nils-herrmann/clean_scirepeval huggingface dataset:
The Multidimensional Wisdom of Crowds

Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different “schools of thought” amongst the annotators, and can group together images belonging to separate categories.
Internet self-efficacy and electronic service acceptance

Internet self-efficacy (ISE), or the beliefs in one’s capabilities to organize and execute courses of Internet actions required to produce given attainments, is a potentially important factor to explain the consumers’ decisions in e-commerce use, such as eservice. In this study, we introduce two types of ISE (i.e., general Internet self-efficacy and Web-specific self-efficacy) as new factors that reflect the user’s behavioral control beliefs in e-service acceptance. Using these two constructs as behavioral control factors, we extend and empirically validate the Theory of Planned Behavior (TPB) for the World Wide Web (WWW) context. D 2003 Elsevier B.V. All rights reserved.
Virtual Wave Optics for Non-Line-of-Sight Imaging

Non-Line-of-Sight (NLOS) imaging allows to observe objects partially or fully occluded from direct view, by analyzing indirect diffuse reflections off a secondary, relay surface. Despite its many potential applications1–9, existing methods lack practical usability due to several shared limitations, including the assumption of single scattering only, lack of occlusions, and Lambertian reflectance. We lift these limitations by transforming the NLOS problem into a virtual Line-Of-Sight (LOS) one. Since imaging information cannot be recovered from the irradiance arriving at the relay surface, we introduce the concept of the phasor field, a mathematical construct representing a fast variation in irradiance. We show that NLOS light transport can be modeled as the propagation of a phasor field wave, which can be solved accurately by the Rayleigh-Sommerfeld diffraction integral. We demonstrate for the first time NLOS reconstruction of complex scenes with strong multiply scattered and ambient light, arbitrary materials, large depth range, and occlusions. Our method handles these challenging cases without explicitly developing a light transport model. By leveraging existing fast algorithms8, we outperform existing methods in terms of execution speed, computational complexity, and memory use. We believe that our approach will help unlock the potential of NLOS imaging, and the development of novel applications not restricted to lab conditions. For example, we demonstrate both refocusing and transient NLOS videos of real-world, complex scenes with large depth.
Inf2vec: Latent Representation Model for Social Influence Embedding

As a fundamental problem in social influence propagation analysis, learning influence parameters has been extensively investigated. Most of the existing methods are proposed to estimate the propagation probability for each edge in social networks. However, they cannot effectively learn propagation parameters of all edges due to data sparsity, especially for the edges without sufficient observed propagation. Different from the conventional methods, we introduce a novel social influence embedding problem, which is to learn parameters for nodes rather than edges. Nodes are represented as vectors in a low-dimensional space, and thus social influence information can be reflected by these vectors. We develop a new model Inf2vec, which combines both the local influence neighborhood and global user similarity to learn the representations. We conduct extensive experiments on two real-world datasets, and the results indicate that Inf2vec significantly outperforms state-of-the-art baseline algorithms.
Fraud detection in water meters using pattern recognition techniques

Water supply utilities have been increasingly looking for solutions to reduce water wastage. Many efforts have been made aiming to promote a better management of this resource. Fraud detection is one of these actions, as the irregular violations are usually held precariously, thus, causing leaks. In this context, the use of technology in order to automate the identification of potential frauds can be an important support tool to avoid water waste. Thus, this research aims to apply pattern recognition techniques in the implementation of an automated detection of suspected irregularities cases in water meters, through image analysis. The proposed computer vision system is composed of three steps: the detection of the water meter location, obtained by OPF classifier and HOG descriptor, detecting the seals through morphological image processing and segmentation methods; and the classification of frauds, in which the condition of the water meter seals is assessed. We validated the proposed framework using a dataset containing images of water meter inspections. At the last step, the proposed framework reached an average accuracy up to 81.29%. We concluded that a computer vision system is a promising strategy and has potential to benefit the analysis of fraud detection.
The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services

