nils-herrmann/clean_scirepeval huggingface dataset:
Car crashes rank among the leading causes of death in the United States .

Overview Aggressive driving is a major concern of the American public, ranking at or near the top of traffic safety issues in national surveys of motorists. However, the concept of aggressive driving is not well defined, and its overall impact on traffic safety has not been well quantified due to inadequacies and limitation of available data. This paper reviews published scientific literature on aggressive driving; discusses various definitions of aggressive driving; cites several specific behaviors that are typically associated with aggressive driving; and summarizes past research on the individuals or groups most likely to behave aggressively. Since adequate data to precisely quantify the percentage of fatal crashes that involve aggressive driving do not exist, in this review, we have quantified the number of fatal crashes in which one or more driver actions typically associated with aggressive driving were reported. We found these actions were reported in 56 percent of fatal crashes from 2003 through 2007, with excessive speed being the number one factor. Ideally, an estimate of the prevalence of aggressive driving would include only instances in which such actions were performed intentionally; however, available data on motor vehicle crashes do not contain such information, thus it is important to recognize that this 56 percent may to some degree overestimate the contribution of aggressive driving to fatal crashes. On the other hand, it is likely that aggressive driving contributes to at least some crashes in which it is not reported due to lack of evidence. Despite the clear limitations associated with our attempt to estimate the contribution of potentially-aggressive driver actions to fatal crashes, it is clear that aggressive driving poses a serious traffic safety threat. In addition, our review further indicated that the " Do as I say, not as I do " culture, previously reported in the Foundation's Traffic Safety Culture Index, very much applies to aggressive driving.
Evaluating usability in learning management system moodle

The electronic way of learning and communicating with students offers a lot of advantages that can be achieved through different solutions. Among them, the most popular approach is the use of a learning management system. Teachers and students do not have the possibility to use all of the available learning system tools and modules. Even for modules that are used it is necessary to find the most effective method of approach for any given situation. Therefore, in this paper we make a usability evaluation of standard modules in Moodle, one of the leading open source learning management systems. With this research, we obtain significant results and informationpsilas for administrators, teachers and students on how to improve effective usage of this system.
The effects of plyometric versus dynamic stabilization and balance training on lower extremity biomechanics.

Neuromuscular training that includes both plyometric and dynamic stabilization/balance exercises alters movement biomechanics and reduces ACL injury risk in female athletes. The biomechanical effects of plyometric and balance training utilized separately are unknown.



A protocol that includes balance training without plyometric training will decrease coronal plane hip, knee, and ankle motions during landing, and plyometric training will not affect coronal plane measures. The corollary hypothesis was that plyometric and balance training effects on knee flexion are dependent on the movement task tested.



Controlled laboratory study.



Eighteen high school female athletes participated in 18 training sessions during a 7-week period. The plyometric group (n = 8) performed maximum-effort jumping and cutting exercises, and the balance group (n = 10) used dynamic stabilization/ balance exercises during training. Lower extremity kinematics were measured during the drop vertical jump and the medial drop landing before and after training using 3D motion analysis techniques.



During the drop vertical jump, both plyometric and balance training reduced initial contact (P = .002), maximum hip adduction angle (P = .015), and maximum ankle eversion angle (P = .020). During the medial drop landing, both groups decreased initial contact (P = .002) and maximum knee abduction angle (P = .038). Plyometric training increased initial contact knee flexion (P = .047) and maximum knee flexion (P = .031) during the drop vertical jump, whereas the balance training increased maximum knee flexion (P = .005) during the medial drop landing.



Both plyometric and balance training can reduce lower extremity valgus measures. Plyometric training affects sagittal plane kinematics primarily during a drop vertical jump, whereas balance training affects sagittal plane kinematics during single-legged drop landing.



