nils-herrmann/clean_scirepeval huggingface dataset:
Multiple extended objects tracking with object-local occupancy grid maps

Improved sensors in the automotive field are leading to multi-object tracking of extended objects becoming more and more important for advanced driver assistance systems and highly automated driving. This paper proposes an approach that combines a PHD filter for extended objects, viz. objects that originate multiple measurements while also estimating the shape of the objects via constructing an object-local occupancy grid map and then extracting a polygonal chain. This allows tracking even in traffic scenarios where unambiguous segmentation of measurements is difficult or impossible. In this work, this is achieved using multiple segmentation assumptions by applying different parameter sets for the DBSCAN clustering algorithm. The proposed algorithm is evaluated using simulated data and real sensor data from a test track including highly accurate D-GPS and IMU data as a ground truth.
A performance evaluation of open source graph databases

With the proliferation of large, irregular, and sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query, and compute on the topological structure of the network represented as sets of edges relating sets of vertices. To store and process Facebook-scale datasets, software and algorithms must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we conduct a qualitative study and a performance comparison of 12 open source graph databases using four fundamental graph algorithms on networks containing up to 256 million edges.
Resolving Ambiguities in Natural Language Software Requirements: A Comprehensive Survey

Requirements Engineering is one of the most vital activities in the entire Software Development Life Cycle. The success of the software is largely dependent on how well the users' requirements have been understood and converted into appropriate functionalities in the software. Typically, the users convey their requirements in natural language statements that initially appear easy to state. However, being stated in natural language, the statement of requirements often tends to suffer from misinterpretations and imprecise inferences. As a result, the requirements specified thus, may lead to ambiguities in the software specifications. One can indeed find numerous approaches that deal with ensuring precise requirement specifications. Naturally, an obvious approach to deal with ambiguities in natural language software specifications is to eliminate ambiguities altogether i.e. to use formal specifications. However, the formal methods have been observed to be cost-effective largely for the development of mission-critical software. Due to the technical sophistication required, these are yet to be accepted in the mainstream. Hence, the other alternative is to let the ambiguities exist in the natural language requirements but deal with the same using proven techniques viz. using approaches based on machine learning, knowledge and ontology to resolve them. One can indeed find numerous automated and semi-automated tools to resolve specific types of natural language software requirement ambiguities. However, to the best of our knowledge there is no published literature that attempts to compare and contrast the prevalent approaches to deal with ambiguities in natural language software requirements. Hence, in this paper, we attempt to survey and analyze the prevalent approaches that attempt to resolve ambiguities in natural language software requirements. We focus on presenting a state-of-the-art survey of the currently available tools for ambiguity resolution. The objective of this paper is to disseminate, dissect and analyze the research work published in the area, identify metrics for a comparative evaluation and eventually do the same. At the end, we identify open research issues with an aim to spark new interests and developments in this field.
Learning Informative Edge Maps for Indoor Scene Layout Prediction

In this paper, we introduce new edge-based features for the task of recovering the 3D layout of an indoor scene from a single image. Indoor scenes have certain edges that are very informative about the spatial layout of the room, namely, the edges formed by the pairwise intersections of room faces (two walls, wall and ceiling, wall and floor). In contrast with previous approaches that rely on area-based features like geometric context and orientation maps, our method attempts to directly detect these informative edges. We learn to predict 'informative edge' probability maps using two recent methods that exploit local and global context, respectively: structured edge detection forests, and a fully convolutional network for pixelwise labeling. We show that the fully convolutional network is quite successful at predicting the informative edges even when they lack contrast or are occluded, and that the accuracy can be further improved by training the network to jointly predict the edges and the geometric context. Using features derived from the 'informative edge' maps, we learn a maximum margin structured classifier that achieves state-of-the-art performance on layout prediction.
ILQUA--An IE-Driven Question Answering System

