nils-herrmann/clean_scirepeval huggingface dataset:
A Multi-Layer Stackable Thin-Film Transistor (TFT) NAND-Type Flash Memory

A double-layer TFT NAND-type flash memory is demonstrated, ushering into the era of three-dimensional (3D) flash memory. A TFT device using bandgap engineered SONOS (BE-SONOS) (Lue et al., 2005, Lai et al., 2006) with fully-depleted (FD) poly silicon (60 nm) channel and tri-gate P+-poly gate is integrated into a NAND array. Small devices (L/W=0.2/0.09 mum) with excellent performance and reliability properties are achieved. The bottom layer shows no sign of reliability degradation compared to the top layer, indicating the potential for further multi-layer stacking. The present work illustrates the feasibility of 3D flash memory
Events Detection for an Audio-Based Surveillance System

The present research deals with audio events detection in noisy environments for a multimedia surveillance application. In surveillance or homeland security most of the systems aiming to automatically detect abnormal situations are only based on visual clues while, in some situations, it may be easier to detect a given event using the audio information. This is in particular the case for the class of sounds considered in this paper, sounds produced by gun shots. The automatic shot detection system presented is based on a novelty detection approach which offers a solution to detect abnormality (abnormal audio events) in continuous audio recordings of public places. We specifically focus on the robustness of the detection against variable and adverse conditions and the reduction of the false rejection rate which is particularly important in surveillance applications. In particular, we take advantage of potential similarity between the acoustic signatures of the different types of weapons by building a hierarchical classification system
Utility function security in artificially intelligent agents

The notion of ‘wireheading’, or direct reward centre stimulation of the brain, is a wellknown concept in neuroscience. In this paper, we examine the corresponding issue of reward (utility) function integrity in artificially intelligent machines. We survey the relevant literature and propose a number of potential solutions to ensure the integrity of our artificial assistants. Overall, we conclude that wireheading in rational selfimproving optimisers above a certain capacity remains an unsolved problem despite opinion of many that such machines will choose not to wirehead. A relevant issue of literalness in goal setting also remains largely unsolved and we suggest that the development of a non-ambiguous knowledge transfer language might be a step in the right direction.
Computer-aided system for defect inspection in the PCB manufacturing process

This paper presents a visual inspection system aimed at the automatic detection and classification of bare-PCB manufacturing errors. The interest of this CAE system lies in a twofold approach. On the one hand, we propose a modification of the subtraction method based on reference images that allows higher performance in the process of defect detection. On the other hand, this method is combined with a particle classification algorithm based on two measures of light intensity. As a result of this strategy, a machine vision application has been implemented to assist people in etching, inspection and verification tasks of PCBs.
Combining regression and classification methods for improving automatic speaker age recognition

We present a novel approach to automatic speaker age classification, which combines regression and classification to achieve competitive classification accuracy on telephone speech. Support vector machine regression is used to generate finer age estimates, which are combined with the posterior probabilities of well-trained discriminative gender classifiers to predict both the age and gender of a speaker. We show that this combination performs better than direct 7-class classifiers. The regressors and classifiers are trained using longterm features such as pitch and formants, as well as short-term (frame-based) features derived from MAP adaptation of GMMs that were trained on MFCCs.
Entity ranking in Wikipedia: utilising categories, links and topic difficulty prediction

Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.
A 32 nm Embedded, Fully-Digital, Phase-Locked Low Dropout Regulator for Fine Grained Power Management in Digital Circuits

The need for fine-grained power management in digital ICs has led to the design and implementation of compact, scalable low-drop out regulators (LDOs) embedded deep within logic blocks. While analog LDOs have traditionally been used in digital ICs, the need for digitally implementable LDOs embedded in digital functional units for ultrafine grained power management is paramount. This paper presents a fully-digital, phase locked LDO implemented in 32 nm CMOS. The control model of the proposed design has been provided and limits of stability have been shown. Measurement results with a resistive load as well as a digital load exhibit peak current efficiency of 98%.
Game-Theoretic Methods for Robustness, Security, and Resilience of Cyberphysical Control Systems: Games-in-Games Principle for Optimal Cross-Layer Resilient Control Systems

