nils-herrmann/clean_scirepeval huggingface dataset:
A Beam Steering Horn Antenna Using Active Frequency Selective Surface

In this communication, a novel filtering antenna is proposed by utilizing active frequency selective surface (FSS), which can simultaneously achieve filtering and beam steering function. The FSS unit is composed of a metallic rectangular ring and a patch, with a pair of microwave varactor diodes inserted in between along incident electric field polarization direction. Transmission phase of the emitted wave can be tuned by changing the bias voltage applied to the varactor diodes. Through different configurations of the bias voltages, we can obtain the gradient phase distribution of the emitted wave along E- and H-plane. This active FSS is then fabricated and utilized as a radome above a conventional horn antenna to demonstrate its ability of steering the beam radiated from the horn. The experimental results agree well with the simulated ones, which show that the horn antenna with the active FSS can realize beam steering in both E- and H-plane in a range of ±30° at 5.3 GHz with a bandwidth of 180 MHz.
What Makes APIs Hard to Learn? Answers from Developers

The paper discusses the application program interface (API). Most software projects reuse components exposed through APIs. In fact, current-day software development technologies are becoming inseparable from the large APIs they provide. An API is the interface to implemented functionality that developers can access to perform various tasks. APIs support code reuse, provide high-level abstractions that facilitate programming tasks, and help unify the programming experience. A study of obstacles that professional Microsoft developers faced when learning to use APIs uncovered challenges and resulting implications for API users and designers. The article focuses on the obstacles to learning an API. Although learnability is only one dimension of usability, there's a clear relationship between the two, in that difficult-to-use APIs are likely to be difficult to learn as well. Many API usability studies focus on situations where developers are learning to use an API. The author concludes that as APIs keep growing larger, developers will need to learn a proportionally smaller fraction of the whole. In such situations, the way to foster more efficient API learning experiences is to include more sophisticated means for developers to identify the information and the resources they need-even for well-designed and documented APIs.
Coverage Analysis for Dense Heterogeneous Networks with Cooperative NOMA

In a heterogeneous cellular network (HetNet) consisting of $M$ tiers of densely-deployed base stations (BSs), consider that each of the BSs in the HetNet that are associated with multiple users is able to simultaneously schedule and serve two users in a downlink time slot by performing the (power-domain) non-orthogonal multiple access (NOMA) scheme. This paper aims at the preliminary study on the downlink coverage performance of the HetNet with the non-cooperative and the proposed cooperative NOMA schemes. First, we study the coverage probability of the NOMA users for the non-cooperative NOMA scheme in which no BSs are coordinated to jointly transmit the NOMA signals for a particular cell and the coverage probabilities of the two NOMA users of the BSs in each tier are derived. We show that the coverage probabilities can be largely reduced if allocated transmit powers for the NOMA users are not satisfied with some constraints. Next, we study and derive the coverage probabilities for the proposed cooperative NOMA scheme in which the void BSs that are not tagged by any users are coordinated to enhance the far NOMA user in a particular cell. Our analyses show that cooperative NOMA can significantly improve the coverage of all NOMA users as long as the transmit powers for the NOMA users are properly allocated.
Evolving Better Stoplists for Document Clustering and Web Intelligence

Text classification, document clustering and similar document analysis tasks are currently the subject of significant global research, since such areas underpin web intelligence, web mining, search engine design, and so forth. A fundamental tool in such document analysis tasks is a list of so-called ‘stop’ words, called a ‘stoplist’. A stoplist is a specific collection of so-called ‘noise’ words, which tend to appear frequently in documents, but are believed to carry no usable information which would aid learning tasks, and so the idea is that the words in the stoplist are removed from the documents concerned before processing begins. It is well-known that the results of document classification experiments (for example) are invariably considerably improved when a stoplist is employed. Current stoplists in regular use are, however, rather outdated. We have explored this claim in recent work which produced new stoplists based on word-entropy over modern collections of documents. In this work we introduce the notion of optimising a stoplist, and use stochastic search in conjunction with k-means clustering to converge on stoplists which lead to better performance on certain tasks than any previously published.
Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
ABOD3: A Graphical Visualization and Real-Time Debugging Tool for BOD Agents

Current software for AI development requires the use of programming languages to develop intelligent agents. This can be disadvantageous for AI designers, as their work needs to be debugged and treated as a generic piece of software code. Moreover, such approaches are designed for experts; often requiring a steep initial learning curve, as they are tailored for programmers. This can be also disadvantageous for implementing transparency to agents, an important ethical consideration , , as additional work is needed to expose and represent information to end users. We are working towards the development of a new editor, ABOD3. It allows the graphical visualisation of Behaviour Oriented Design based plans , including its two major derivatives: Parallel-rooted, Ordered Slip-stack Hierarchical (POSH) and Instinct . The new editor is designed to allow not only the development of reactive plans, but also to debug such plans in real time to reduce the time required to develop an agent. This allows the development and testing of plans from a same application.
Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.
Neuroticism, marital interaction, and the trajectory of marital satisfaction.