Both plyometric and dynamic stabilization/balance exercises should be included in injury-prevention protocols.
A Study of RPL DODAG Version Attacks

The IETF designed the Routing Protocol for Low power and Lossy Networks (RPL) as a candidate for use in constrained networks. Keeping in mind the different requirements of such networks, the protocol was designed to support multiple routing topologies, called DODAGs, constructed using different objective functions, so as to optimize routing based on divergent metrics. A DODAG versioning system is incorporated into RPL in order to ensure that the topology does not become stale and that loops are not formed over time. However, an attacker can exploit this versioning system to gain an advantage in the topology and also acquire children that would be forced to route packets via this node. In this paper we present a study of possible attacks that exploit the DODAG version system. The impact on overhead, delivery ratio, end-to-end delay, rank inconsistencies and loops is studied.
Modelling website user behaviors by combining the EM and DBSCAN algorithms

Web logs can provide a wealth of information on user access patterns of a corresponding website, when they are properly analyzed. However, finding interesting patterns hidden in the low-level log data is non-trivial due to large log volumes, and the distribution of the log files in cluster environments. This paper presents a novel technique, the application of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation Maximization (EM) algorithms in an iterative manner for clustering web user sessions. Each cluster corresponds to one or more web user activities. The unique user access pattern of each cluster is identified by frequent pattern mining and sequential pattern mining techniques. When compared with the clustering output of EM, DBSCAN, and k-means algorithms, this technique shows better accuracy in web session mining, and it is more effective in identifying cluster changes with time. We demonstrate that the implemented system is capable of not only identifying common user behaviors, but also of identifying cyber-attacks.
On Latent Fingerprint Enhancement

Automatic feature extraction in latent fingerprints is a challenging problem due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. We proposed a latent fingerprint enhancement algorithm which requires manually marked region of interest (ROI) and singular points. The core of the proposed enhancement algorithm is a novel orientation field estimation algorithm, which fits orientation field model to coarse orientation field estimated from skeleton outputted by a commercial fingerprint SDK. Experimental results on NIST SD27 latent fingerprint database indicate that by incorporating the proposed enhancement algorithm, the matching accuracy of the commercial matcher was significantly improved.
Specifying and Annotating Reduced Argument Span Via QA-SRL

Prominent semantic annotations take an inclusive approach to argument span annotation, marking arguments as full constituency subtrees. Some works, however, showed that identifying a reduced argument span can be beneficial for various semantic tasks. While certain practical methods do extract reduced argument spans, such as in Open-IE , these solutions are often ad-hoc and system-dependent, with no commonly accepted standards. In this paper we propose a generic argument reduction criterion, along with an annotation procedure, and show that it can be consistently and intuitively annotated using the recent QA-SRL paradigm.
A survey of digital forensics tools for database extraction

As secure processing as well as correct recovery of data getting more important, digital forensics gain more value each day. This paper investigates the digital forensics tools available on the market and analyzes each tool based on the database perspective. We present a survey of digital forensics tools that are either focused on data extraction from databases or assist in the process of database recovery. In our work, a detailed list of current database extraction software is provided. We demonstrate examples of database extractions executed on representative selections from among tools provided in the detailed list. We use a standard sample database with each tool for comparison purposes. Based on the execution results obtained, we compare these tools regarding different criteria such as runtime, static or live acquisition, and more.
Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB

Graph databases are a very powerful solution for storing and searching for data designed for data rich in relationships, such as Facebook and Twitter. With data multiplication and data type diversity there has been a need to create new storage and analysis platforms that structure irregular data with a flexible schema, maintaining a high level of performance and ensuring data scalability effectively, which is a problem that relational databases cannot handle. In this paper, we analyse the most popular graph databases: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J and OrientDB. We study the most important features for a complete and effective application, such as flexible schema, query language, sharding and scalability.
A stigmergic approach to indoor localization using Bluetooth Low Energy beacons

Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.
Diametral Tensile Strength, Flexural Strength, and Surface Microhardness of Bioactive Bulk Fill Restorative.

The aim was to perform comparative analysis of bioactive, contemporary bulk-fill resin-based composites (RBCs) and conventional glass-ionomer materials for flexural strength (FS), diametral tensile strength (DTS), and Vickers hardness number (VHN) in the presence of thermocycling.



