transparent. An idle virtual machine remains fully operational. It is able to respond to external events with a delay comparable to a delay under a medium load. To achieve desired degree of consolidation, we identify and leave in memory only a minimal working set of pages required to maintain the illusion of running VM and respond to external events. To keep the number of active pages small without harming performance dramatically, we build a correspondence between every event and its working set. Reducing a working set further, we implement a copy-on-write page sharing across virtual machines running on the same host. To decrease resources occupied by a virtual machine’s file system, we implement a copy-on-write storage and golden imaging.
An Ethological Approach to Personality Development

attachment that we have jointly developed is that it is an ethological approach to personality development. We have had a long and happy partnership in pursuing this approach. In this article we wish to give a brief historical account of the initially separate but compatible approaches that eventually merged in the partnership, and how our contributions have intertwined in the course of developing an ethologically oriented theory of attachment and a body of research that has both stemmed from the theory and served to extend and elaborate it.
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.



To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.



A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.



Deep learning-trained algorithm.



The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity.



The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.



In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.
A Sparse Matrix Library in C + + for High PerformanceArchitectures

We describe an object oriented sparse matrix library in C++ built upon the Level 3 Sparse BLAS proposal [5] for portability and performance across a wide class of machine architectures. The C++ library includes algorithms for various iterative methods and supports the most common sparse data storage formats used in practice. Besides simplifying the subroutine interface, the object oriented design allows the same driving code to be used for various sparse matrix formats, thus addressing many of the di culties encountered with the typical approach to sparse matrix libraries. We emphasize the fundamental design issues of the C++ sparse matrix classes and illustrate their usage with the preconditioned conjugate gradient (PCG) method as an example. Performance results illustrate that these codes are competitive with optimized Fortran 77. We discuss the impact of our design on elegance, performance, maintainability, portability, and robustness.
An Investigation into the Pedagogical Features of Documents

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner.While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.
Pixel Recurrent Neural Networks

Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast twodimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.