Theories of how initially satisfied marriages deteriorate or remain stable over time have been limited by a failure to distinguish between key facets of change. The present study defines the trajectory of marital satisfaction in terms of 2 separate parameters--(a) the initial level of satisfaction and (b) the rate of change in satisfaction over time--and seeks to estimate unique effects on each of these parameters with variables derived from intrapersonal and interpersonal models of marriage. Sixty newlywed couples completed measures of neuroticism, were observed during a marital interaction and provided reports of marital satisfaction every 6 months for 4 years. Neuroticism was associated with initial levels of marital satisfaction but had no additional effects on rates of change. Behavior during marital interaction predicted rates of change in marital satisfaction but was not associated with initial levels.
Comparison of Sensory Profile scores of young children with and without autism spectrum disorders.

The purpose of this study was to describe the sensory-based behaviors of young children with autism as reported by their parents on the Sensory Profile. Factor scores of children with autism were compared with those of children without autism.



The Sensory Profile questionnaire was completed by parents of 40 children with autism 3 through 6 years of age and parents of 40 children without autism 3 through 6 years of age.



The performance of children with autism was significantly different from that of children without autism on 8 of 10 factors. Factors where differences were found included Sensory Seeking, Emotionally Reactive, Low Endurance/Tone, Oral Sensitivity, Inattention/Distractibility, Poor Registration, Fine Motor/Perceptual, and Other.



Findings from the study suggest that young children with autism have deficits in a variety of sensory processing abilities as measured by the Sensory Profile. Further research is needed to replicate these findings, to examine the possibility of subgroups on the basis of sensory processing, and to contrast the sensory processing abilities of children with other disabilities to those of children with autism.
Building trusted path on untrusted device drivers for mobile devices

Mobile devices are frequently used as terminals to interact with many security-critical services such as mobile payment and online banking. However, the large client software stack and the continuous proliferation of malware expose such interaction under various threats, including passive attacks like phishing and active ones like direct code manipulation. This paper proposes TrustUI, a new trusted path design for mobile devices that enables secure interaction between end users and services based on ARM's TrustZone technology. TrustUI is built with a combination of key techniques including cooperative randomization of the trusted path and secure delegation of network interaction. With such techniques, TrustUI not only requires no trust of the commodity software stack, but also takes a step further by excluding drivers for user-interacting devices like touch screen from its trusted computing base (TCB). Hence, TrustUI has a much smaller TCB, requires no access to device driver code, and may easily adapt to many devices. A prototype of TrustUI has been implemented on a Samsung Exynos 4412 board and evaluation shows that TrustUI provides strong protection of users interaction.
Perceived usefulness, ease of use and electronic supermarket use

Information Technology has permeated many facets of work life in industrialized nations. With the expansion of Internet access we are now witnessing an expansion of the use of information technology in the form of electronic commerce. This current study tests the applicability of one prominent information technology uptake model, the Technology Acceptance Model ( (1993) 475), within an electronic commerce setting. Specifically, the relationship between the perceived ease of use, usefulness and three electronically recorded indicators of use were assessed within the context of an electronic supermarket. A total of 247 participants completed the attitudinal measures. Electronically recorded indicators of use in the form of deliveries, purchase value and number of log-ons to the system were also recorded for the month the participants completed the questionnaire and 6 further months. Results indicated that the Technology Acceptance Model could be successfully applied to an electronic supermarket setting, providing empirical support for the ability of the Technology Acceptance Model to predict actual behaviour. The Technology Acceptance Model explained up to 15% of the variance in the behavioural indicators through perceived ease of use and usefulness of the system. However, the perceived ease of use of the system did not uniquely contribute to the prediction of behaviour when usefulness was considered, indicating a mediation effect. Future research should now focus on product and service attributes to more fully explain the use of electronic commerce services. r 2003
TunnelSlice: Freehand Subspace Acquisition Using an Egocentric Tunnel for Wearable Augmented Reality

In this paper, we propose TunnelSlice, which enables natural acquisition of subspace in an augmented scene from an egocentric view, even for scenarios involving ambiguous center objects or object occlusion. In wearable augmented reality (AR), approaching a three-dimensional (3-D) region including the objects of interest has become more important than approaching distant objects one by one. However, existing ray-based volumetric selection through a head worn display accompanies difficulties in defining a desired 3-D region due to obstacles by occlusion and depth perception. The proposed TunnelSlice effectively determines a cuboid transform, excluding unnecessary areas of a user-defined tunnel via two-handed pinch-based procedural slicing from an egocentric view. Through six scenarios involving central object status and different occlusion levels, we conducted a user study of TunnelSlice. Compared with two existing approaches, TunnelSlice was preferred by the subjects and showed greater stability for all scenarios, and outperformed the other approaches in a scenario involving strong occlusion without a central object. TunnelSlice is thus expected to serve as a key technology for spatial protocol and interaction using a subspace in wearable AR.
Feature Generation for Text Categorization Using World Knowledge

