nils-herrmann/clean_scirepeval huggingface dataset:
Dialog Act Modeling for Conversational Speech

We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42`dialog acts' which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classiiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achieve 84% on this task (with chance performance at 35%) we nd these results encouraging.
Evaluation of Computer Games Developed by Primary School Children to Gauge Understanding of Programming Concepts

Under the Curriculum for Excellence (CfE) in Scotland, newer approaches such as games-based learning and games-based construction are being adopted to motivate and engage students. Construction of computer games is seen by some to be a highly motivational and practical approach at engaging children at Primary Education (PE) level in computer programming concepts. Gamesbased learning (GBL) and games-based construction both suffer from a dearth of empirical evidence supporting their validity as teaching and learning approaches. To address this issue, this paper will present the findings of observational research at PE level using Scratch as a tool to develop computer games using rudimentary programming concepts. A list of criteria will be compiled for reviewing the implementation of each participant to gauge the level of programming proficiency demonstrated. The study will review 29 games from Primary 4 to Primary 7 level and will present the overall results and results for each individual year. This study will contribute to the empirical evidence in games-based construction by providing the results of observational research across different levels of PE and will provide pedagogical guidelines for assessing programming ability using a games-based construction approach.
Non-linear Canonical Correlation Analysis using a RBF networks

A non-linear version of the multivariate statistical technique of canonical correlation analysis (CCA) is proposed through the integration of a radial basis function (RBF) network. The advantage of the RBF network is that the solution of linear CCA can be used to train the network and hence the training effort is minimal. Also the canonical variables can be extracted simultaneously. It is shown that the proposed technique can be used to extract non-linear structures inherent within a data set.
Fast penetration depth computation for physically-based animation

We present a novel and fast algorithm to compute penetration depth (PD) between two polyhedral models for physically-based animation. Given two overlapping polyhedra, it computes the minimal translation distance to separate them using a combination of object-space and image-space techniques. The algorithm computes pairwise Minkowski sums of decomposed convex pieces and performs a closest point query using rasterization hardware. It uses bounding volume hierarchies, object-space and image-space culling algorithms to further accelerate the computation and refines the estimated PD in a hierarchical manner. We demonstrate its application to contact response computation and a time-stepping method for dynamic simulation.
Focused Crawler Optimization Using Genetic Algorithm

As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.
FIRST EXPERIMENTAL INVESTIGATIONS ON WHEEL-WALKING FOR IMPROVING TRIPLE-BOGIE ROVER LOCOMOTION PERFORMANCES

Deployment actuators of a triple-bogie rover locomotion platform can be used to perform Wheel-Walking (WW) manoeuvres. How WW could affect the traversing capabilities of rovers is a recurrent debate in the planetary robotics community. The Automation and Robotics Section of ESTEC has initiated a long term project to evaluate the performance of WW manoeuvres in different scenarios. This paper presents the first experimental results on this project, obtained during the test campaign run on November 2014 at the Planetary Robotics Lab (PRL) of ESTEC, and shows the performance analysis made when comparing WW with standard rolling. The PRL rover prototype ExoTeR was used to test three different scenarios: entrapment in loose soil, up-slope traverse and lander egressing. WW locomotion showed increased capabilities in all scenarios and proved its relevance and advantages for planetary exploration missions.
Preprocessing and Classification of Electrophoresis Gel Images Using Dynamic Time Warping

1 Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejni 2906/4, CZ-616 00 Brno, Czech Republic 2 Department of Chemistry and Biochemistry, Faculty of Agronomy, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic 3 International Clinical Research Center – Center of Biomedical Engineering, St. Anne's University Hospital Brno, Brno, Czech Republic * E-mail: provaznik@feec.vutbr.cz
Comprehensive Two-Level Analysis of Static and Dynamic RBAC Constraints with UML and OCL

Organizations with stringent security requirements like banks or hospitals frequently adopt role-based access control (RBAC) principles to simplify their internal permission management. Authorization constraints represent a fundamental advanced RBAC concept enabling precise restrictions on access rights. Thereby, the complexity of the resulting security policies increases so that tool support for comfortable creation and adequate validation is required. We propose a new approach to developing and analyzing RBAC policies using UML for modeling RBAC core concepts and OCL to realize authorization constraints. Dynamic (i. e., time-dependent) constraints, their visual representation in UML and their analysis are of special interest. The approach results in a domain-specific language for RBAC which is highly configurable and extendable with respect to new RBAC concepts and classes of authorization constraints and allows the developer to validate RBAC policies in an effective way. The approach is supported by a UML and OCL validation tool.
A Model of User Adoption of Mobile Portals

The purpose of the study is to present a conceptual model of user adoption of mobile portals. This model identifies factors which may potentially influence an individual’s decision whether to start or continue utilizing wireless portals. The major distinction of the proposed model from those of prior MIS technology adoption projects is that it includes not only widely employed MIS constructs but also the perceived value construct of a mobile portal. This construct is adapted from the marketing literature. It reflects the perceived level of a wireless service quality relative to the airtime cost. The rationale for the introduction of perceived value in terms of an individual’s direct financial expenses lies in the unique nature of mobile communication devices usage. The proposed model also identifies two individual-specific antecedents and five portal-specific antecedents of those key constructs because they may potentially explicate the variance of users’ perceptions of portal experiences. In addition, the paper presents a survey of real-life users of mobile portals, designs a questionnaire, and selects appropriate data analysis techniques.
A user's guide to cannabinoid therapies in oncology.

