nils-herrmann/clean_scirepeval huggingface dataset:
Small-size circular polarized patch antenna

Designing of an microstrip patch antenna with circular polarization (CP) and reduced size is a challenging and complex task. Here such an antenna is proposed and experimentally studied. Deployment of meandering technique and shorting pins together at the four corners of patch gives less back radiation along with acheiving CP and fairly reduce the size of antenna. Also it can provide inductive and capacitive loading effect to the patch which in turn control the frequency of operation. In this paper, a study is conducted with different shorting strip structures such as rectangle, U-shape, and meandering. Simulations were carried out in HFSS and compared the simulated results with different structures. It is found that meandering technique gives better size reduction than other two because the strongest current strengths concentrates at the meandering shorting strips. In addition, meandering technique provides high front-to-back ratio than others.
Document-Level Adaptation for Neural Machine Translation

It is common practice to adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator’s corrections within the document itself. We focus on adaptation within a single document – appropriate for an interactive translation scenario where a model adapts to a human translator’s input over the course of a document. We propose two methods: single-sentence adaptation (which performs online adaptation one sentence at a time) and dictionary adaptation (which specifically addresses the issue of translating novel words). Combining the two models results in improvements over both approaches individually, and over baseline systems, even on short documents. On WMT news test data, we observe an improvement of +1.8 BLEU points and +23.3% novel word translation accuracy and on EMEA data (descriptions of medications) we observe an improvement of +2.7 BLEU points and +49.2% novel word translation accuracy.
An improved LoRaWAN protocol based on adaptive duty cycle

In the low power wide area network, the LoRaWAN protocol adopts ALOHA type to access channel without duty cycle limited. The energy-efficient of the node and the robustness for varying traffic load are not considered in the situation which may lead to unfairness of node. To balance the energy consumption and network performance, an adaptive duty cycle medium access control (ADC-MAC) protocol is proposed. The basic idea of the protocol is that each sensor node maintains three indicators including the residual energy, the node load and the network congestion rate. The sensor node selects a different duty cycle to control the minimum interval of the uplinks. The asynchronous scheme is easier to implement as time synchronization is not necessary. The results, acquired by OPNET, demonstrate that the proposed ADC-MAC protocol have obvious advantages in extending the battery lifetime and improving packet delivery rate.
Effects of Small-Group Learning on Undergraduates in Science , Mathematics , Engineering , and Technology : A Meta-Analysis

Metric for Expressing Effect Sizes .............................................................................. 5 Calculations of Average Effect Sizes .......................................................................... 5 Tests for Conditional Effects ....................................................................................... 6 Study Coding ............................................................................................................... 7 Meta Analysis  1 7 Robust Main Effects .................................................................................................. 1 7 Conditional Effects of Small-Group Learning .......................................................... 1 8 Limitations of the Study ............................................................................................ 2 0 Implications for Theory, Research, Policy, and Practice .......................................... 2 1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon

Do people really care about their privacy? Surveys show that privacy is a primary concern for citizens in the digital age. On the other hand, individuals reveal personal information for relatively small rewards, often just for drawing the attention of peers in an online social network. This inconsistency of privacy attitudes and privacy behavior is often referred to as the “privacy paradox”. In this paper, we present the results of a review of research literature on the privacy paradox. We analyze studies that provide evidence of a paradoxical dichotomy between attitudes and behavior and studies that challenge the existence of such a phenomenon. The diverse research results are explained by the diversity in research methods, the different contexts and the different conceptualizations of the privacy paradox. We also present several interpretations of the privacy paradox, stemming from social theory, psychology, behavioral economics and, in one case, from quantum theory. We conclude that current research has improved our understanding of the privacy paradox phenomenon. It is, however, a complex phenomenon that requires extensive further research. Thus, we call for synthetic studies to be based on comprehensive theoretical models that take into account the diversity of personal information and the diversity of privacy concerns. We suggest that future studies should use evidence of actual behavior rather than self-reported behavior.
3D face recognition using local binary patterns

It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition. & 2012 Elsevier B.V. All rights reserved.
Dense Multimodal Fusion for Hierarchically Joint Representation

