nils-herrmann/clean_scirepeval huggingface dataset:
Monocular-vision based SLAM using Line Segments

This paper presents a method to incorporate 3D line segments in vision based SLAM. A landmark initialization method that relies on the Plucker coordinates to represent a 3D line is introduced: a Gaussian sum approximates the feature initial state and is updated as new observations are gathered by the camera. Once initialized, the landmarks state is estimated along an EKF-based SLAM approach: constraints associated with the Plucker representation are considered during the update step of the Kalman filter. The whole SLAM algorithm is validated in simulation runs and results obtained with real data are presented.
Multichannel Convolutional Neural Network for Biological Relation Extraction

The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.
Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding

The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it entails when deployed with learned dictionaries. A way around this problem has been proposed recently, adopting a convolutional sparse representation model. This approach assumes that the global dictionary is a concatenation of banded Circulant matrices. Although several works have presented algorithmic solutions to the global pursuit problem under this new model, very few trulyeffective guarantees are known for the success of such methods. In the first of this two-part work, we address the theoretical aspects of the sparse convolutional model, providing the first meaningful answers to corresponding questions of uniqueness of solutions and success of pursuit algorithms. To this end, we generalize mathematical quantities, such as the `0 norm, the mutual coherence and the Spark, to their counterparts in the convolutional setting, which intrinsically capture local measures of the global model. In a companion paper, we extend the analysis to a noisy regime, addressing the stability of the sparsest solutions and pursuit algorithms, and demonstrate practical approaches for solving the global pursuit problem via simple local processing.
Spoken Content Retrieval—Beyond Cascading Speech Recognition with Text Retrieval

Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences.
A Virtual Assembly Design Environment

The Virtual Assembly Design Environment (VADE) is a Virtual Reality (VR)-based engineering application that allows engineers to evaluate, analyze, and plan the assembly of mechanical systems. This system focuses on utilizing an immersive, virtual environment tightly coupled with commercial computer aided design (CAD) systems. Salient features of VADE include: 1) data integration (two-way) with a parametric CAD system, 2) realistic interaction of user with parts in the virtual environment, 3) creation of valued design information in the virtual environment, 4) reverse data transfer of design information back to the CAD system, 5) significant interactivity in the virtual environment, 6) collision detection, and 7) physically-based modeling. This paper describes the functionality and applications of VADE. A discussion of the limitations of virtual assembly and a comparison with automated assembly planning systems are presented. Experiments conducted using real-world engineering models are also described.
Males' and Females' Script Debugging Strategies

Little research has addressed IT professionals’ script debugging strategies, or considered whether there may be gender differences in these strategies. What strategies do male and female scripters use and what kinds of mechanisms do they employ to successfully fix bugs? Also, are scripters’ debugging strategies similar to or different from those of spreadsheet debuggers? Without the answers to these questions, tool designers do not have a target to aim at for supporting how male and female scripters want to go about debugging. We conducted a think-aloud study to bridge this gap. Our results include (1) a generalized understanding of debugging strategies used by spreadsheet users and scripters, (2) identification of the multiple mechanisms scripters employed to carry out the strategies, and (3) detailed examples of how these debugging strategies were employed by males and females to successfully fix bugs.
Secure communication for the robot operating system

The boom for robotics technologies in recent years has also empowered a new generation of robotics software. The Robot Operating System (ROS) is one of the most popular frameworks for robotics researchers and makers which is moving towards commercial and industrial use. Security-wise however, ROS is vulnerable to attacks. It is rather easy to inject or eavesdrop data in a ROS application. This opens many different ways to attack a ROS application resulting in data loss, monetary damage or even physical injury. In this paper we present a secure communication channel enabling ROS-nodes to communicate with authenticity and confidentiality. We secure ROS on a peer-to-peer basis in the direct interaction between publishers and subscribers. We describe the implementation changes we have made to the ROS core and assess the overhead introduced by the new security functions.
Design and Demonstration of High Power Density Inverter for Aircraft Applications

This paper presents a design methodology for a high power density converter, which emphasizes weight minimization. The design methodology considers various inverter topologies and semiconductor devices with application of cold plate cooling and LCL filter. Design for a high-power inverter is evaluated with demonstration of a 50 kVA 2-level 3-phase SiC inverter operating at 60 kHz switching frequency. The prototype achieves high gravimetric power density of 6.49 kW/kg.
Low-Profile Top-Hat Monopole Yagi Antenna for End-Fire Radiation

