nils-herrmann/clean_scirepeval huggingface dataset:
Automation of Question Generation From Sentences

Question Generation (QG) and Question Answering (QA) are key challenges facing systems that interact with natural languages. The potential benefits of using automated systems to generate questions helps reduce the dependency on humans to generate questions and other needs associated with systems interacting with natural languages. In this paper we consider a system that automates generation of questions from a sentence, given a sentence, the system, will generate all possible questions which this sentence contain these questions answers. Since the given sentence may be a complex sentence, the system will generate elementary sentences, from the input complex sentences, using a syntactic parser. A part of speech tagger and a named entity recogniser are used to encode needed information. Based on the subject, verb, object and preposition the sentence will be classified, in order determine the type of questions that can possibly be generated from this sentence. We use development data provided by the Question Generation Shared Task Evaluation Challenge 2010.
A review of Planar Ultrawideband Modular Antenna (PUMA) Arrays

Planar Ultrawideband Modular Antenna (PUMA) arrays are low-cost, wide-scan, and low-cross polarization dual-polarized UWB arrays that combine excellent electrical performance with convenient and practical feeding/fabrication processes. Each member of the PUMA array family consists of tightly coupled horizontal dipoles over a ground plane with novel feeding schemes that enable simple PCB fabrication. This feeding eliminates the need for baluns, “cable organizers,” and other external support mechanisms to produce stand-alone, high-efficiency radiators. Additionally, all PUMA arrays consist of dual-offset dual-polarized lattice arrangements for modular, tile-based assembly. This paper will review the basic operation principles of the PUMA arrays followed by the technological evolution of the PUMA array family. Fabricated PUMA arrays and full-wave simulations of structures that can be manufactured with standard fabrication technologies will be shown along with results.
An efficient multi-task PaaS cloud infrastructure based on docker and AWS ECS for application deployment

The setup environment and deployment of distributed applications is a human intensive and highly complex process that poses significant challenges. Nowadays many applications are developed in the cloud and existing applications are migrated to the cloud because of the promising advantages of cloud computing. Presenting two common serious challenging scenarios in the application development environment, we propose a multi-task PaaS cloud infrastructure using Docker and AWS services for application isolation, optimization and rapid deployment of distributed applications. We fully utilized Docker, a lightweight containerization technology that uses a host of the Linux kernel’s features such as namespaces and cgroup’s to sandbox processes into configurable virtual environments. The Amazon EC2 container service helps our container management framework. The cluster management framework uses optimistic, shared state scheduling to execute processes on EC2 instances using Docker containers. Several experimentations were carried out, one of the experimentation focused on a simulation of application deployment scheduling that shows our propose infrastructure is flexible, efficient and well optimized.
Robust techniques for background subtraction in urban traffic video

Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
A comparative analysis of edge and color based segmentation for orange fruit recognition

In this paper, we presented two segmentation methods. Edge based and color based detection methods were used to segment images of orange fruits obtained under natural lighting conditions. Twenty digitized images of orange fruits were randomly selected from the Internet in order to find an orange in each image and to determine its location. We compared the results of both segmentation results and the color based segmentation outperforms the edge based segmentation in all aspects. The MATLAB image processing toolbox is used for the computation and comparison results are shown in the segmented image results.
Mobile Payment Service Development - Managerial Implications Of Consumer Value Perceptions

In recent years, a number of new payment solutions have been introduced with little success. The solutions have mostly been technology driven at the cost of convenience and value to the consumer. In this research in progress we investigate what characteristics of mobile payment solutions increase or decrease consumers' willingness to adopt these solutions. We also specify managerial implications for the guidance of consumers' payment solution adoption. As a frame of reference for understanding and explaining consumer value perceptions we use three befitting theories: Consumer perceived value (Grönroos, 1997), Technology acceptance model (Davis et. al., 1989), and network externalities theory (Shapiro & Varian, 1999). We conducted four focus group interviews for data collection. The results imply that mobile payment solutions are perceived to be valuable for small everyday purchases and other small purchases. There are, at the same time, still many concerns related to e.g. security and pricing. Ease of use, security, low transaction costs, and wide applicability of the solutions increase perceived customer value and should be managed by mobile payment solution providers. This research indicates clearly that only technically driven development may lack relevance for the potential users of mobile payment solutions.
Image Retrieval using Harris Corners and Histogram of Oriented Gradients

Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.
Managing information sharing within an organizational setting: A social network perspective

In the current information-driven and technologically based global economy, organizations are becoming increasingly dependent on the cumulative knowledge of their employees, suppliers, customers, and other key stakeholders. An organization’s ability to share this knowledge among organizational members is key to its competitive advantage (Bock, Zmud, Kim, & Lee, 2007; Brown & Duguid, 2000; Small & Sage, 2006). Information sharing is critical to an organization’s competitiveness and requires a free flow of information among members that is undistorted and up-to-date (Childhouse & Towill, 2003; Li & Lin, 2006; Moberg, Cutler, Gross, & Speh, 2002; Rahman, 2004; Tan, Lyman, & Wisner, 2002). However, extensive information sharing within organizations still appears to be the exception rather than the rule (Bock et al., 2007; Davenport & Prusack, 1998; Li & Lin, 2006). According to Li and Lin (2006), intensified competition and globalized markets are some of the challenges associated with getting products and services to the right place at the right time and at the lowest cost. These challenges, for instance, have forced organizations to realize that it is not enough to improve their efficiencies; rather, their entire supply chains have to be made competitive. One way for organizations to do this is to support information sharing among their work groups (Li & Lin, 2006). This article explores organizational information sharing as it relates to individuals within and between work groups. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example then illustrates how a social network approach (SNA) was used to explore the process of measuring the social
Addressing the Winograd Schema Challenge as a Sequence Ranking Task

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.
A mixed-signal X-band SiGe multi-function control MMIC for phased array radar applications

In this paper is reported the design, fabrication and test of a mixed-signal SiGe X-band multi-function control MMIC for phased array radar applications. Said MMIC, fabricated, with the ST-Microelectronics BiCMOS7RF SiGe technology, comprises a 5-bit phase shifter, 5-bit attenuator, SPDT switches, several gain amplifiers and a digital serial to parallel converter to reduce the number of MMIC I/O control lines. The gain amplifiers are implemented using SiGe HBTs, while phase shifter, attenuator and SPDT switches are based on CMOS transistors. The measurement results show a return loss better than 15 dB and a gain of 17 dB, equal in both RX and TX state. In RX mode the obtained noise figure is lower than 10 dB, while in TX mode the output P1dB is higher than 12 dBm. The achieved RF performance, the low power consumption and associated low cost, make this SiGe control-chip an attractive solution for high performance/low cost Tx/Rx components for phased array radar applications.
A Novel Solid-State Fault Current-Limiting Circuit Breaker for Medium-Voltage Network Applications

In this paper, a novel solid-state fault current-limiting circuit breaker (SSFCLCB) based on a series resonance LC tank is proposed. Since the series configuration of the resonance structure (a capacitor and a reactor) of the SSFCLCB is invisible during normal operation mode, it shows negligible impedance in the line. In fault conditions, the SSFCLCB topology changes to a rectifier bridge and feeds the resonance structure with rectified ac voltage. With the rectified voltage, the series capacitor is charged and, as a result, the faulty line is opened and the fault current is seized. Hence, the suggested SSFCLCB cannot only limit the fault current but can also open the faulty line and acts as a circuit breaker. For confirmation of these aspects, simulations are performed using MATLAB software. Also, a prototype structure is designed and built for results confirmation. This experimental setup is established to prove that SSFCLCB has excellent performance in its startup, normal operation, and current-limiting/breaking conditions. The results show that the SSFCLCB has the ability to improve distribution network reliability and it can decrease the network fault current level successfully.
Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system

The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.
Attacking the Washington, D.C. Internet Voting System

In 2010, Washington, D.C. developed an Internet voting pilot project that was intended to allow overseas absentee voters to cast their ballots using a website. Prior to deploying the system in the general election, the District held a unique public trial: a mock election during which anyone was invited to test the system or attempt to compromise its security. This paper describes our experience participating in this trial. Within 48 hours of the system going live, we had gained nearcomplete control of the election server. We successfully changed every vote and revealed almost every secret ballot. Election officials did not detect our intrusion for nearly two business days—and might have remained unaware for far longer had we not deliberately left a prominent clue. This case study—the first (to our knowledge) to analyze the security of a government Internet voting system from the perspective of an attacker in a realistic pre-election deployment—attempts to illuminate the practical challenges of securing online voting as practiced today by a growing number of jurisdictions.
Adaptive Apriori Algorithm for frequent itemset mining

