nils-herrmann/clean_scirepeval huggingface dataset:
Fingerprint-Based Attendance Management System

In recent time, there has been high level of impersonation experienced on a daily basis in both privateand public sectors, the ghost worker syndrome which has become a menace across all tiers of government, employers concerns over the levels of employee absence in their workforce and the difficulty in managing student attendance during lecture periods. Fingerprints are a form of biometric identification which is unique and does not change in one’s entire lifetime. This paper presents the attendance management system using fingerprint technology in a university environment. It consists of two processes namely; enrolment and authentication. During enrolment, the fingerprint of the user is captured and its unique features extracted and stored in a database along with the users identity as a template for the subject. The unique features called minutiae points were extracted using the Crossing Number (CN) method which extracts the ridge endings and bifurcations from the skeleton image by examining the local neighborhoods of each ridge pixel using a 3 x 3 window. During authentication, the fingerprint of the user is captured again and the extracted features compared with the template in the database to determine a match before attendance is made. The fingerprint-based attendance management system was implemented with Microsoft’s C# on the. NET framework and Microsoft’s Structured Query Language (SQL) Server 2005 as the backend. The experimental result shows that the developed system is highly efficient in the verification of users fingerprint with an accuracy level of 97.4%. The average execution time for the developed system was 4.29 seconds as against 18.48 seconds for the existing system. Moreover, the result shows a well secured and reliable system capable of preventing impersonation.
Control system design of a battery discharge unit for space applications

The problem of control system design is discussed for a high frequency DC-DC converter which is based on the push-pull circuit with autotransformer. The proposed converter may be used as a battery discharge unit in power supply system of satellites. The mathematical model of the converter is described. The proportional-integral (PI) current controller and PI voltage controller are designed based on singular perturbation technique. Procedure for selection of controller parameters is highlighted. Numerical simulations are included in order to show the applicability of the proposed design methodology in practice.
The EUROHIS-QOL 8-item index: psychometric results of a cross-cultural field study.

Survey research including multiple health indicators requires brief indices for use in cross-cultural studies, which have, however, rarely been tested in terms of their psychometric quality. Recently, the EUROHIS-QOL 8-item index was developed as an adaptation of the WHOQOL-100 and the WHOQOL-BREF. The aim of the current study was to test the psychometric properties of the EUROHIS-QOL 8-item index.



In a survey on 4849 European adults, the EUROHIS-QOL 8-item index was assessed across 10 countries, with equal samples adjusted for selected sociodemographic data. Participants were also investigated with a chronic condition checklist, measures on general health perception, mental health, health-care utilization and social support.



Findings indicated good internal consistencies across a range of countries, showing acceptable convergent validity with physical and mental health measures, and the measure discriminates well between individuals that report having a longstanding condition and healthy individuals across all countries. Differential item functioning was less frequently observed in those countries that were geographically and culturally closer to the UK, but acceptable across all countries. A universal one-factor structure with a good fit in structural equation modelling analyses (SEM) was identified with, however, limitations in model fit for specific countires.



The short EUROHIS-QOL 8-item index showed good cross-cultural field study performance and a satisfactory convergent and discriminant validity, and can therefore be recommended for use in public health research. In future studies the measure should also be tested in multinational clinical studies, particularly in order to test its sensitivity.
An Approach to the Use of Depth Cameras for Weed Volume Estimation

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.
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The evolution of modern wireless communications systems has increased dramatically the demand for antennas, capable to be embedded in portable, or not, devices which serve a wireless land mobile or terrestrial-satellite network. With time and requirements, these devices become smaller in size and hence the antennas required for transmit and receive signals have also to be smaller and lightweight. As a matter of fact, microstrip antennas can meet these requirements. As they are lightweight and have low profile it is feasible them to be structured conformally to the mounting hosts. Moreover, they are easy fabricated, have low cost and are easy integrated into arrays or into microwave printed circuits. So, they are attractive choices for the above mentioned type of applications. For all that, the design of a microstrip antenna is not always an easy problem and the antenna designer is faced with difficulties coming from a) the inherent disadvantages of a printed resonant antenna element, for example the narrow impedance bandwidth, and b) the various requirements of the specific applications, which concern the operation of the radiating element, and can not be satisfied by a printed scheme with an ordinary configuration. For example, it would be demanded, the microstrip element to have gain characteristics that potentially incommensurate to its size or/and frequency bandwidth greater than the element could give, taking into account that it operates as a resonant cavity. Moreover, the rapid development in the field of Land Mobile Telephony as well as in the field of Wireless Local Area Networks(WLANs) demands devices capable to operate in more than one frequency bands. So the design of a printed antenna with intend to conform to multiple communications protocols, for example the IEEE 802.11b/g, in the band of 2.4GHz, and the IEEE 802.11a at 5.3GHz and 5.8GHz, would be a difficult task but at the same time a challenge for the designer. Counting in the above the possibility the device, and so the antenna, to serve terrestrial and also satellite navigation systems the problem of the antenna design is even more complicated. In this chapter techniques will be analysed, to design microstrip antennas that combine the attributes mentioned above which make them suitable for modern communications applications. Specific examples will be also presented for every case.
Underwater remotely operated vehicle for surveillance and marine study

Oceanic exploration has become an emerging field of research due to many resources located in the deep sea. Robots are used in sea exploration as it is very difficult for the human beings to carry out exploration work due to harsh sea environment. Development of underwater Remotely Operated Vehicles (ROVs) is an important area of research. This paper describes a simple ROV prototype based on low cost boards, Raspberry Pi and Arduino UNO. The underwater platform consists of simple control system, camera, gripper and basic equipment. The manoeuvrability consists of forward-reverse motion, submerge under surface motion. The ROV has been tested for the above basic operations.
Shooter Localization based on DoA Estimation of Gunshot Signals and Digital Map Information

This paper discusses a technique to estimate the position of a sniper using a spatial microphone array placed on elevated platforms. The shooter location is obtained from the exact location of the microphone array, from topographic information of the area and from an estimated direction of arrival (DoA) of the acoustic wave related to the explosion in the gun barrel, which is known as muzzle blast. The estimation of the DOA is based on the time differences the sound wavefront arrives at each pair of microphones, employing a technique known as Generalized Cross Correlation (GCC) with phase transform. The main idea behind the localization procedure used herein is that, based on the DoA, the acoustical path of the muzzle blast (from the weapon to the microphone) can be marked as a straight line on a terrain profile obtained from an accurate digital map, allowing the estimation of the shooter location whenever the microphone array is located on an dominant position. In addition, a new approach to improve the DoA estimation from a cognitive selection of microphones is introduced. In this technique, the microphones selected must form a consistent (sum of delays equal to zero) fundamental loop. The results obtained after processing muzzle blast gunshot signals recorded in a typical scenario, show the effectiveness of the proposed method.
A Wideband Isotropic Radiated Planar Antenna Using Sequential Rotated L-Shaped Monopoles

In this communication, we have proposed a compact planar antenna for isotropic radiation pattern in a wide operating band. The proposed antenna consists of four sequential rotated L-shaped monopoles that are fed by a compact uniform sequential-phase (SP) feeding network with equal amplitude and incremental 90 ° phase delay. Based on the rotated field method, a full spatial coverage with gain deviation less than 6 dB is achieved in a wide operating band from 2.3 to 2.61 GHz, also with well-impedance matching. A prototype of the proposed antenna has been built and tested. The measured results, including the reflection coefficient, gain, and radiation patterns, are analyzed and compared with the simulated results.
A Dataset of Operator-client Dialogues Aligned with Database Queries for End-to-end Training

This paper presents a novel dataset for training end-to-end task oriented conversational agents. The dataset contains conversations between an operator – a task expert, and a client who seeks information about the task. Along with the conversation transcriptions, we record database API calls performed by the operator, which capture a distilled meaning of the user query. We expect that the easy-to-get supervision of database calls will allow us to train end-to-end dialogue agents with significantly less training data. The dataset is collected using crowdsourcing and the conversations cover the well-known restaurant domain. Quality of the data is enforced by mutual control among contributors. The dataset is available for download under the Creative Commons 4.0 BY-SA license.
Deep Learning Hierarchical Representations for Image Steganalysis

Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i.e., residual computation, feature extraction, and binary classification. In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images. The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks. Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively. To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model. Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel. Three state-of-the-art steganographic algorithms in spatial domain, e.g., WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model. Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloads.
Hybrid Euclidean-and-Riemannian Metric Learning for Image Set Classification

We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with d-dimension often lies in Euclidean space R, whereas the covariance matrix typically resides on Riemannian manifold Sym+d . Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1. To fuse these statistics from heterogeneous spaces, we propose a Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to exploit both Euclidean and Riemannian metrics for embedding their original spaces into high dimensional Hilbert spaces and then jointly learn hybrid metrics with discriminant constraint. The proposed method is evaluated on two tasks: set-based object categorization and video-based face recognition. Extensive experimental results demonstrate that our method has a clear superiority over the state-of-the-art methods.
Comparison of Different Super-Resolution Methods for HD Video Endoscopy

The main question we try to answer in this work is whether it is feasible to employ super-resolution (SR) algorithms to increase the spatial resolution of endoscopic high-definition (HD) images in order to reveal new details which may have got lost due to the limited endoscope magnification of the HD endoscope used (e.g. mucosal structures). For this purpose we compare the quality achieved of different SR methods. This is done on standard test images as well as on images obtained from endoscopic video frames. We also investigate whether compression artifacts have a noticeable effect on the SR results. We show that, due to several limitations in case of endoscopic videos, we are not consistently able to achieve a higher visual quality when using SR algorithms instead of bicubic interpolation.
An Effective Immersive Cyber Security Awareness Learning Platform for Businesses in the Hospitality Sector

