snu-aidas/HVMap-dataset huggingface dataset:
Comparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study

Context: Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020. Objective: International competitions like the 2007 DARPA Urban Challenge did not focus on efficient usage of resources to realize the self-driving vehicular functionality. Since the automotive industry is very cost-sensitive, realizing reliable and robust selfdriving functionality is challenging when expensive and sophisticated sensors mounted very visibly on the vehicle’s roof for example cannot be used. Therefore, the goal for this study is to investigate how architectural design decisions of recent self-driving vehicular technology consider resource-efficiency. Method: In a multiple case study, the architectural design decisions derived for resourceconstrained self-driving miniature cars for the international competition CaroloCup are compared with architectural designs from recent real-scale self-driving cars. Results: Scaling down available resources for realizing self-driving vehicular technology puts additional constraints on the architectural design; especially reusability of software components in platform-independent algorithmic concepts are prevailing. Conclusion: Software frameworks like the robotic operating system (ROS) enable fast prototypical solutions; however, architectural support for resource-constrained devices is limited. Here, architectural design drivers as realized in AUTOSAR are more suitable.
Predictors of physical restraint use in Canadian intensive care units

INTRODUCTION
Physical restraint (PR) use in the intensive care unit (ICU) has been associated with higher rates of self-extubation and prolonged ICU length of stay. Our objectives were to describe patterns and predictors of PR use.


METHODS
We conducted a secondary analysis of a prospective observational study of analgosedation, antipsychotic, neuromuscular blocker, and PR practices in 51 Canadian ICUs. Data were collected prospectively for all mechanically ventilated adults admitted during a two-week period. We tested for patient, treatment, and hospital characteristics that were associated with PR use and number of days of use, using logistic and Poisson regression respectively.


RESULTS
PR was used on 374 out of 711 (53%) patients, for a mean number of 4.1 (standard deviation (SD) 4.0) days. Treatment characteristics associated with PR were higher daily benzodiazepine dose (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.00 to 1.11), higher daily opioid dose (OR 1.04, 95% CI 1.01 to 1.06), antipsychotic drugs (OR 3.09, 95% CI 1.74 to 5.48), agitation (Sedation-Agitation Scale (SAS) >4) (OR 3.73, 95% CI 1.50 to 9.29), and sedation administration method (continuous and bolus versus bolus only) (OR 3.09, 95% CI 1.74 to 5.48). Hospital characteristics associated with PR indicated patients were less likely to be restrained in ICUs from university-affiliated hospitals (OR 0.32, 95% CI 0.17 to 0.61). Mainly treatment characteristics were associated with more days of PR, including: higher daily benzodiazepine dose (incidence rate ratio (IRR) 1.07, 95% CI 1.01 to 1.13), daily sedation interruption (IRR 3.44, 95% CI 1.48 to 8.10), antipsychotic drugs (IRR 15.67, 95% CI 6.62 to 37.12), SAS <3 (IRR 2.62, 95% CI 1.08 to 6.35), and any adverse event including accidental device removal (IRR 8.27, 95% CI 2.07 to 33.08). Patient characteristics (age, gender, Acute Physiology and Chronic Health Evaluation II score, admission category, prior substance abuse, prior psychotropic medication, pre-existing psychiatric condition or dementia) were not associated with PR use or number of days used.


CONCLUSIONS
PR was used in half of the patients in these 51 ICUs. Treatment characteristics predominantly predicted PR use, as opposed to patient or hospital/ICU characteristics. Use of sedative, analgesic, and antipsychotic drugs, agitation, heavy sedation, and occurrence of an adverse event predicted PR use or number of days used.
The Internet of Things: A survey

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details. 2010 Elsevier B.V. All rights reserved.
ERP in action - Challenges and benefits for management control in SME context

