nils-herrmann/clean_scirepeval huggingface dataset:
On the Waveform for 5G

In this article, an overview and an in-depth analysis of the most discussed 5G waveform candidates are presented. In addition to general requirements, the nature of each waveform is revealed including the motivation, the underlying methodology, and the associated advantages and disadvantages. Furthermore, these waveform candidates are categorized and compared both qualitatively and quantitatively. By doing all these, the study in this work offers not only design guidelines but also operational suggestions for the 5G waveform.
Combining Misuse Cases with Attack Trees and Security Activity Models

Misuse cases and attack trees have been suggested for security requirements elicitation and threat modeling in software projects. Their use is believed to increase security awareness throughout the software development life cycle. Experiments have identified strengths and weaknesses of both model types. In this paper we present how misuse cases and attack trees can be linked to get a high-level view of the threats towards a system through misuse case diagrams and a more detailed view on each threat through attack trees. Further, we introduce links to security activity descriptions in the form of UML activity graphs. These can be used to describe mitigating security activities for each identified threat. The linking of different models makes most sense when security modeling is supported by tools, and we present the concept of a security repository that is being built to store models and relations such as those presented in this paper.
Postural control in 13-year-old soccer players

To evaluate the effect of early systematic soccer training on postural control we measured center-of-pressure (COP) variability, range, mean velocity and frequency in bipedal quiet stance with eyes open (EO) and closed (EC) in 44 boys aged 13 (25 boys who practiced soccer for 5–6 years and 19 healthy boys who did not practice sports). The soccer players had better stability, particularly in the medial–lateral plane (M/L); their COP variability and range were lower than in controls in both EO (p < 0.05) and EC (p < 0.0005) condition indicating that the athletes were less dependent on vision than non-athletes. Improved stability of athletes was accompanied by a decrease in COP frequency (p < 0.001 in EO, and p < 0.04 in EC) which accounted for lower regulatory activity of balance system in soccer players. The athletes had lower COP mean velocity than controls (p < 0.0001 in both visual condition), with larger difference in the M/L than A/P plane (p < 0.00001 and p < 0.05, respectively). Postural behavior was more variable within the non-athletes than soccer players, mainly in the EC stances (p < 0.005 for all COP parameters). We conclude that: (1) soccer training described was efficient in improving the M/L postural control in young boys; (2) athletes developed specific postural strategies characterized by decreased COP frequency and lower reliance on vision.
Stitch meshes for modeling knitted clothing with yarn-level detail

Recent yarn-based simulation techniques permit realistic and efficient dynamic simulation of knitted clothing, but producing the required yarn-level models remains a challenge. The lack of practical modeling techniques significantly limits the diversity and complexity of knitted garments that can be simulated. We propose a new modeling technique that builds yarn-level models of complex knitted garments for virtual characters. We start with a polygonal model that represents the large-scale surface of the knitted cloth. Using this mesh as an input, our interactive modeling tool produces a finer mesh representing the layout of stitches in the garment, which we call the stitch mesh. By manipulating this mesh and assigning stitch types to its faces, the user can replicate a variety of complicated knitting patterns. The curve model representing the yarn is generated from the stitch mesh, then the final shape is computed by a yarn-level physical simulation that locally relaxes the yarn into realistic shape while preserving global shape of the garment and avoiding "yarn pull-through," thereby producing valid yarn geometry suitable for dynamic simulation. Using our system, we can efficiently create yarn-level models of knitted clothing with a rich variety of patterns that would be completely impractical to model using traditional techniques. We show a variety of example knitting patterns and full-scale garments produced using our system.
How to Track Your Data: The Case for Cloud Computing Provenance

Provenance, a meta-data describing the derivation history of data, is crucial for the uptake of cloud computing to enhance reliability, credibility, accountability, transparency, and confidentiality of digital objects in a cloud. In this paper, we survey current mechanisms that support provenance for cloud computing, we classify provenance according to its granularities encapsulating the various sets of provenance data for different use cases, and we summarize the challenges and requirements for collecting provenance in a cloud, based on which we show the gap between current approaches to requirements. Additionally, we propose our approach, Data PROVE, that aims to effectively and efficiently satisfy those challenges and requirements in cloud provenance, and to provide a provenance supplemented cloud for better integrity and safety of customers' data.
Iterative detection and decoding for SCMA systems with LDPC codes

