nils-herrmann/clean_scirepeval huggingface dataset:
Software-Defined Mobile Networks Security

The future 5G wireless is triggered by the higher demand on wireless capacity. With Software Defined Network (SDN), the data layer can be separated from the control layer. The development of relevant studies about Network Function Virtualization (NFV) and cloud computing has the potential of offering a quicker and more reliable network access for growing data traffic. Under such circumstances, Software Defined Mobile Network (SDMN) is presented as a promising solution for meeting the wireless data demands. This paper provides a survey of SDMN and its related security problems. As SDMN integrates cloud computing, SDN, and NFV, and works on improving network functions, performance, flexibility, energy efficiency, and scalability, it is an important component of the next generation telecommunication networks. However, Yongfeng Qian yongfeng.hust@gmail.com Min Chen minchen@ieee.org Shiwen Mao smao@ieee.org Wan Tang tangwan@scuec.edu.cn Ximin Yang yangximin@scuec.edu.cn 1 Embedded and Pervasive Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China 2 Department of Electrical & Computer Engineering, Auburn University, 200 Broun Hall, Auburn, AL, 36849-5201, USA 3 College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China the SDMN concept also raises new security concerns. We explore relevant security threats and their corresponding countermeasures with respect to the data layer, control layer, application layer, and communication protocols. We also adopt the STRIDE method to classify various security threats to better reveal them in the context of SDMN. This survey is concluded with a list of open security challenges in SDMN.
Trajectory tracking control for hovering and acceleration maneuver of Quad Tilt Rotor UAV

The ability to transition between vertical and horizontal flight is of great importance to convertible unmanned aerial vehicles. A novel convertible UAV-a Quad Tilt Rotor UAV (QTR UAV) was designed, which could operate as a quad rotor as well as an airplane by means of transition maneuver. This paper focuses on the trajectory tracking control for the hovering mode and acceleration maneuver which is the basis for the transition maneuver. First, a prototype QTR UAV was constructed to prove the concept and validate the proposed layout, which demonstrated good stability and control characteristics under manual control. Then dynamic model for full flight profile was constructed according to the first principle. A control strategy was scheduled for the transition maneuver. A nonlinear controller was designed based on dynamic inversion method. The effectiveness of the proposed control methodology was shown through numerical simulations.
Image analysis and machine learning in digital pathology: Challenges and opportunities

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.
A behavior model for persuasive design

This paper presents a new model for understanding human behavior. In this model (FBM), behavior is a product of three factors: motivation, ability, and triggers, each of which has subcomponents. The FBM asserts that for a person to perform a target behavior, he or she must (1) be sufficiently motivated, (2) have the ability to perform the behavior, and (3) be triggered to perform the behavior. These three factors must occur at the same moment, else the behavior will not happen. The FBM is useful in analysis and design of persuasive technologies. The FBM also helps teams work together efficiently because this model gives people a shared way of thinking about behavior change.
Technical Challenges of Forensic Investigations in Cloud Computing Environments

Cloud Computing is arguably one of the most discussed information technology topics in recent times. It presents many promising technological and economical opportunities. However, many customers remain reluctant to move their business IT infrastructure completely to “the Cloud“. One of the main concerns of customers is Cloud security and the threat of the unknown. Cloud Service Providers (CSP) encourage this perception by not letting their customers see what is behind their “virtual curtain“. A seldomly discussed, but in this regard highly relevant open issue is the ability to perform digital investigations. This continues to fuel insecurity on the sides of both providers and customers. In Cloud Forensics, the lack of physical access to servers constitutes a completely new and disruptive challenge for investigators. Due to the decentralized nature of data processing in the Cloud, traditional approaches to evidence collection and recovery are no longer practical. This paper focuses on the technical aspects of digital forensics in distributed Cloud environments. We contribute by assessing whether it is possible for the customer of Cloud Computing services to perform a traditional digital investigation from a technical standpoint. Furthermore we discuss possible new methodologies helping customers to perform such investigations and discuss future issues.
A new optimization method: Big Bang-Big Crunch

Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang–Big Crunch (BB–BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB–BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions. q 2005
Adaptive polynomial rendering

In this paper, we propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges. Our method locally approximates an image with polynomial functions and the optimal order of each polynomial function is estimated so that our reconstruction error can be minimized. To robustly estimate the optimal order, we propose a multi-stage error estimation process that iteratively estimates our reconstruction error. In addition, we present an energy-preserving outlier removal technique to remove spike noise without causing noticeable energy loss in our reconstruction result. Also, we adaptively allocate additional ray samples to high error regions guided by our error estimation. We demonstrate that our approach outperforms state-of-the-art methods by controlling the tradeoff between reconstruction bias and variance through locally defining our polynomial order, even without need for filtering bandwidth optimization, the common approach of other recent methods.
Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons.

