nils-herrmann/clean_scirepeval huggingface dataset:
OFDM and TDM based sensing method for large projected mutual-capacitance touch screens

This paper presents a new technique for reading out projected mutual capacitance touch screen panels. It models touch screen panel as a communication system. Orthogonal frequency division multiplexing (OFDM) is used along with time division multiplexing (TDM) to read out all channels of touch screen panels. Different carrier frequencies are applied to touch screen panel concurrently using OFDM, while the sensing lines are sensed sequentially using TDM. The proposed technique ensures a high frame scan rate by using simultaneous driving signals, and it provides high signal to noise ratio (SNR) by avoiding frequency band with high noise power. Simulation results demonstrate a SNR improvement of 8.2 dB by selecting adequate carrier frequencies.
A Tagged Corpus and a Tagger for Urdu

In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.
A spatio-temporal reference model of the aging brain

Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9-91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.
Hull-form optimization in calm and rough water

The paper presents a formal methodology for the hull form optimization in calm and rough water using wash waves and selected dynamic responses, respectively. Parametric hull form modeling is used to generate the variant hull forms with some of the form parameters modified, which are evaluated in the optimization scheme based on evolutionary strategies. Rankine-source panel method and strip theories are used for the hydrodynamic evaluation. The methodology is implemented in the optimization of a double-chine, planing hull form. Furthermore, a dual-stage optimization strategy is applied on a modern fast displacement ferry. The effect of the selected optimization parameters is presented and discussed.
A Novel Differential-Fed Patch Antenna on Stepped-Impedance Resonator With Enhanced Bandwidth Under Dual-Resonance

A novel design concept to enhance the bandwidth of a differential-fed patch antenna using the dual-resonant radiation of a stepped-impedance resonator (SIR) is proposed. The SIR is composed of two distinctive portions: the radiating patch and a pair of open stubs. Initially, based on the transmission line model, the first and second odd-order radiative resonant modes, i.e., TM10 and TM30, of this SIR-typed patch antenna are extensively investigated. It is demonstrated that the frequency ratio between the dual-resonant modes can be fully controlled by the electrical length and the impedance ratios between the open stub and radiating patch. After that, the SIR-typed patch antenna is reshaped with stepped ground plane in order to increase the impedance ratio as highly required for wideband radiation. With this arrangement, these two radiative modes are merged with each other, resulting in a wide impedance bandwidth with a stable radiation pattern under dual-resonant radiation. Finally, the proposed antenna is designed, fabricated, and measured. It is verified in experiment that the impedance bandwidth (|Sdd11| <; -10 dB) of the proposed antenna has gained tremendous increment up to 10% (0.85-0.94 GHz) with two attenuation poles. Most importantly, the antenna has achieved a stable gain varying from 7.4 to 8.5 dB within the whole operating band, while keeping low-cross polarization.
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices

We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet. Our model is based on the well-known depthwise convolution, and on two new layers, which we introduce in this work: a wavelet convolution and a depthwise fast wavelet transform. By breaking the symmetry in channel dimensions and applying a fast algorithm, WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D) factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet classification show superior and comparable performances of WaveletNet compared to state-of-the-art models such as MobileNetV2.
New bottle but old wine: A research of cyberbullying in schools

This study investigates the nature and the extent of adolescences experience of cyberbullying. A survey study of 177 grade seven students in an urban city is conducted. In this paper, ‘‘cyberbullying’’ refers to bullying via electronic communication tools. The results show that almost 54% of the students were victims of traditional bullying and over a quarter of them had been cyber-bullied. Almost one in three students had bullied others in the traditional form, and almost 15% had bullied others using electronic communication tools. Almost 60% of the cyber victims are females, while over 52% of cyber-bullies are males. Majority of the cyber-bully victims and bystanders did not report the incidents to adults.
Factors affecting the Choice of High Tech Engineering majors for University women and Men in Bangladesh

