nils-herrmann/clean_scirepeval huggingface dataset:
A Survey and a Layered Taxonomy of Software-Defined Networking

Software-defined networking (SDN) has recently gained unprecedented attention from industry and research communities, and it seems unlikely that this will be attenuated in the near future. The ideas brought by SDN, although often described as a “revolutionary paradigm shift” in networking, are not completely new since they have their foundations in programmable networks and control-data plane separation projects. SDN promises simplified network management by enabling network automation, fostering innovation through programmability, and decreasing CAPEX and OPEX by reducing costs and power consumption. In this paper, we aim at analyzing and categorizing a number of relevant research works toward realizing SDN promises. We first provide an overview on SDN roots and then describe the architecture underlying SDN and its main components. Thereafter, we present existing SDN-related taxonomies and propose a taxonomy that classifies the reviewed research works and brings relevant research directions into focus. We dedicate the second part of this paper to studying and comparing the current SDN-related research initiatives and describe the main issues that may arise due to the adoption of SDN. Furthermore, we review several domains where the use of SDN shows promising results. We also summarize some foreseeable future research challenges.
A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles.
FVC2004: Third Fingerprint Verification Competition

A new technology evaluation of fingerprint verification algorithms has been organized following the approach of the previous FVC2000 and FVC2002 evaluations, with the aim of tracking the quickly evolving state-ofthe-art of fingerprint recognition systems. Three sensors have been used for data collection, including a solid state sweeping sensor, and two optical sensors of different characteristics. The competition included a new category dedicated to “ light” systems, characterized by limited computational and storage resources. This paper summarizes the main activities of the FVC2004 organization and provides a first overview of the evaluation. Results will be further elaborated and officially presented at the International Conference on Biometric Authentication (Hong Kong) on July 2004.
Skippy: single view 3D curve interactive modeling

We introduce Skippy, a novel algorithm for 3D interactive curve modeling from a single view. While positing curves in space can be a tedious task, our rapid sketching algorithm allows users to draw curves in and around existing geometry in a controllable manner. The key insight behind our system is to automatically infer the 3D curve coordinates by enumerating a large set of potential curve trajectories. More specifically, we partition 2D strokes into continuous segments that land both on and off the geometry, duplicating segments that could be placed in front or behind, to form a directed graph. We use distance fields to estimate 3D coordinates for our curve segments and solve for an optimally smooth path that follows the curvature of the scene geometry while avoiding intersections. Using our curve design framework we present a collection of novel editing operations allowing artists to rapidly explore and refine the combinatorial space of solutions. Furthermore, we include the quick placement of transient geometry to aid in guiding the 3D curve. Finally we demonstrate our interactive design curve system on a variety of applications including geometric modeling, and camera motion path planning.
Analysis and visualization of gene expression data using Self-Organizing Maps

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U-matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.
Efficient large-scale sequence comparison by locality-sensitive hashing

Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences efficiently, existing algorithms find alignments by expanding around short runs of matching bases with no substitutions or other differences. Unfortunately, exact matches that are short enough to occur often in significant alignments also occur frequently by chance in the background sequence. Thus, these algorithms must trade off between efficiency and sensitivity to features without long exact matches.



We introduce a new algorithm, LSH-ALL-PAIRS, to find ungapped local alignments in genomic sequence with up to a specified fraction of substitutions. The length and substitution rate of these alignments can be chosen so that they appear frequently in significant similarities yet still remain rare in the background sequence. The algorithm finds ungapped alignments efficiently using a randomized search technique, locality-sensitive hashing. We have found LSH-ALL-PAIRS to be both efficient and sensitive for finding local similarities with as little as 63% identity in mammalian genomic sequences up to tens of megabases in length
Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks

Recently, as biometric technology grows rapidly, the importance of fingerprint spoof detection technique is emerging. In this paper, we propose a technique to detect forged fingerprints using contrast enhancement and Convolutional Neural Networks (CNNs). The proposed method detects the fingerprint spoof by performing contrast enhancement to improve the recognition rate of the fingerprint image, judging whether the sub-block of fingerprint image is falsified through CNNs composed of 6 weight layers and totalizing the result. Our fingerprint spoof detector has a high accuracy of 99.8% on average and has high accuracy even after experimenting with one detector in all datasets.
Design and measurement of array antennas for 77GHz automotive radar application

Array antennas for 77GHz automotive radar application are designed and measured. Linear series-fed patch array (SFPA) antenna is designed for transmitters of middle range radar (MRR) and all the receivers. A planar SFPA based on the linear one and substrate integrated waveguide (SIW) feeding network is proposed for transmitter of long range radar (LRR), which can decline the radiation from feeding network itself. The array antennas are fabricated, both the performances with and without radome of these array antennas are measured. Good agreement between simulation and measurement has been achieved. They can be good candidates for 77GHz automotive application.
Gender differences in the viscoelastic properties of tendon structures