Article history: Received 31 January 2011 Received in revised form 7 February 2012 Accepted 2 March 2012 ERP systems have fundamentally re-shaped the way business data is collected, stored, disseminated and used throughout the world. However, the existing research in accounting has provided only relatively few empirical findings on the implications for management control when companies implement ERP systems as the technological platform. Especially scarce are the findings concerning the production phase, after implementation, when the information processes, related work practices and the new information contents can be seen as established. In this paper we explored and theorized the benefits, challenges and problems for management control when an ERP system is in use, four years after the implementation. Our findings also illustrate why and under what circumstances these challenges and benefits may exist. For a holistic view of the organization our findings, based on a qualitative case study, are constructed from the viewpoints of people at different levels and functions of the organization. Top management expected a new strategic control system, but due to the many challenges it ended up with merely financial accounting based control. At the operational level, serious challenges lead to inadequate usage of the ERP system. Management control produces the financial basic data and must contend with many practical problems caused by ERP implementation. © 2012 Elsevier Inc. All rights reserved.
Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques

Crime analysis has become a critical area for helping law enforcement agencies to protect civilians. As a result of a rapidly increasing population, crime rates have increased dramatically, and appropriate analysis has become a timeconsuming effort. Text mining is an effective tool that may help to solve this problem to classify crimes in effective manner. The proposed system aims to detect and classify crimes in Twitter posts that written in the Arabic language, one of the most widespread languages today. In this paper, classification techniques are used to detect crimes and identify their nature by different classification algorithms. The experiments evaluate different algorithms, such as SVM, DT, CNB, and KNN, in terms of accuracy and speed in the crime domain. Also, different features extraction techniques are evaluated, including rootbased stemming, light stemming, n-gram. The experiments revealed the superiority of n-gram over other techniques. Specifically, the results indicate the superiority of SVM with trigram over other classifiers, with a 91.55% accuracy. Keywords—Crimes; text mining; classification; features extraction techniques; arabic posts; twitter
Tracking down software bugs using automatic anomaly detection

This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.We implemented the DIDUCE system for Java programs and applied it to four programs of significant size and complexity. DIDUCE succeeded in identifying the root causes of programming errors in each of the programs quickly and automatically. In particular, DIDUCE is effective in isolating a timing-dependent bug in a released JSSE (Java Secure Socket Extension) library, which would have taken an experienced programmer days to find. Our experience suggests that detecting and checking program invariants dynamically is a simple and effective methodology for debugging many different kinds of program errors across a wide variety of application domains.
HIGH DIMENSIONAL SPACES

In this paper, we analyze deep learning from a mathematical point of view and derive several novel results. The results are based on intriguing mathematical properties of high dimensional spaces. We first look at perturbation based adversarial examples and show how they can be understood using topological arguments in high dimensions. We point out fallacy in an argument presented in a published paper in 2015 by Goodfellow et al., see reference [10], and we present a more rigorous, general and correct mathematical result to explain adversarial examples in terms of topology of image manifolds. Second, we look at optimization landscapes of deep neural networks and examine the number of saddle points relative to that of local minima. Third, we show how multiresolution nature of images explains perturbation based adversarial examples in form of a stronger result. Our results state that expectation of L2-norm of adversarial perturbations shrinks to 0 as image resolution becomes arbitrarily large. Finally, by incorporating the parts-whole manifold learning hypothesis for natural images, we investigate the working of deep neural networks and root causes of adversarial examples and discuss how future improvements can be made and how adversarial examples can be eliminated.
On pricing discrete barrier options using conditional expectation and importance sampling Monte Carlo

Estimators for the price of a discrete barrier option based on conditional expectation and importance sampling variance reduction techniques are given. There are erroneous formulas for the conditional expectation estimator published in the literature: we derive the correct expression for the estimator. We use a simulated annealing algorithm to estimate the optimal parameters of exponential twisting in importance sampling, and compare it with a heuristic used in the literature. Randomized quasi-Monte Carlo methods are used to further increase the accuracy of the estimators. c © 2007 Elsevier Ltd. All rights reserved.
Five Styles of Customer Knowledge Management , and How Smart Companies Use Them To Create Value

Corporations are beginning to realize that the proverbial ‘if we only knew what we know’ also includes ‘if we only knew what our customers know.’ The authors discuss the concept of Customer Knowledge Management (CKM), which refers to the management of knowledge from customers, i.e. knowledge resident in customers. CKM is contrasted with knowledge about customers, e.g. customer characteristics and preferences prevalent in previous work on knowledge management and customer relationship management. Five styles of CKM are proposed and practically illustrated by way of corporate examples. Implications are discussed for knowledge management, the resource based view, and strategy process research.  2002 Elsevier Science Ltd. All rights reserved
gNUFFTW : Auto-Tuning for High-Performance GPU-Accelerated Non-Uniform Fast Fourier Transforms by Teresa