This work aims to advance Web Service retrieval, also known as Matching, in two directions. First, it introduces a matching algorithm for SAWSDL, which adapts and extends known concepts with novel strategies. Effective logic-based and syntactic strategies are introduced and combined in a novel hybrid strategy, targeting an envisioned well-defined, real-world scenario for matching. The algorithm is evaluated in a universal environment for matching algorithms, SME2, in an objective, reproducible manner. Evaluation ranks Tomaco high amongst state of the art, especially for early recall levels (first in macro-averaging precision, up to 0.7 recall). Secondly, this work introduces the Tomaco web application, which aims to promote wide-spread adoption of Semantic Web Services while targeting the lack of user-friendly applications in this field, by integrating a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.
Hierarchical Online Intrusion Detection for SCADA Networks

We propose a novel hierarchical online intrusion detection system (HOIDS) for supervisory control and data acquisition (SCADA) networks based on machine learning algorithms. By utilizing the server-client topology while keeping clients distributed for global protection, high detection rate is achieved with minimum network impact. We implement accurate models of normal-abnormal binary detection and multi-attack identification based on logistic regression and quasi-Newt on optimization algorithm using the Broyden-Fletcher-GoldfarbShanno approach. The detection system is capable of acceler ating detection by information gain based feature selection or pr inciple component analysis based dimension reduction. By evaluati ng our system using the KDD99 dataset and the industrial contro l system dataset, we demonstrate that HOIDS is highly scalabl e, efficient and cost effective for securing SCADA infrastruct ures.
Combining instance-based learning and logistic regression for multilabel classification

Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms relying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hitherto existing algorithms do not take correlations and interdependencies between labels into account, their potential has not yet been fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a framework that unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allows one to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabel classification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms of several evaluation criteria for multilabel prediction.
Information ethics: On the philosophical foundation of computer ethics

The essential difficulty about Computer Ethics' (CE) philosophical status is a methodological problem: standard ethical theories cannot easily be adapted to deal with CE-problems, which appear to strain their conceptual resources, and CE requires a conceptual foundation as an ethical theory. Information Ethics (IE), the philosophical foundational counterpart of CE, can be seen as a particular case of “environmental” ethics or ethics of the infosphere. What is good for an information entity and the infosphere in general? This is the ethical question asked by IE. The answer is provided by a minimalist theory of deseerts: IE argues that there is something more elementary and fundamental than life and pain, namely being, understood as information, and entropy, and that any information entity is to be recognised as the centre of a minimal moral claim, which deserves recognition and should help to regulate the implementation of any information process involving it. IE can provide a valuable perspective from which to approach, with insight and adequate discernment, not only moral problems in CE, but also the whole range of conceptual and moral phenomena that form the ethical discourse.
Look me in the eyes: constraining gaze in the eye-region provokes abnormally high subcortical activation in autism

Individuals with Autism Spectrum Disorder (ASD) seem to have difficulties looking others in the eyes, but the substrate for this behavior is not well understood. The subcortical pathway, which consists of superior colliculus, pulvinar nucleus of the thalamus, and amygdala, enables rapid and automatic face processing. A specific component of this pathway – i.e., the amygdala – has been shown to be abnormally activated in paradigms where individuals had to specifically attend to the eye-region; however, a direct examination of the effect of manipulating the gaze to the eye-regions on all the components of the subcortical system altogether has never been performed. The subcortical system is particularly important as it shapes the functional specialization of the face-processing cortex during development. Using functional MRI, we investigated the effect of constraining gaze in the eye-region during dynamic emotional face perception in groups of participants with ASD and typical controls. We computed differences in activation in the subcortical face processing system (superior colliculus, pulvinar nucleus of the thalamus and amygdala) for the same stimuli seen freely or with the gaze constrained in the eye-region. Our results show that when constrained to look in the eyes, individuals with ASD show abnormally high activation in the subcortical system, which may be at the basis of their eye avoidance in daily life.
The Adult Sensory Profile: measuring patterns of sensory processing.

This article describes a series of studies designed to evaluate the reliability and validity of the Adult Sensory Profile.