The purpose of this study was to investigate the differences in the viscoelastic properties of human tendon structures (tendon and aponeurosis) in the medial gastrocnemius muscle between men (n=16) and women (n=13). The elongation of the tendon and aponeurosis of the medial gastrocnemius muscle was measured directly by ultrasonography, while the subjects performed ramp isometric plantar flexion up to the voluntary maximum, followed by a ramp relaxation. The relationship between the estimated muscle force (Fm) and tendon elongation (L) during the ascending phase was fitted to a linear regression, the slope of which was defined as stiffness. The percentage of the area within the Fm-L loop to the area beneath the curve during the ascending phase was calculated as hysteresis. The L values at force production levels beyond 50 N were significantly greater for women than for men. The maximum strain (100×ΔL/initial tendon length) was significantly greater in women [9.5 (1.1)%] than in men [8.1 (1.6)%]. The stiffness and Young's modulus were significantly lower in women [16.5 (3.4) N/mm, 277 (25) MPa] than in men [25.9 (7.0) N/mm, 356 (32) MPa]. Furthermore, the hysteresis was significantly lower in women [11.1 (5.9)%] than in men [18.7 (8.5)%, P=0.048]. These results suggest that there are gender differences in the viscoelastic properties of tendon structures and that these might in part account for previously observed performance differences between the genders.
Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking

While there is little doubt that risk-taking is generally more prevalent during adolescence than before or after, the underlying causes of this pattern of age differences have long been investigated and debated. One longstanding popular notion is the belief that risky and reckless behavior in adolescence is tied to the hormonal changes of puberty. However, the interactions between pubertal maturation and adolescent decision making remain largely understudied. In the current review, we discuss changes in decision making during adolescence, focusing on the asynchronous development of the affective, reward-focused processing system and the deliberative, reasoned processing system. As discussed, differential maturation in the structure and function of brain systems associated with these systems leaves adolescents particularly vulnerable to socio-emotional influences and risk-taking behaviors. We argue that this asynchrony may be partially linked to pubertal influences on development and specifically on the maturation of the affective, reward-focused processing system.
A Single-Phase Integrated Onboard Battery Charger Using Propulsion System for Plug-in Electric Vehicles

Plug-in electric vehicles (PEVs) are equipped with onboard level-1 or level-2 chargers for home overnight or office daytime charging. In addition, off-board chargers can provide fast charging for traveling long distances. However, off-board high-power chargers are bulky, expensive, and require comprehensive evolution of charging infrastructures. An integrated onboard charger capable of fast charging of PEVs will combine the benefits of both the conventional onboard and off-board chargers, without additional weight, volume, and cost. In this paper, an innovative single-phase integrated charger, using the PEV propulsion machine and its traction converter, is introduced. The charger topology is capable of power factor correction and battery voltage/current regulation without any bulky add-on components. Ac machine windings are utilized as mutually coupled inductors, to construct a two-channel interleaved boost converter. The circuit analyses of the proposed technology, based on a permanent magnet synchronous machine (PMSM), are discussed in details. Experimental results of a 3-kW proof-of-concept prototype are carried out using a  ${\textrm{220-V}}_{{\rm{rms}}}$, 3-phase, 8-pole PMSM. A nearly unity power factor and 3.96% total harmonic distortion of input ac current are acquired with a maximum efficiency of 93.1%.
Declining loneliness over time: evidence from american colleges and high schools.

We examined changes in loneliness over time. Study 1 was a cross-temporal meta-analysis of 48 samples of American college students who completed the Revised UCLA Loneliness Scale (total N = 13,041). In Study 1, loneliness declined from 1978 to 2009 (d = -0.26). Study 2 used a representative sample of high school students from the Monitoring the Future project (total N = 385,153). In Study 2, loneliness declined from 1991 to 2012. Declines were similar among White students (d = -0.14), Black students (d = -0.17), male students (d = -0.11), and female students (d = -0.11). Different loneliness factors showed diverging trends. Subjective isolation declined (d = -0.20), whereas social network isolation increased (d = 0.06). We discuss the declines in loneliness within the context of other cultural changes, including changes to group membership and personality.
Design of a solar powered smart irrigation system (SPSIS) using WSN as an IoT device

The Internet has always been a means of communication between people, but with the technological development and changing requirements and lifestyle, this network has become a tool of communication between things of all types and sizes, and is known as Internet of things (IoT) for this reason.
 One of the most promising applications of IoT technology is the automated irrigation systems. The aim of this paper is to propose a methodology of the implementation of wireless sensor networks as an IoT device to develop a smart irrigation management system powered by solar energy.
Generative Choreography using Deep Learning

Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. In this paper we present a system chorrnn for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is a deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.
From Contract Drafting to Software Specification: Linguistic Sources of Ambiguity

This handbook is about writing software requirements specifications and legal contracts, two kinds of documents with similar needs for completeness, consistency, and precision. Particularly when these are written, as they usually are, in natural language, ambiguity—by any definition—is a major cause of their not specifying what they should. Simple misuse of the language in which the document is written is one source of these ambiguities.
Antibacterial activity of Curcuma longa varieties against different strains of bacteria.