We enhance machine learning algorithms for text categorization with generated features based on domain-specific and common-sense knowledge. This knowledge is represented using publicly available ontologies that contain hundreds of thousands of concepts, such as the Open Directory; these ontologies are further enriched by several orders of magnitude through controlled Web crawling. Prior to text categorization, a feature generator analyzes the documents and maps them onto appropriate ontology concepts, which in turn induce a set of generated features that augment the standard bag of words. Feature generation is accomplished through contextual analysis of document text, implicitly performing word sense disambiguation. Coupled with the ability to generalize concepts using the ontology, this approach addresses the two main problems of natural language processing—synonymy and polysemy. Categorizing documents with the aid of knowledge-based features leverages information that cannot be deduced from the documents alone. Experimental results confirm improved performance, breaking through the plateau previously reached in the field.
All-Hex Mesh Generation via Volumetric PolyCube Deformation

While hexahedral mesh elements are preferred by a variety of simulation techniques, constructing quality all-hex meshes of general shapes remains a challenge. An attractive hex-meshing approach, often referred to as submapping, uses a low distortion mapping between the input model and a PolyCube (a solid formed from a union of cubes), to transfer a regular hex grid from the PolyCube to the input model. Unfortunately, the construction of suitable PolyCubes and corresponding volumetric maps for arbitrary shapes remains an open problem. Our work introduces a new method for computing low-distortion volumetric PolyCube deformations of general shapes and for subsequent all-hex remeshing. For a given input model, our method simultaneously generates an appropriate PolyCube structure and mapping between the input model and the PolyCube. From these we automatically generate good quality all-hex meshes of complex natural and man-made shapes.
Selection of Support Vector Machines based classifiers for credit risk domain

This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning issue. It is compared to other classifiers in terms of accuracy and identification of each class. Experimental classification performance results, obtained using real world financial dataset from SEC EDGAR database, lead to conclusion that proposed technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network, and thus be can be an appealing option for future development of real credit risk evaluation models.
A STAP overview

This tutorial provides a brief overview of space-time adaptive processing (STAP) for radar applications. We discuss space-time signal diversity and various forms of the adaptive processor, including reduced-dimension and reduced-rank STAP approaches. Additionally, we describe the space-time properties of ground clutter and noise-jamming, as well as essential STAP performance metrics. We conclude this tutorial with an overview of some current STAP topics: space-based radar, bistatic STAP, knowledge-aided STAP, multi-channel synthetic aperture radar and non-sidelooking array configurations.
Animated People Textures

This paper introduces a technique to create controllable animations of realistic figures of people starting from live-action video. The described synthesis of such ‘people textures’ extends previous work in video textures to allow the ‘texturing’ of human movement through human-specific feature extraction, coupled with carefuldata mining. In our approach, the video database is pre-processed to classify the motion of the human figures and identify the movements of repeated sequences using data motifs. Then, based on user input, novel sequences of video are computed with edits that are selected based on the raw footage found in the video database and performed based on morphing between segments to generate the transitions automatically. Applications for such animated people textures include video based animations for electronic games and creating background elements and special effects for movies.
Actively predicting diverse search intent from user browsing behaviors

This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. However, the prediction was mostly passive because it was performed only after users submitted their queries to search engines. It is not considered why users issued these queries, and what triggered their information needs. According to our study, many information needs of users were actually triggered by what they have browsed. That is, after reading a page, if a user found something interesting or unclear, he/she might have the intent to obtain further information and accordingly formulate a search query. Actively predicting such search intent can benefit both search engines and their users. In this paper, we propose a series of technologies to fulfill this task. First, we extract all the queries that users issued after reading a given page from user browsing behavior data. Second, we learn a model to effectively rank these queries according to their likelihoods of being triggered by the page. Third, since search intents can be quite diverse even if triggered by the same page, we propose an optimization algorithm to diversify the ranked list of queries obtained in the second step, and then suggest the list to users. We have tested our approach on large-scale user browsing behavior data obtained from a commercial search engine. The experimental results have shown that our approach can predict meaningful queries for a given page, and the search performance for these queries can be significantly improved by using the triggering page as contextual information.
Using Neural Networks to Forecast Stock Market Prices

This paper is a survey on the application of neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Common market analysis techniques such as technical analysis, fundamental analysis, and regression are discussed and compared with neural network performance. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with chaos theory and neural networks. This paper refutes the EMH based on previous neural network work. Finally, future directions for applying neural networks to the financial markets are discussed.