nils-herrmann/clean_scirepeval huggingface dataset:
Alignment of continuous video onto 3D point clouds

We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are difficult to model; for example, trees. To compute 3D point clouds from video, motion stereo is used along with a state-of-the-art algorithm for camera pose estimation. Our experiments with real data demonstrate the advantages of the proposed registration algorithm for texturing models in large-scale semi-urban environments. The capability to align video before a 3D model is built from the 3D sensor data offers new practical opportunities for 3D modeling. We introduce a novel modeling-through-registration approach that fuses 3D information from both the 3D sensor and the video. Initial experiments with real data illustrate the potential of the proposed approach.
Bayes classifiers for imbalanced traffic accidents datasets.

Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.
Blepharophimosis mental retardation syndrome Say-Barber/Biesecker/Young-Simpson type - new findings with neuroimaging.

We report on a female patient with blepharophimosis mental retardation syndrome of Say/Barber/Biesecker/Young-Simpson (SBBYS) type. Main findings in her were marked developmental delay, blepharophimosis, ptosis, cleft palate, external auditory canal stenosis, small and malformed teeth, hypothyroidism, hearing impairment, and joint limitations. We performed diffusion tensor magnetic resonance imaging (MRI) and tractography of the brain which showed inappropriate myelination and disturbed white matter integrity. Cytogenetic analysis, subtelomeric fluorescence in situ hybridization and comparative genomic hybridization failed to identify an abnormality. It remains uncertain whether the MRI findings are specific to the present patient or form part of the SBBYS syndrome.
Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition

Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.
Chinese Word Segmentation and Named Entity Recognition Based on Conditional Random Fields

Chinese word segmentation (CWS), named entity recognition (NER) and part-ofspeech tagging is the lexical processing in Chinese language. This paper describes the work on these tasks done by France Telecom Team (Beijing) at the fourth International Chinese Language Processing Bakeoff. In particular, we employ Conditional Random Fields with different features for these tasks. In order to improve NER relatively low recall; we exploit non-local features and alleviate class imbalanced distribution on NER dataset to enhance the recall and keep its relatively high precision. Some other post-processing measures such as consistency checking and transformation-based error-driven learning are used to improve word segmentation performance. Our systems participated in most CWS and POS tagging evaluations and all the NER tracks. As a result, our NER system achieves the first ranks on MSRA open track and MSRA/CityU closed track. Our CWS system achieves the first rank on CityU open track, which means that our systems achieve state-of-the-art performance on Chinese lexical processing.
Traffic sign recognition and analysis for intelligent vehicles

This paper deals with object recognition in outdoor environments. In this type of environments, lighting conditions cannot be controlled and predicted, objects can be partially occluded, and their position and orientation is not known a priori. The chosen type of objects is traffic or road signs, due to their usefulness for sign maintenance, inventory in highways and cities, Driver Support Systems and Intelligent Autonomous Vehicles. A genetic algorithm is used for the detection step, allowing an invariance localisation to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same colour. A neural network achieves the classification. The global system not only recognises the traffic sign but also provides information about its condition or state. q 2002 Elsevier Science B.V. All rights reserved.
Advanced PROPHET Routing in Delay Tolerant Network

To solve routing jitter problem in PROPHET in delay tolerant network, advanced PROPHET routing is proposed in this paper. Average delivery predictabilities are used in advanced PROPHET to avoid routing jitter. Furthermore, we evaluate it through simulations versus PROPHET routing protocol. The experimental results show there has higher average delivery rates and shorter average delays in advanced PROPHET. Thus, it is fair to say that advanced PROPHET gives better performance than PROPHET.
Inverted Files Versus Signature Files for Text Indexing

Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available
Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research

This whitepaper summarizes issues raised during the First International Workshop on Serverless Computing (WoSC) 2017 held June 5th 2017 and especially in the panel and associated discussion that concluded the workshop. We also include comments from the keynote and submitted papers. A glossary at the end (section 8) defines many technical terms used in this report.
Privacy and contextual integrity: framework and applications

