nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Technology Adoption : Theory and Future Directions for Informal Learning

How and why individuals adopt innovations has motivated a great deal of research. This article examines individuals' computing adoption processes through the lenses of three adoption theories: Rogers's innovation diffusion theory, the Concerns-Based Adoption Model, the Technology Acceptance Model, and the United Theory of Acceptance and Use of Technology. Incorporating all three models, this article suggests technology adoption is a complex, inherently social, developmental process; individuals construct unique yet malleable perceptions of technology that influence their adoption decisions. Thus, successfully facilitating technology adoption must address cognitive, emotional, and contextual concerns. This article also focuses specific attention on adoption theory outside of a formal organization and the implications of adoption theory on informal environments. For a crime against the gods—the specifics of which are variously reported—he was condemned to an eternity at hard labor. And frustrating labor at that. For his assignment was to roll a great boulder to the top of a hill. Only every time Sisyphus, by the greatest of exertion and toil, attained the summit, the darn thing rolled back down again.
Improving the Performance of the FFT-based Parallel Code-phase Search Acquisition of GNSS Signals by Decomposition of the Circular Correlation

Dr. Cyril Botteron leads the GNSS and UWB groups in the electronics and signal processing laboratory at EPFL. He received his PhD degree from the University of Calgary, Canada, in 2003. His current research interests comprise the development of low power radio frequency (RF) integrated circuits and advanced signal processing techniques for ultra-low power communications and global and local positioning applications.
Deep Creations: Intellectual Property and the Automata

The rapid progress of deep neural network architectures is allowing both to automate the production of artworks and to extend the domain of creative expression. As such, it is opening new ground for professional and amateur artists alike. A major asset of these new computer processes is their capacity to derive, from a training phase, a generative model from which new artifacts can be produced. This attribute allows for a wide range of novel applications. New music or paintings in the style of famous artists can be produced at the click of a button, or combined to form new artworks. New graphical compositions can be “hallucinated” by the deep algorithmic models to produce striking, unexpected, visual forms. By the same token, the dependence on preexisting, protected, artworks lays the ground for potential zones of friction with the rights holders of the source data that helped shape the generative model. This articulation, between the popular creative movement initiated by the deep neural architectures and the preexisting rights of the authors, leads to a confrontation between the present legal framework for the protection of artistic creations and the new modalities offered by these new technological objects. The present work will address the conditions of protection of creations generated by deep neural networks under the main copyright regimes.
Survey of Maneuvering Target Tracking . Part I : Dynamic Models

The key to successful target tracking lies in the effective extraction of useful information about the target’s state from observations. A good model of the target will certainly facilitate this information extraction to a great extent. In general, one can say without exaggeration that a good model is worth a thousand pieces of data. This statement has an even stronger positive connotation in target tracking where observation data are rather limited. Most tracking algorithms are model based because knowledge of target motion is available and a good model-based tracking algorithm will greatly outperform any model-free tracking algorithm if the underlying model turns out to be a good one. As such, it is hard to overstate the importance of the role of a good model here. Various mathematical models of target motion have been developed over the past three decades. They are, however, scattered in the literature. Many of them have never appeared in any periodical in the public domain. As a result, few people have a good knowledge of these models. This is partly due to a lack of a comprehensive survey. The importance of such a survey for both practitioners and researchers in the tracking community is evident. The single best source so far is, in our opinion, the recent book by Blackman and Popoli , which is nonetheless far from complete. Some more or less standard models for target motion can be found in established books on target tracking and/or estimation, such as [2–12]. This paper is the first part of a comprehensive and up-to-date survey of the techniques for maneuvering target tracking. The survey is an ongoing project. The conference versions of its first several parts have appeared in [13–17]. It is well known that the so-called measurement-origin uncertainty and target motion uncertainty are two major challenges in target tracking. To limit the scope of the work, this survey deals only with the second uncertainty, leaving the techniques unique for the data-association problems untouched. Target detection, tracking, and recognition are closely interrelated areas, with significant overlaps. It is not easy to draw a clear line to separate them. To be relatively more focused, this part covers mainly dynamic models of a “point target,” that is, those of the dynamic (temporal) behaviors, rather than spatial characteristics, of a target. While many of these models are also useful for target detection and recognition, this survey is only concerned with their value for target tracking. This of course does not prevent us from developing or applying a model that describes both the temporal evolution and spatial characteristics of a target. Needless to say, target dynamic models and tracking algorithms have intimate ties.
The Missing Link of Jewish European Ancestry: Contrasting the Rhineland and
the Khazarian Hypotheses

