nils-herrmann/clean_scirepeval huggingface dataset:
A General Lower Bound on the Number of Examples Needed for Learning

We prove a lower bound of ( ln + VCdim(C) ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and and are the accuracy and con dence parameters. This improves the previous best lower bound of ( ln + VCdim(C)), and comes close to the known general upper bound of O( ln + VCdim(C) ln ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. A. Ehrenfeucht was supported by NSF grant MCS-8305245, D. Haussler by ONR grant N00014-86-K-0454, M. Kearns by ONR grant N00014-86-K-0454 and an A.T. & T. Bell Laboratories Scholarship, and L. Valiant by grants ONR-N00014-85-K-0445, NSF-DCR-8600379, DAAL03-86-K-0171 and by the SERC of the U.K. Part of this research was done while M. Kearns was visiting U.C. Santa Cruz.
Metric localization using Google Street View

Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.
Entity Synonyms for Structured Web Search

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.
Design of tri-band Planar Inverted F Antenna (PIFA) with parasitic elements for UMTS2100, LTE and WiMAX mobile applications

In this paper, the multiband Planar Inverted F Antenna (PIFA) for mobile communication applications has been designed and constructed by introducing two rectangular shape parasitic elements which are located under the main radiating patch of the PIFA in order to obtain a triple-band operation. This triple-band PIFA antenna can be operated at three different operating frequencies which are 2100 MHz for UMTS2100, 2600 MHz for LTE Application and 3500 MHz for WiMAX Application. The main radiating patch of this antenna will control the upper band frequency at 3500 MHz, while the first and second parasitic elements located at the left and right edge of the ground plane will resonate at the middle band (2600MHz) and the lower band (2100 MHz) frequency operation. The proposed triple-band antenna is fabricated and experimentally tested. A good agreement between simulated and measured reflection coefficent of the antenna is achieved where the experimental impedance bandwidth covering the three applications. In these frequency bands, the antenna has nearly omni-directional radiation pattern with the peak gain between 2 dBi~ 5 dBi.
A Study of Influencing Factors for Repurchase Intention in Internet Shopping Malls

This research studied the effect of 15 variables on the consumers' overall satisfaction and repurchase intention in Internet shopping malls. As the value of loyal customers is incomparably high in Electronic Commerce, winning customers’ loyalty is vital to success of shopping malls. In this study, a customer is defined as one who has purchased goods or services at least once from Internet shopping malls. The research variables are demographic factors, product perceptions, customer service, perceived ease of use, site image, promotion, perceived consumer risk, personal characteristics and Internet communications environments. The outcome of the research is as follows: Perceived consumer risk shows a negative relationship with the repurchase intention, and all the other variables product perceptions, customer service, perceived ease of use, site image, promotion, communications environments are positively related with the repurchase intention. Also, the overall satisfaction level of customers for the Internet shopping malls positively influences repurchase intention.
Chinese/English mixed Character Segmentation as Semantic Segmentation

OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual text and are not suitable for multi-lingual cases. In this work, we particularly tackle the Chinese/English mixed case by reframing it as a semantic segmentation problem. We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation. As a deep architecture, FCN can automatically learn useful features without traditional feature engineering. Given wide enough receptive field, it can utilize the necessary context around a position to better determinate whether this is a splitting point or not. Trained on synthesized samples with simulated random disturbances, FCN can effectively split characters without any hand-crafted features. The experimental results show that our model significantly outperforms the previous methods. It is able to generalize from simulated disturbances to realworld disturbances, generalize from one text content style to another, generalize from seen font styles to unseen ones, and correctly handle disconnected structures and touching characters.
Soft Biometrics in Online Social Networks: A Case Study on Twitter User Gender Recognition

The proliferate unstructured data generated in online social networks leads to significant research advances in the recognition of user profiles (e.g., age, gender, ethnicity, etc.), but meanwhile brings new challenges. These attributes are referred to as soft biometrics and provide a semantic description of users. Identifying users' soft biometric traits in online social networks is crucial for a variety of applications such as customized marketing, personalized recommendation, and urban planning. Compared to conventional studies, we address the importance and challenges of identifying soft biometrics in online social networks and provide a case study on gender recognition of Twitter users based on their tweets texts and profile images. We first apply an efficient approach to label the soft biometric attributes of users using their self-reported names, instead of labor-forced methodology such as manually labeling and crowdsourcing. Then we investigate the approaches using texts and profile images, respectively, including Hashtag-based TF-IDF, LDA Topic Distribution, SIFT-based TF-IDF and Convolutional Neural Networks (CNN). Finally, we propose an ensemble method by combining models learned from both texts and images to enhance gender identification. The performance in our extensive experiments is demonstrated to be comparative with state-of the-art work.
A Survey of Outlier Detection Methods in Network Anomaly Identification