Critical infrastructures, such as power grids and transportation systems, are increasingly using open networks for operation. The use of open networks poses many challenges for control systems. The classical design of control systems takes into account modeling uncertainties as well as physical disturbances, providing a multitude of control design methods such as robust control, adaptive control, and stochastic control. With the growing level of integration of control systems with new information technologies, modern control systems face uncertainties not only from the physical world but also from the cybercomponents of the system. The vulnerabilities of the software deployed in the new control system infrastructure will expose the control system to many potential risks and threats from attackers. Exploitation of these vulnerabilities can lead to severe damage as has been reported in various news outlets [1], . More recently, it has been reported in  and [4] that a computer worm, Stuxnet, was spread to target Siemens supervisory control and data acquisition (SCADA) systems that are configured to control and monitor specific industrial processes.
The Design of Rijndael: AES - The Advanced Encryption Standard

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Recognizing emotions expressed by body pose: A biologically inspired neural model

Research into the visual perception of human emotion has traditionally focused on the facial expression of emotions. Recently researchers have turned to the more challenging field of emotional body language, i.e. emotion expression through body pose and motion. In this work, we approach recognition of basic emotional categories from a computational perspective. In keeping with recent computational models of the visual cortex, we construct a biologically plausible hierarchy of neural detectors, which can discriminate seven basic emotional states from static views of associated body poses. The model is evaluated against human test subjects on a recent set of stimuli manufactured for research on emotional body language.
Dynamic behavioral modeling of 3G power amplifiers using real-valued time-delay neural networks

In this paper, we propose a novel real-valued time-delay neural network (RVTDNN) suitable for dynamic modeling of the baseband nonlinear behaviors of third-generation (3G) base-station power amplifiers (PA). Parameters (weights and biases) of the proposed model are identified using the back-propagation algorithm, which is applied to the input and output waveforms of the PA recorded under real operation conditions. Time- and frequency-domain simulation of a 90-W LDMOS PA output using this novel neural-network model exhibit a good agreement between the RVTDNN behavioral model's predicted results and measured ones along with a good generality. Moreover, dynamic AM/AM and AM/PM characteristics obtained using the proposed model demonstrated that the RVTDNN can track and account for the memory effects of the PAs well. These characteristics also point out that the small-signal response of the LDMOS PA is more affected by the memory effects than the PAs large-signal response when it is driven by 3G signals. This RVTDNN model requires a significantly reduced complexity and shorter processing time in the analysis and training procedures, when driven with complex modulated and highly varying envelope signals such as 3G signals, than previously published neural-network-based PA models.
Potential effect of physical activity based menu labels on the calorie content of selected fast food meals

In this study we examined the effect of physical activity based labels on the calorie content of meals selected from a sample fast food menu. Using a web-based survey, participants were randomly assigned to one of four menus which differed only in their labeling schemes (n=802): (1) a menu with no nutritional information, (2) a menu with calorie information, (3) a menu with calorie information and minutes to walk to burn those calories, or (4) a menu with calorie information and miles to walk to burn those calories. There was a significant difference in the mean number of calories ordered based on menu type (p=0.02), with an average of 1020 calories ordered from a menu with no nutritional information, 927 calories ordered from a menu with only calorie information, 916 calories ordered from a menu with both calorie information and minutes to walk to burn those calories, and 826 calories ordered from the menu with calorie information and the number of miles to walk to burn those calories. The menu with calories and the number of miles to walk to burn those calories appeared the most effective in influencing the selection of lower calorie meals (p=0.0007) when compared to the menu with no nutritional information provided. The majority of participants (82%) reported a preference for physical activity based menu labels over labels with calorie information alone and no nutritional information. Whether these labels are effective in real-life scenarios remains to be tested.
Composing Video Game Levels with Music Metaphors through Functional Scaffolding

