nils-herrmann/clean_scirepeval huggingface dataset:
Predicting protein structures with a multiplayer online game

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully ‘crowd-sourced’ through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.
A Methodology for Architecting Collaborative Product Service System of Systems

Product service systems (PSS) can be understood as an innovation / business strategy that includes a set of products and services that are realized by an actor network. More recently, PSS that comprise System of Systems (SoS) have been of increasing interest, notably in the transportation (autonomous vehicle infrastructures, multi-modal transportation) and energy sector (smart grids). Architecting such PSS-SoS goes beyond classic SoS engineering, as they are often driven by new technology, without an a priori client and actor network, and thus, a much larger number of potential architectures. However, it seems that neither the existing PSS nor SoS literature provides solutions to how to architect such PSS. This paper presents a methodology for architecting PSS-SoS that are driven by technological innovation. The objective is to design PSS-SoS architectures together with their value proposition and business model from an initial technology impact assessment. For this purpose, we adapt approaches from the strategic management, business modeling, PSS and SoS architecting literature. We illustrate the methodology by applying it to the case of an automobile PSS.
Knowledge-based Supervision for Domain-adaptive Semantic Role Labeling

Semantic role labeling (SRL) is a method for the semantic analysis of texts that adds a level of semantic abstraction on top of syntactic analysis, for instance adding semantic role labels like Agent on top of syntactic functions like Subject . SRL has been shown to benefit various natural language processing applications such as question answering, information extraction, and summarization. Automatic SRL systems are typically based on a predefined model of semantic predicate argument structure incorporated in lexical knowledge bases like PropBank or FrameNet. They are trained using supervised or semi-supervisedmachine learningmethods using training data labeled with predicate (word sense) and role labels. Even state-of-the-art systems based on deep learning still rely on a labeled training set. However, despite the success in an experimental setting, the real-world application of SRL methods is still prohibited by severe coverage problems (lexicon coverage problem) and lack of domain-relevant training data for training supervised systems (domain adaptation problem). These issues apply to English, but are even more severe for other languages, for which only small resources exist. The goal of this thesis is to develop knowledge-based methods to improve lexicon coverage and training data coverage for SRL. We use linked lexical knowledge bases to extend the lexicon coverage and as a basis for automatic training data generation across languages and domains. Links between lexical resources have already been previously used to address this problem, but the linkings have not been explored and applied at a large scale and the resulting generated training data only contained predicate (word sense) labels, but no role labels. To create predicate and role labels, corpus-based methods have been used. These rely on the existence of labeled training data as sources for label transfer to unlabeled corpora. For certain languages, like German or Spanish, several lexical knowledge bases, but only small amounts of labeled training data exist. For such languages, knowledge-based methods promise greater improvements. In our experiments, we target FrameNet, a lexical-semantic resource with a strong focus on semantic abstraction and generalization, but the methods developed in this thesis can be extended to other models of predicate argument structure, like VerbNet and PropBank.
Space Object Classification using Fused Features of Time Series Data

In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results show that the classification algorithms using the fused feature vector achieve better performance than those using raw time series or texture features only.
User-Generated Content in Social Media

This report documents the program and the outcomes of Dagstuhl Seminar 17301 “User-Generated Content in Social Media”. Social media have a profound impact on individuals, businesses, and society. As users post vast amounts of text and multimedia content every minute, the analysis of this user generated content (UGC) can offer insights to individual and societal concerns and could be beneficial to a wide range of applications. In this seminar, we brought together researchers from different subfields of computer science, such as information retrieval, multimedia, natural language processing, machine learning and social media analytics. We discussed the specific properties of UGC, the general research tasks currently operating on this type of content, identifying their limitations, and imagining new types of applications. We formed two working groups, WG1 “Fake News and Credibility”, WG2 “Summarizing and Story Telling from UGC”. WG1 invented an “Information Nutrition Label” that characterizes a document by different features such as e.g. emotion, opinion, controversy, and topicality; For computing these feature values, available methods and open research issues were identified. WG2 developed a framework for summarizing heterogeneous, multilingual and multimodal data, discussed key challenges and applications of this framework. Seminar July 23–28, 2017 – http://www.dagstuhl.de/17301 1998 ACM Subject Classification H Information Systems, H.5 Information Interfaces and Presentation, H.5.1 Multimedia Information Systems, H.3 Information Storage and Retrieval, H.1 Models and principles, I Computing methodologies, I.2 Artificial Intelligence, I.2.6 Learning, I.2.7 Natural language processing, J Computer Applications, J.4 Social and behavioural sciences, K Computing Milieux, K.4 Computers and Society, K.4.1 Public policy issues
Originator of Theory : Albert Bandura