The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers. Detection of outliers can lead to identification of system faults so that administrators can take preventive measures before they escalate. It is possible that anomaly detection may enable detection of new attacks. Outlier detection is an important anomaly detection approach. In this paper, we present a comprehensive survey of well known distance-based, density-based and other techniques for outlier detection and compare them. We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.
One Century of Brain Mapping Using Brodmann Areas*

100 years after their publication, Brodmann’s maps of the cerebral cortex are universally used to locate neuropsychological functions. On the occasion of this jubilee the life and work of Korbinian Brodmann are reported. The core functions of each single Brodmann area are described and Brodmann’s views on neuropsychological processes are depicted. 100 Jahre nach ihrer Veröffentlichung wird Brodmanns Kartierung des zerebralen Kortex universell zur Lokalisation neuropsychologischer Funktionen eingesetzt. Anlässlich dieses Jubiläums werden Leben und Werk von Korbinian Brodmann dargestellt. Die wesentlichen Funktionen der einzelnen Brodmann-Areale werden beschrieben und Brodmanns Ansichten über neuropsychologische Prozesse wiedergegeben.
Media and children's aggression, fear, and altruism.

Noting that the social and emotional experiences of American children today often heavily involve electronic media, Barbara Wilson takes a close look at how exposure to screen media affects children's well-being and development. She concludes that media influence on children depends more on the type of content that children find attractive than on the sheer amount of time they spend in front of the screen. Wilson begins by reviewing evidence on the link between media and children's emotions. She points out that children can learn about the nature and causes of different emotions from watching the emotional experiences of media characters and that they often experience empathy with those characters. Although research on the long-term effects of media exposure on children's emotional skill development is limited, a good deal of evidence shows that media exposure can contribute to children's fears and anxieties. Both fictional and news programming can cause lasting emotional upset, though the themes that upset children differ according to a child's age. Wilson also explores how media exposure affects children's social development. Strong evidence shows that violent television programming contributes to children's aggressive behavior. And a growing body of work indicates that playing violent video games can have the same harmful effect. Yet if children spend time with educational programs and situation comedies targeted to youth, media exposure can have more prosocial effects by increasing children's altruism, cooperation, and even tolerance for others. Wilson also shows that children's susceptibility to media influence can vary according to their gender, their age, how realistic they perceive the media to be, and how much they identify with characters and people on the screen. She concludes with guidelines to help parents enhance the positive effects of the media while minimizing the risks associated with certain types of content.
FPGA based real-time lane detection and tracking implementation

In this article, we present a system for robust lane detection and tracking. The proposed algorithm has two main parts. The first one is for lane detection using the Sobel operator with an adaptive threshold and the Hough transform, and the second part deals with lane tracking using the Kalman filter. To meet real time requirements, we use gradient directions of edge stage to simplify CORDIC algorithm. The obtained results show that the proposed algorithm can reliably detect and track lanes in different illumination condition.
Single-Switch High-Power-Factor Inverter Driving Piezoelectric Ceramic Transducer for Ultrasonic Cleaner

A novel cost-effective and high-efficiency single-switch resonant inverter with a power-factor-correction (PFC) scheme is proposed to drive a piezoelectric ceramic transducer (PCT) for an ultrasonic cleaner. The circuit topology is derived by integrating a flyback converter that served as the PFC stage with a Class E resonant inverter for driving a thin PCT at its resonance frequency. A unity power factor is achieved by operating the flyback converter in discontinuous conduction mode. With tactfully designed circuit parameters, the Class E resonant inverter is operated at the suboptimum mode to minimize switching losses of the active switch, leading to a high circuit efficiency. The active switch is controlled by pulsewidth modulation at a fixed switching frequency and a constant duty ratio to adjust the output power of the PCT. The operation modes, design equations, and steps for the circuit parameters are proposed. A prototype circuit designed for a 42-W ultrasonic cleaner was built and tested to verify the analytical predictions. Satisfactory performances are obtained from the experimental results.
How does knowledge management influence innovation and competitiveness?