The question of Jewish ancestry has been the subject of controversy for over two centuries and has yet to be resolved. The "Rhineland hypothesis" depicts Eastern European Jews as a "population isolate" that emerged from a small group of German Jews who migrated eastward and expanded rapidly. Alternatively, the "Khazarian hypothesis" suggests that Eastern European Jews descended from the Khazars, an amalgam of Turkic clans that settled the Caucasus in the early centuries CE and converted to Judaism in the 8th century. Mesopotamian and Greco-Roman Jews continuously reinforced the Judaized empire until the 13th century. Following the collapse of their empire, the Judeo-Khazars fled to Eastern Europe. The rise of European Jewry is therefore explained by the contribution of the Judeo-Khazars. Thus far, however, the Khazars' contribution has been estimated only empirically, as the absence of genome-wide data from Caucasus populations precluded testing the Khazarian hypothesis. Recent sequencing of modern Caucasus populations prompted us to revisit the Khazarian hypothesis and compare it with the Rhineland hypothesis. We applied a wide range of population genetic analyses to compare these two hypotheses. Our findings support the Khazarian hypothesis and portray the European Jewish genome as a mosaic of Near Eastern-Caucasus, European, and Semitic ancestries, thereby consolidating previous contradictory reports of Jewish ancestry. We further describe a major difference among Caucasus populations explained by the early presence of Judeans in the Southern and Central Caucasus. Our results have important implications for the demographic forces that shaped the genetic diversity in the Caucasus and for medical studies.
A Tool for Visualizing the Topology of Three-Dimensional Vector Fields

We describe a software system, TOPO, that numerically analyzes and graphically displays topological aspects of a three dimensional vector field, v, to produce a single relatively simple picture that characterizes v. The topology of v that we consider consists of its critical points (where v = 0), their invariant manifolds, and the integral curves connecting these invariant manifolds. Many of the interesting features of v are associated with its critical points. The field in the neighborhood of each critical point is approximated by the Taylor expansion. The coefficients of the first non-zero term of the Taylor expansion around a critical point are the 3x3 matrix ▿v. Critical points are classified by examining ▿v's eigenvalues. The eigenvectors of ▿v span the invariant manifolds of the linearized field around a critical point. Curves integrated from initial points on the eigenvectors a small distance from a critical point connect with other critical points (or the boundary) to complete the topology. In addition, one class of critical surfaces important in computational fluid dynamics is analyzed.TOPO is implemented as a module in the FAST  visualization environment. FAST is general purpose visualization software with modules for isosurface generation, particle tracing, etc. TOPO operates on curvilinear, structured grids, including large multi-zone grids. We have used TOPO to visualize a number of computational fluid dynamics (CFD) data sets. The results agree well with other topology software and hand generated topologies. TOPO has proved useful in finding surface topology, flow attachment and separation points, vortex cores, scalar field local extrema, and generally interesting regions of v. We believe there may be other interesting applications yet to be discovered. This paper, along with the references, contains most of the information needed for a scientific programmer to code a topology module in another environment.
Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar

Ultra-wideband (UWB) pulse Doppler radars can be used for noncontact vital signs monitoring of more than one subject. However, their detected signals typically have low signal-to-noise ratio (SNR) causing significant heart rate (HR) detection errors, as the spurious harmonics of respiration signals and mixed products of respiration and heartbeat signals (that can be relatively higher than heartbeat signals) corrupt conventional fast Fourier transform spectrograms. In this paper, we extend the complex signal demodulation (CSD) and arctangent demodulation (AD) techniques previously used for accurately detecting the phase variations of reflected signals of continuous wave radars to UWB pulse radars as well. These detection techniques reduce the impact of the interfering harmonic signals, thus improving the SNR of the detected vital sign signals. To further enhance the accuracy of the HR estimation, a recently developed state-space method has been successfully combined with CSD and AD techniques and over 10 dB improvements in SNR is demonstrated. The implementation of these various detection techniques has been experimentally investigated and full error and SNR analysis of the HR detection are presented.
A comparison-based approach to mispronunciation detection