Expert judges evaluated the construct validity of the items. Coefficient alpha, factor analysis, and correlations of items with subscales determined item reliability, using data from 615 adult sensory profiles. A subsample of 20 adults furnished skin conductance data. A heterogeneous group of 93 adults completed the revised Adult Sensory Profile, and item reliability was reexamined.



Expert judgment indicated that items could be categorized according to Dunn's Model of Sensory Processing. Results suggested reasonable item reliability for all subscales except for the Sensation Avoiding subscale. Skin conductance measures detected distinct patterns of physiological responses consistent with the four-quadrant model. Revision of the Adult Sensory Profile resulted in improved reliability of the Sensation Avoiding subscale.



The series of studies provides evidence to support the four subscales of the Adult Sensory Profile as distinct constructs of sensory processing preferences.
Moving Right Along: A Computational Model of Metaphoric Reasoning about Events

This paper describes the results of an implemented computational model that cashes out the belief that metaphor interpretation is grounded in embodied primitives. The speci c task addressed is the interpretation of simple causal narratives in the domains of Politics and Economics. The stories are taken from newspaper articles in these domains. When presented with a preparsed version of these narratives as input, the system described is able to generate commonsense inferences consistent with the input.
Behavioral and Neural Adaptation in Approach Behavior

People often make approachability decisions based on perceived facial trustworthiness. However, it remains unclear how people learn trustworthiness from a population of faces and whether this learning influences their approachability decisions. Here we investigated the neural underpinning of approach behavior and tested two important hypotheses: whether the amygdala adapts to different trustworthiness ranges and whether the amygdala is modulated by task instructions and evaluative goals. We showed that participants adapted to the stimulus range of perceived trustworthiness when making approach decisions and that these decisions were further modulated by the social context. The right amygdala showed both linear response and quadratic response to trustworthiness level, as observed in prior studies. Notably, the amygdala's response to trustworthiness was not modulated by stimulus range or social context, a possible neural dynamic adaptation. Together, our data have revealed a robust behavioral adaptation to different trustworthiness ranges as well as a neural substrate underlying approach behavior based on perceived facial trustworthiness.
Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy ( $$>$$ > 90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://www.plant-phenotyping.org/datasets ) to support future challenges beyond segmentation within this application domain.
A Low-Power Wideband Transmitter Front-End Chip for 80 GHz FMCW Radar Systems With Integrated 23 GHz Downconverter VCO

A low-power FMCW 80 GHz radar transmitter front-end chip is presented, which was fabricated in a SiGe bipolar production technology ( fT=180 GHz, fmax=250 GHz ). Additionally to the fundamental 80 GHz VCO, a 4:1-frequency divider (up to 100 GHz), a 23 GHz local oscillator (VCO) with a low phase noise of -112 dBc/Hz (1 MHz offset), a PLL-mixer and a static frequency divider is integrated together with several output buffers. This chip was designed for low power consumption (in total <; 0.5 W, i.e., 100 mA at 5 V supply voltage), which is dominated by the 80 GHz VCO due to the demands for high output power (≈ 12 dBm) and low phase noise (minimum -97 dBc/Hz at 1 MHz offset) within the total wide tuning range from 68 GHz to 92.5 GHz (Δf = 24.5 GHz). Measurements of the double-PLL system at 80 GHz showed a low phase noise of -88 dBc/Hz at 10 kHz offset frequency.
Developing a Concept-Level Knowledge Base for Sentiment Analysis in Singlish

In this paper, we present Singlish sentiment lexicon, a conceptlevel knowledge base for sentiment analysis that associates multiword expressions to a set of emotion labels and a polarity value. Unlike many other sentiment analysis resources, this lexicon is not built by manually labeling pieces of knowledge coming from general NLP resources such as WordNet or DBPedia. Instead, it is automatically constructed by applying graph-mining and multi-dimensional scaling techniques on the affective common-sense knowledge collected from three different sources. This knowledge is represented redundantly at three levels: semantic network, matrix, and vector space. Subsequently, the concepts are labeled by emotions and polarity through the ensemble application of spreading activation, neural networks and an emotion categorization model.
A user experience framework and model within experiential living labs for Internet of Things