Five restorative materials [Tetric N-Ceram Bulk Fill; smart dentin replacement (SDR) Flowable Material; Bioactive restorative material (ACTIVA Bulk Fill); Ketac Universal Aplicap; and GC Fuji II] were evaluated for DTS, FS, and VHN. Half the samples in each material group were ther-mocycled. The DTS was performed under compressive load at a cross-head speed of 1.0 mm/min. The FS was assessed by three-point bending test at a cross-head speed of 0.5 mm/min. The VHN was determined using a Vickers diamond indenter at 50 gf load for 15 seconds. Differences in FS, DTS, and VHN were analyzed using analysis of variance (ANOVA) and Tukey post hoc tests at a = 0.05 level of significance.



N-Ceram, ACTIVA, and SDR demonstrated the highest and comparable (p > 0.05) FS. The SDR had the highest DTS value (141.28 ± 0.94), followed by N-Ceram (136.61 ± 1.56) and ACTIVA (129.05 ± 1.78). Ketac had the highest VHN value before and after thermocycling.



ACTIVA showed mechanical properties (FS and DTS) comparable with bulk-fill resin composite materials. ACTIVA showed potential for durability, as VHN was comparable post-thermocycling.



Bioactive materials showed acceptable DTS and FS values. However, hardness was compromised compared with included materials. ACTIVA Bulk Fill shows potential for dentin replacement but it needs to be covered with a surface-resistant restorative material. Further studies to improve surface characteristics of ACTIVA Bulk Fill are recommended.
A Software Engineering Perspective on SDN Programmability

Software-defined networking (SDN) has received a great deal of attention from both academia and industry in recent years. Studies on SDN have brought a number of interesting technical discussions on network architecture design, along with scientific contributions. Researchers, network operators, and vendors are trying to establish new standards and provide guidelines for proper implementation and deployment of such novel approach. It is clear that many of these research efforts have been made in the southbound of the SDN architecture, while the northbound interface still needs improvements. By focusing in the SDN northbound, this paper surveys the body of knowledge and discusses the challenges for developing SDN software. We investigate the existing solutions and identify trends and challenges on programming for SDN environments. We also discuss future developments on techniques, specifications, and methodologies for programmable networks, with the orthogonal view from the software engineering discipline.
Evaluating Topic Coherence Using Distributional Semantics

This paper introduces distributional semantic similarity methods for automatically measuring the coherence of a set of words generated by a topic model. We construct a semantic space to represent each topic word by making use of Wikipedia as a reference corpus to identify context features and collect frequencies. Relatedness between topic words and context features is measured using variants of Pointwise Mutual Information (PMI). Topic coherence is determined by measuring the distance between these vectors computed using a variety of metrics. Evaluation on three data sets shows that the distributional-based measures outperform the state-of-the-art approach for this task.
Conical Conformal Shaped-Beam Substrate-Integrated Waveguide Slot Array Antenna With Conical-to-Cylindrical Transition

The analysis and design of a millimeter-wave conical conformal shaped-beam substrate-integrated waveguide (SIW) array antenna is demonstrated in this paper. After investigating the influence of the conical surface on the propagation characteristics of a conformal SIW, a modification for the width of a conical conformal SIW is proposed to obtain the same propagation characteristic along the longitudinal direction. This feature is indispensable to employ the classic equivalent circuit of a planar slot array antenna in the design of a conical conformal antenna. In this case, the design process of the conformal antenna can be simplified. An efficient and accurate model method of the conical conformal SIW antenna is presented as well. Then, a design process of the conical conformal SIW slot array antenna is introduced. Furthermore, to implement the transition between a conical surface and a cylindrical surface, a flexible SIWtransition is designed with a good impedance matching. Finally, two low sidelobe level (SLL) SIW conical conformal antennas with and without the flexible transitions are designed. Both of them have −28 dB SLLs in H-plane at the center frequency of 35 GHz.
Information-theoretic algorithm for feature selection

Feature selection is used to improve efficiency of learning algorithms by finding an optimal subset of features. However, most feature selection techniques can handle only certain types of data. Additional limitations of existing methods include intensive computational requirements and inability to identify redundant variables. In this paper, we are presenting a novel, information-theoretic algorithm for feature selection, which finds an optimal set of attributes by removing both irrelevant and redundant features. The algorithm has a polynomial computational complexity and it is applicable to datasets of mixed nature. The method performance is evaluated on several benchmark datasets by using a standard classifier (C4.5).
Automatic skin lesion segmentation with optimal colour channel from dermoscopic images