Contextual integrity is a conceptual framework for understanding privacy expectations and their implications developed in the literature on law, public policy, and political philosophy. We formalize some aspects of contextual integrity in a logical framework for expressing and reasoning about norms of transmission of personal information. In comparison with access control and privacy policy frameworks such as RBAC, EPAL, and P3P, these norms focus on who personal information is about, how it is transmitted, and past and future actions by both the subject and the users of the information. Norms can be positive or negative depending on whether they refer to actions that are allowed or disallowed. Our model is expressive enough to capture naturally many notions of privacy found in legislation, including those found in HIPAA, COPPA, and GLBA. A number of important problems regarding compliance with privacy norms, future requirements associated with specific actions, and relations between policies and legal standards reduce to standard decision procedures for temporal logic
Multi-tenancy Performance Benchmark for Web Application Platforms

Cloud environments reduce data center operating costs through resource sharing and economies of scale. Infrastructure-as-a-Service is one example that leverages virtualization to share infrastructure resources. However, virtualization is often insufficient to provide Software-as-aService applications due to the need to replicate the operating system, middleware and application components for each customer. To overcome this problem, multi-tenancy has emerged as an architectural style that allows to share a single Web application instance among multiple independent customers, thereby significantly improving the efficiency of Software-as-a-Service offerings. A number of platforms are available today that support the development and hosting of multi-tenant applications by encapsulating multi-tenancy specific functionality. Although a lack of performance guarantees is one of the major obstacles to the adoption of cloud computing, in general, and multi-tenant applications, in particular, these kinds of applications and platforms have so far not been in the focus of the performance and benchmarking community. In this paper, we present an extended version of an existing and widely accepted application benchmark adding support for multi-tenant platform features. The benchmark is focused on evaluating the maximum throughput and the amount of tenants that can be served by a platform. We present a case study comparing virtualization and multi-tenancy. The results demonstrate the practical usability of the proposed benchmark in evaluating multi-tenant platforms and gives insights that help to decide for one sharing approach.
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

We introduce a novel, simple convolution neural network (CNN) architecture – multi-group norm constraint CNN (MGNC-CNN) – that capitalizes on multiple sets of word embeddings for sentence classification. MGNCCNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.
OWLIM: A family of scalable semantic repositories

An explosion in the use of RDF for representing information about resources has driven the requirements for Webscale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform. Ontotext AD, 135 Tsarigradsko Chaussee, Sofia 1784, Bulgaria
Dynamics of Consumer Demand for New Durable Goods ∗

Most new consumer durable goods experience rapid declines in prices and improvements in quality, suggesting the importance of modeling dynamics. This paper estimates a dynamic model of consumer preferences for new durable goods with persistent heterogeneous consumer tastes, rational expectations and repeat purchases over time. We estimate the model on the digital camcorder industry using panel data on prices, sales and characteristics. We find that standard COLIs overstate welfare gain in later periods due to a changing composition of buyers. The one-year industry elasticity in response to a transitory industry-wide price shock is about 25% less than the one-month elasticity. ∗We thank Dan Ackerberg, Victor Aguirregabiria, Ana Aizcorbe, Rabah Amir, Lanier Benkard, Steve Berry, Sofronis Clerides, Simon Gilchrist, Avi Goldfarb, Igal Hendel, Kei Hirano, Firat Inceoglu, Sam Kortum, John Krainer, Ariel Pakes, Minsoo Park, Rob Porter, Jeff Prince, Pasquale Schiraldi, Andy Skrzypacz, Tim Erickson, Mo Xiao and seminar participants at several institutions for helpful comments; Haizhen Lin, Ryan Murphy, Kathleen Nosal, David Rapson, Alex Shcherbakov, and Shirley Wong for research assistance; and the NPD Group, ICR-CENTRIS, Ali Hortaçsu and Jeff Prince for providing data. The comments of the editor and anonymous referees substantially improved the paper. We acknowledge funding from the National Science Foundation. All errors are our own.
Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement - part II: the variations