Multiple modalities can provide more valuable information than single one by describing the same contents in various ways. Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities. However, previous methods mainly focus on fusing the shallow features or high-level representations generated by unimodal deep networks, which only capture part of the hierarchical correlations across modalities. In this paper, we propose to densely integrate the representations by greedily stacking multiple shared layers between different modality-specific networks, which is named as Dense Multimodal Fusion (DMF). The joint representations in different shared layers can capture the correlations in different levels, and the connection between shared layers also provides an efficient way to learn the dependence among hierarchical correlations. These two properties jointly contribute to the multiple learning paths in DMF, which results in faster convergence, lower training loss, and better performance. We evaluate our model on three typical multimodal learning tasks, including audiovisual speech recognition, cross-modal retrieval, and multimodal classification. The noticeable performance in the experiments demonstrates that our model can learn more effective joint representation.
A Comparative Study of Customer Relationship Management ( CRM ) How Strategies and Applications Vary Across Industries

Over the past decade, there has been an explosion of interest in customer relationship management (CRM) by both academics and executives. However, despite an increasing amount of published material, most of which is practitioner oriented, there remains a lack of agreement about what CRM is, what CRM means to different industries and companies and how CRM strategy should be developed. The purpose of this article is to develop a process-oriented conceptual framework that positions CRM at a strategic level by identifying the key cross-functional processes involved in the development of CRM strategy in retail, manufacturing and service industries. CRM is increasingly important to firms as they seek to improve their profits through longerterm relationships with customers. In recent years, many have invested heavily in information technology (IT) assets to better manage their interactions with customers before, during and after purchase (Bohling, Bowman, LaValle, Mittal, Narayandas, Ramani, & Varadarajan, R 2006). Yet, measurable returns from IT investment programs rarely arise from a narrow concentration on IT alone, with the most successful programs combining technology with the effective organisation of people and their skills (Bharadwaj, 2000; Piccoli & Ives, 2005). It follows that the greater the knowledge about how firms successfully build and combine their technological and organisational capabilities, the greater will be our understanding of how CRM influences performance. We have found through the study that CRM is best performed if it is designed to meet the specific needs of each company. And we have also found that for any CRM strategy to be successful it needs the support from the management team, from the top to the bottom, a customer-centric culture has to be incorporated into one’s daily operation.
A Variable Step Size INC MPPT Method for PV Systems

Maximum power point tracking (MPPT) techniques are employed in photovoltaic (PV) systems to make full utilization of PV array output power which depends on solar irradiation and ambient temperature. Among all the MPPT strategies, the incremental conductance (INC) algorithm is widely used due to the high tracking accuracy at steady state and good adaptability to the rapidly changing atmospheric conditions. In this paper, a modified variable step size INC MPPT algorithm is proposed, which automatically adjusts the step size to track the PV array maximum power point. Compared with the conventional fixed step size method, the proposed approach can effectively improve the MPPT speed and accuracy simultaneously. Furthermore, it is simple and can be easily implemented in digital signal processors. A theoretical analysis and the design principle of the proposed method are provided and its feasibility is also verified by simulation and experimental results.
Performance analysis of latent fingerprint enhancement techniques

Fingerprint Enhancement is an important step used to improve the accuracy and rate of automated detection of fingerprints. Enhancing the latent fingerprints facilitates the extraction of minutiae from the fingerprint image. In this paper, two methods of latent fingerprint enhancement are carried out. The first uses Histogram Equalization for enhancing the latent fingerprint. The second method uses a novel approach of Binarization followed by Wiener filtering and an additional Median filter. The improvements in the image are then analyzed based on their entropy, contrast and moment.
Queer diagnoses revisited: The past and future of homosexuality and gender diagnoses in DSM and ICD.