A top-hat monopole Yagi antenna is presented to produce an end-fire radiation beam. The antenna has an extremely low profile and wide operating bandwidth. It consists of a folded top-hat monopole as the driven element and four short-circuited top-hat monopoles as parasitic elements. A broad bandwidth can be achieved by adjusting the different resonances introduced by the driven and parasitic elements. A prototype operating at the UHF band (f0 = 550 MHz) is fabricated and tested. Measured results show that a fractional bandwidth (|S11| <; -10 dB) of 20.5% is obtained while the antenna height is only λ0/28 at the center frequency.
Automatic complex schema matching across Web query interfaces: A correlation mining approach

To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this article takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this “deep Web ” query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preprocessing, dual mining of positive and negative correlations, and finally matching construction. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the entire matching process, we incorporate automatic techniques for interface extraction. Executing the DCM framework on automatically extracted interfaces, we find that the inevitable errors in automatic interface extraction may significantly affect the matching result. To make the DCM framework robust against such “noisy” schemas, we integrate it with a novel “ensemble” approach, which creates an ensemble of DCM matchers, by randomizing the schema data into many trials and aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the robustness of the ensemble approach. Empirically, our experiments show that the “ensemblization” indeed significantly boosts the matching accuracy, over automatically extracted and thus noisy schema data. By employing the DCM framework with the ensemble approach, we thus complete an automatic process of matchings Web query interfaces.
Context-based vision system for place and object recognition

While navigating in an environment, a vision system has to be able to recognize where it is and what the main objects in the scene are. In this paper we present a contextbased vision system for place and object recognition. The goal is to identify familiar locations (e.g., office 610, conference room 941, Main Street), to categorize new environments (office, corridor, street) and to use that information to provide contextual priors for object recognition (e.g., tables are more likely in an office than a street). We present a low-dimensional global image representation that provides relevant information for place recognition and categorization, and show how such contextual information introduces strong priors that simplify object recognition. We have trained the system to recognize over 60 locations (indoors and outdoors) and to suggest the presence and locations of more than 20 different object types. The algorithm has been integrated into a mobile system that provides realtime feedback to the user.
The Labeled Multi-Bernoulli Filter

This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the δ-Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the δ-Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance.
Scratching middle schoolers' creative itch

Each July since 2003, the author has directed summer camps that introduce middle school boys and girls to the basic ideas of computer programming. Prior to 2009, the author used Alice 2.0 to introduce object-based computing. In 2009, the author decided to offer these camps using Scratch, primarily to engage repeat campers but also for variety. This paper provides a detailed overview of this outreach, and documents its success at providing middle school girls with a positive, engaging computing experience. It also discusses the merits of Alice and Scratch for such outreach efforts; and the use of these visually oriented programs by students with disabilities, including blind students.
Dynamic Modeling and Control of a Grid-Connected Hybrid Generation System With Versatile Power Transfer

This paper presents power-control strategies of a grid-connected hybrid generation system with versatile power transfer. The hybrid system is the combination of photovoltaic (PV) array, wind turbine, and battery storage via a common dc bus. Versatile power transfer was defined as multimodes of operation, including normal operation without use of battery, power dispatching, and power averaging, which enables grid- or user-friendly operation. A supervisory control regulates power generation of the individual components so as to enable the hybrid system to operate in the proposed modes of operation. The concept and principle of the hybrid system and its control were described. A simple technique using a low-pass filter was introduced for power averaging. A modified hysteresis-control strategy was applied in the battery converter. Modeling and simulations were based on an electromagnetic-transient-analysis program. A 30-kW hybrid inverter and its control system were developed. The simulation and experimental results were presented to evaluate the dynamic performance of the hybrid system under the proposed modes of operation.
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

The recently released Rodinia benchmark suite enables users to evaluate heterogeneous systems including both accelerators, such as GPUs, and multicore CPUs. As Rodinia sees higher levels of acceptance, it becomes important that researchers understand this new set of benchmarks, especially in how they differ from previous work. In this paper, we present recent extensions to Rodinia and conduct a detailed characterization of the Rodinia benchmarks (including performance results on an NVIDIA GeForce GTX480, the first product released based on the Fermi architecture). We also compare and contrast Rodinia with Parsec to gain insights into the similarities and differences of the two benchmark collections; we apply principal component analysis to analyze the application space coverage of the two suites. Our analysis shows that many of the workloads in Rodinia and Parsec are complementary, capturing different aspects of certain performance metrics.
Snore Sound Classification Using Image-Based Deep Spectrum Features