This article provides an improved automated skin lesion segmentation method for dermoscopic images. There are several stages for this method. These include the pre-processing steps such as resizing the images and eliminating noise. Hair was removed and reflective light was reduced using morphological operations and a median filter. The single green channel was rescaled into new intensities, as it provided the highest segmentation accuracy. The threshold value was calculated to separate the skin lesion region from healthy skin. Morphological operations were implemented to merge the small lesion areas around the bigger lesion areas with similar features and trace the boundary of the melanoma. The accuracy of the segmentation was evaluated by comparing the automatic boundary and manual boundary. Compared to other studies, our proposed method achieved the highest average accuracy of 97%.
Rotated region based CNN for ship detection

The state-of-the-art object detection networks for natural images have recently demonstrated impressive performances. However the complexity of ship detection in high resolution satellite images exposes the limited capacity of these networks for strip-like rotated assembled object detection which are common in remote sensing images. In this paper, we embrace this observation and introduce the rotated region based CNN (RR-CNN), which can learn and accurately extract features of rotated regions and locate rotated objects precisely. RR-CNN has three important new components including a rotated region of interest (RRoI) pooling layer, a rotated bounding box regression model and a multi-task method for non-maximal suppression (NMS) between different classes. Experimental results on the public ship dataset HRSC2016 confirm that RR-CNN outperforms baselines by a large margin.
Non-Parametric Transformation Networks

ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn general invariances and symmetries directly from data. NPTNs are a natural generalization of ConvNets and can be optimized directly using gradient descent. Unlike almost all previous works in deep architectures, they make no assumption regarding the structure of the invariances present in the data and in that aspect are flexible and powerful. We also model ConvNets and NPTNs under a unified framework called Transformation Networks (TN), which yields a better understanding of the connection between the two. We demonstrate the efficacy of NPTNs on data such as MNIST with extreme transformations and CIFAR10 where they outperform baselines, and further outperform several recent algorithms on ETH-80. They do so while having the same number of parameters. We also show that they are more effective than ConvNets in modelling symmetries and invariances from data, without the explicit knowledge of the added arbitrary nuisance transformations. Finally, we replace ConvNets with NPTNs within Capsule Networks and show that this enables Capsule Nets to perform even bet-
Realtime Depth Estimation and Obstacle Detection from Monocular Video

This paper deals with the detection of arbitrary static objects in traffic scenes from monocular video using structure from motion. A camera in a moving vehicle observes the road course ahead. The camera translation in depth is known. Many structure from motion algorithms were proposed for detecting moving or nearby objects. However, detecting stationary distant obstacles in the focus of expansion remains quite challenging due to very small subpixel motion between frames. In this work the scene depth is estimated from the scaling of supervised image regions. We generate obstacle hypotheses from these depth estimates in image space. A second step then performs testing of these by comparing with the counter hypothesis of a free driveway. The approach can detect obstacles already at distances of 50m and more with a standard focal length. This early detection allows driver warning and safety precaution in good time.
Combining Agile with Traditional V Model for Enhancement of Maturity in Software Development

In the field of software engineering there are many new archetypes are introducing day to day Improve the efficiency and effectiveness of software development. Due to dynamic environment organizations are frequently exchanging their software constraint to meet their objectives. The propose research is a new approach by integrating the traditional V model and agile methodology to combining the strength of these models while minimizing their individual weakness.The fluctuating requirements of emerging a carried software system and accumulative cost of operational software are imposing researchers and experts to determine innovative and superior means for emerging software application at slight business or at enterprise level are viewing for. Agile methodology has its own benefits but there are deficiency several of the features of traditional software development methodologies that are essential for success. That’s why an embedded approach will be the right answer for software industry rather than a pure agile approach. This research shows how agile embedded traditional can play a vital role in development of software. A survey conducted to find the impact of this approach in industry. Both qualitative and quantitative analysis performed.
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems. In this paper, we present a new chest X-ray database, namely ChestX-ray8, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based reading chest X-rays (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems.