nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing DBSCAN algorithm for data mining

Today data mining is widely used by companies with a strong consumer focus like retail, financial, communication and marketing organizations. Here technically data mining is the process of extraction of required information from huge databases. It allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. The ultimate goal of this paper is to propose a methodology for the improvement in DB-SCAN algorithm to improve clustering accuracy. The proposed improvement is based on back propagation algorithm to calculate Euclidean distance in the dynamic manner. Also this paper shows the obtained results of implemented proposed and existing methods and it compares the results in terms of its execution time and accuracy.
The Case for Learned Index Structures

Indexes are models: a \btree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term \em learned indexes. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show that our learned indexes can have significant advantages over traditional indexes. More importantly, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work provides just a glimpse of what might be possible.
Unconstrained Speech Segmentation using Deep Neural Networks

We propose a method for improving the unconstrained segmentation of speech into phoneme-like units using deep neural networks. The proposed approach is not dependent on acoustic models or forced alignment, but operates using the acoustic features directly. Previous solutions of this type were plagued by the tendency to hypothesise additional incorrect phoneme boundaries near the phoneme transitions. We show that the application of deep neural networks is able to reduce this over-segmentation substantially, and achieve improved segmentation accuracies. Furthermore, we find that generative pre-training offers an additional benefit.
Multiple amino acid residues confer temperature sensitivity to human influenza virus vaccine strains (FluMist) derived from cold-adapted A/Ann Arbor/6/60.

FluMist influenza A vaccine strains contain the PB1, PB2, PA, NP, M, and NS gene segments of ca A/AA/6/60, the master donor virus-A strain. These gene segments impart the characteristic cold-adapted (ca), attenuated (att), and temperature-sensitive (ts) phenotypes to the vaccine strains. A plasmid-based reverse genetics system was used to create a series of recombinant hybrids between the isogenic non-ts wt A/Ann Arbor/6/60 and MDV-A strains to characterize the genetic basis of the ts phenotype, a critical, genetically stable, biological trait that contributes to the attenuation and safety of FluMist vaccines. PB1, PB2, and NP derived from MDV-A each expressed determinants of temperature sensitivity and the combination of all three gene segments was synergistic, resulting in expression of the characteristic MDV-A ts phenotype. Site-directed mutagenesis analysis mapped the MDV-A ts phenotype to the following four major loci: PB1(1195) (K391E), PB1(1766) (E581G), PB2(821) (N265S), and NP(146) (D34G). In addition, PB1(2005) (A661T) also contributed to the ts phenotype. The identification of multiple genetic loci that control the MDV-A ts phenotype provides a molecular basis for the observed genetic stability of FluMist vaccines.
Distributed Formation Control of Multi-Agent Systems Using Complex Laplacian

The paper concentrates on the fundamental coordination problem that requires a network of agents to achieve a specific but arbitrary formation shape. A new technique based on complex Laplacian is introduced to address the problems of which formation shapes specified by inter-agent relative positions can be formed and how they can be achieved with distributed control ensuring global stability. Concerning the first question, we show that all similar formations subject to only shape constraints are those that lie in the null space of a complex Laplacian satisfying certain rank condition and that a formation shape can be realized almost surely if and only if the graph modeling the inter-agent specification of the formation shape is 2-rooted. Concerning the second question, a distributed and linear control law is developed based on the complex Laplacian specifying the target formation shape, and provable existence conditions of stabilizing gains to assign the eigenvalues of the closed-loop system at desired locations are given. Moreover, we show how the formation shape control law is extended to achieve a rigid formation if a subset of knowledgable agents knowing the desired formation size scales the formation while the rest agents do not need to re-design and change their control laws.
ICLR 2019 Deep Lagrangian Networks : Using Physics as Model Prior for Deep Learning

Deep learning has achieved astonishing results onmany taskswith large amounts of data and generalization within the proximity of training data. For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems. In particular, learning physics models for model-based control requires robust extrapolation from fewer samples – often collected online in real-time – and model errors may lead to drastic damages of the system. Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples. As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed. DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility. The resulting DeLaN network performs very well at robot tracking control. The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time.
Communication Centric Design in Complex Automotive Embedded Systems

Automotive embedded applications like the engine management system are composed of multiple functional components that are tightly coupled via numerous communication dependencies and intensive data sharing, while also having real-time requirements. In order to cope with complexity, especially in multi-core settings, various communication mechanisms are used to ensure data consistency and temporal determinism along functional cause-effect chains. However, existing timing analysis methods generally only support very basic communication models that need to be extended to handle the analysis of industry grade problems which involve more complex communication semantics. In this work, we give an overview of communication semantics used in the automotive industry and the different constraints to be considered in the design process. We also propose a method for model transformation to increase the expressiveness of current timing analysis methods enabling them to work with more complex communication semantics. We demonstrate this transformation approach for concrete implementations of two communication semantics, namely, implicit and LET communication. We discuss the impact on end-to-end latencies and communication overheads based on a full blown engine management system.
ROSPlan: Planning in the Robot Operating System

The Robot Operating System (ROS) is a set of software libraries and tools used to build robotic systems. ROS is known for a distributed and modular design. Given a model of the environment, task planning is concerned with the assembly of actions into a structure that is predicted to achieve goals. This can be done in a way that minimises costs, such as time or energy. Task planning is vital in directing the actions of a robotic agent in domains where a causal chain could lock the agent into a dead-end state. Moreover, planning can be used in less constrained domains to provide more intelligent behaviour. This paper describes the ROSPLAN framework, an architecture for embedding task planning into ROS systems. We provide a description of the architecture and a case study in autonomous robotics. Our case study involves autonomous underwater vehicles in scenarios that demonstrate the flexibility and robustness of our approach.
Tree Based Indexes vs. Bitmap Indexes - a Performance Study

Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP). Short response times are essential for on-line decision support. Common approaches to reach this goal in read-mostly environments are the precomputation of materialized views and the use of index structures. In this paper, a framework is presented to evaluate different index structures analytically depending on nine parameters for the use in a data warehouse environment. The framework is applied to four different index structures to evaluate which structure works best for range queries. We show that all parameters influence the performance. Additionally, we show why bitmap index structures use modern disks better than traditional tree structures and why bitmaps will supplant the tree based index structures in the future.
A randomised, double-blind, placebo-controlled, parallel-group study of the standardised extract shr-5 of the roots of Rhodiola rosea in the treatment of subjects with stress-related fatigue.

The aim of the study was to assess the efficacy of the standardised extract SHR-5 of roots of Rhodiola Rosea L. in the treatment of individuals suffering from stress-related fatigue. The phase III clinical trial took the form of a randomised, double-blind, placebo-controlled study with parallel groups. Participants, males and females aged between 20 and 55 years, were selected according to the Swedish National Board of Health and Welfare diagnostic criteria for fatigue syndrome. A total of 60 individuals were randomised into two groups, one ( N = 30) of which received four tablets daily of SHR-5 extract (576 mg extract/day), while a second ( N = 30) received four placebo tablets daily. The effects of the extract with respect to quality of life (SF-36 questionnaire), symptoms of fatigue (Pines' burnout scale), depression (Montgomery -Asberg depression rating scale - MADRS), attention (Conners' computerised continuous performance test II - CCPT II), and saliva cortisol response to awakening were assessed on day 1 and after 28 days of medication. Data were analysed by between-within analyses of variance. No serious side effects that could be attributed to the extract were reported. Significant post-treatment improvements were observed for both groups (placebo effect) in Pines' burnout scale, mental health (SF-36), and MADRS and in several CCPT II indices of attention, namely, omissions, commissions, and Hit RT SE. When the two groups were compared, however, significant effects of the SHR-5 extract in comparison with the placebo were observed in Pines' burnout scale and the CCPT II indices omissions, Hit RT SE, and variability. Pre- VERSUS post-treatment cortisol responses to awakening stress were significantly different in the treatment group compared with the control group. It is concluded that repeated administration of R. ROSEA extract SHR-5 exerts an anti-fatigue effect that increases mental performance, particularly the ability to concentrate, and decreases cortisol response to awakening stress in burnout patients with fatigue syndrome.
Mapping slums using spatial features in Accra, Ghana