In this paper, we propose a method for automatically detecting various types of snore sounds using image classification convolutional neural network (CNN) descriptors extracted from audio file spectrograms. The descriptors, denoted as deep spectrum features, are derived from forwarding spectrograms through very deep task-independent pre-trained CNNs. Specifically, activations of fully connected layers from two common image classification CNNs, AlexNet and VGG19, are used as feature vectors. Moreover, we investigate the impact of differing spectrogram colour maps and two CNN architectures on the performance of the system. Results presented indicate that deep spectrum features extracted from the activations of the second fully connected layer of AlexNet using a viridis colour map are well suited to the task. This feature space, when combined with a support vector classifier, outperforms the more conventional knowledge-based features of 6 373 acoustic functionals used in the INTERSPEECH ComParE 2017 Snoring sub-challenge baseline system. In comparison to the baseline, unweighted average recall is increased from 40.6% to 44.8% on the development partition, and from 58.5% to 67.0% on the test partition.
Automatic Extraction of Data from Bar Charts

Scientific charts are an effective tool to visualize numerical data trends. They appear in a wide range of contexts, from experimental results in scientific papers to statistical analyses in business reports. The abundance of scientific charts in the web has made it inevitable for search engines to include them as indexed content. However, the queries based on only the textual data used to tag the images can limit query results. Many studies exist to address the extraction of data from scientific diagrams in order to improve search results. In our approach to achieving this goal, we attempt to enhance the semantic labeling of the charts by using the original data values that these charts were designed to represent. In this paper, we describe a method to extract data values from a specific class of charts, bar charts. The extraction process is fully automated using image processing and text recognition techniques combined with various heuristics derived from the graphical properties of bar charts. The extracted information can be used to enrich the indexing content for bar charts and improve search results. We evaluate the effectiveness of our method on bar charts drawn from the web as well as charts embedded in digital documents.
A Secure System For Pervasive Social Network-Based Healthcare

Modern technologies of mobile computing and wireless sensing prompt the concept of pervasive social network (PSN)-based healthcare. To realize the concept, the core problem is how a PSN node can securely share health data with other nodes in the network. In this paper, we propose a secure system for PSN-based healthcare. Two protocols are designed for the system. The first one is an improved version of the IEEE 802.15.6 display authenticated association. It establishes secure links with unbalanced computational requirements for mobile devices and resource-limited sensor nodes. The second protocol uses blockchain technique to share health data among PSN nodes. We realize a protocol suite to study protocol runtime and other factors. In addition, human body channels are proposed for PSN nodes in some use cases. The proposed system illustrates a potential method of using blockchain for PSN-based applications.
Meltdown: Reading Kernel Memory from User Space

The security of computer systems fundamentally relies on memory isolation, e.g., kernel address ranges are marked as non-accessible and are protected from user access. In this paper, we present Meltdown. Meltdown exploits side effects of out-of-order execution on modern processors to read arbitrary kernel-memory locations including personal data and passwords. Out-of-order execution is an indispensable performance feature and present in a wide range of modern processors. The attack is independent of the operating system, and it does not rely on any software vulnerabilities. Meltdown breaks all security guarantees provided by address space isolation as well as paravirtualized environments and, thus, every security mechanism building upon this foundation. On affected systems, Meltdown enables an adversary to read memory of other processes or virtual machines in the cloud without any permissions or privileges, affecting millions of customers and virtually every user of a personal computer. We show that the KAISER defense mechanism for KASLR has the important (but inadvertent) side effect of impeding Meltdown. We stress that KAISER must be deployed immediately to prevent largescale exploitation of this severe information leakage.
An Analysis of BGP Convergence Properties

The Border Gateway Protocol (BGP) is the de facto inter-domain routing protocol used to exchange reachability information between Autonomous Systems in the global Internet. BGP is a path-vector protocol that allows each Autonomous System to override distance-based metrics with policy-based metrics when choosing best routes. Varadhan et al.  have shown that it is possible for a group of Autonomous Systems to independently define BGP policies that together lead to BGP protocol oscillations that never converge on a stable routing. One approach to addressing this problem is based on static analysis of routing policies to determine if they are safe. We explore the worst-case complexity for convergence-oriented static analysis of BGP routing policies. We present an abstract model of BGP and use it to define several global sanity conditions on routing policies that are related to BGP convergence/divergence. For each condition we show that the complexity of statically checking it is either NP-complete or NP-hard.
K-means with Three different Distance Metrics

The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper, the results obtained by implementing the k-means algorithm using three different metrics Euclidean, Manhattan and Minkowski distance metrics along with the comparative study of results of basic k-means algorithm which is implemented through Euclidian distance metric for two-dimensional data, are discussed. Results are displayed with the help of histograms.