ILQUA first participated in TREC QA main task in 2003. This year we have made modifications to the system by removing some components with poor performance and enhanced the system with new methods and new components. The newly built ILQUA is an IE-driven QA system. To answer “Factoid” and “List” questions, we apply our answer extraction methods on NE-tagged passages. The answer extraction methods adopted here are surface text pattern matching, n-gram proximity search and syntactic dependency matching. Surface text pattern matching has been applied in some previous TREC QA systems. However, the patterns used in ILQUA are automatically generated by a supervised learning system and represented in a format of regular expressions which can handle up to 4 question terms. N-gram proximity search and syntactic dependency matching are two steps of one component. N-grams of question terms are matched around every named entity in the candidate passages and a list of named entities are generated as answer candidate. These named entities go through a multi-level syntactic dependency matching until a final answer is generated. To answer “Other” questions, we parse the answer sentences of “Other” questions in 2004 main task and built syntactic patterns combined with semantic features. These patterns are applied to the parsed candidate sentences to extract answers of “Other” questions. The evaluation results showed ILQUA has reached an accuracy of 30.9% for factoid questions. ILQUA is an IE-driven QA system without any pre-compiled knowledge base of facts and it doesn’t get reference from any other external search engine such as Google. The disadvantage of an IE-driven QA system is that there are some types of questions that can’t be answered because the answer in the passages can’t be tagged as appropriate NE types. Figure 1 shows the diagram of the ILQUA architecture.
Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent

We propose a generic method for iteratively approximating various second-order gradient steps-Newton, Gauss-Newton, Levenberg-Marquardt, and natural gradient-in linear time per iteration, using special curvature matrix-vector products that can be computed in O(n). Two recent acceleration techniques for on-line learning, matrix momentum and stochastic meta-descent (SMD), implement this approach. Since both were originally derived by very different routes, this offers fresh insight into their operation, resulting in further improvements to SMD.
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks

Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging task. To remedy this unsatisfactory situation, a number of groups have recently proposed different methods to visualize the learned models. In this work we suggest a general taxonomy to classify and compare these methods, subdividing the literature into three main categories and providing researchers with a terminology to base their works on. Furthermore, we introduce the FeatureVis library for MatConvNet: an extendable, easy to use open source library for visualizing CNNs. It contains implementations from each of the three main classes of visualization methods and serves as a useful tool for an enhanced understanding of the features learned by intermediate layers, as well as for the analysis of why a network might fail for certain examples.
Generalized Two-Way Two-Section Dual-Band Wilkinson Power Divider With Two Absorption Resistors and Its Miniaturization

A generalized two-way two-section dual-band Wilkinson power divider with two absorption resistors and its size-reduced model are presented. Design equations for dual-band and arbitrary power division are derived from modified even- and odd-mode analysis. The frequency ratio of the lower and upper band frequencies must be lower than three. The channel bandwidth is presented as a function of frequency ratio with mathematical expressions. A coupled line section is introduced to reduce circuit size based on the assumptions of linear phase characteristics and equal phase velocities of the even and odd modes. Two two-way dual-band power dividers, one three-way planar dual-band power divider, and one size-reduced dual-band power divider were fabricated. Experimental results were in good agreement with predicted values.
Enhancing personalized search by mining and modeling task behavior

Personalized search systems tailor search results to the current user intent using historic search interactions. This relies on being able to find pertinent information in that user's search history, which can be challenging for unseen queries and for new search scenarios. Building richer models of users' current and historic search tasks can help improve the likelihood of finding relevant content and enhance the relevance and coverage of personalization methods. The task-based approach can be applied to the current user's search history, or as we focus on here, all users' search histories as so-called "groupization" (a variant of personalization whereby other users' profiles can be used to personalize the search experience). We describe a method whereby we mine historic search-engine logs to find other users performing similar tasks to the current user and leverage their on-task behavior to identify Web pages to promote in the current ranking. We investigate the effectiveness of this approach versus query-based matching and finding related historic activity from the current user (i.e., group versus individual). As part of our studies we also explore the use of the on-task behavior of particular user cohorts, such as people who are expert in the topic currently being searched, rather than all other users. Our approach yields promising gains in retrieval performance, and has direct implications for improving personalization in search systems.
Teacher efficacy : capturing an elusive construct