Non-uniform sampling of the Fourier transform appears in many important applications such as magnetic resonance imaging (MRI), optics, tomography and radio interferometry. Computing the inverse often requires fast application of the non-uniform discrete Fourier transform (NUDFT) and its adjoint operation. Non-Uniform Fast Fourier Transform (NUFFT) methods, such as gridding/regridding, are approximate algorithms which often leverage the highly-optimized Fast Fourier Transform (FFT) and localized interpolations. These approaches require selecting several parameters, such as interpolation and FFT grid sizes, which affect both the accuracy and runtime. In addition, different implementations lie on a spectrum of precomputation levels, which can further speed up repeated computations, with various trade-offs in planning time, execution time and memory usage. Choosing the optimal parameters and implementations is important for performance speed, but difficult to do manually since the performance of NUFFT is not well-understood for modern parallel processors. Inspired by the FFTW library, we demonstrate an empirical auto-tuning approach for the NUFFT on General Purpose Graphics Processors Units (GPGPU). We demonstrate order-of-magnitude speed improvements with autotuning compared to typical default choices. Our auto-tuning is implemented in an easy to use proof-of-concept library called gNUFFTW, which leverages existing open-source NUFFT packages, cuFFT and cuSPARSE libraries, as well as our own NUFFT implementations for high performance. Keywords—non-uniform, non-Cartesian, FFT, NUFFT, GPU, auto-tuning, image processing.
Automatic Medical Concept Extraction from Free Text Clinical Reports, a New Named Entity Recognition Approach

Actually in the Hospital Information Systems, there is a wide range of clinical information representation from the Electronic Health Records (EHR), and most of the information contained in clinical reports is written in natural language free text. In this context, we are researching the problem of automatic clinical named entities recognition from free text clinical reports. We are using Snomed-CT (Systematized Nomenclature of Medicine – Clinical Terms) as dictionary to identify all kind of clinical concepts, and thus the problem we are considering is to map each clinical entity named in a free text report with its Snomed-CT unique ID. More in general, we are developed a new approach for the named entity recognition (NER) problem in specific domains, and we have applied it to recognize clinical concepts in free text clinical reports. In our approach we apply two types of NER approaches, dictionary-based and machine learning-based. We use a specific domain dictionary-based gazetteer (using Snomed-CT to get the standard clinical code for the clinical concept), and the main approach that we introduce is using a unsupervised shallow learning neural network, word2vec from Mikolov et al., to represent words as vectors, and then making the recognition based on the distance between candidates and dictionary terms. We have applied our approach on a Dataset with 318.585 clinical reports in Spanish from the emergency service of the Hospital “Rafael Méndez” from Lorca (Murcia) Spain, and preliminary results are encouraging. Key-Words: Snomed-CT, word2vec, doc2vec, clinical information extraction, skipgram, medical terminologies, search semantic, named entity recognition, ner, medical entity recognition
Forecasting and Trading Currency Volatility : An Application of Recurrent Neural Regression and Model Combination by

In this paper, we examine the use of GARCH models, Neural Network Regression (NNR), Recurrent Neural Network (RNN) regression and model combinations for forecasting and trading currency volatility, with an application to the GBP/USD and USD/JPY exchange rates. Both the results of the NNR/RNN models and the model combination results are benchmarked against the simpler GARCH alternative. The idea of developing a nonlinear nonparametric approach to forecast FX volatility, identify mispriced options and subsequently develop a trading strategy based upon this process is intuitively appealing. Using daily data from December 1993 through April 1999, we develop alternative FX volatility forecasting models. These models are then tested out-of-sample over the period April 1999-May 2000, not only in terms of forecasting accuracy, but also in terms of trading efficiency: In order to do so, we apply a realistic volatility trading strategy using FX option straddles once mispriced options have been identified. Allowing for transaction costs, most trading strategies retained produce positive returns. RNN models appear as the best single modelling approach yet, somewhat surprisingly, model combination which has the best overall performance in terms of forecasting accuracy, fails to improve the RNN-based volatility trading results. Another conclusion from our results is that, for the period and currencies considered, the currency option market was inefficient and/or the pricing formulae applied by market participants were inadequate. * Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: cdunis@globalnet.co.uk). The opinions expressed herein are not those of Girobank. ** Xuehuan Huang is an Associate Researcher with CIBEF. *** CIBEF – Centre for International Banking, Economics and Finance, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ. We are grateful to Professor Ken Holden and Professor John Thompson of Liverpool Business School for helpful comments on an earlier version of this paper.
Training Support Vector Machines: an Application to Face Detection