Obtaining frequent itemsets from the dataset is one of the most promising area of data mining. The Apriori algorithm is one of the most important algorithm for obtaining frequent itemsets from the dataset. But the algorithm fails in terms of time required as well as number of database scans. Hence a new improved version of Apriori is proposed in this paper which is efficient in terms of time required as well as number of database scans than the Apriori algorithm. It is well known that the size of the database for defining candidates has great effect on running time and memory need. We presented experimental results, showing that the proposed algorithm always outperform Apriori. To evaluate the performance of the proposed algorithm, we have tested it on Turkey student's database as well as a real time dataset.
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow

We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.
Adversarial Learning for Fine-grained Image Search

Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. In practice, a dynamic inventory with new fine-grained categories adds another dimension to this challenge. In this work, we propose an end-to-end network, called FGGAN, that learns discriminative representations by implicitly learning a geometric transformation from multi-view images for fine-grained image search. We integrate a generative adversarial network (GAN) that can automatically handle complex view and pose variations by converting them to a canonical view without any predefined transformations. Moreover, in an open-set scenario, our network is able to better match images from unseen and unknown fine-grained categories. Extensive experiments on two public datasets and a newly collected dataset have demonstrated the outstanding robust performance of the proposed FGGAN in both closed-set and open-set scenarios, providing as much as 10% relative improvement compared to baselines.
W-BAND MICROSTRIP-TO-WAVEGUIDE TRANSITION USING VIA FENCES

The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.
Understanding Online Consumer Stickiness in E-commerce Environment : A Relationship Formation Model

Consumers with online shopping experience often stick to some special websites. Does it mean that long-term relationships between consumers and the websites form? To solve the problem, this paper analyzed the belief and attitude factors influencing online consumer stickiness intention. Based on the relationships between belief-attitude-intention implied in TPB (Theory of Planned Behavior), according to Expectation-Confirmation theory and Commitment-trust theory, we developed a concept model on online consumer stickiness. Six research hypotheses derived from this model were empirically validated using a survey of online shoppers. SEM ( Modeling) was as a data analysis method. The results suggest that online consumer stickiness intention is influenced by consumers’ commitment to the website (e-vendors) and their overall satisfaction to the transaction process, and commitment’s effect is stronger; in turn, both consumers’ commitment and overall satisfaction are influenced by consumers’ ongoing trust significantly; confirmation has a significant effect on overall satisfaction but has not the same effect on commitment. The findings show that the long-term relationships between consumers and special websites exactly exist during online repeat consumption.
A novel SVM-kNN-PSO ensemble method for intrusion detection system

In machine learning, a combination of classifiers, known as an ensemble classifier, often outperforms individual ones. While many ensemble approaches exist, it remains, however, a difficult task to find a suitable ensemble configuration for a particular dataset. This paper proposes a novel ensemble construction method that uses PSO generated weights to create ensemble of classifiers with better accuracy for intrusion detection. Local unimodal sampling (LUS) method is used as a meta-optimizer to find better behavioral parameters for PSO. For our empirical study, we took five random subsets from the well-known KDD99 dataset. Ensemble classifiers are created using the new approaches as well as the weighted majority algorithm (WMA) approach. Our experimental results suggest that the new approach can generate ensembles that outperform WMA in terms of classification accuracy .
Range-IT: detection and multimodal presentation of indoor objects for visually impaired people

In the paper we present our Range-IT prototype, which is a 3D depth camera based electronic travel aid (ETA) to assist visually impaired people in finding out detailed information of surrounding objects. In addition to detecting indoor obstacles and identifying several objects of interest (e.g., walls, open doors and stairs) up to 7 meters, the Range-IT system employs a multimodal audio-vibrotactile user interface to present this spatial information.
A software-based fault detection scheme for wireless sensor networks

Recently, wireless sensor networks are being increasingly integrated with consumer electronic devices to deliver more intelligent services. Usually, the wireless sensor networks are expected to provide continuous, unattended service for months or even years. However, hardware reliability poses a major challenge to this expectation. To address this issue, this paper designs and implements a software-based minimal-overhead fault detection method to detect failures in various hardware components. The fault detection scheme has been implemented in the SOS kernel on the sensor nodes. Experimental results indicate that except for the memory access trap, most failures can be successfully detected through low-cost software detectors.
Deep Relevance Ranking using Enhanced Document-Query Interactions

We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR’s (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.