nils-herrmann/clean_scirepeval huggingface dataset:
Spectrum sharing scenarios and resulting technical requirements for 5G systems

Cellular networks today are designed for and operate in dedicated licensed spectrum. At the same time there are other spectrum usage authorization models for wireless communication, such as unlicensed spectrum or, as widely discussed currently but not yet implemented in practice, various forms of licensed shared spectrum. Hence, cellular technology as of today can only operate in a subset of the spectrum that is in principle available. Hence, a future wireless system may benefit from the ability to access also spectrum opportunities other than dedicated licensed spectrum. It is therefore important to identify which additional ways of authorizing spectrum usage are deemed to become relevant in the future and to analyze the resulting technical requirements. The implications of sharing spectrum between different technologies are analyzed in this paper, both from efficiency and technology neutrality perspective. Different known sharing techniques are outlined and their applicability to the relevant range of future spectrum regulatory regimes is discussed. Based on an assumed range of relevant (according to the views of the authors) future spectrum sharing scenarios, a toolbox of certain spectrum sharing techniques is proposed as the basis for the design of spectrum sharing related functionality in future mobile broadband systems.
DBDC: Density Based Distributed Clustering

Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. In this paper, we propose a different approach. We cluster the data locally and extract suitable representatives from these clusters. These representatives are sent to a global server site where we restore the complete clustering based on the local representatives. This approach is very efficient, because the local clustering can be carried out quickly and independently from each other. Furthermore, we have low transmission cost, as the number of transmitted representatives is much smaller than the cardinality of the complete data set. Based on this small number of representatives, the global clustering can be done very efficiently. For both the local and the global clustering, we use a density based clustering algorithm. The combination of both the local and the global clustering forms our new DBDC (Density Based Distributed Clustering) algorithm. Furthermore, we discuss the complex problem of finding a suitable quality measure for evaluating distributed clusterings. We introduce two quality criteria which are compared to each other and which allow us to evaluate the quality of our DBDC algorithm. In our experimental evaluation, we will show that we do not have to sacrifice clustering quality in order to gain an efficiency advantage when using our distributed clustering approach.
Field Programmable Gate Array-based Number Plate Binarisation and Adjustment for ANPR Systems

Number Plate (NP) binarisation and adjustment are very important pre-processing stages in Automatic Number Plate Recognition (ANPR) systems and are used to link the Number Plate Localisation (NPL) and Character segmentation (CS) stages. Successfully linking these two stages will improve the performance of the entire ANPR system. This paper presents two optimised low complexity NP binarisation and adjustment algorithms. Efficient area/speed architectures based on the proposed algorithms are also presented and have been successfully implemented and tested using the Mentor Graphics RC240 FPGA development board, which together require only 9% of the available on-chip resources of a Virtex-4 FPGA, run with a maximum frequency of 95.8 MHz and are capable of processing one image in 0.07 0.17 . ms 
Memory encryption: A survey of existing techniques

Memory encryption has yet to be used at the core of operating system designs to provide confidentiality of code and data. As a result, numerous vulnerabilities exist at every level of the software stack. Three general approaches have evolved to rectify this problem. The most popular approach is based on complex hardware enhancements; this allows all encryption and decryption to be conducted within a well-defined trusted boundary. Unfortunately, these designs have not been integrated within commodity processors and have primarily been explored through simulation with very few prototypes. An alternative approach has been to augment existing hardware with operating system enhancements for manipulating keys, providing improved trust. This approach has provided insights into the use of encryption but has involved unacceptable overheads and has not been adopted in commercial operating systems. Finally, specialized industrial devices have evolved, potentially adding coprocessors, to increase security of particular operations in specific operating environments. However, this approach lacks generality and has introduced unexpected vulnerabilities of its own. Recently, memory encryption primitives have been integrated within commodity processors such as the Intel i7, AMD bulldozer, and multiple ARM variants. This opens the door for new operating system designs that provide confidentiality across the entire software stack outside the CPU. To date, little practical experimentation has been conducted, and the improvements in security and associated performance degradation has yet to be quantified. This article surveys the current memory encryption literature from the viewpoint of these central issues.
Unifying local and non-local signal processing with graph CNNs

This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. This also allows us to learn through training the optimal mixing of locality and non-locality, in cases where the graph is built on the input signal itself. We demonstrate the versatility and the effectiveness of our framework on several types of signals (greyscale and color images, color palettes and speech signals) and on several applications (style transfer, color transfer, and denoising).
Light Field Image Processing: An Overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.
Radio Frequency Identification: Applications and Security Issues

Radio Frequency Identification (RFID) is name given to technology that uses tags, readers and backend servers to form a system that has numerous applications in many areas, much discovered and rest are to be explored. Before implementing RFID system security issues must be considered carefully, taking not care of security issues could lead to severe consequences. This paper is overview of Introduction to RFID, RFID Fundamentals, basic structure of a RFID system, some of its numerous applications, security issues and their remedies.
Wireless power transfer: A survey of EV battery charging technologies

