nils-herrmann/clean_scirepeval huggingface dataset:
Urban Transport Trends and Policies in China and India : Impacts of Rapid Economic Growth

This article provides a comparative overview of urban transport in the world’s two most populous countries: China and India. Cities in both countries are suffering from severe and worsening transport problems: air pollution, noise, traffic injuries and fatalities, congestion, parking shortages, energy use, and lack of mobility for the poor. The urban transport crisis in China and India results from continuing population growth, urbanization, suburban sprawl, rising incomes, and skyrocketing motor vehicle ownership and use. This article critically assesses government policies in each country and suggests a range of specific improvements. The authors advocate a slowdown in the massive roadway investment in recent years and a shift in emphasis to expanding and improving public transport, cycling, and walking facilities. While continued growth in motor vehicle use is inevitable, China and India should restrict motor vehicle use in congested city centers and increase taxes, fees, and charges to reflect the enormous social and environmental costs of motor vehicle use. At the same time, much stricter regulations should be imposed on manufacturers to produce cleaner, more energy-efficient, quieter, and safer cars, motorcycles, buses, and trucks. Mitigating the many social and environmental impacts of rising motorization is obviously important for the future well being of Chinese and Indian cities. It is also crucial to the future of the rest of the world. Unless the problems of motorization in China and India can be effectively dealt with, the world faces sharp increases in Greenhouse gases, accelerating climate change, and rapid depletion of a range of non-renewable resources.
Stabilizing Dynamic Controllers for Hybrid Systems: A Hybrid Control Lyapunov Function Approach

This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.
Joint topology design and mapping of service function chains for efficient, scalable, and reliable network functions virtualization

NFV is promising to lower the network operator's capital expenditure and operational expenditure by replacing proprietary hardware-based network equipment with software-based VNFs that can be consolidated into telecom clouds. In particular, NFV provides an efficient way to deploy network services using SFCs that consist of a set of VNFs interconnected by virtual links. A practical but theoretically challenging problem related to NFV management and orchestration is how to jointly optimize the topology design and mapping of multiple SFCs such that the TBC is minimized, which is called the JTDM problem. In this article, we propose a novel heuristic algorithm, Closed-Loop with Critical Mapping Feedback, to efficiently address the JTDM problem. While minimizing the TBC, we also propose scalable and reliable JTDM strategies that can significantly reduce the network reconfigurations and enhance the service reliability, respectively.
Using virtual data effects to stabilize pilot run neural network modeling

Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive, Artificial Neural Networks are widely utilized to extract management knowledge from acquired data for its non-linear properties; however, getting as large a number of training data as needed is the fundamental assumption. This is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize virtual sample generation techniques and the corresponding data effects to stabilize the prediction model. This research derives from using extreme value theory to estimate the domain range of a small data set, which is used for virtual sample production to fill the information gaps of sparse data. Further, for the virtual samples, a fuzzy-based data effect calculation system is developed to determine the comprehensive importance of each datum. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.
Impact of interleaving on common-mode EMI filter weight reduction of paralleled three-phase voltage-source converters

This paper presents a detailed analysis on the impact of the interleaving angle on EMI filter weight reduction in a paralleled three-phase VSI motor drive system. The EMI noise analysis equivalent circuits are given in frequency domain and EMI filter design methods for a motor drive system are discussed. Based on the analysis, design examples are given for DC and AC common-mode (CM) EMI filters showing that minimum corner frequency in this case cannot ensure minimum EMI filter weight. In effect, the EMI filter weight is also determined by the volt-seconds on the inductor. With this consideration, the impact of interleaving is analyzed based on the equivalent circuit developed. Specifically, it is shown that interleaving can either reduce the volt-second on the inductor or help reduce the inductance needed, which has a different impact on the filter weight when the design condition is different. EMI filters are designed for both AC and DC sides as examples, showing the impact that different interleaving angles can have on filter weight reduction. Verifications are carried out both in simulation and experimentally on a 2 kW converter system.
Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts

This paper considers a shortcutting heuristic to smooth jerky trajectories for many-DOF robot manipulators subject to collision constraints, velocity bounds, and acceleration bounds. The heuristic repeatedly picks two points on the trajectory and attempts to replace the intermediate trajectory with a shorter, collision-free segment. Here, we construct segments that interpolate between endpoints with specified velocity in a time-optimal fashion, while respecting velocity and acceleration bounds. These trajectory segments consist of parabolic and straight-line curves, and can be computed in closed form. Experiments on reaching tasks in cluttered human environments demonstrate that the technique can generate smooth, collision-free, and natural-looking motion in seconds for a PUMA manipulator and the Honda ASIMO robot.
COLOR EYE FUNDUS IMAGES

This paper presents an approach for automatic detection of microaneurysms and hemorrhages in fundus images. These lesions are considered the earliest signs of diabetic retinopathy. The diabetic retinopathy is a disease caused by diabetes and is considered as the major cause of blindness in working age population. The proposed method is based on mathematical morphology and consists in removing components of retinal anatomy to reach the lesions. This method consists of five stages: a) pre-processing; b) enhancement of low intensity structures; c) detection of blood vessels; d) elimination of blood vessels; e) elimination of the fovea. The accuracy of the method was tested on a public database of fundus images, where it achieved satisfactory results, comparable to other methods from the literature, reporting 87.69% and 92.44% of mean sensitivity and specificity, respectively.
Latent Users in an Online User-Generated Content Community

Online communities depend on the persistent contributions of heterogeneous users with diverse motivations and ways of participating. As these online communities exist over time, it is possible that users change the way in which they contribute to the site. Through interviews with 31 long-term members of a user-generated content community who have decreased their participation on the site, we examined the meaning that these users gave to their contribution and how their new participation patterns related to their initial motivations. We complement the reader-to-leader framework (Preece and Shneiderman: AIS Transactions on Human-Computer Interaction, vol. 1, no. 1, pp. 13–32, 2009) by propounding the concept of latent user to understand decreasing content contribution and user life-cycles in online communities. We showed that even though latent users decrease their content contribution, their participation becomes more selective and remained consistent with initial motivations to participate.
Stable Marriage with Ties and Unacceptable Partners

An instance of the classical stable marriage problem involves n men and n women, and each person ranks all n members of the opposite sex in strict order of preference. The effect of allowing ties in the preference lists has been investigated previously, and three natural definitions of stability arise. In this paper, we extend this study by allowing a preference list to involve ties and/or be incomplete. We show that, under the weakest notion of stability, the stable matchings need not be all of the same cardinality, and the decision problem related to finding a maximum cardinality stable matching is NP-complete, even if the ties occur in the preference lists of one sex only. This result has important implications for practical matching schemes such as the well-known National Resident Matching Program . In the cases of the other two notions of stability, Irving [5] has described algorithms for testing whether a stable matching exists, and for constructing such a matching if one does exist, where a preference list is complete but may involve ties. We demonstrate how to extend these algorithms to the case where a preference list may be incomplete and/or involve ties.
Solving Infinite Kolam in Knot Theory

In southern India, there are traditional patterns of line-drawings encircling dots, called “Kolam”, among which one-line drawings or the “infinite Kolam” provide very interesting questions in mathematics. For example, we address the following simple question: how many patterns of infinite Kolam can we draw for a given grid pattern of dots? The simplest way is to draw possible patterns of Kolam while judging if it is infinite Kolam. Such a search problem seems to be NP complete: almost all cases should be examined for a solution. However, it is certainly not. In this paper, we focus on diamondshaped grid patterns of dots, (1-3-5-3-1) and (1-3-5-7-5-3-1) in particular. By using the knot-theory description of the infinite Kolam, we show how to find the solution, which inevitably gives a sketch of the proof for the statement “infinite Kolam is not NP complete.” Its further discussion will be given in the final section.
GaAs Mixed Signal Multi-Function X-Band MMIC with 7 Bit Phase and Amplitude Control and Integrated Serial to Parallel Converter