In contrast to western countries which are experiencing declines in female enrollment in high tech engineering (HTE), Bangladesh has experienced a continuous increase in female enrollment in engineering. In order to better understand the factors driving the choice of HTE as a major, survey data was collected from 590 male and female students in HTE majors in Bangladesh. Hypotheses related to gender differences in the influence of various factors on major choice were examined using t-tests. Results revealed that, while several factors had a similar impact for women and men, gender differences in selfefficacy and socio-economic status persist and may represent barriers to women’s participation.
Loosely-Coupled Benchmark Framework Automates Performance Modeling on IaaS Clouds

Cloud computing is under rapid development, which brings the urgent need of evaluation and comparison of cloud systems. The performance testers often struggle in tedious manual operations, when carrying out lots of similar experiments on the cloud systems. However, few of current benchmark tools provide both flexible workflow controlling methodology and extensible workload abstraction at the same time. We present a modeling methodology to compare the performance from multiple aspects based on a loosely coupled benchmark framework, which automates experiments under agile workflow controlling and achieves broad cloud supports, as well as good workload extensibility. With several built-in workloads and scenario templates, we performed a series of tests on Amazon EC2 services and our private Open Stack-based cloud, and analyze the elasticity and scalability based on the performance models. Experiments show the robustness and compatibility of our framework, which makes remarkable guarantee that it can be leveraged in practice for researchers and testers to perform their further study.
In search of how people change. Applications to addictive behaviors.

How people intentionally change addictive behaviors with and without treatment is not well understood by behavioral scientists. This article summarizes research on self-initiated and professionally facilitated change of addictive behaviors using the key trans-theoretical constructs of stages and processes of change. Modification of addictive behaviors involves progression through five stages--pre-contemplation, contemplation, preparation, action, and maintenance--and individuals typically recycle through these stages several times before termination of the addiction. Multiple studies provide strong support for these stages as well as for a finite and common set of change processes used to progress through the stages. Research to date supports a trans-theoretical model of change that systematically integrates the stages with processes of change from diverse theories of psychotherapy.
A 60 GHz Broadband Low-Noise Amplifier With Variable-Gain Control in 65 nm CMOS

A 60 GHz low-noise amplifier (LNA) implemented in a 65 nm CMOS process is presented. Due to the use of a gain-boosted input stage and binary controlled attenuators, the LNA exhibits a broadband response and four programmable gain levels from 18.9 to 7.9 dB while maintaining impedance matching at the 60 GHz frequency band. The fabricated circuit consumes a dc current of 25 mA from a 1.8 V supply.
Mining time-changing data streams

Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.
Opinion mining and sentiment analysis on a Twitter data stream

Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives before purchasing an item, but today the trend is to identify the opinions of a variety of individuals around the globe using microblogging data. This paper discusses an approach where a publicised stream of tweets from the Twitter microblogging site are preprocessed and classified based on their emotional content as positive, negative and irrelevant; and analyses the performance of various classifying algorithms based on their precision and recall in such cases. Further, the paper exemplifies the applications of this research and its limitations.
Computationally efficient link prediction in a variety of social networks

Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between individuals with at least one common friend. We also present a method for calculating the amount of data needed in order to build more accurate classifiers. The new Friends measure and Same community features we developed are shown to be good predictors for missing links. An evaluation experiment was performed on ten large social networks datasets: Academia.edu, DBLP, Facebook, Flickr, Flixster, Google+, Gowalla, TheMarker, Twitter, and YouTube. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in online social networks.
Regional cerebral blood flow correlates of visuospatial tasks in Alzheimer's disease.