In this paper, a comprehensive review of existing technological solutions for wireless power transfer used in electric vehicle battery chargers is given. The concept of each solution is thoroughly reviewed and the feasibility is evaluated considering the present limitations in power electronics technology, cost and consumer acceptance. In addition, the challenges and advantages of each technology are discussed. Finally a thorough comparison is made and a proposed mixed conductive/wireless charging system solution is suggested to solve the inherent existing problems.
Sequentiality and Prefetching in Database Systems

Sequentiality of access is an inherent characteristic of many database systems. We use this observation to develop an algorithm which selectively prefetches data blocks ahead of the point of reference. The number of blocks prefetched is chosen by using the empirical run length distribution and conditioning on the observed number of sequential block references immediately preceding reference to the current block. The optimal number of blocks to prefetch is estimated as a function of a number of “costs,” including the cost of accessing a block not resident in the buffer (a miss), the cost of fetching additional data blocks at fault times, and the cost of fetching blocks that are never referenced. We estimate this latter cost, described as memory pollution, in two ways. We consider the treatment (in the replacement algorithm) of prefetched blocks, whether they are treated as referenced or not, and find that it makes very little difference. Trace data taken from an operational IMS database system is analyzed and the results are presented. We show how to determine optimal block sizes. We find that anticipatory fetching of data can lead to significant improvements in system operation.
Motion Compensation for UAV SAR Based on Raw Radar Data

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is very important for battlefield awareness. For SAR systems mounted on a UAV, the motion errors can be considerably high due to atmospheric turbulence and aircraft properties, such as its small size, which makes motion compensation (MOCO) in UAV SAR more urgent than other SAR systems. In this paper, based on 3-D motion error analysis, a novel 3-D MOCO method is proposed. The main idea is to extract necessary motion parameters, i.e., forward velocity and displacement in line-of-sight direction, from radar raw data, based on an instantaneous Doppler rate estimate. Experimental results show that the proposed method is suitable for low- or medium-altitude UAV SAR systems equipped with a low-accuracy inertial navigation system.
A survey of trust management systems for online social communities - Trust modeling, trust inference and attacks

Trust can help participants in online social communities to make decisions; however, it is a challenge for systems to map trust into computational models because of its subjective properties. Also, many online social communities are sparsely connected. Therefore, it is necessary to introduce mechanisms which can infer indirect trust among participants who are not directly connected. We provide a survey of existing trust management systems for online social communities. We also list four types of attacks, and analyze existing systems’ vulnerabilities. Compared with previous surveys, our survey takes trust modeling, trust inference, and attacks into account. Although there are several survey papers about global trust/reputation related attacks, the main contribution of this paper is that we consider trust inference and potential local trust related attacks.
Wideband Bandstop Filter With Cross-Coupling

A general circuit configuration for cross-coupled wideband bandstop filters is proposed. The distinct filtering characteristics of this new type of transmission line filter are investigated theoretically and experimentally. It is shown that a ripple stopband can be created, leading to a quasi-elliptic function response that enhances the rejection bandwidth. A demonstrator with approximately 80% fractional bandwidth at a mid-stopband frequency of 4 GHz is developed and presented. The proposed filter is successfully realized in theory and verified by full-wave electromagnetic simulation and the experiment. Theoretical, simulated, and measured results are in excellent agreement.
Fuzzy logic , trading uncertainty and technical trading

From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.
Sensor enabled wearable RFID technology for mitigating the risk of falls near beds

The increasing ageing population around the world and the increased risk of falling among this demographic, challenges society and technology to find better ways to mitigate the occurrence of such costly and detrimental events as falls. The most common activity associated with falls is bed transfers; therefore, the most significant high risk activity. Several technological solutions exist for bed exiting detection using a variety of sensors which are attached to the body, bed or floor. However, lack of real life performance studies, technical limitations and acceptability are still key issues. In this research, we present and evaluate a novel method for mitigating the high falls risk associated with bed exits based on using an inexpensive, privacy preserving and passive sensor enabled RFID device. Our approach is based on a classification system built upon conditional random fields that requires no preprocessing of sensorial and RF metrics data extracted from an RFID platform. We evaluated our classification algorithm and the wearability of our sensor using elderly volunteers (66-86 y.o.). The results demonstrate the validity of our approach and the performance is an improvement on previous bed exit classification studies. The participants of the study also overwhelmingly agreed that the sensor was indeed wearable and presented no problems.
NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains

Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and loss) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.
Small Antennas: Miniaturization Techniques and Applications