"Cannabinoid" is the collective term for a group of chemical compounds that either are derived from the Cannabis plant, are synthetic analogues, or occur endogenously. Although cannabinoids interact mostly at the level of the currently recognized cannabinoid receptors, they might have cross reactivity, such as at opioid receptors. Patients with malignant disease represent a cohort within health care that have some of the greatest unmet needs despite the availability of a plethora of guideline-driven disease-modulating treatments and pain and symptom management options. Cannabinoid therapies are varied and versatile, and can be offered as pharmaceuticals (nabilone, dronabinol, and nabiximols), dried botanical material, and edible organic oils infused with cannabis extracts. Cannabinoid therapy regimens can be creative, involving combinations of all of the aforementioned modalities. Patients with malignant disease, at all points of their disease trajectory, could be candidates for cannabinoid therapies whether as monotherapies or as adjuvants. The most studied and established roles for cannabinoid therapies include pain, chemotherapy-induced nausea and vomiting, and anorexia. Moreover, given their breadth of activity, cannabinoids could be used to concurrently optimize the management of multiple symptoms, thereby reducing overall polypharmacy. The use of cannabinoid therapies could be effective in improving quality of life and possibly modifying malignancy by virtue of direct effects and in improving compliance or adherence with disease-modulating treatments such as chemotherapy and radiation therapy.
Regional coherence changes in the early stages of Alzheimer’s disease: A combined structural and resting-state functional MRI study

Recent functional imaging studies have indicated that the pathophysiology of Alzheimer's disease (AD) can be associated with the changes in spontaneous low-frequency (<0.08 Hz) blood oxygenation level-dependent fluctuations (LFBF) measured during a resting state. The purpose of this study was to examine regional LFBF coherence patterns in early AD and the impact of regional brain atrophy on the functional results. Both structural MRI and resting-state functional MRI scans were collected from 14 AD subjects and 14 age-matched normal controls. We found significant regional coherence decreases in the posterior cingulate cortex/precuneus (PCC/PCu) in the AD patients when compared with the normal controls. Moreover, the decrease in the PCC/PCu coherence was correlated with the disease progression measured by the Mini-Mental State Exam scores. The changes in LFBF in the PCC/PCu may be related to the resting hypometabolism in this region commonly detected in previous positron emission tomography studies of early AD. When the regional PCC/PCu atrophy was controlled, these results still remained significant but with a decrease in the statistical power, suggesting that the LFBF results are at least partly explained by the regional atrophy. In addition, we also found increased LFBF coherence in the bilateral cuneus, right lingual gyrus and left fusiform gyrus in the AD patients. These regions are consistent with previous findings of AD-related increased activation during cognitive tasks explained in terms of a compensatory-recruitment hypothesis. Finally, our study indicated that regional brain atrophy could be an important consideration in functional imaging studies of neurodegenerative diseases.
Predicting Student Retention in Massive Open Online Courses using Hidden Markov Models

Massive Open Online Courses (MOOCs) have a high attrition rate: most students who register for a course do not complete it. By examining a student's history of actions during a course, we can predict whether or not they will drop out in the next week, facilitating interventions to improve retention. We compare predictions resulting from several modeling techniques and several features based on different student behaviors. Our best predictor uses a Hidden Markov Model (HMM) to model sequences of student actions over time, and encodes several continuous features into a single discrete observable state using a simple cross­product method. It yielded an ROC AUC (Receiver Operating Characteristic Area Under the Curve score) of 0.710, considerably better than a random predictor. We also use simpler HMM models to derive information about which student behaviors are most salient in determining student retention.
Opensmile: the munich versatile and fast open-source audio feature extractor

We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake

In this work, we conducted a case study of a popular Chinese microblogging site, Sina-Weibo, to investigate how Chinese netizens used microblogging in response to a major disaster: the 2010 Yushu Earthquake. We combined multiple analysis methods in this case study, including content analysis of microblog messages, trend analysis of different topics, and an analysis of the information spreading process. This study helped us understand the roles played by microblogging systems in response to major disasters and enabled us to gain insight into how to harness the power of microblogging to facilitate disaster response. In addition, this work supplements existing works with an exploration of a non-Western socio-cultural system: how Chinese Internet users used microblogging in disaster response.
Robot pebbles: One centimeter modules for programmable matter through self-disassembly

This paper describes the design, fabrication, and experimental results of a programmable matter system capable of 2D shape formation through subtraction. The system is composed of autonomous 1cm modules which use custom-designed electropermanent magnets to bond, communicate, and share power with their neighbors. Given an initial block composed of many of these modules latched together in a regular crystalline structure, our system is able to form shapes by detaching the unnecessary modules. Many experiments show that the modules in our system are able to distribute data at 9600bps to their neighbors with a 98.5% success rate after four retries, and the connectors are able to support over 85 times the weight of a single module.
Who can catch a liar?