Teacher efficacy has proved to be powerfully related to many meaningful educational outcomes such as teachers’ persistence, enthusiasm, commitment and instructional behavior, as well as student outcomes such as achievement, motivation, and self-efficacy beliefs. However, persistent measurement problems have plagued those who have sought to study teacher efficacy. We review many of the major measures that have been used to capture the construct, noting problems that have arisen with each. We then propose a promising new measure of teacher efficacy along with validity and reliability data from three separate studies. Finally, new directions for research made possible by this instrument are explored.
fTPM: A Software-Only Implementation of a TPM Chip

Commodity CPU architectures, such as ARM and Intel CPUs, have started to offer trusted computing features in their CPUs aimed at displacing dedicated trusted hardware. Unfortunately, these CPU architectures raise serious challenges to building trusted systems because they omit providing secure resources outside the CPU perimeter. This paper shows how to overcome these challenges to build software systems with security guarantees similar to those of dedicated trusted hardware. We present the design and implementation of a firmware-based TPM 2.0 (fTPM) leveraging ARM TrustZone. Our fTPM is the reference implementation of a TPM 2.0 used in millions of mobile devices. We also describe a set of mechanisms needed for the fTPM that can be useful for building more sophisticated trusted applications beyond just a TPM.
Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G

Fifth-generation (5G) networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This article gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls.
Parking Slot Markings Recognition for Automatic Parking Assist System

This paper describes a monocular vision based parking-slot-markings recognition algorithm, which is used to automate the target position selection of automatic parking assist system. Peak-pair detection and clustering in Hough space recognize marking lines. Specially, one-dimensional filter in Hough space is designed to utilize a priori knowledge about the characteristics of marking lines in bird's eye view edge image. Modified distance between point and line-segment is used to distinguish guideline from recognized marking line-segments. Once the guideline is successfully recognized, T-shape template matching easily recognizes dividing marking line-segments. Experiments show that proposed algorithm successfully recognizes parking slots even when adjacent vehicles occlude parking-slot-markings severely
Community evolution detection in dynamic heterogeneous information networks

As the rapid development of all kinds of online databases, huge heterogeneous information networks thus derived are ubiquitous. Detecting evolutionary communities in these networks can help people better understand the structural evolution of the networks. However, most of the current community evolution analysis is based on the homogeneous networks, while a real community usually involves different types of objects in a heterogeneous network. For example, when referring to a research community, it contains a set of authors, a set of conferences or journals and a set of terms.
 In this paper, we study the problem of detecting evolutionary multi-typed communities defined as net-clusters in dynamic heterogeneous networks. A Dirichlet Process Mixture Model-based generative model is proposed to model the community generations. At each time stamp, a clustering of communities with the best cluster number that can best explain the current and historical networks are automatically detected. A Gibbs sampling-based inference algorithm is provided to inference the model. Also, the evolution structure can be read from the model, which can help users better understand the birth, split and death of communities. Experiments on two real datasets, namely DBLP and Delicious.com, have shown the effectiveness of the algorithm.
Phase-change memory technology for embedded applications

A novel p-trench phase-change memory (PCM) cell and its integration with a MOSFET selector in a standard 0.18 /spl mu/m CMOS technology are presented. The high-performance capabilities of PCM cells are experimentally investigated and their application in embedded systems is discussed. Write times as low as 10 ns and 20 ns have been measured for the RESET and SET operation, respectively, still granting a 10/spl times/ read margin. The impact of the RESET pulse on PCH cell endurance has been also evaluated. Finally, cell distributions and first statistical endurance measurements on a 4 Mbit MOS demonstrator clearly assess the feasibility of the PCM technology.
Combining feature norms and text data with topic models.