The rapid digitalisation of the hospitality industry over recent years has brought forth many new points of attack for consideration. The hasty implementation of these systems has created a reality in which businesses are using the technical solutions, but employees have very little awareness when it comes to the threats and implications that they might present. This gap in awareness is further compounded by the existence of preestablished, often rigid, cultures that drive how hospitality businesses operate. Potential attackers are recognising this and the last two years have seen a huge increase in cyber-attacks within the sector.Attempts at addressing the increasing threats have taken the form of technical solutions such as encryption, access control, CCTV, etc. However, a high majority of security breaches can be directly attributed to human error. It is therefore necessary that measures for addressing the rising trend of cyber-attacks go beyond just providing technical solutions and make provision for educating employees about how to address the human elements of security. Inculcating security awareness amongst hospitality employees will provide a foundation upon which a culture of security can be created to promote the seamless and secured interaction of hotel users and technology.One way that the hospitality industry has tried to solve the awareness issue is through their current paper-based training. This is unengaging, expensive and presents limited ways to deploy, monitor and evaluate the impact and effectiveness of the content. This leads to cycles of constant training, making it very hard to initiate awareness, particularly within those on minimum waged, short-term job roles.This paper presents a structured approach for eliciting industry requirement for developing and implementing an immersive Cyber Security Awareness learning platform. It used a series of over 40 interviews and threat analysis of the hospitality industry to identify the requirements for designing and implementing cyber security program which encourage engagement through a cycle of reward and recognition. In particular, the need for the use of gamification elements to provide an engaging but gentle way of educating those with little or no desire to learn was identified and implemented. Also presented is a method for guiding and monitoring the impact of their employee’s progress through the learning management system whilst monitoring the levels of engagement and positive impact the training is having on the business.
Central Pattern Generators for the control of robotic systems

Bio-inspired control of motion is an active field of research with many applications in real world tasks. In the case of robotic systems that need to exhibit oscillatory behaviour (i.e. locomotion of snake-type or legged robots), Central Pattern Generators (CPGs) are among the most versatile solutions. These controllers are often based on loosely-coupled oscillators similar to those found in the neural circuits of many animal species, and can be more robust to uncertainty (i.e. external perturbations) than traditional control approaches. This project provides an overview of the state-of-the-art in the field of CPGs, and in particular their applications within robotic systems. The project also tackles the implementation of a CPG-based controller in a small 3D-printed hexapod.
Real-Time Lane and Obstacle Detection on the System ∗

This paper describes the GOLD (Generic Obstacle and Lane Detection) system, a stereo vision-based hardware and software architecture developed to increment road safety of moving vehicles: it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings). It has been implemented on the PAPRICA system and works at a rate of 10 Hz.
3-D MIMO: How Much Does It Meet Our Expectations Observed From Channel Measurements?

By taking advantage of the elevation domain, three-dimensional (3-D) multiple input and multiple output (MIMO) with massive antenna elements is considered as a promising and practical technique for the fifth Generation mobile communication system. So far, 3-D MIMO is mostly studied by simulation and a few field trials have been launched recently. It still remains unknown how much does the 3-D MIMO meet our expectations in versatile scenarios. In this paper, we answer this based on measurements with  $56\times 32$  antenna elements at 3.5 GHz with 100-MHz bandwidth in three typical deployment scenarios, including outdoor to indoor (O2I), urban microcell (UMi), and urban macrocell (UMa). Each scenario contains two different site locations and 2–5 test routes under the same configuration. Based on the measured data, both elevation and azimuth angles are extracted and their stochastic behaviors are investigated. Then, we reconstruct two-dimensional and 3-D MIMO channels based on the measured data, and compare the capacity and eigenvalues distribution. It is observed that 3-D MIMO channel which fully utilizes the elevation domain does improve capacity and also enhance the contributing eigenvalue number. However, this gain varies from scenario to scenario in reality, O2I is the most beneficial scenario, then followed by UMi and UMa scenarios. More results of multiuser capacity varying with the scenario, antenna number and user number can provide the experimental insights for the efficient utilization of 3-D MIMO in future.
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification

Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of unsupervised learning. Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction. First, we demonstrate that the intermediate activations of pretrained large-scale classification networks preserve almost all the information of input images except a portion of local spatial details. Then, by end-to-end training of the entire augmented architecture with the reconstructive objective, we show improvement of the network performance for supervised tasks. We evaluate several variants of autoencoders, including the recently proposed “what-where" autoencoder that uses the encoder pooling switches, to study the importance of the architecture design. Taking the 16-layer VGGNet trained under the ImageNet ILSVRC 2012 protocol as a strong baseline for image classification, our methods improve the validation-set accuracy by a noticeable margin.