Circumstances that led to the development of the Theory: The SCT has its origins in the discipline of psychology, with its early foundation being laid by behavioral and social psychologists. The SLT evolved under the umbrella of behaviorism, which is a cluster of psychological theories intended to explain why people and animals behave the way that they do. Behaviorism, introduced by John Watson in 1913, took an extremely mechanistic approach to understanding human behavior. According to Watson, behavior could be explained in terms of observable acts that could be described by stimulus-response sequences (Crosbie-Brunett and Lewis, 1993; Thomas, 1990). Also central to behaviorist study was the notion that contiguity between stimulus and response determined the likelihood that learning would occur.
Implementation of Home Security System using GSM module and Microcontroller

Home security system is needed for convenience and safety. This system invented to keep home safe from intruder. In this work, we present the design and implementation of a GSM based wireless home security system. which take a very less power. The system is a wireless home network which contains a GSM modem and magnet with relay which are door security nodes. The system can response rapidly as intruder detect and GSM module will do alert home owner. This security system for alerting a house owner wherever he will. In this system a relay and magnet installed at entry point to a precedence produce a signal through a public telecom network and sends a message or redirect a call that that tells about your home update or predefined message which is embedded in microcontroller. Suspected activities are conveyed to remote user through SMS or Call using GSM technology.
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy

Apolipoprotein E (Apo-E) is a major cholesterol carrier that supports lipid transport and injury repair in the brain. APOE polymorphic alleles are the main genetic determinants of Alzheimer disease (AD) risk: individuals carrying the ε4 allele are at increased risk of AD compared with those carrying the more common ε3 allele, whereas the ε2 allele decreases risk. Presence of the APOE ε4 allele is also associated with increased risk of cerebral amyloid angiopathy and age-related cognitive decline during normal ageing. Apo-E–lipoproteins bind to several cell-surface receptors to deliver lipids, and also to hydrophobic amyloid-β (Aβ) peptide, which is thought to initiate toxic events that lead to synaptic dysfunction and neurodegeneration in AD. Apo-E isoforms differentially regulate Aβ aggregation and clearance in the brain, and have distinct functions in regulating brain lipid transport, glucose metabolism, neuronal signalling, neuroinflammation, and mitochondrial function. In this Review, we describe current knowledge on Apo-E in the CNS, with a particular emphasis on the clinical and pathological features associated with carriers of different Apo-E isoforms. We also discuss Aβ-dependent and Aβ-independent mechanisms that link Apo-E4 status with AD risk, and consider how to design effective strategies for AD therapy by targeting Apo-E.
Knowledge Management in Software Engineering: A Systematic Review of Studied Concepts, Findings and Research Methods Used

Software engineering is knowledge-intensive work, and how to manage software engineering knowledge has received much attention. This systematic review identifies empirical studies of knowledge management initiatives in software engineering, and discusses the concepts studied, the major findings, and the research methods used. Seven hundred and sixty-two articles were identified, of which 68 were studies in an industry context. Of these, 29 were empirical studies and 39 reports of lessons learned. More than half of the empirical studies were case studies. The majority of empirical studies relate to technocratic and behavioural aspects of knowledge management, while there are few studies relating to economic, spatial and cartographic approaches. A finding reported across multiple papers was the need to not focus exclusively on explicit knowledge, but also consider tacit knowledge. We also describe implications for research and for practice.
Next Generation 5G Wireless Networks: A Comprehensive Survey