The American Psychiatric Association (APA) recently completed a several year process of revising the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). During that time, there were objections raised to retaining DSM's gender identity disorder diagnoses and calls to remove them, just as homosexuality had been removed from DSM-II in 1973. At the conclusion of the DSM-5 revision process, the gender diagnoses were retained, albeit in altered form and bearing the new name of 'gender dysphoria'. The author of this paper was a member of the DSM-5 Workgroup on Sexual and Gender Identity Disorders and presently serves on the WHO Working Group on Sexual Disorders and Sexual Health. Both groups faced similar tasks: reconciling patients' needs for access to care with the stigma of being given a psychiatric diagnosis. The differing nature of the two diagnostic manuals led to two different outcomes. As background, this paper updates the history of homosexuality and the gender diagnoses in the DSM and in the International Statistical Classification of Diseases and Related Health Problems (ICD) as well as what is expected to happen to the homosexuality and gender diagnoses following the current ICD-11 revision process.
Spatio-temporal proximity and social distance: a confirmation framework for social reporting

Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.
Enhancement and bandwidth compression of noisy speech

Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.
A Syllable-based Technique for Word Embeddings of Korean Words

Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem.
Agile methods and organizational culture: reflections about cultural levels

Agile methods emerge as an alternative to improve quality and performance in software development processes. However, as agile methods are essentially focused on human aspects, their application in companies depends mostly on their adequacy to the current organizational culture. This study explores the view of the organizational culture in three levels as a theoretical framework to allow early detection of problems that could jeopardize the adoption of agile methods by a company. This paper points out that many facilitators or obstacles to the adoption of an agile method can be hidden in the lower levels of the organizational culture. Additionally, the paper shows that a superficial analysis of those issues can lead to a miscomprehension about the possibility of applying an agile method in a software company. This paper also evidences that the interpretation of the levels of organizational culture improves the understanding on how an agile culture should be established.
Automatic Definition of Modular Neural Networks

This paper illustrates an artiicial developmental system that is a computationally ee-cient technique for the automatic generation of complex Artiicial Neural Networks (ANN). Artiicial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic deenition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic deenition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures are evolved, and how the ANNs compute.matic deention of sub-neural networks.
Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.
Modeling and optimal three-dimensional trajectory tracking for an autonomous underwater vehicle

This paper introduces a new approach for designing a simplified control law, which controls an autonomous underwater vehicle (AUV) to track a given 3D trajectory. To get a Linear Time Variant (LTV) state space model, the 6 degrees of freedom (6DOF) dynamics and kinematics model of the AUV is linearized about the given desired 3D trajectory it needs to track. A linear quadratic regulator (LQR) is designed based on the linear model and is applied to the nonlinear model to track the desired trajectory. Simulation results show that the linear control law provides satisfactory results when applied to the nonlinear model for tracking basic trajectory maneuvers: helix, saw-tooth path, and 3D Dubin's path.
Highways Traffic Surveillance System (HTSS) using OpenCV

Due to the traffic accidents over the last few years; the development of surveillance systems with multifunctional techniques has received increasing attention. The use of the smart camera is one solution to solve the traffic problems, Smart cameras are cameras that can perform tasks far beyond simply taking photos and recording videos. Highways Traffic Surveillance System (HTSS) is used to monitor the roads in preventing accidents at the same time finding what causes the accidents. This is done by implementing some image vision protocols as that of Gaussian and Canny. This paper will discuss about the camera-video-surveillance capabilities of tracking across different and varied road environments including detection of moving vehicles. The programming method used will be the functional programming of OpenCV which could be operated under both, Windows and Linux OS. This paper will also examine the result of the combination of computer vision programming and the network socket programming under GNU Linux. At the end, this paper will attempt to discuss the design issues and challenges.
ALVINN: An Autonomous Land Vehicle in a Neural Network

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand. This research was^supported by the Office of Naval Research under Contracts N00014-87K-0385 and N00014-87-K-0533, by National Science Foundation Grant EET-8716324, by the Defense Advanced Research Projects Agency (DOD) monitored by the Space and Naval Warfare Systems Command under Contract N0O039-87-C-0251, and by the Strategic Computing Initiative of DARPA, through ARPA Order 5351, and monitored by the U.S. Army Engineer Topographic Laboratories under contract DACA76-85-C-0003 titled "Road Following".