In order to map the spatial extent and location of slum settlements multiple methodologies have been devised including remote sensing based methods and field based methods using surveys and census data. In this study we utilize spatial, structural, and contextual features (e.g., PanTex, Histogram of Oriented Gradients, Line Support Regions, Hough transforms and others) calculated at multiple spatial scales from high spatial resolution satellite data to map slum areas and compare these estimates to three field based slum maps: one from the UN Habitat/Accra Metropolitan Assembly (UNAMA) and two census data derived maps based on the UN Habitat definition of a slum, a simple slum/non-slum dichotomy map, and a slum index map. When comparing the remotely sensed derived slum areas to the UNAMA slum definition results indicate an overall accuracy of 94.3% and a Kappa of 0.91. When compared to the dichotomous, census derived slum maps the results are not as accurate. This reduced accuracy is due to the substantial over prediction of slums, especially if only one criterion was missing, using the census data. In relation to the slum index, the remote sensing estimates of slums were significantly correlated with an r2 of 0.45 and when population density was taken into account, the correlation increased to an r2 of 0.78. Overall, the remote sensing methodology provides a reasonable estimate of slum areas and variations within the city.
Backward bifurcation in SIRS malaria model

We present a deterministic mathematical model for malaria transmission with waning immunity. The model consists of five non-linear system of differential equations. We used next generation matrix to derive the basic reproduction number R0. The disease free equilibrium was computed and its local stability has been shown by the virtue of the Jacobean matrix. Moreover, using Lyapunov function theory and LaSalle Invariance Principle we have proved that the disease free equilibrium is globally asymptotically stable. Conditions for existence of endemic equilibrium point have been established. A qualitative study based on bifurcation theory reveals that backward bifurcation occur in the model. The stable disease free equilibrium of the model coexists with the stable endemic equilibrium when R0 < 1. Furthermore, we have shown that bringing the number of disease (malaria) induced death rate below some threshold is sufficient enough to eliminate backward bifurcation in the model.
Automated Detection of Firearms and Knives in a CCTV Image

Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.
Functional neurogenesis in the adult hippocampus

There is extensive evidence indicating that new neurons are generated in the dentate gyrus of the adult mammalian hippocampus, a region of the brain that is important for learning and memory. However, it is not known whether these new neurons become functional, as the methods used to study adult neurogenesis are limited to fixed tissue. We use here a retroviral vector expressing green fluorescent protein that only labels dividing cells, and that can be visualized in live hippocampal slices. We report that newly generated cells in the adult mouse hippocampus have neuronal morphology and can display passive membrane properties, action potentials and functional synaptic inputs similar to those found in mature dentate granule cells. Our findings demonstrate that newly generated cells mature into functional neurons in the adult mammalian brain.
Enhancing EEG Surface Resolution by Using a Combination of Kalman Filter and Interpolation Method

With recent progress in the medical signals processing, the EEG allows to study the Brain functioning with a high temporal and spatial resolution. This approach is possible by combining the standard processing algorithms of cortical brain waves with characterization and interpolation methods. First, a new vector of characteristics for each EEG channel was introduced using the Extended Kalman filter (EKF). Next, the spherical spline interpolation technique was applied in order to rebuild other vectors corresponding to virtual electrodes. The temporal variation of these vectors was restored by applying the EKF. Finally, the accuracy of the method has been estimated by calculating the error between the actual and interpolated signal after passing by the characterization method with the Root Mean Square Error algorithm (RMSE).
Weakly Supervised Memory Networks

In this paper we introduce a variant of Memory Networks (Weston et al., 2015b) that needs significantly less supervision to perform question and answering tasks. The original model requires that the sentences supporting the answer be explicitly indicated during training. In contrast, our approach only requires the answer to the question during training. We apply the model to the synthetic bAbI tasks, showing that our approach is competitive with the supervised approach, particularly when trained on a sufficiently large amount of data. Furthermore, it decisively beats other weakly supervised approaches based on LSTMs. The approach is quite general and can potentially be applied to many other tasks that require capturing long-term dependencies.
An Experimental Analysis of Multi-Perspective Convolutional Neural Networks