The performance of the brain is constrained by wiring length and maintenance costs. The apparently inverse relationship between number of neurons in the various interneuron classes and the spatial extent of their axon trees suggests a mathematically definable organization, reminiscent of 'small-world' or scale-free networks observed in other complex systems. The wiring-economy-based classification of cortical inhibitory interneurons is supported by the distinct physiological patterns of class members in the intact brain. The complex wiring of diverse interneuron classes could represent an economic solution for supporting global synchrony and oscillations at multiple timescales with minimum axon length.
In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring

We introduce a novel in-ear sensor which satisfies key design requirements for wearable electroencephalography (EEG)-it is discreet, unobtrusive, and capable of capturing high-quality brain activity from the ear canal. Unlike our initial designs, which utilize custom earpieces and require a costly and time-consuming manufacturing process, we here introduce the generic earpieces to make ear-EEG suitable for immediate and widespread use. Our approach represents a departure from silicone earmoulds to provide a sensor based on a viscoelastic substrate and conductive cloth electrodes, both of which are shown to possess a number of desirable mechanical and electrical properties. Owing to its viscoelastic nature, such an earpiece exhibits good conformance to the shape of the ear canal, thus providing stable electrode-skin interface, while cloth electrodes require only saline solution to establish low impedance contact. The analysis highlights the distinguishing advantages compared with the current state-of-the-art in ear-EEG. We demonstrate that such a device can be readily used for the measurement of various EEG responses.
Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing

Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries. Over the next two years, this column on IoT will explore many challenges and technologies on intelligent use and applications of IoT data.
Tunable Bandpass Filter With Independently Controllable Dual Passbands

This paper presents a two-pole dual-band tunable bandpass filter (BPF) with independently controllable dual passbands based on a novel tunable dual-mode resonator. This resonator principally comprises a λ/2 resonator and two varactor diodes. One varactor is placed at the center of the resonator to determine the dominant even-mode resonant frequency; the other is installed between two ends of the resonator to control the dominant odd-mode resonant frequency. These two distinct odd- and even-mode resonances can be independently generated, and they are used to realize the two separated passbands as desired. Detailed discussion is carried on to provide a set of closed-form design equations for determination of all of the elements involved in this tunable filter, inclusive of capacitively loaded quarter-wavelength or λ/2 resonators, external quality factor, and coupling coefficient. Finally, a prototype tunable dual-band filter is fabricated and measured. Measured and simulated results are found in good agreement with each other. The results show that the first passband can be tuned in a frequency range from 0.77 to 1.00 GHz with the 3-dB fractional-bandwidth of 20.3%-24.7%, whereas the second passband varies from 1.57 to 2.00 GHz with the 3-dB absolute-bandwidth of 120 ± 8 MHz.
Stock Market Prediction Using Support Vector Machine

a lot of studies provide strong evidence that traditional predictive regression models face significant challenges in out-of sample predictability tests due to model uncertainty and parameter instability. Recent studies introduce particular strategies that overcome these problems. Support Vector Machine (SVM) is a relatively new learning algorithm that has the desirable characteristics of the control of the decision function, the use of the kernel method, and the sparsely of the solution. In this paper, we present a theoretical and empirical framework to apply the Support Vector Machines strategy to predict the stock market. Firstly, four company-specific and six macroeconomic factors that may influence the stock trend are selected for further stock multivariate analysis. Secondly, Support Vector Machine is used in analyzing the relationship of these factors and predicting the stock performance. Our results suggest that SVM is a powerful predictive tool for stock predictions in the financial market.
Generating Different Story Tellings from Semantic Representations of Narrative

In order to tell stories in different voices for different audiences, interactive story systems require: (1) a semantic representation of story structure, and (2) the ability to automatically generate story and dialogue from this semantic representation using some form of Natural Language Generation (nlg). However, there has been limited research on methods for linking story structures to narrative descriptions of scenes and story events. In this paper we present an automatic method for converting from Scheherazade’s story intention graph, a semantic representation, to the input required by the personage nlg engine. Using 36 Aesop Fables distributed in DramaBank, a collection of story encodings, we train translation rules on one story and then test these rules by generating text for the remaining 35. The results are measured in terms of the string similarity metrics Levenshtein Distance and BLEU score. The results show that we can generate the 35 stories with correct content: the test set stories on average are close to the output of the Scheherazade realizer, which was customized to this semantic representation. We provide some examples of story variations generated by personage. In future work, we will experiment with measuring the quality of the same stories generated in different voices, and with techniques for making storytelling interactive.
A review of clinical upper limb assessments within the framework of the WHO ICF.

It considers 25 upper limb assessments used in musculoskeletal care and presents a simple, straightforward comparative review of each. The World Health Organization International Classification on Functioning, Disability and Health (WHO ICF) is used to provide a relative summary of purpose between each assessment. Measurement properties of each assessment are provided, considering the type of data generated, availability of reliability estimates and normative data for the assessment.
Reducing the population's sodium intake: the UK Food Standards Agency's salt reduction programme.

To describe the UK Food Standards Agency's (FSA) salt reduction programme undertaken between 2003 and 2010 and to discuss its effectiveness.