Crude extracts of curcuminoids and essential oil of Curcuma longa varieties Kasur, Faisalabad and Bannu were studied for their antibacterial activity against 4 bacterial strains viz., Bacillus subtilis, Bacillus macerans, Bacillus licheniformis and Azotobacter using agar well diffusion method. Solvents used to determine antibacterial activity were ethanol and methanol. Ethanol was used for the extraction of curcuminoids. Essential oil was extracted by hydrodistillation and diluted in methanol by serial dilution method. Both Curcuminoids and oil showed zone of inhibition against all tested strains of bacteria. Among all the three turmeric varieties, Kasur variety had the most inhibitory effect on the growth of all bacterial strains tested as compared to Faisalabad and Bannu varieties. Among all the bacterial strains B. subtilis was the most sensitive to turmeric extracts of curcuminoids and oil. The MIC value for different strains and varieties ranged from 3.0 to 20.6 mm in diameter.
Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.
Stereolithographic (SLA) 3D printing of oral modified-release dosage forms.

The aim of this work was to evaluate the suitability of stereolithography (SLA) to fabricate drug-loaded tablets with modified-release characteristics. The SLA printer creates solid objects by using a laser beam to photopolymerise monomers. In this work polyethylene glycol diacrylate (PEGDA) was used as a monomer and diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide was used as a photo-initiator. 4-aminosalicylic acid (4-ASA) and paracetamol (acetaminophen) were selected as model drugs. Tablets were successfully printed and formulations with different properties were fabricated by adding polyethylene glycol 300 (PEG 300) to the printing solution. The loading of paracetamol and 4-ASA in the printed tablets was 5.69% and 5.40% respectively. In a realistic dynamic dissolution simulation of the gastrointestinal tract, drug release from the tablets was dependent on the composition of the formulations, but independent of dissolution pH. In conclusion SLA 3DP technology allows the manufacture of drug loaded tablets with specific extended-release profiles. In the future this technology could become a manufacturing technology for the elaboration of oral dosage forms, for industrial production or even for personalised dose.
Effect of Multisensory Training on Writing Skills of Children with Dysgraphia

To find the effect of multisensory training on writing skills in children with dysgraphia.  To assess the writing skills in children with dysgraphia. To assess visual perception, motor coordination and visuomotor integration skills in children with dysgraphia. To evaluate the effect of multisensory training on writing skills in children with dysgraphia.  30 dysgraphic children were selected for study according to screening test of writing assessment measure (WAM) and Berry visual motor integration scale. Participants were into two groups .15 samples in experimental group and 15samples in control group. Experimental group underwent 12 sessions of intervention.  Statistical significant is present in experimental group with regard to the effectiveness of multisensory training on writing skills in children with dysgraphia. Statistical significance have been observed in experimental group pre and post test “t” values of VMI, VP, MC are 5.29,14.78 and 13.22.  The result of this study shows there is a significant improvement in writing skills for dysgraphic children after multisensory training programs.
Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison

The task of network management and monitoring relies on an accurate characterization of network traffic generated by different applications and network protocols. We employ three supervisedmachine learning (ML) algorithms, BayesianNetworks, Decision Trees and Multilayer Perceptrons for the flow-based classification of six different types of Internet traffic including peer-to-peer (P2P) and content delivery (Akamai) traffic. The dependency of the traffic classification performance on the amount and composition of training data is investigated followed by experiments that show that ML algorithms such as Bayesian Networks and Decision Trees are suitable for Internet traffic flow classification at a high speed, and prove to be robust with respect to applications that dynamically change their source ports. Finally, the importance of correctly classified training instances is highlighted by an experiment that is conducted with wrongly labeled training data.
Data warehouse architecture classification

The purpose of this study is to give an overlook and comparison of best known data warehouse architectures. Single-layer, two-layer, and three-layer architectures are structure-oriented one that are depending on the number of layers used by the architecture. In independent data marts architecture, bus, hub-and-spoke, centralized and distributed architectures, the main layers are differently combined. Listed data warehouse architectures are compared based on organizational structures, with its similarities and differences. The second comparison gives a look into information quality (consistency, completeness, accuracy) and system quality (integration, flexibility, scalability). Bus, hub-and-spoke and centralized data warehouse architectures got the highest scores in information and system quality assessment.
A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars

Most state-of-the-art robotic cars’ perception systems are quite different from the way a human driver understands traffic environments. First, humans assimilate information from the traffic scene mainly through visual perception, while the machine perception of traffic environments needs to fuse information from several different kinds of sensors to meet safety-critical requirements. Second, a robotic car requires nearly 100% correct perception results for its autonomous driving, while an experienced human driver works well with dynamic traffic environments, in which machine perception could easily produce noisy perception results. In this paper, we propose a vision-centered multi-sensor fusing framework for a traffic environment perception approach to autonomous driving, which fuses camera, LIDAR, and GIS information consistently via both geometrical and semantic constraints for efficient self-localization and obstacle perception. We also discuss robust machine vision algorithms that have been successfully integrated with the framework and address multiple levels of machine vision techniques, from collecting training data, efficiently processing sensor data, and extracting low-level features, to higher-level object and environment mapping. The proposed framework has been tested extensively in actual urban scenes with our self-developed robotic cars for eight years. The empirical results validate its robustness and efficiency.