Sparse code multiple access (SCMA) is a promising multiplexing approach to achieve high system capacity. In this paper, we develop a novel iterative detection and decoding scheme for SCMA systems combined with Low-density Parity-check (LDPC) decoding. In particular, we decompose the output of the message passing algorithm (MPA) based SCMA multiuser detection into intrinsic part and prior part. Then we design a joint detection and decoding scheme which iteratively exchanges the intrinsic information between the detector and the decoder, yielding a satisfied performance gain. Moreover, the proposed scheme has almost the same complexity compared with the traditional receiver for LDPC-coded SCMA systems. As numerical results demonstrate, the proposed scheme has a substantial gain over the traditional SCMA receiver on AWGN channels and Rayleigh fading channels.
Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus

In this paper we present an annotated corpus created with the aim of analyzing the informative behaviour of emoji – an issue of importance for sentiment analysis and natural language processing. The corpus consists of 2475 tweets all containing at least one emoji, which has been annotated using one of the three possible classes: Redundant, Non Redundant, and Non Redundant + POS. We explain how the corpus was collected, describe the annotation procedure and the interface developed for the task. We provide an analysis of the corpus, considering also possible predictive features, discuss the problematic aspects of the annotation, and suggest future improvements.
User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities

Information Visualization systems have traditionally followed a one-size-fits-all model, typically ignoring an individual user's needs, abilities and preferences. However, recent research has indicated that visualization performance could be improved by adapting aspects of the visualization to each individual user. To this end, this paper presents research aimed at supporting the design of novel user-adaptive visualization systems. In particular, we discuss results on using information on user eye gaze patterns while interacting with a given visualization to predict the user's visualization tasks, as well as user cognitive abilities including perceptual speed, visual working memory, and verbal working memory. We show that such predictions are significantly better than a baseline classifier even during the early stages of visualization usage. These findings are discussed in view of designing visualization systems that can adapt to each individual user in real-time.
The Feature Selection and Intrusion Detection Problems

Cyber security is a serious global concern. The potential of cyber terrorism has posed a threat to national security; meanwhile the increasing prevalence of malware and incidents of cyber attacks hinder the utilization of the Internet to its greatest benefit and incur significant economic losses to individuals, enterprises, and public organizations. This paper presents some recent advances in intrusion detection, feature selection, and malware detection. In intrusion detection, stealthy and low profile attacks that include only few carefully crafted packets over an extended period of time to delude firewalls and the intrusion detection system (IDS) have been difficult to detect. In protection against malware (trojans, worms, viruses, etc.), how to detect polymorphic and metamorphic versions of recognized malware using static scanners is a great challenge. We present in this paper an agent based IDS architecture that is capable of detecting probe attacks at the originating host and denial of service (DoS) attacks at the boundary controllers. We investigate and compare the performance of different classifiers implemented for intrusion detection purposes. Further, we study the performance of the classifiers in real-time detection of probes and DoS attacks, with respect to intrusion data collected on a real operating network that includes a variety of simulated attacks. Feature selection is as important for IDS as it is for many other modeling problems. We present several techniques for feature selection and compare their performance in the IDS application. It is demonstrated that, with appropriately chosen features, both probes and DoS attacks can be detected in real time or near real time at the originating host or at the boundary controllers. We also briefly present some encouraging recent results in detecting polymorphic and metamorphic malware with advanced static, signature-based scanning techniques.
Curve-fitting with piecewise parametric cubics

Parametric piecewise-cubic functions are used throughout the computer graphics industry to represent curved shapes. For many applications, it would be useful to be able to reliably derive this representation from a closely spaced set of points that approximate the desired curve, such as the input from a digitizing tablet or a scanner. This paper presents a solution to the problem of automatically generating efficient piecewise parametric cubic polynomial approximations to shapes from sampled data. We have developed an algorithm that takes a set of sample points, plus optional endpoint and tangent vector specifications, and iteratively derives a single parametric cubic polynomial that lies close to the data points as defined by an error metric based on least-squares. Combining this algorithm with dynamic programming techniques to determine the knot placement gives good results over a range of shapes and applications.
Convolutional Experts Constrained Local Model for Facial Landmark Detection

Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. In our work, we present a novel local detector – Convolutional Experts Network (CEN) – that brings together the advantages of neural architectures and mixtures of experts in an end-toend framework. We further propose a Convolutional Experts Constrained Local Model (CE-CLM) algorithm that uses CEN as a local detector. We demonstrate that our proposed CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial landmark detection by a large margin, especially on challenging profile images.
RAMOBoost: Ranked Minority Oversampling in Boosting

In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imbalanced data. However, because of the complex characteristics of imbalanced data, many real-world solutions struggle to provide robust efficiency in learning-based applications. In an effort to address this problem, this paper presents Ranked Minority Oversampling in Boosting (RAMOBoost), which is a RAMO technique based on the idea of adaptive synthetic data generation in an ensemble learning system. Briefly, RAMOBoost adaptively ranks minority class instances at each learning iteration according to a sampling probability distribution that is based on the underlying data distribution, and can adaptively shift the decision boundary toward difficult-to-learn minority and majority class instances by using a hypothesis assessment procedure. Simulation analysis on 19 real-world datasets assessed over various metrics-including overall accuracy, precision, recall, F-measure, G-mean, and receiver operation characteristic analysis-is used to illustrate the effectiveness of this method.
Ant System : An Autocatalytic Optimizing Process

A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.
A pitch determination algorithm based on subharmonic-to-harmonic ratio

In the present paper, a pitch determination algorithm (PDA) based on Subharmonic-to-Harmonic Ratio (SHR) is proposed. The algorithm is motivated by the results of a recent study on the perceived pitch of alternate pulse cycles in speech . The algorithm employs a logarithmic frequency scale and a spectrum shifting technique to obtain the amplitude summation of harmonics and subharmonics, respectively. Through comparing the amplitude ratio of subharmonics and harmonics with the pitch perception results, the pitch of normal speech as well as speech with alternate pulse cycles (APC) can be determined. . Evaluation of the algorithm is performed on CSTR’s database and on synthesized speech with APC. The results show that this algorithm is one of the most reliable PDAs. Furthermore, superior to most other algorithms, it handles subharmonics reasonably well.
Basic vibration signal processing for bearing fault detection

Faculty in the College of Engineering at the University of Alabama developed a multidisciplinary course in applied spectral analysis that was first offered in 1996. The course is aimed at juniors majoring in electrical, mechanical, industrial, or aerospace engineering. No background in signal processing or Fourier analysis is assumed; the requisite fundamentals are covered early in the course and followed by a series of laboratories in which the fundamental concepts are applied. In this paper, a laboratory module on fault detection in rolling element bearings is presented. This module is one of two laboratory modules focusing on machine condition monitoring applications that were developed for this course. Background on the basic operational characteristics of rolling element bearings is presented, and formulas given for the calculation of the characteristic fault frequencies. The shortcomings of conventional vibration spectral analysis for the detection of bearing faults is examined in the context of a synthetic vibration signal that students generate in MATLAB. This signal shares several key features of vibration signatures measured on bearing housings. Envelope analysis and the connection between bearing fault signatures and amplitude modulation/demodulation is explained. Finally, a graphically driven software utility (a set of MATLAB m-files) is introduced. This software allows students to explore envelope analysis using measured data or the synthetic signal that they generated. The software utility and the material presented in this paper constitute an instructional module on bearing fault detection that can be used as a stand-alone tutorial or incorporated into a course.
Young adults' use of communication technology within their romantic relationships and associations with attachment style

In an online survey with two cohorts (2009 and 2011) of undergraduates in dating relationshi ps, we examined how attachment was related to communication technology use within romantic relation ships. Participants reported on their attachment style and frequency of in-person communication as well as phone, text messaging, social network site (SNS), and electronic mail usage with partners. Texting and SNS communication were more frequent in 2011 than 2009. Attachment avoidance was related to less frequent phone use and texting, and greater email usage. Electronic communication channels (phone and texting) were related to positive relationship qualities, however, once accounting for attachment, only moderated effects were found. Interactions indicated texting was linked to more positive relationships for highly avoidant (but not less avoidant) participants. Additionally, email use was linked to more conflict for highly avoidant (but not less avoidant) participants. Finally, greater use of a SNS was positively associated with intimacy/support for those higher (but not lower) on attachment anxiety. This study illustrates how attachment can help to explain why the use of specific technology-based communication channels within romantic relationships may mean different things to different people, and that certain channels may be especially relevant in meeting insecurely attached individuals’ needs.
Mobile phones and paper documents: evaluating a new approach for capturing microfinance data in rural India

