nils-herrmann/clean_scirepeval huggingface dataset:
Literature review of spatio-temporal database models

Recent efforts in spatial and temporal data models and database systems attempt to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.
Understanding the smart city Domain : A Literature Review

Smart Cities appeared in literature in late ‘90s and various approaches have been developed so far. Until today, smart city does not describe a city with particular attributes but it is used to describe different cases in urban spaces: web portals that virtualize cities or city guides; knowledge bases that address local needs; agglomerations with Information and Communication Technology (ICT) infrastructure that attract business relocation; metropolitan-wide ICT infrastructures that deliver e-services to the citizens; ubiquitous environments; and recently ICT infrastructure for ecological use. Researchers, practicians, businessmen and policy makers consider smart city from different perspectives and most of them agree on a model that measures urban economy, mobility, environment, living, people and governance. On the other hand, ICT and construction industries stress to capitalize smart city and a new market seems to be generated in this domain. This chapter aims to perform a literature review, discover and classify the particular schools of thought, universities and research centres as well as companies that deal with smart city domain and discover alternative approaches, models, architecture and frameworks with this regard.
Analyzing animal behavior via classifying each video frame using convolutional neural networks

High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals' body parts. But the image analysis rarely attempts to recognize "behavioral states"-e.g., actions or facial expressions-directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)-a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition-were able to recognize directly from images whether Drosophila were "on" (standing or walking) or "off" (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%. Classifying one of our 8 h videos took less than 3 h using a fast GPU. The approach enabled uncovering a novel egg-laying-induced behavior modification in Drosophila. Furthermore, it should be readily applicable to other behavior analysis tasks.
Ensemble Classifiers for Steganalysis of Digital Media

Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets-two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on three steganographic methods that hide messages in JPEG images.
Music Structure Discovery in Popular Music using Non-negative Matrix Factorization

We introduce a method for the automatic extraction of musical structures in popular music. The proposed algorithm uses non-negative matrix factorization to segment regions of acoustically similar frames in a self-similarity matrix of the audio data. We show that over the dimensions of the NMF decomposition, structural parts can easily be modeled. Based on that observation, we introduce a clustering algorithm that can explain the structure of the whole music piece. The preliminary evaluation we report in the the paper shows very encouraging results.
iDice: Problem Identification for Emerging Issues

One challenge for maintaining a large-scale software system, especially an online service system, is to quickly respond to customer issues. The issue reports typically have many categorical attributes that reflect the characteristics of the issues. For a commercial system, most of the time the volume of reported issues is relatively constant. Sometimes, there are emerging issues that lead to significant volume increase. It is important for support engineers to efficiently and effectively identify and resolve such emerging issues, since they have impacted a large number of customers. Currently, problem identification for an emerging issue is a tedious and error-prone process, because it requires support engineers to manually identify a particular attribute combination that characterizes the emerging issue among a large number of attribute combinations. We call such an attribute combination effective combination, which is important for issue isolation and diagnosis. In this paper, we propose iDice, an approach that can identify the effective combination for an emerging issue with high quality and performance. We evaluate the effectiveness and efficiency of iDice through experiments. We have also successfully applied iDice to several Microsoft online service systems in production. The results confirm that iDice can help identify emerging issues and reduce maintenance effort.
Design, analysis and experiments of an omni-directional spherical robot

This paper presents a novel design of an omni-directional spherical robot that is mainly composed of a lucent ball-shaped shell and an internal driving unit. Two motors installed on the internal driving unit are used to realize the omni-directional motion of the robot, one motor is used to make the robot move straight and another is used to make it steer. Its motion analysis, kinematics modeling and controllability analysis are presented. Two typical motion simulations show that the unevenness of ground has big influence on the open-loop trajectory tracking of the robot. At last, motion performance of this spherical robot in several typical environments is presented with prototype experiments.
Detection of Face Morphing Attacks by Deep Learning

Identification by biometric features has become more popular in the last decade. High quality video and fingerprint sensors have become less expensive and are nowadays standard components in many mobile devices. Thus, many devices can be unlocked via fingerprint or face verification. The state of the art accuracy of biometric facial recognition systems prompted even systems that need high security standards like border control at airports to rely on biometric systems. While most biometric facial recognition systems perform quite accurate under a controlled environment, they can easily be tricked by morphing attacks. The concept of a morphing attack is to create one synthetic face image that contains characteristics of two different individuals and to use this image on a document or as reference image in a database. Using this image for authentication, a biometric facial recognition system accepts both individuals. In this paper, we propose a morphing attack detection approach based on convolutional neural networks. We present an automatic morphing pipeline to generate morphing attacks, train neural networks based on this data and analyze their accuracy. The accuracy of different well-known network architectures are compared and the advantage of using pretrained networks compared to networks learned from scratch is studied.
One-Shot Learning of Object Categories Using Dependent Gaussian Processes