This is Part II of the paper, "Gray-Level Grouping (GLG): an Automatic Method for Optimized Image Contrast Enhancement". Part I of this paper introduced a new automatic contrast enhancement technique: gray-level grouping (GLG). GLG is a general and powerful technique, which can be conveniently applied to a broad variety of low-contrast images and outperforms conventional contrast enhancement techniques. However, the basic GLG method still has limitations and cannot enhance certain classes of low-contrast images well, e.g., images with a noisy background. The basic GLG also cannot fulfill certain special application purposes, e.g., enhancing only part of an image which corresponds to a certain segment of the image histogram. In order to break through these limitations, this paper introduces an extension of the basic GLG algorithm, selective gray-level grouping (SGLG), which groups the histogram components in different segments of the grayscale using different criteria and, hence, is able to enhance different parts of the histogram to various extents. This paper also introduces two new preprocessing methods to eliminate background noise in noisy low-contrast images so that such images can be properly enhanced by the (S)GLG technique. The extension of (S)GLG to color images is also discussed in this paper. SGLG and its variations extend the capability of the basic GLG to a larger variety of low-contrast images, and can fulfill special application requirements. SGLG and its variations not only produce results superior to conventional contrast enhancement techniques, but are also fully automatic under most circumstances, and are applicable to a broad variety of images.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Detecting False Data Injection Attacks on DC State Estimation

• Y. Liu, M. K. Reiter, and P. Ning. False Data Injection Attacks Against State Estimation in Electric Power Grids. In 16th ACM Conference on Computer and Communications Security (CCS ’09). Defense Against False Data Injection Attacks on DC State Estimation Miao Lu, University of Illinois at Urbana- Rakesh Bobba, Kate Morrow I T I S U M M E R U N D E R G R A D U A T E I N T E R N P R O G R A M, 2 0 1 1
Smartphone Background Activities in the Wild: Origin, Energy Drain, and Optimization

As new iterations of more powerful and better connected smartphones emerge, their limited battery life remains a leading factor adversely affecting the mobile experience of millions of smartphone users. While it is well-known that many apps can drain battery even while running in background, there has not been any study that quantifies the extent and severity of such background energy drain for users in the wild. To extend battery life, various new features are being incorporated within the phone, one of them being preventing applications from running in background, i.e., when the screen is off, but their impact is largely unknown. This paper makes several contributions. First, we present a large-scale measurement study that performs an in-depth analysis of the activities of various apps running in background on thousands of phones in the wild. Second, we quantify the amount of battery drain by all such background activities and possible energy saving. Third, we develop a metric to measure the usefulness of background activities that is personalized to each user. Finally, we present a system called HUSH (screen-off optimizer) that monitors the metric online and automatically identifies and suppresses background activities during screen-off periods that are not useful to the user experience. In doing so, our proposed HUSH saves screen-off energy of smartphones by 15.7% on average while incurring minimal impact on the user experience with the apps.
Web question answering: is more always better?

This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Most question answering systems use a wide variety of linguistic resources. We focus instead on the redundancy available in large corpora as an important resource. We use this redundancy to simplify the query rewrites that we need to use, and to support answer mining from returned snippets. Our system performs quite well given the simplicity of the techniques being utilized. Experimental results show that question answering accuracy can be greatly improved by analyzing more and more matching passages. Simple passage ranking and n-gram extraction techniques work well in our system making it efficient to use with many backend retrieval engines.
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images, thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel-level annotations. Then, we address the question of how useful such data can be for semantic segmentation - in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.
On enhancing accessible smart buildings using IoT

Moving is one of the most important issues as regards independence while conducting daily activities, in both indoor and outdoor environments. This is considered particularly true for people with disabilities and it should be guaranteed in order to enable and facilitate their integration and their independence. In this context, IoT could be a means to define and obtain smart and more accessible buildings. In this paper, we present UniSAS, a system which has been designed to improve the accessibility of buildings thanks to an architecture based on Raspberry Pi and users' mobile devices. The paper describes the system architecture and a prototype we have developed. Some users' scenarios are illustrated, so as to discuss the feasibility of our approach.
On the limited memory BFGS method for large scale optimization

We study the numerical performance of a limited memory quasi Newton method for large scale optimization which we call the L BFGS method We compare its performance with that of the method developed by Buckley and LeNir which combines cyles of BFGS steps and conjugate direction steps Our numerical tests indicate that the L BFGS method is faster than the method of Buckley and LeNir and is better able to use additional storage to accelerate convergence We show that the L BFGS method can be greatly accelerated by means of a simple scaling We then compare the L BFGSmethod with the partitioned quasi Newton method of Griewank and Toint a The results show that for some problems the partitioned quasi Newton method is clearly superior to the L BFGS method However we nd that for other problems the L BFGS method is very competitive due to its low iteration cost We also study the convergence properties of the L BFGS method and prove global convergence on uniformly convex problems