CAM is a user interface toolkit that allows a camera-equipped mobile phone to interact with paper documents. It is designed to automate inefficient, paper-intensive information processes in the developing world. In this paper we present a usability evaluation of an application built using CAM for collecting data from microfinance groups in rural India. This application serves an important and immediate need in the microfinance industry. Our quantitative results show that the user interface is efficient, accurate and can quickly be learned by rural users. The results were competitive with an equivalent PC-based UI. Qualitatively, the interface was found easy to use by almost all users. This shows that, with a properly designed user interface, mobile phones can be a preferred platform for many rural computing applications. Voice feedback and numeric data entry were particularly well-received by users. We are conducting a pilot of this application with 400 microfinance groups in India.
Dual-polarized corporate-feed plate-laminated waveguide slot array antenna for 60 GHz-band

The dual-polarized corporate-feed waveguide slot array antenna is designed for the 60 GHz band. Using the multi-layer structure, we have realized dual-polarization operation. Even though the gain is approximately 1 dB lower than the antenna for the single polarization due to the -15dB cross-polarization level in 8=58°, this antenna still shows very high gain over 32 dBi over the broad bandwidth. This antenna will be fabricated and measured in future.
Step-climbing wheelchair with lever propelled rotary legs

There are a few types of step-climbing wheelchairs in the world, but most of them are large and heavy because they are power-assisted. Therefore, they require large space to maneuver, which is not always feasible with existing house architectures. This study proposes a novel step-climbing wheelchair based on lever propulsion control using human upper limbs. The developed step-climbing wheelchair device consists of manual wheels with casters for moving around and a rotary-legs mechanism that is capable of climbing steps. The wheelchair also has a passive mechanism for posture transition to shift the center of gravity of the person between the desired positions for planar locomotion and step-climbing. The proposed design consists of passive parts, and this leads the wheelchair being compact and lightweight. In this paper, we present the design of this step-climbing wheelchair and some preliminary experiments to test its usability.
Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features

One of important cues of deception detection is micro-expression. It has three characteristics: short duration, low intensity and usually local movements. These characteristics imply that micro-expression is sparse. In this paper, we use the sparse part of Robust PCA (RPCA) to extract the subtle motion information of micro-expression. The local texture features of the information are extracted by Local Spatiotemporal Directional Features (LSTD). In order to extract more effective local features, 16 Regions of Interest (ROIs) are assigned based on the Facial Action Coding System (FACS). The experimental results on two micro-expression databases show the proposed method gain better performance. Moreover, the proposed method may further be used to extract other subtle motion information (such as lip-reading, the human pulse, and micro-gesture etc.) from video.
An Analysis of RFID Authentication Schemes for Internet of Things in Healthcare Environment Using Elliptic Curve Cryptography

Advances in information and communication technologies have led to the emergence of Internet of Things (IoT). In the healthcare environment, the use of IoT technologies brings convenience to physicians and patients as they can be applied to various medical areas (such as constant real-time monitoring, patient information management, medical emergency management, blood information management, and health management). The radio-frequency identification (RFID) technology is one of the core technologies of IoT deployments in the healthcare environment. To satisfy the various security requirements of RFID technology in IoT, many RFID authentication schemes have been proposed in the past decade. Recently, elliptic curve cryptography (ECC)-based RFID authentication schemes have attracted a lot of attention and have been used in the healthcare environment. In this paper, we discuss the security requirements of RFID authentication schemes, and in particular, we present a review of ECC-based RFID authentication schemes in terms of performance and security. Although most of them cannot satisfy all security requirements and have satisfactory performance, we found that there are three recently proposed ECC-based authentication schemes suitable for the healthcare environment in terms of their performance and security.