Knowledge transfer from related object categories is a key concept to allow learning with few training examples. We present how to use dependent Gaussian processes for transferring knowledge from a related category in a non-parametric Bayesian way. Our method is able to select this category automatically using efficient model selection techniques. We show how to optionally incorporate semantic similarities obtained from the hierarchical lexical database WordNet  into the selection process. The framework is applied to image categorization tasks using state-of-the-art image-based kernel functions. A large scale evaluation shows the benefits of our approach compared to independent learning and a SVM based approach.
Permacoin: Repurposing Bitcoin Work for Data Preservation

Bit coin is widely regarded as the first broadly successful e-cash system. An oft-cited concern, though, is that mining Bit coins wastes computational resources. Indeed, Bit coin's underlying mining mechanism, which we call a scratch-off puzzle (SOP), involves continuously attempting to solve computational puzzles that have no intrinsic utility. We propose a modification to Bit coin that repurposes its mining resources to achieve a more broadly useful goal: distributed storage of archival data. We call our new scheme Perm coin. Unlike Bit coin and its proposed alternatives, Perm coin requires clients to invest not just computational resources, but also storage. Our scheme involves an alternative scratch-off puzzle for Bit coin based on Proofs-of-Retrievability (PORs). Successfully minting money with this SOP requires local, random access to a copy of a file. Given the competition among mining clients in Bit coin, this modified SOP gives rise to highly decentralized file storage, thus reducing the overall waste of Bit coin. Using a model of rational economic agents we show that our modified SOP preserves the essential properties of the original Bit coin puzzle. We also provide parameterizations and calculations based on realistic hardware constraints to demonstrate the practicality of Perm coin as a whole.
Design compact planar monopole antenna for LTE mobile handset

Development of Mobile handset applications, new standard for cellular networks have been defined. In this Paper, author intend to propose a Novel mobile Antenna that can cover more of LTE (Long Term Evolution) Bands (4G cellular networks). The proposed antenna uses structure of planar monopole antenna. Bandwidth of antenna is 0.87-0.99 GHz, 1.65-3.14 GHz and has high efficiency unlike the previous structures. The dimension of the antenna is 18mm×21mm and has FR4 substrate by 1.5mm thickness that is very compact antenna respect to the other expressed antenna.
Automated generation of interactive 3D exploded view diagrams

We present a system for creating and viewing interactive exploded views of complex 3D models. In our approach, a 3D input model is organized into an explosion graph that encodes how parts explode with respect to each other. We present an automatic method for computing explosion graphs that takes into account part hierarchies in the input models and handles common classes of interlocking parts. Our system also includes an interface that allows users to interactively explore our exploded views using both direct controls and higher-level interaction modes.
An Arabic question-answering system for factoid questions

In this paper, we propose an Arabic Question-Answering (Q-A) system called QASAL «Question -Answering system for Arabic Language». QASAL accepts as an input a natural language question written in Modern Standard Arabic (MSA) and generates as an output the most efficient and appropriate answer. The proposed system is composed of three modules: A question analysis module, a passage retrieval module and an answer extraction module. To process these three modules we use the NooJ Platform which represents a linguistic development environment.
Design of a compact planar MIMO antenna for LTE mobile application

Recently, the long-term evolution (LTE) is considered as one of the most promising 4th generation (4G) mobile standards to increase the capacity and speed of mobile handset networks . In order to realize the LTE wireless communication system, the diversity and multiple-input multiple-output (MIMO) systems have been introduced . In a MIMO mobile user terminal such as handset or USB dongle, at least two uncorrelated antennas should be placed within an extremely restricted space. This task becomes especially difficult when a MIMO planar antenna is designed for LTE band 13 (the corresponding wavelength is 390 mm). Due to the limited space available for antenna elements, the antennas are strongly coupled with each other and have narrow bandwidth.
Digital Innovation as a Fundamental and Powerful Concept in the Information Systems Curriculum