Many psychological theories of semantic cognition assume that concepts are represented by features. The empirical procedures used to elicit features from humans rely on explicit human judgments which limit the scope of such representations. An alternative computational framework for semantic cognition that does not rely on explicit human judgment is based on the statistical analysis of large text collections. In the topic modeling approach, documents are represented as a mixture of learned topics where each topic is represented as a probability distribution over words. We propose feature-topic models, where each document is represented by a mixture of learned topics as well as predefined topics that are derived from feature norms. Results indicate that this model leads to systematic improvements in generalization tasks. We show that the learned topics in the model play in an important role in the generalization performance by including words that are not part of current feature norms.
Limits on the majority vote accuracy in classifier fusion

We derive upper and lower limits on the majority vote accuracy with respect to individual accuracy p, the number of classifiers in the pool (L), and the pairwise dependence between classifiers, measured by Yule’s Q statistic. Independence between individual classifiers is typically viewed as an asset in classifier fusion. We show that the majority vote with dependent classifiers can potentially offer a dramatic improvement both over independent classifiers and over an individual classifier with accuracy p. A functional relationship between the limits and the pairwise dependence Q is derived. Two patterns of the joint distribution for classifier outputs (correct/incorrect) are identified to derive the limits: the pattern of success and the pattern of failure. The results support the intuition that negative pairwise dependence is beneficial although not straightforwardly related to the accuracy. The pattern of success showed that for the highest improvement over p, all pairs of classifiers in the pool should have the same negative dependence.
Inferring binary trust relationships in Web-based social networks

The growth of Web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in Web-based social networks and investigates how trust information can be mined and integrated into applications. This article introduces a definition of trust suitable for use in Web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.
Evaluating Topic-Driven Web Crawlers

Due to limited bandwidth, storage, and computational resources, and to the dynamic nature of the Web, search engines cannot index every Web page, and even the covered portion of the Web cannot be monitored continuously for changes. Therefore it is essential to develop effective crawling strategies to prioritize the pages to be indexed. The issue is even more important for topic-specific search engines, where crawlers must make additional decisions based on the relevance of visited pages. However, it is difficult to evaluate alternative crawling strategies because relevant sets are unknown and the search space is changing. We propose three different methods to evaluate crawling strategies. We apply the proposed metrics to compare three topic-driven crawling algorithms based on similarity ranking, link analysis, and adaptive agents.
Trajectory Tracking Control for a 3-DOF Parallel Manipulator Using Fractional-Order $\hbox{PI}^{\lambda}\hbox{D}^{\mu}$ Control

In this paper, a 3-degrees-of-freedom parallel manipulator developed by Tsai and Stamper known as the Maryland manipulator is considered. In order to provide dynamic analysis, three different sequential trajectories are taken into account. Two different control approaches such as the classical proportional-integral-derivative (PID) and fractional-order PID control are used to improve the tracking performance of the examined manipulator. Parameters of the controllers are determined by using pattern search algorithm and mathematical methods for the classical PID and fractional-order PID controllers, respectively. Design procedures for both controllers are given in detail. Finally, the corresponding results are compared. Performance analysis for both of the proposed controllers is confirmed by simulation results. It is observed that not only transient but also steady-state error values have been reduced with the aid of the PIλDμ controller for tracking control purpose. According to the obtained results, the fractional-order PIλDμ controller is more powerful than the optimally tuned PID for the Maryland manipulator tracking control. The main contribution of this paper is to determine the control action with the aid of the fractional-order PI λDμ controller different from previously defined controller structures. The determination of correct and accurate control action has great importance when high speed, high acceleration, and high accuracy needed for the trajectory tracking control of parallel mechanisms present unique challenges.
A Study on Difficulty Level Recognition of Piano Sheet Music

Looking for a piano sheet music with proper difficulty for a piano learner is always an important work to his/her teacher. In the paper, we study on a new and challenging issue of recognizing the difficulty level of piano sheet music. To analyze the semantic content of music, we focus on symbolic music, i.e., sheet music or score. Specifically, difficulty level recognition is formulated as a regression problem to predict the difficulty level of piano sheet music. Since the existing symbolic music features are not able to capture the characteristics of difficulty, we propose a set of new features. To improve the performance, a feature selection approach, RReliefF, is used to select relevant features. An extensive performance study is conducted over two real datasets with different characteristics to evaluate the accuracy of the regression approach for predicting difficulty level. The best performance evaluated in terms of the R2 statistics over two datasets reaches 39.9% and 38.8%, respectively.