The paper presents research results in the field of small antennas obtain ed at the Department of Wireless Communications, Faculty of Electrical Engineering and Computing, University of Zagreb. A study comparing the application of several miniaturization techniques on a shorted patch antenn is presented. Single and dual band shorted patch antennas with notches and/or slot are introduced. A PIFA d esigned for application in mobile GSM terminals is described. The application of stacked shorted patches as arr ay elements for a mobile communication base station as well as for electromagnetic field sensor is presented. The design of single and dual band folded monopoles is described. Prototypes of the presented antennas have be en manufactured and their characteristics were verified by measurements.
Service-Oriented Sharding with Aspen

The rise of blockchain-based cryptocurrencies has led to an explosion of services using distributed ledgers as their underlying infrastructure. However, due to inherently single-service oriented blockchain protocols, such services can bloat the existing ledgers, fail to provide sufficient security, or completely forego the property of trustless auditability. Security concerns, trust restrictions, and scalability limits regarding the resource requirements of users hamper the sustainable development of loosely-coupled services on blockchains. This paper introduces Aspen, a sharded blockchain protocol designed to securely scale with increasing number of services. Aspen shares the same trust model as Bitcoin in a peer-to-peer network that is prone to extreme churn containing Byzantine participants. It enables introduction of new services without compromising the security, leveraging the trust assumptions, or flooding users with irrelevant messages.
Color image decomposition and restoration

Meyer has recently introduced an image decomposition model to split an image into two components: a geometrical component and a texture (oscillatory) component. Inspired by his work, numerical models have been developed to carry out the decomposition of gray scale images. In this paper, we propose a decomposition algorithm for color images. We introduce a generalization of Meyer s G norm to RGB vectorial color images, and use Chromaticity and Brightness color model with total variation minimization. We illustrate our approach with numerical examples.
The digital divide and t-government in the United States: using the technology acceptance model to understand usage

1 September 2008  30 May 2009 2nd   1 November 2010  This paper applies the technology acceptance model to explore the digital divide and transformational government (t-government) in the United States. Successful t-government is predicated on citizen adoption and usage of e-government services. The contribution of this research is to enhance our understanding of the factors associated with the usage of e-government services among members of a community on the unfortunate side of the divide. A questionnaire was administered to members, of a techno-disadvantaged public housing community and neighboring households, who partook in training or used the community computer lab. The results indicate that perceived access barriers and perceived ease of use (PEOU) are significantly associated with usage, while perceived usefulness (PU) is not. Among the demographic characteristics, educational level, employment status, and household income all have a significant impact on access barriers and employment is significantly associated with PEOU. Finally, PEOU is significantly related to PU. Overall, the results emphasize that t-government cannot cross the digital divide without accompanying employment programs and programs that enhance citizens’ ease in using such services.  (2011) 20, 308–328. doi:10.1057/ejis.2010.64;
A high efficiency low cost direct battery balancing circuit using a multi-winding transformer with reduced switch count

This paper presents a simple circuit for balancing battery cells. The circuit is composed of one low voltage MOSFET for each battery cell and a multi-winding transformer for a group of battery cells. Only one identical gate drive signal for all the MOSFETs is needed to control the balance current. Energy can be directly transferred from higher voltage cells to lower voltage cells. The balancing circuit can achieve ideal balance results as long as the multi-winding transformer has ideal symmetry. Other circuit components' asymmetry would only affect the balance speed. Simulation results are given to show the effectiveness. A circuit for balancing a four-cell battery group is tested. The experiment shows the energy transfer efficiency is up to 93% between cell energy transfers.
DeepPermNet: Visual Permutation Learning

We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii).
28 GHz Compact Omnidirectional Circularly Polarized Antenna for Device-to-Device Communications in the Future 5G Systems

An omnidirectional circularly polarized (OCP) antenna operating at 28 GHz is reported and has been found to be a promising candidate for device-to-device (D2D) communications in the next generation (5G) wireless systems. The OCP radiation is realized by systematically integrating electric and magnetic dipole elements into a compact disc-shaped configuration (9.23 mm $^{3} =0.008~\lambda _{0}^{3}$  at 28 GHz) in such a manner that they are oriented in parallel and radiate with the proper phase difference. The entire antenna structure was printed on a single piece of dielectric substrate using standard PCB manufacturing technologies and, hence, is amenable to mass production. A prototype OCP antenna was fabricated on Rogers 5880 substrate and was tested. The measured results are in good agreement with their simulated values and confirm the reported design concepts. Good OCP radiation patterns were produced with a measured peak realized RHCP gain of 2.2 dBic. The measured OCP overlapped impedance and axial ratio bandwidth was 2.2 GHz, from 26.5 to 28.7 GHz, an 8 % fractional bandwidth, which completely covers the 27.5 to 28.35 GHz band proposed for 5G cellular systems.