Relevant scientific papers, campaign materials and evaluations and consultation responses to the FSA's salt reduction programme were used.



Adult salt intakes, monitored using urinary Na data collected from UK-wide surveys, indicate a statistically significant reduction in the population's average salt intake from 9·5 g/d in 2000-2001 to 8·6 g/d in 2008, which is likely to have health benefits.



Reducing salt intake will have an impact on blood pressure; an estimated 6 % of deaths from CHD in the UK can be avoided if the number of people with high blood pressure is reduced by 50 %.



Salt levels in food, monitored using commercial label data and information collected through an industry self-reporting framework, indicated that substantial reductions of up to 70 % in some foods had been achieved. The FSA's consumer campaign evaluation showed increased awareness of the benefits of reducing salt intake on health, with 43 % of adults in 2009 claiming to have made a special effort to reduce salt in their diet compared with 34 % of adults in 2004, before the campaign commenced.



The UK's salt reduction programme successfully reduced the average salt intake of the population and increased consumers' awareness. Significant challenges remain in achieving the population average salt intake of 6 g/d recommended by the UK's Scientific Advisory Committee on Nutrition. However, the UK has demonstrated the success of its programme and this approach is now being implemented elsewhere in the world.
A Trench-Gate High-Conductivity IGBT (HiGT) With Short-Circuit Capability

This paper describes a new 600-V trench-gate high-conductivity insulated gate bipolar transistor (trench HiGT) that has both a low collector-emitter saturation voltage of 1.55 V at 200 and a tough short-circuit capability of more than 10 . The trench HiGT also has better tradeoff relationship between turn-off switching loss and collector-emitter saturation voltage compared to either an insulated gate bipolar transistor (IGBT) with a planar gate or a conventional trench gate. A reverse transfer capacitance that is 50% lower than that of the planar-gate IGBT and an input capacitance that is 40% lower than that of a conventional trench gate IGBT have been obtained for the trench HiGT.
Evaluating Computational Creativity: An Interdisciplinary Tutorial

This article is a tutorial for researchers who are designing software to perform a creative task and want to evaluate their system using interdisciplinary theories of creativity. Researchers who study human creativity have a great deal to offer computational creativity. We summarize perspectives from psychology, philosophy, cognitive science, and computer science as to how creativity can be measured both in humans and in computers. We survey how these perspectives have been used in computational creativity research and make recommendations for how they should be used.
Feel the presence : technologies of touch and distance

Haptic devices for computers and video-game consoles aim to reproduce touch and to engage the user with `force feedback'. Although physical touch is often associated with proximity and intimacy, technologies of touch can reproduce such sensations over a distance, allowing intricate and detailed operations to be conducted through a network such as the Internet. The `virtual handshake' between Boston and London in 2002 is given as an example. This paper is therefore a critical investigation into some technologies of touch, leading to observations about the sociospatial framework in which this technological touching takes place. Haptic devices have now become routinely included with videogame consoles, and have started to be used in computer-aided design and manufacture, medical simulation, and even the cybersex industry. The implications of these new technologies are enormous, as they remould the human ^ computer interface from being primarily audiovisual to being more truly multisensory, and thereby enhance the sense of `presence' or immersion. But the main thrust of this paper is the development of ideas of presence over a large distance, and how this is enhanced by the sense of touch. By using the results of empirical research, including interviews with key figures in haptics research and engineering and personal experience of some of the haptic technologies available, I build up a picture of how `presence', `copresence', and `immersion', themselves paradoxically intangible properties, are guiding the design, marketing, and application of haptic devices, and the engendering and engineering of a set of feelings of interacting with virtual objects, across a range of distances. DOI:10.1068/d394t
Optimisation of the SHA-2 family of hash functions on FPGAs

Hash functions play an important role in modern cryptography. This paper investigates optimisation techniques that have recently been proposed in the literature. A new VLSI architecture for the SHA-256 and SHA-512 hash functions is presented, which combines two popular hardware optimisation techniques, namely pipelining and unrolling. The SHA processors are developed for implementation on FPGAs, thereby allowing rapid prototyping of several designs. Speed/area results from these processors are analysed and are shown to compare favourably with other FPGA-based implementations, achieving the fastest data throughputs in the literature to date
A Novel Hybrid Angle Tracking Observer for Resolver to Digital Conversion

Resolvers are absolute angle transducers that are usually used for position and speed measurement in permanent magnet motors. An observer that uses the sinusoidal signals of the resolver for this measurement is called an Angle Tracking Observer (ATO). Current designs for such observers are not stable in high acceleration and high-speed applications. This paper introduces a novel hybrid scheme for ATO design, in which a closed-loop LTI observer is combined with a quadrature encoder. Finite gain stability of the proposed design is proven based on the circle theorem in input-output stability theory. Simulation results show that the proposed ATO design is stable in two cases where an LTI observer and an extended Kalman filter are unstable due to high speed and acceleration,. In addition, the tracking accuracy of our hybrid scheme is substantially higher than a single quadrature encoder.