The design and measured performance of a GaAs multi-function X-band MMIC for space-based synthetic aperture radar (SAR) applications with 7-bit phase and amplitude control and integrated serial to parallel converter (including level conversion) is presented. The main application for the multi-function chip (MFC) is to provide full amplitude and phase control in the transmit path and the receive path of T/R modules. The MFC has been realised in the OMMIC ED02AH 0.2 ¿m PHEMT process providing enhancement and depletion FET's.
An Explication of Secret Sharing Schemes

This paper is an explication of secret sharing schemes, emphasizing combinatorial construction methods. The main problem we consider is the construction of perfect secret sharing schemes, for specified access structures, with the maximum possible information rate. In this paper, we present numerous direct constructions for secret sharing schemes, such as the Shamir threshold scheme, the Boolean circuit construction of Benaloh and Leichter (for general access structures), the vector space construction of BrickeU, and the Simmons geometric construction. We discuss the connections between ideal schemes (i.e., those with information rate equal to one) and matroids. We also mention the entropy bounds of Capocelli et al. Then we give a very general construciton, called the decomposition construction, and numerous applications of it. In particular, we study schemes for access structures based on graphs and the many interesting bounds that can be proved; and we determine the exact value of the optimal information rate for all access structures on at most four participants.
COCOTS : a COTS software integration cost model-model overview and preliminary data findings

As the use of commercial-of-the-shelf (COTS) components becomes ever more prevalent in the creation of large software systems, the need for the ability to reasonably predict the true lifetime cost of using such software components grows accordingly. In using COTS components, immediate short-term gains in direct development effort & schedule are possible, but usually as a trade-off for a more complicated long-term post-deployment maintenance environment. This paper discusses a COTS cost model being developed as an extension of the COCOMO II  cost model. COCOTS attempts to predict the lifecycle costs of using COTS components by capturing the more significant COTS risks in its modeling parameters. The current state of the model is presented, along with some preliminary findings suggested by an analysis of calibration data collected to date. The paper concludes with a discussion of the on-going effort to further refine the accuracy and scope of COCOTS.
The changing paradigm of air pollution monitoring.

The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.
Machine-Learning Based Threat-Aware System in Software Defined Networks

Software-Defined Networking (SDN) is an emerging network architecture that decouples the control plane and the data plane to provide unprecedented programmability, automation, and network control. The SDN controller exercises centralized control over network software, and in doing so, it can monitor and respond to malicious traffic for network protection. This paper proposes a threat-aware system based on machine-learning for timely detection and response against network intrusion in SDN. Our proposed system consists of data preprocessing for feature selection, predictive data modeling for machine-learning and anomaly detection, and decision making for intrusion response in SDN. Due to the time-critical nature of SDN, we propose a practical approach utilizing machine-learning techniques to protect against network intrusion and reduce uncertainty in decision-making outcomes. The maliciousness of most uncertain network traffic subsets is evaluated with selected significant feature sets. Our experimental results show that the proposed approach achieves high performance and significantly reduces uncertainty in the decision process with a small number of feature sets. The results help the SDN controller to properly react against known or unknown attacks that cannot be prevented by signature-based network intrusion detection systems.
Multi words quran and hadith searching based on news using TF-IDF

Each week religious leaders need to give advice to their community. Religious advice matters ideally contain discussion and solution of the problem that arising in society. But the lot of religious resources that must be considered agains many arising problems make this religious task is not easy. Especially in moslem community, the religious resources are Quran and Kutubus Sitah, the six most referenced collection of Muhammad (pbuh) news (hadith). The problem that arising in society can be read from various online mass media. Doing manually, they must know the Arabic word of the problem, and make searching manually from Mu'jam, Quran and Hadith index, then write out the found verses or hadith. TF-IDF method is often used in the weighting informational retrieval and text mining. This research want to make tools that get input from mass media news, make multi words searching from database using TF-IDF (Term Frequency - Inverse Document Frequency), and give relevan verse of Quran and hadith. Top five the most relevan verse of Quran and hadith will be displayed. Justified by religious leader, application give 60% precision for Quranic verses, and 53% for hadith, with the average query time 2.706 seconds.
Building a vocabulary self-learning speech recognition system