Artists and other creators naturally draw inspiration for new works based on previous artifacts in their own fields. Some of the most profound examples of creativity, however, also transform the field by redefining and combining rules from other domains. In procedural content generation for games, representations and constraints are typically modeled on the target domain. In contrast, this paper examines representing and generating video game levels through a representation called functional scaffolding for musical composition originally designed to procedurally compose music. Viewing music as a means to re-frame the way we think about, represent and computationally design video game levels, this paper presents a method for deconstructing game levels into multiple “instruments” or “voices,” wherein each voice represents a tile type. We then use functional scaffolding to automatically generate “accompaniment” to individual voices. Complete new levels are subsequently synthesized from generated voices. Our proof-of-concept experiments showcase that music is a rich metaphor for representing naturalistic, yet unconventional and playable, levels in the classic platform game Super Mario Bros, demonstrating the capacity of our approach for potential applications in computational creativity and game design.
Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the “ground-truth” reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.
Bacteriocins — a viable alternative to antibiotics?

Solutions are urgently required for the growing number of infections caused by antibiotic-resistant bacteria. Bacteriocins, which are antimicrobial peptides produced by certain bacteria, might warrant serious consideration as alternatives to traditional antibiotics. These molecules exhibit significant potency against other bacteria (including antibiotic-resistant strains), are stable and can have narrow or broad activity spectra. Bacteriocins can even be produced in situ in the gut by probiotic bacteria to combat intestinal infections. Although the application of specific bacteriocins might be curtailed by the development of resistance, an understanding of the mechanisms by which such resistance could emerge will enable researchers to develop strategies to minimize this potential problem.
An Approach for Text Summarization using Deep Learning Algorithm

Now days many research is going on for text summari zation. Because of increasing information in the internet, these kind of research are gaining more a nd more attention among the researchers. Extractive text summarization generates a brief summary by extracti ng proper set of sentences from a document or multi ple documents by deep learning. The whole concept is to reduce or minimize the important information prese nt in the documents. The procedure is manipulated by Rest rict d Boltzmann Machine (RBM) algorithm for better efficiency by removing redundant sentences. The res tricted Boltzmann machine is a graphical model for binary random variables. It consist of three layers input, hidden and output layer. The input data uni formly distributed in the hidden layer for operation. The experimentation is carried out and the summary is g enerated for three different document set from different kno wledge domain. The f-measure value is the identifie r to the performance of the proposed text summarization meth od. The top responses of the three different knowle dge domain in accordance with the f-measure are 0.85, 1 .42 and 1.97 respectively for the three document se t.
A New Brillouin Dispersion Diagram for 1-D Periodic Printed Structures

Dispersion and radiation properties for bound and leaky modes supported by 1-D printed periodic structures are investigated. A new type of Brillouin diagram is presented that accounts for different types of physical leakage, namely, leakage into one or more surface waves or also simultaneously into space. This new Brillouin diagram not only provides a physical insight into the dispersive behavior of such periodic structures, but it also provides a simple and convenient way to correctly choose the integration paths that arise from a spectral-domain moment-method analysis. Numerical results illustrate the usefulness of this new Brillouin diagram in explaining the leakage and stopband behavior for these types of periodic structures.
A Principled Approach to Score Level Fusion in Multimodal Biometric Systems

A multimodal biometric system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. We propose an optimal framework for combining the matching scores from multiple modalities using the likelihood ratio statistic computed using the generalized densities estimated from the genuine and impostor matching scores. The motivation for using generalized densities is that some parts of the score distributions can be discrete in nature; thus, estimating the distribution using continuous densities may be inappropriate. We present two approaches for combining evidence based on generalized densities: (i) the product rule, which assumes independence between the individual modalities, and (ii) copula models, which consider the dependence between the matching scores of multiple modalities. Experiments on the MSU and NIST multimodal databases show that both fusion rules achieve consistently high performance without adjusting for optimal weights for fusion and score normalization on a case-by-case basis.
Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as $$0.8683 \pm 0.0056$$ 0.8683 ± 0.0056 , $$0.9224 \pm 0.0027$$ 0.9224 ± 0.0027 , $$0.915 \pm 0.0077$$ 0.915 ± 0.0077 , $$0.0669 \pm 0.0032$$ 0.0669 ± 0.0032 , $$0.6228 \pm 0.1414$$ 0.6228 ± 0.1414 on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.
Predicting iPhone Sales from iPhone Tweets

Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.