The vision of next generation 5G wireless communications lies in providing very high data rates (typically of Gbps order), extremely low latency, manifold increase in base station capacity, and significant improvement in users' perceived quality of service (QoS), compared to current 4G LTE networks. Ever increasing proliferation of smart devices, introduction of new emerging multimedia applications, together with an exponential rise in wireless data (multimedia) demand and usage is already creating a significant burden on existing cellular networks. 5G wireless systems, with improved data rates, capacity, latency, and QoS are expected to be the panacea of most of the current cellular networks' problems. In this survey, we make an exhaustive review of wireless evolution toward 5G networks. We first discuss the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN. Subsequently, we make an in-depth survey of underlying novel mm-wave physical layer technologies, encompassing new channel model estimation, directional antenna design, beamforming algorithms, and massive MIMO technologies. Next, the details of MAC layer protocols and multiplexing schemes needed to efficiently support this new physical layer are discussed. We also look into the killer applications, considered as the major driving force behind 5G. In order to understand the improved user experience, we provide highlights of new QoS, QoE, and SON features associated with the 5G evolution. For alleviating the increased network energy consumption and operating expenditure, we make a detail review on energy awareness and cost efficiency. As understanding the current status of 5G implementation is important for its eventual commercialization, we also discuss relevant field trials, drive tests, and simulation experiments. Finally, we point out major existing research issues and identify possible future research directions.
User and entity behavior analytics for enterprise security

This paper presents an overview of an intelligence platform we have built to address threat hunting and incident investigation use-cases in the cyber security domain. Specifically, we focus on User and Entity Behavior Analytics (UEBA) modules that track and monitor behaviors of users, IP addresses and devices in an enterprise. Anomalous behavior is automatically detected using machine learning algorithms based on Singular Values Decomposition (SVD). Such anomalous behavior indicative of potentially malicious activity is alerted to analysts with relevant contextual information for further investigation and action. We provide a detailed description of the models, algorithms and implementation underlying the module and demonstrate the functionality with empirical examples.
Flow Benefits of Autonomous Intelligent Cruise Control in Mixed Manual and Automated Traffic

Autonomous intelligent cruise control (AICC) is a technology for driver convenience, increased safety, and smoother traffic flow. AICC also has been proposed for increasing traffic flow by allowing shorter intervehicle headways. Because an AI CC-equipped vehicle operates using only information available from its own sensors, there is no requirement for communication and cooperation between vehicles. This format allows gradual market penetration of AICC systems, which makes the technology attractive from a systems implementation standpoint. The potential flow increases when only a proportion of vehicles on a highway are equipped with AICC were examined, and theoretical upper limits on flows as a function of pertinent variables were derived. Because of the limitations of the theoretical models, a simulator was used that models interactions between vehicles to give detailed information on achievable capacity and traffic stream stability. Results showed that AICC can lead to potentially large gains in capacity only if certain highly unrealistic assumptions hold. In reality, the capacity gains from AICC are likely to be small.
THE STRUCTURE OF CREDIT RISK

Knowing the relative riskiness of di erent types of credit exposure is important for policymakers designing regulatory capital requirements and for rms allocating economic capital. This paper analyzes the risk structure of credit exposures with di erent maturities and credit qualities. We focus particularly on risks associated with (i) ratings transitions and (ii) spread changes for given ratings. We show that, for high quality debt, most risk stems from spread changes. This is signi cant because several recently proposed pricing and credit risk models assume zero spread risk.
Recording and controlling the 4 D light field in a microscope using microlens arrays

By inserting a microlens array at the intermediate image plane of an optical microscope, one can record four-dimensional light fields of biological specimens in a single snapshot. Unlike a conventional photograph, light fields permit manipulation of viewpoint and focus after the snapshot has been taken, subject to the resolution of the camera and the diffraction limit of the optical system. By inserting a second microlens array and video projector into the microscope’s illumination path, one can control the incident light field falling on the specimen in a similar way. In this paper, we describe a prototype system we have built that implements these ideas, and we demonstrate two applications for it: simulating exotic microscope illumination modalities and correcting for optical aberrations digitally.
Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