This paper presents initial studies on building a vocabulary selflearning speech recognition system that can automatically learn unknown words and expand its recognition vocabulary. Our recognizer can detect and recover out-of-vocabulary (OOV) words in speech, then incorporate OOV words into its lexicon and language model (LM). As a result, these unknown words can be correctly recognized when encountered by the recognizer in future. Specifically, we apply the word-fragment hybrid system framework to detect the presence of OOV words. We propose a better phoneme-to-grapheme (P2G) model so as to correctly recover the written form for more OOV words. Furthermore, we estimate LM scores for OOV words using their syntactic and semantic properties. The experimental results show that more than 40% OOV words are successfully learned from the development data, and about 60% learned OOV words are recognized in the testing data.
Endovascular therapy for ischemic stroke with perfusion-imaging selection.

Trials of endovascular therapy for ischemic stroke have produced variable results. We conducted this study to test whether more advanced imaging selection, recently developed devices, and earlier intervention improve outcomes.



We randomly assigned patients with ischemic stroke who were receiving 0.9 mg of alteplase per kilogram of body weight less than 4.5 hours after the onset of ischemic stroke either to undergo endovascular thrombectomy with the Solitaire FR (Flow Restoration) stent retriever or to continue receiving alteplase alone. All the patients had occlusion of the internal carotid or middle cerebral artery and evidence of salvageable brain tissue and ischemic core of less than 70 ml on computed tomographic (CT) perfusion imaging. The coprimary outcomes were reperfusion at 24 hours and early neurologic improvement (≥8-point reduction on the National Institutes of Health Stroke Scale or a score of 0 or 1 at day 3). Secondary outcomes included the functional score on the modified Rankin scale at 90 days.



The trial was stopped early because of efficacy after 70 patients had undergone randomization (35 patients in each group). The percentage of ischemic territory that had undergone reperfusion at 24 hours was greater in the endovascular-therapy group than in the alteplase-only group (median, 100% vs. 37%; P<0.001). Endovascular therapy, initiated at a median of 210 minutes after the onset of stroke, increased early neurologic improvement at 3 days (80% vs. 37%, P=0.002) and improved the functional outcome at 90 days, with more patients achieving functional independence (score of 0 to 2 on the modified Rankin scale, 71% vs. 40%; P=0.01). There were no significant differences in rates of death or symptomatic intracerebral hemorrhage.



In patients with ischemic stroke with a proximal cerebral arterial occlusion and salvageable tissue on CT perfusion imaging, early thrombectomy with the Solitaire FR stent retriever, as compared with alteplase alone, improved reperfusion, early neurologic recovery, and functional outcome.
Multidisciplinary Instruction with the Natural Language Toolkit

The Natural Language Toolkit ( NLTK ) is widely used for teaching natural language processing to students majoring in linguistics or computer science. This paper describes the design ofNLTK , and reports on how it has been used effectively in classes that involve different mixes of linguistics and computer science students. We focus on three key issues: getting started with a course, delivering interactive demonstrations in the classroom, and organizing assignments and projects. In each case, we report on practical experience and make recommendations on how to useNLTK to maximum effect.
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism

Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. In this paper, we propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.
Computationally Efficient M-Estimation of Log-Linear Structure Models

We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum (conditional) likelihood estimation of conditional random fields (Lafferty et al., 2001; an order of magnitude or more). We compare its performance and training time to an HMM, a CRF, an MEMM, and pseudolikelihood on a shallow parsing task. These experiments help tease apart the contributions of rich features and discriminative training, which are shown to be more than additive.