Is concerned with human value management, examines the relationships between organizations' competitiveness, innovation advancements, and knowledge management and presents a set of considerations regarding how these relationships affect strategic management and the formulation of competitive strategies. By considering how knowledge development is related with personal characteristics and personal development, this article attempts to provide useful insights on the linkages between innovation and competitiveness. These considerations point out the importance of knowledge development and the role of knowledge management in order to assure competitiveness. This work proposes also a conceptual model, with special focus on the relationships between knowledge management, competitiveness, and innovation. The major factors are presented and directions for future research are suggested according to the proposed model.
THE JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT

Landfill design needs to consider dynamic properties for safety not only under static conditions, but also under dynamic loading conditions. In recent years, geophysical surveys are becoming popular mainly to understand the profiles within the landfills and estimate its dynamic properties like shear wave velocity, shear modulus and Poisson’s ratio. Shear wave and P-wave velocity (Vs, Vp) are very well related to the dynamic properties of soil at lower strains. Geophysical tests provide a direct measurement of Vs and hence widely accepted for the seismic site characterization purpose. In this study, seismic survey was performed using Multi channel Analyzer of Surface Waves (MASW) technique at Mavallipura landfill site, Bangalore. MASW test involves three steps: data acquisition, construction of a dispersion curve (showing the variation of phase velocity among various frequencies), and inversion of the dispersion curve to get shear wave (Vs) profile from the calculated dispersion curve. The typical MASW test setup used for the present study consists of 24-channel geode seismograph and 24 geophones of 4.5 Hz capacity. Seismic data were recorded using geode seismograph with sledge hammer source on one side of the landfill and geophones on the other side with 1m spacing of geophones. The landfill was surveyed up to a length of about 25m from the top level. The deposit consisted of uncompacted waste up to a depth 6m with Shear wave velocity of 74 to 130m/s. Series of 1-D MASW tests have been carried out to map the entire solid waste site and the results will be presented in the paper. Overall, the results from the study showed that seismic surveys have the potential to capture the changes in dynamic properties like shear wave velocity and Poisson’s ratio with respect to depth of MSW landfill to infer the extent of degradation and provide dynamic properties needed for seismic stability evaluations.
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments

In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery.
A study on e-mail image spam filtering techniques

Spam filters are the most used software tool used by businesses and individual to block spam mails entering into their mail boxes. Until recently, majority of research effort was expended on controlling text-based spam emails. However, the past few years have envisaged a novel approach, where the spammers embed the text message into an image. Thus, the anti-spam filtering research is forced to move from text-based techniques to image-based techniques. Spam and the spam blockers designed to combat it have spawned an upsurge in creativity and innovation. Many software developers are developing new and every more effective spam filtering software. All the methods have a common dream that is to eliminate 100% of the spam, which is still not a reality. To reduce the gap between this reality and dream, researchers have proposed many different types of spam filters and this paper provides a review of them.
Monolithic CMOS distributed amplifier and oscillator

CMOS implementations for RF applications often employ technology modifications to reduce the silicon substrate loss at high frequencies. The most common techniques include the use of a high-resistivity substrate (/spl rho/>10 /spl Omega/-cm) or silicon-on-insulator (SOI) substrate and precise bondwire inductors. However, these techniques are incompatible with low-cost CMOS manufacture. This design demonstrates use of CMOS with a conventional low-resistivity epi-substrate and on-chip inductors for applications above 10 GHz.
PAGE: A Partition Aware Engine for Parallel Graph Computation

Graph partition quality affects the overall performance of parallel graph computation systems. The quality of a graph partition is measured by the balance factor and edge cut ratio. A balanced graph partition with small edge cut ratio is generally preferred since it reduces the expensive network communication cost. However, according to an empirical study on Giraph, the performance over well partitioned graph might be even two times worse than simple random partitions. This is because these systems only optimize for the simple partition strategies and cannot efficiently handle the increasing workload of local message processing when a high quality graph partition is used. In this paper, we propose a novel partition aware graph computation engine named PAGE, which equips a new message processor and a dynamic concurrency control model. The new message processor concurrently processes local and remote messages in a unified way. The dynamic model adaptively adjusts the concurrency of the processor based on the online statistics. The experimental evaluation demonstrates the superiority of PAGE over the graph partitions with various qualities.
Large Receptive Field Networks for High-Scale Image Super-Resolution

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.
Design of a Compact UWB Out-of-Phase Power Divider

The design of a compact out-of-phase uniplanar power divider operating over an ultra wide frequency band is presented. To achieve an out-of-phase signal division over a large frequency range, a T-junction formed by a slotline and a microstrip line accompanied by wideband microstrip to slotline transitions is employed. The simulated and experimental results of the developed divider show a low insertion loss and good return loss performance of the three ports across the band 3.1-10.6 GHz
Towards a Future Reallocation of Work between Humans and Machines - Taxonomy of Tasks and Interaction Types in the Context of Machine Learning

In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.