The task of mispronunciation detection for language learning is typically accomplished via automatic speech recognition (ASR). Unfortunately, less than 2% of the world's languages have an ASR capability, and the conventional process of creating an ASR system requires large quantities of expensive, annotated data. In this paper we report on our efforts to develop a comparison-based framework for detecting word-level mispronunciations in nonnative speech. Dynamic time warping (DTW) is carried out between a student's (non-native speaker) utterance and a teacher's (native speaker) utterance, and we focus on extracting word-level and phone-level features that describe the degree of mis-alignment in the warping path and the distance matrix. Experimental results on a Chinese University of Hong Kong (CUHK) nonnative corpus show that the proposed framework improves the relative performance on a mispronounced word detection task by nearly 50% compared to an approach that only considers DTW alignment scores.
Clustering Validity Based on the Improved Hubert \Gamma Statistic and the Separation of Clusters

The validity of clustering is one important research field in clustering analysis, and many clustering validity functions have been proposed, especially those based on the geometrical structure of data set, such as Dunn's index and Xie-Beni index. In this way, the compactness and the separation of clusters are usually taken into account. Xie-Beni index decreases with the number of partitions increasing. It is difficult to choose the optimal number of clusters when there are lots of clusters in data. In this paper, a novel clustering validity function is proposed, which is based on the improved Huber Gamma statistic combined with the separation of clusters. Unlike other clustering validity, the function has the only maximum with the clustering number increasing. The experiments indicate that the function can be used as the optimal index for the choice of the clustering numbers
Toward crowdsourcing micro-level behavior annotations: the challenges of interface, training, and generalization

Research that involves human behavior analysis usually requires laborious and costly efforts for obtaining micro-level behavior annotations on a large video corpus. With the emerging paradigm of crowdsourcing however, these efforts can be considerably reduced. We first present OCTAB (Online Crowdsourcing Tool for Annotations of Behaviors), a web-based annotation tool that allows precise and convenient behavior annotations in videos, directly portable to popular crowdsourcing platforms. As part of OCTAB, we introduce a training module with specialized visualizations. The training module's design was inspired by an observational study of local experienced coders, and it enables an iterative procedure for effectively training crowd workers online. Finally, we present an extensive set of experiments that evaluates the feasibility of our crowdsourcing approach for obtaining micro-level behavior annotations in videos, showing the reliability improvement in annotation accuracy when properly training online crowd workers. We also show the generalization of our training approach to a new independent video corpus.
Simulation on Simulink AC4 model (200hp DTC induction motor drive) using Fuzzy Logic controller

Classical direct torque control (DTC) has advantage in absence of coordinate transform and voltage modulator block. However, it may have disadvantage in controlling electromagnetic torque. DTC produces high ripple in electromagnetic torque as it is not directly controlled. High torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. Thus, Fuzzy Logic controller is applied to reduce electromagnetic torque ripple. This paper presents the simulation analysis of Fuzzy Logic Direct Torque Controller (FLDTC) of induction machine drives. However, only the design of the controller using built in DTC induction motor drive model of Simulink AC4 will be discussed in this paper. Using FLDTC, the resulting electromagnetic torque from produced less ripple than classical DTC.
A Collection for Detecting Triggers of Sentiment Spikes

The advent of social media has given the opportunity to users to publicly express and share their opinion about any topic. Public opinion is very important for the interested entities that can leverage such information in the process of making decisions. In addition, identifying sentiment changes and the likely causes that have triggered them allows interested parties to adjust their strategies and attract more positive sentiment. With the aim to facilitate research on this problem, we describe a collection of tweets that can be used for detecting and ranking the likely triggers of sentiment spikes towards different entities. To build the collection, we first group tweets by topic which are then manually annotated according to sentiment polarity and strength. We believe that this collection can be useful for further research on detecting sentiment change triggers, sentiment analysis and sentiment prediction.
A Critical Evaluation of User Participation Research: Gaps and Future Directions

In this theoretical synthesis, we juxtapose three traditions of prior research on user participation and involvement: the survey and experimental literature on the relationship between user participation and IS success, the normative literature on alternative development approaches, and qualitative studies that examine user participation from a variety of theoretical perspectives. We also assess progress made in the three bodies of literature, and identify gaps and directions of future research for improving user participation.
An embedded artificial skin for humanoid robots