Current-generation Deep Neural Networks (DNNs), such as AlexNet and VGG, rely heavily on dense floating-point matrix multiplication (GEMM), which maps well to GPUs (regular parallelism, high TFLOP/s). Because of this, GPUs are widely used for accelerating DNNs. Current FPGAs offer superior energy efficiency (Ops/Watt), but they do not offer the performance of today's GPUs on DNNs. In this paper, we look at upcoming FPGA technology advances, the rapid pace of innovation in DNN algorithms, and consider whether future high-performance FPGAs will outperform GPUs for next-generation DNNs. The upcoming Intel® 14-nm Stratix? 10 FPGAs will have thousands of hard floating-point units (DSPs) and on-chip RAMs (M20K memory blocks). They will also have high bandwidth memories (HBMs) and improved frequency (HyperFlex? core architecture). This combination of features brings FPGA raw floating point performance within striking distance of GPUs. Meanwhile, DNNs are quickly evolving. For example, recent innovations that exploit sparsity (e.g., pruning) and compact data types (e.g., 1-2 bit) result in major leaps in algorithmic efficiency. However, these innovations introduce irregular parallelism on custom data types, which are difficult for GPUs to handle but would be a great fit for FPGA's extreme customizability.
 This paper evaluates a selection of emerging DNN algorithms on two generations of Intel FPGAs (Arria'10, Stratix'10) against the latest highest performance Titan X Pascal GPU. We created a customizable DNN accelerator template for FPGAs and used it in our evaluations. First, we study various GEMM operations for next-generation DNNs. Our results show that Stratix 10 FPGA is 10%, 50%, and 5.4x better in performance (TOP/sec) than Titan X Pascal GPU on GEMM operations for pruned, Int6, and binarized DNNs, respectively. Then, we present a detailed case study on accelerating Ternary ResNet which relies on sparse GEMM on 2-bit weights (i.e., weights constrained to 0,+1,-1) and full-precision neurons. The Ternary ResNet accuracy is within ~1% of the full-precision ResNet which won the 2015 ImageNet competition. On Ternary-ResNet, the Stratix 10 FPGA can deliver 60% better performance over Titan X Pascal GPU, while being 2.3x better in performance/watt. Our results indicate that FPGAs may become the platform of choice for accelerating next-generation DNNs.
Multi-Mode Inference Engine for Convolutional Neural Networks

During the past few years, interest in convolutional neural networks (CNNs) has risen constantly, thanks to their excellent performance on a wide range of recognition and classification tasks. However, they suffer from the high level of complexity imposed by the high-dimensional convolutions in convolutional layers. Within scenarios with limited hardware resources and tight power and latency constraints, the high computational complexity of CNNs makes them difficult to be exploited. Hardware solutions have striven to reduce the power consumption using low-power techniques, and to limit the processing time by increasing the number of processing elements (PEs). While most of ASIC designs claim a peak performance of a few hundred giga operations per seconds, their average performance is substantially lower when applied to state-of-the-art CNNs such as AlexNet, VGGNet and ResNet, leading to low resource utilization. Their performance efficiency is limited to less than 55% on average, which leads to unnecessarily high processing latency and silicon area. In this paper, we propose a dataflow which enables to perform both the fully-connected and convolutional computations for any filter/layer size using the same PEs. We then introduce a multi-mode inference engine (MMIE) based on the proposed dataflow. Finally, we show that the proposed MMIE achieves a performance efficiency of more than 84% when performing the computations of the three renown CNNs (i.e., AlexNet, VGGNet and ResNet), outperforming the best architecture in the state-of-the-art in terms of energy consumption, processing latency and silicon area.
A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles

We developed a novel computational framework to predict the perceived trustworthiness of host profile texts in the context of online lodging marketplaces. To achieve this goal, we developed a dataset of 4,180 Airbnb host profiles annotated with perceived trustworthiness. To the best of our knowledge, the dataset along with our models allow for the first computational evaluation of perceived trustworthiness of textual profiles, which are ubiquitous in online peer-to-peer marketplaces. We provide insights into the linguistic factors that contribute to higher and lower perceived trustworthiness for profiles of different lengths.