This paper is a follow-up of a previous paper, dedicated to the description of a holistic view of the user experience (UX) model, presenting the refined user experience framework and model within the Experiential Living Labs for Internet of Things (IoT) applications. The holistic model of UX was instantiated for six different use case domains appertaining to the Internet of Things (IoT) based services, namely: wellbeing, environment, logistics, energy efficiency, eBusiness and health. These six use cases demonstrate the difficulty to select the appropriate model elements and properties according to the IoT application context as well as to identify proper metrics for measuring UX. However, based on the six use cases experiments, a bottom-up (grounded) UX model applicable to any IoT use case appears to emerge from the empirical results. The experiments conducted within the use cases show the interest of the iterative nature of the Living Lab experiential design process for improving the design until it reaches the highest level of user satisfaction inducing an appropriate technology adoption rate by consumers. Finally, the refined Experiential Design process and UX model are presented with a set of conclusions.
Optimizing 360 video delivery over cellular networks

As an important component of the virtual reality (VR) technology, 360-degree videos provide users with panoramic view and allow them to freely control their viewing direction during video playback. Usually, a player displays only the visible portion of a 360 video. Thus, fetching the entire raw video frame wastes bandwidth. In this paper, we consider the problem of optimizing 360 video delivery over cellular networks. We first conduct a measurement study on commercial 360 video platforms. We then propose a cellular-friendly streaming scheme that delivers only 360 videos' visible portion based on head movement prediction. Using viewing data collected from real users, we demonstrate the feasibility of our approach, which can reduce bandwidth consumption by up to 80% based on a trace-driven simulation.
Double-Balanced Gilbert Mixer with Current Bleeding for RF Front-End Using 0.13µm SiGe BiCMOS Technology

This paper presents the design of a differential double-balanced Gilbert mixer in 0.13 um SiGe BiCMOS technology. A current-bleeding injection technique is adopted to increase the bias current at the driver stage without causing overvoltage headroom at the differential pair stage. This mechanism improves the performance in terms of conversion gain, linearity and noise figure. The proposed mixer achieves 10.7 dB conversion gain, 15 dB noise figure, -1.67 dBm 1-dB compression point, and 5 dBm IIP3. The designed double balanced Gilbert mixer with current bleeding is part of an integrated RF front-end for full duplex radio applications in the 2.4 GHz band and occupies an area of 0.1002 × 0.0748 mm2 excluding the pads.
A morphologic two-stage approach for automated optic disk detection in color eye fundus images

A new adaptive morphological method for the automatic detection of the optic disk in digital color eye fundus images is presented in this paper. This method has been designed to detect the optic disk center and the optic disk rim. In our experiments with the DRIVE and DIARETDB1 databases, the proposed method was able to detect the optic disk center with 100% and 97.75% of accuracy, respectively. We considered correct all automatically detected optic disk location that is within the borders of the optic disk marked manually. Using our proposed method, the rim of the optic disk was detected in all images of the DRIVE database with average sensitivity and specificity of 83.54% and 99.81%, respectively, and on the DIARETDB1 database with average sensitivity and specificity of 92.51% and 99.76%, respectively. 2013
Analysis of hidden units in a layered network trained to classify sonar targets

-A neural network learning procedure has been applied to the classification ~/sonar returns [kom two undersea targets, a metal cylinder and a similarly shaped rock. Networks with an intermediate layer ~/ hidden processing units achieved a classification accuracy as high as 100% on a training set of l04 returns. These net~orks correctly classified up to 90.4% of 104 test returns not contained in the training set. This perfi~rmance was better than that of a nearest neighbor classifier, which was 82.7%. and was close to that of an optimal Bayes classifie~ Specific signal features extracted by hidden units in a trained network were identified and related to coding schemes in the pattern of connection strengths between the input and the hidden units. Network perlbrmance and class[/~cation strategy was comparable to that of trained human listeners. Learning algorithms, Hidden units. Multilayered neural network, Sonar, Signal processing.