Detection (To appear in the Proceedings of CVPR'97, June 17-19, 1997, Puerto Rico.) Edgar Osunay? Robert Freund? Federico Girosiy yCenter for Biological and Computational Learning and ?Operations Research Center Massachusetts Institute of Technology Cambridge, MA, 02139, U.S.A. Abstract We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classi ers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.
Review of Dercum’s disease and proposal of diagnostic criteria, diagnostic methods, classification and management

UNLABELLED
DEFINITION AND CLINICAL PICTURE: We propose the minimal definition of Dercum's disease to be generalised overweight or obesity in combination with painful adipose tissue. The associated symptoms in Dercum's disease include fatty deposits, easy bruisability, sleep disturbances, impaired memory, depression, difficulty concentrating, anxiety, rapid heartbeat, shortness of breath, diabetes, bloating, constipation, fatigue, weakness and joint aches.


CLASSIFICATION
We suggest that Dercum's disease is classified into: I. Generalised diffuse form A form with diffusely widespread painful adipose tissue without clear lipomas, II. Generalised nodular form - a form with general pain in adipose tissue and intense pain in and around multiple lipomas, and III. Localised nodular form - a form with pain in and around multiple lipomas IV. Juxtaarticular form - a form with solitary deposits of excess fat for example at the medial aspect of the knee.


EPIDEMIOLOGY
Dercum's disease most commonly appears between the ages of 35 and 50 years and is five to thirty times more common in women than in men. The prevalence of Dercum's disease has not yet been exactly established.


AETIOLOGY
Proposed, but unconfirmed aetiologies include: nervous system dysfunction, mechanical pressure on nerves, adipose tissue dysfunction and trauma. DIAGNOSIS AND DIAGNOSTIC METHODS: Diagnosis is based on clinical criteria and should be made by systematic physical examination and thorough exclusion of differential diagnoses. Advisably, the diagnosis should be made by a physician with a broad experience of patients with painful conditions and knowledge of family medicine, internal medicine or pain management. The diagnosis should only be made when the differential diagnoses have been excluded.


DIFFERENTIAL DIAGNOSIS
Differential diagnoses include: fibromyalgia, lipoedema, panniculitis, endocrine disorders, primary psychiatric disorders, multiple symmetric lipomatosis, familial multiple lipomatosis, and adipose tissue tumours. GENETIC COUNSELLING: The majority of the cases of Dercum's disease occur sporadically. A to G mutation at position A8344 of mitochondrial DNA cannot be detected in patients with Dercum's disease. HLA (human leukocyte antigen) typing has not revealed any correlation between typical antigens and the presence of the condition. MANAGEMENT AND TREATMENT: The following treatments have lead to some pain reduction in patients with Dercum's disease: Liposuction, analgesics, lidocaine, methotrexate and infliximab, interferon α-2b, corticosteroids, calcium-channel modulators and rapid cycling hypobaric pressure. As none of the treatments have led to long lasting complete pain reduction and revolutionary results, we propose that Dercum's disease should be treated in multidisciplinary teams specialised in chronic pain.


PROGNOSIS
The pain in Dercum's disease seems to be relatively constant over time.
Mapping the game landscape: Locating genres using functional classification

Are typical computer game genres still valid descriptors and useful for describing game structure and game content? Games have changed from simple to complex and from single function to multi function. By identifying structural differences in game elements we develop a more nuanced model to categorized games and use cluster analysis as a descriptive tool in order to do so. The cluster analysis of 75 functionally different games shows that the two perspectives (omnipresent and vagrant), as well as challenges, mutability and savability are important functional categories to use in order to describe games. Author