Applegate et al. (2008), Corporate Information Strategy and Management: Text and Cases 1 IT and Strategy The relationship between IT and business models & strategies Partially The effects of IT on business models are relevant, but overall it lacks a specific focus on digital innovation 2 IT and Organization The effect of IT on an organization's capability, coordination, learning, etc. Partially Some of the discussions are relevant to how IT can enable innovation in an organization 3 Extending the Enterprise Challenges of IT in enterprise, how IT creates new opportunities, reduces cost, helps gain sustainable competitive advantage Yes Concise discussions of the new opportunities that IT enables in an enterprise 4 Making the Case for IT Highlights the importance of IT in organizations Somewhat Some discussions touched on digital innovation 5 Understanding Internetworking Infrastructure Networking, Internet infrastructure and management No Focus is on technology itself, not how network technology enables innovation 6 Assuring Reliable and Secure IT Services Security, backup, recovery etc. No Very detailed discussion of the technology (e.g. security management), but not much on how technology enables innovation MIS Quarterly Vol. 38 No. 2—Appendices/June 2014 A1 Fichman et al./Digital Innovation in the Information Systems Curriculum Textbook Ch.  Text and Cases 7 Managing Diverse IT Infrastructures The management aspects of IT infrastructure No Not an innovation-focused presentation 8 Organizing and Leading the IT Function IT management, governance and functions Partially Some of the IT governance discussion and the Enron case are relevant, but the chapter as a whole does not address digital innovation 9 Managing IT Outsourcing Outsourcing Very little The part on the new service model is related, but the bulk of the discussion on outsourcing, vendor selection and contract management are not innovation-related 10 A Portfolio Approach to IT Projects IT project management Very little Some discussions of project management and system analysis & design are relevant, but much of the discussion lacks an innovation focus Gallaugher (2012), Information Systems: A Manager's Guide to Harnessing Technology 1 Setting the Stage: Technology and Modern Enterprise Introduction on how technology is reshaping business disciplines such as finance, marketing, operations, and IS Yes A nice, brief overview on how digital innovation is enabling changes across the board 2 Strategy and Technology Competitive advantage, resource based view, value chain …
Vision-Based SLAM: Stereo and Monocular Approaches

Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.
The Meme Ranking Problem: Maximizing Microblogging Virality

Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a “meme” of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
A Practical Analysis of Smartphone Security

Recent developments in mobile technologies have produced a new kind of device, a programmable mobile phone, the smartphone. Generally, smartphone users can program any application which is customized for needs. Furthermore, they can share these applications in online market. Therefore, smartphone and its application are now most popular keywords in mobile technology. However, to provide these customized services, smartphone needs more private information and this can cause security vulnerabilities. Therefore, in this work, we analyze security of smartphone based on its environments and describe countermeasures.
Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form

We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent variables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally deterministic given their parents and a set of independent auxiliary variables. Variables of models in this auxiliary form have much larger Markov blankets, leading to significant speedups in gradient-based inference, e.g. rapid mixing Hybrid Monte Carlo and efficient gradient-based optimization. The relative efficiency is confirmed in experiments.
Named Entity Recognition in Wikipedia

Named entity recognition (NER) is used in many domains beyond the newswire text that comprises current gold-standard corpora. Recent work has used Wikipedia’s link structure to automatically generate near gold-standard annotations. Until now, these resources have only been evaluated on newswire corpora or themselves. We present the first NER evaluation on a Wikipedia gold standard (WG) corpus. Our analysis of cross-corpus performance on WG shows that Wikipedia text may be a harder NER domain than newswire. We find that an automatic annotation of Wikipedia has high agreement with WG and, when used as training data, outperforms newswire models by up to 7.7%.
Random Regression Forests for Acoustic Event Detection and Classification

Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an approach based on random forest regression. Furthermore, event localization in time can be efficiently handled as a joint problem. We first decompose the training audio signals into multiple interleaved superframes which are annotated with the corresponding event class labels and their displacements to the temporal onsets and offsets of the events. For a specific event category, a random-forest regression model is learned using the displacement information. Given an unseen superframe, the learned regressor will output the continuous estimates of the onset and offset locations of the events. To deal with multiple event categories, prior to the category-specific regression phase, a superframe-wise recognition phase is performed to reject the background superframes and to classify the event superframes into different event categories. While jointly posing event detection and localization as a regression problem is novel, the superior performance on two databases ITC-Irst and UPC-TALP demonstrates the efficiency and potential of the proposed approach.
EEG-based vigilance estimation using extreme learning machines

For many human machine interaction systems, techniques for continuously estimating the vigilance of operators are highly desirable to ensure work safety. Up to now, various signals are studied for vigilance analysis. Among them, electroencephalogram (EEG) is the most commonly used signal. In this paper, extreme learning machine (ELM) and its modifications with L1 norm and L2 norm penalties are adopted for EEG-based vigilance estimation. A comparative study on system performance is conducted among ordinary ELM, its modifications, and support vector machines (SVMs). Experimental results show that, compared with SVMs, the ordinary ELM and its modifications can all dramatically speed up the training process while still achieving similar or better vigilance estimation accuracy. In addition, the following three observations have been made from the experiment results: (a) the ordinary ELM and the ELM with L1 norm penalty (LARS-ELM) are sensitive on the number of hidden nodes; (b) the ELM with L2 norm penalty (regularized-ELM) and the ELMs with both L1 norm and L2 norm penalties (LARS-EN-ELM, TROP-ELM) are stable and insensitive on the number of hidden nodes; and (c) regularized-ELM has a much faster training speed, while LARS-EN-ELM can achieve better vigilance estimation accuracy. & 2012 Elsevier B.V. All rights reserved.