This study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 +/- 6.6, MMSE > or = 27), 86 patients with mild AD (age: 69.18 +/- 8.28, MMSE > or = 21) and 42 patients moderate/severe AD (age: 68.86 +/- 10.69, MMSE 8-20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology.
A 130 mV SRAM With Expanded Write and Read Margins for Subthreshold Applications

SRAM suffers read-disturb and write failures at a low supply voltage, especially at deep subthreshold operation. This study proposes a 9T-SRAM cell with a data-aware-feedback-cutoff (DAFC) scheme to enlarge the write margin and dynamic-read-decoupled (DRD) scheme to prevent read-disturb for achieving deep subthreshold operation. A 30 mV negative-pumped wordline scheme is employed to suppress bitline leakage current. The fabricated 90 nm 32 Kb 9T-SRAM macro achieves 130 mV VDDmin. All the 32 Kb 9T cells are stable across read and write operations when operated at 105 mV.
The Impact of Social Engineering on Industrial Control System Security

In assessing the security posture of Industrial Control Systems (ICS), several approaches have been proposed, including attack graphs, attack trees, Bayesian networks and security ideals. Predominantly focusing on technical vulnerabilities, challenges stemming from social and organisational factors are often reviewed in isolation, if at all. Taking a mean time-to-compromise (MTTC) metric as a base for expansion, we explore the impact social engineering attack vectors (malicious e-mails) could have on such assessments. The applied method takes a holistic view, to better understand the potential impact of social engineering across a small European utility company. The results of this review are analysed and discussed, highlighting the level of access an attacker could gain through social engineering, and the need for assessment metrics to include vulnerabilities stemming not only from technical factors, but social and organisational ones as well.
Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

To investigate the impact of visualizing car uncertainty on drivers' trust during an automated driving scenario, a simulator study was conducted. A between-group design experiment with 59 Swedish drivers was carried out where a continuous representation of the uncertainty of the car's ability to autonomously drive during snow conditions was displayed to one of the groups, whereas omitted for the control group. The results show that, on average, the group of drivers who were provided with the uncertainty representation took control of the car faster when needed, while they were, at the same time, the ones who spent more time looking at other things than on the road ahead. Thus, drivers provided with the uncertainty information could, to a higher degree, perform tasks other than driving without compromising with driving safety. The analysis of trust shows that the participants who were provided with the uncertainty information trusted the automated system less than those who did not receive such information, which indicates a more proper trust calibration than in the control group.
Fully integrated CMOS power amplifier design using the distributed active-transformer architecture

A novel on-chip impedance matching and powercombining method, the distributed active transformer is presented. It combines several low-voltage push–pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any offchip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50input and output matching has been fabricated using 0.35m CMOS transistors. It achieves a power added efficiency (PAE) of 41% at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time.
Deep Noise Tracking Network: A Hybrid Signal Processing/Deep Learning Approach to Speech Enhancement

Noise statistics and speech spectrum characteristics are the essential information for the single channel speech enhancement. The signal processing-based methods mainly rely on noise statistics estimation. They perform very well for stationary noise, but have remained difficult to cope with non-stationary noise. While the deep learning-based methods mainly focus on the perception on the spectrum characteristics of speech and have a capacity in dealing with non-stationary noise. However, the performance would degrade dramatically for the unseen noise types, which could be due to the over-reliance on data and the ignorance to domain knowledge of signal process. Obviously, the hybrid signal processing/deep learning scheme may be a smart alternative. In this paper, we incorporate the powerful perceptual capabilities of deep learning in the conventional speech enhancement framework. Deep learning is used to estimate the speech presence probability and the update factor of noise statistics, which are then integrated into the Wiener filter-based speech enhancement structure to enhance the desired speech. All components are jointly optimized by a spectrum approximation objective. Systematic experiments on CHiME-4 and NOISEX-92 demonstrate the proposed hybrid signal processing/deep learning approach to noise suppression in noise-unmatched and noise-matched conditions.
On the Geo-Indicativeness of Non-Georeferenced Text

Geographic location is a key component for information retrieval on the Web, recommendation systems in mobile computing and social networks, and placebased integration on the Linked Data cloud. Previous work has addressed how to estimate locations by named entity recognition, from images, and via structured data. In this paper, we estimate geographic regions from unstructured, non geo-referenced text by computing a probability distribution over the Earth’s surface. Our methodology combines natural language processing, geostatistics, and a data-driven bottom-up semantics. We illustrate its potential for mapping geographic regions from non geo-referenced text.
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.