A novel artificial skin for covering the whole body of a humanoid robot is presented. It provides pressure measurements and shape information about the contact surfaces between the robot and the environment. The system is based on a mesh of sensors interconnected in order to form a networked structure. Each sensor has 12 capacitive taxels, has a triangular shape and is supported by a flexible substrate in order to conform to smooth curved surfaces. Three communications ports placed along the sides of each sensor sides allow communications with adjacent sensors. The tactile measurements are sent to embed microcontroller boards using serial bus communication links. The system can adaptively reduce its spatial resolution, improving the response time. This feature is very useful for detecting the first contact very rapidly, at a lower spatial resolution, and then increase the spatial resolution in the region of contact for accurate reconstruction of the contact pressure distribution.
An Image Inpainting Technique Based on the Fast Marching Method

Digital inpainting provides a means for reconstruction of small damaged portions of an image. Although the inpainting basics are straightforward, most inpainting techniques published in the literature are complex to understand and implement. We present here a new algorithm for digital inpainting based on the fast marching method for level set applications. Our algorithm is very simple to implement, fast, and produces nearly identical results to more complex, and usually slower, known methods.
Quaternion Convolutional Neural Networks for Heterogeneous Image Processing

Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edges or shapes, making it efficient with small or heterogeneous datasets. Quaternion-valued convolutional neural networks (QCNN) solved this problematic by introducing multidimensional algebra to CNN. This paper proposes to explore the fundamental reason of the success of QCNN over CNN, by investigating the impact of the Hamilton product on a color image reconstruction task performed from a gray-scale only training. By learning independently both internal and external relations and with less parameters than real valued convolutional encoder-decoder (CAE), quaternion convolutional encoder-decoders (QCAE) perfectly reconstructed unseen color images while CAE produced worst and gray-scale versions.
Silk-based biomaterials.

Silk from the silkworm, Bombyx mori, has been used as biomedical suture material for centuries. The unique mechanical properties of these fibers provided important clinical repair options for many applications. During the past 20 years, some biocompatibility problems have been reported for silkworm silk; however, contamination from residual sericin (glue-like proteins) was the likely cause. More recent studies with well-defined silkworm silk fibers and films suggest that the core silk fibroin fibers exhibit comparable biocompatibility in vitro and in vivo with other commonly used biomaterials such as polylactic acid and collagen. Furthermore, the unique mechanical properties of the silk fibers, the diversity of side chain chemistries for 'decoration' with growth and adhesion factors, and the ability to genetically tailor the protein provide additional rationale for the exploration of this family of fibrous proteins for biomaterial applications. For example, in designing scaffolds for tissue engineering these properties are particularly relevant and recent results with bone and ligament formation in vitro support the potential role for this biomaterial in future applications. To date, studies with silks to address biomaterial and matrix scaffold needs have focused on silkworm silk. With the diversity of silk-like fibrous proteins from spiders and insects, a range of native or bioengineered variants can be expected for application to a diverse set of clinical needs.
Establishment of tumor-specific copy number alterations from plasma DNA of patients with cancer

With the increasing number of available predictive biomarkers, clinical management of cancer is becoming increasingly reliant on the accurate serial monitoring of tumor genotypes. We tested whether tumor-specific copy number changes can be inferred from the peripheral blood of patients with cancer. To this end, we determined the plasma DNA size distribution and the fraction of mutated plasma DNA fragments with deep sequencing and an ultrasensitive mutation-detection method, i.e., the Beads, Emulsion, Amplification, and Magnetics (BEAMing) assay. When analyzing the plasma DNA of 32 patients with Stage IV colorectal carcinoma, we found that a subset of the patients (34.4%) had a biphasic size distribution of plasma DNA fragments that was associated with increased circulating tumor cell numbers and elevated concentration of mutated plasma DNA fragments. In these cases, we were able to establish genome-wide tumor-specific copy number alterations directly from plasma DNA. Thus, we could analyze the current copy number status of the tumor genome, which was in some cases many years after diagnosis of the primary tumor. An unexpected finding was that not all patients with progressive metastatic disease appear to release tumor DNA into the circulation in measurable quantities. When we analyzed plasma DNA from 35 patients with metastatic breast cancer, we made similar observations suggesting that our approach may be applicable to a variety of tumor entities. This is the first description of such a biphasic distribution in a surprisingly high proportion of cancer patients which may have important implications for tumor diagnosis and monitoring.
Learning Human Identity From Motion Patterns

We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind data set of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We compare several neural architectures for efficient learning of temporal multi-modal data representations, propose an optimized shift-invariant dense convolutional mechanism, and incorporate the discriminatively trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems. Finally, we demonstrate that the proposed model can also be successfully applied in a visual context.