Modelling the similarity of sentence pairs is an important problem in natural language processing and information retrieval, with applications in tasks such as paraphrase identification and answer selection in question answering. The Multi-Perspective Convolutional Neural Network (MP-CNN) is a model that improved previous state-of-the-art models in 2015 and has remained a popular model for sentence similarity tasks. However, until now, there has not been a rigorous study of how the model actually achieves competitive accuracy. In this thesis, we report on a series of detailed experiments that break down the contribution of each component of MP-CNN towards its statistical accuracy and how they affect model robustness. We find that two key components of MP-CNN are non-essential to achieve competitive accuracy and they make the model less robust to changes in hyperparameters. Furthermore, we suggest simple changes to the architecture and experimentally show that we improve the accuracy of MP-CNN when we remove these two major components of MP-CNN and incorporate these small changes, pushing its scores closer to more recent works on competitive semantic textual similarity and answer selection datasets, while using eight times fewer parameters.
Hardening the OAuth-WebView Implementations in Android Applications by Re-Factoring the Chromium Library

Today, the Open Standard for Authorization (OAuth) is widely used by many service providers such as Google, Github, and Facebook. The OAuth-WebView implementation is the most widely used approach despite explicit warnings to the developers of its security and privacy risks. Previous researches have discussed these risks and proposed solutions that mandate numerous implementation's changes and/or do not assume strong attacking assumptions. In this work, we introduce SecureOAuth, a whitelist access control protection framework for the Android platform. SecureOAuth is composed of: Android library modifications, service creation, and system app creation. We have implemented a prototype of the SecureOAuth framework and evaluated it on performance and memory overhead. We also showcase examples of security threats that this framework counters. The framework hardens the OAuth-WebView implementation with bounded overhead while keeping the user's involvement to minimum. Moreover, the framework requires no implementations' changes and it assumes attackers with advanced and expert skill levels.
Radio resource allocation scheme for device-to-device communication in cellular networks using fractional frequency reuse

Device-to-Device (D2D) communication is the technology enabling user equipments (UEs) to directly communicate with each other without help of evolved nodeB (eNB). Due to this characteristic, D2D communication can reduce end-to-end delay and traffic load offered to eNB. However, by applying D2D communication into cellular systems, interference between D2D and eNB relaying UEs can occur if D2D UEs reuse frequency band for eNB relaying UEs. In cellular systems, fractional frequency reuse (FFR) is used to reduce inter-cell interference of cell outer UEs. In this paper, we propose a radio resource allocation scheme for D2D communication underlaying cellular networks using FFR. In the proposed scheme, D2D and cellular UEs use the different frequency bands chosen as users' locations. The proposed radio resource allocation scheme can alleviate interference between D2D and cellular UEs if D2D device is located in cell inner region. If D2D UEs is located in cell outer region, D2D and cellular UEs experience tolerable interference. By simulations, we show that the proposed scheme improves the performance of D2D and cellular UEs by reducing interference between them.
Using machine learning to refine Category-Partition test specifications and test suites

In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box, Category-Partition testing, we propose a methodology and a tool based on machine learning that has shown promising results on a case study involving students as testers.
On-line signature veri!cation

We describe a method for on-line handwritten signature veri!cation. The signatures are acquired using a digitizing tablet which captures both dynamic and spatial information of the writing. After preprocessing the signature, several features are extracted. The authenticity of a writer is determined by comparing an input signature to a stored reference set (template) consisting of three signatures. The similarity between an input signature and the reference set is computed using string matching and the similarity value is compared to a threshold. Several approaches for obtaining the optimal threshold value from the reference set are investigated. The best result yields a false reject rate of 2.8% and a false accept rate of 1.6%. Experiments on a database containing a total of 1232 signatures of 102 individuals show that writer-dependent thresholds yield better results than using a common threshold. ? 2002 Pattern Recognition Society.