The ability to detect lying was evaluated in 509 people including law-enforcement personnel, such as members of the U.S. Secret Service, Central Intelligence Agency, Federal Bureau of Investigation, National Security Agency, Drug Enforcement Agency, California police and judges, as well as psychiatrists, college students, and working adults. A videotape showed 10 people who were either lying or telling the truth in describing their feelings. Only the Secret Service performed better than chance, and they were significantly more accurate than all of the other groups. When occupational group was disregarded, it was found that those who were accurate apparently used different behavioral clues and had different skills than those who were inaccurate.
Poisson surface reconstruction

We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.
Graph-based term weighting for text categorization

Text categorization is an important task with plenty of applications, ranging from sentiment analysis to automated news classification. In this paper, we introduce a novel graph-based approach for text categorization. Contrary to the traditional Bag-of-Words model for document representation, we consider a model in which each document is represented by a graph that encodes relationships between the different terms. The importance of a term to a document is indicated using graph-theoretic node centrality criteria. The proposed weighting scheme is able to meaningfully capture the relationships between the terms that co-occur in a document, creating feature vectors that can improve the categorization task. We perform experiments in well-known document collections, applying popular classification algorithms. Our preliminary results indicate that the proposed graph-based weighting mechanism is able to outperform existing frequency-based term weighting criteria, under appropriate parameter setting.
Lung nodule detection in CT using 3D convolutional neural networks

We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter and further reduce the structure variability by estimating the local orientation. The nodule candidates in the form of 3D cubes are fed into a deep 3D convolutional neural network that is trained to differentiate nodule and non-nodule inputs. We use data augmentation techniques to generate a large number of training examples and apply regularization to avoid overfitting. On a set of 99 CT scans, the proposed system achieved state-of-the-art performance and significantly outperformed a similar hybrid system that uses conventional shallow learning. The experimental results showed benefits of using a priori models to reduce the problem space for data-driven machine learning of complex deep neural networks. The results also showed the advantages of 3D CNN over 2D CNN in volumetric medical image analysis.
Comprehension of Argument Structure and Semantic Roles: Evidence from English-Learning Children and the Forced-Choice Pointing Paradigm

Research using the intermodal preferential looking paradigm (IPLP) has consistently shown that English-learning children aged 2 can associate transitive argument structure with causal events. However, studies using the same methodology investigating 2-year-old children's knowledge of the conjoined agent intransitive and semantic role assignment have reported inconsistent findings. The aim of the present study was to establish at what age English-learning children have verb-general knowledge of both transitive and intransitive argument structure using a new method: the forced-choice pointing paradigm. The results suggest that young 2-year-olds can associate transitive structures with causal (or externally caused) events and can use transitive structure to assign agent and patient roles correctly. However, the children were unable to associate the conjoined agent intransitive with noncausal events until aged 3;4. The results confirm the pattern from previous IPLP studies and indicate that children may develop the ability to comprehend different aspects of argument structure at different ages. The implications for theories of language acquisition and the nature of the language acquisition mechanism are discussed.
A 280nW, 100kHz, 1-cycle start-up time, on-chip CMOS relaxation oscillator employing a feedforward period control scheme

A sub-μW, 1-cycle start-up CMOS relaxation oscillator has been developed with a feedforward period control scheme and a digitally-controlled boost charging technique. The oscillator is implemented in 90nm CMOS and we sucessfuly have demonstrated 100kHz clock generation with ±1%-accuracy and an extremely low power consumption of 280nW.
On the Impossibility of Basing Identity Based Encryption on Trapdoor Permutations

We ask whether an identity based encryption (IBE) system can be built from simpler public-key primitives. We show that there is no black-box construction of IBE from trapdoor permutations (TDP) or even from chosen ciphertext secure public key encryption (CCA-PKE). These black-box separation results are based on an essential property of IBE, namely that an IBE system is able to compress exponentially many public-keys into a short public parameters string.
Perceiving affective content in ambiguous visual stimuli: a component of emotional intelligence.

Emotional intelligence involves the accurate appraisal and expression of emotions in oneself and others and the regulation of emotion in a way that enhances living. One aspect of emotional intelligence is the ability to recognize the consensually agreed upon emotional qualities of objects in the environment. One hundred thirty-nine adults viewed 18 reproductions of faces, color swatches, and abstract designs and rated the emotional content of these visual stimuli. Three scores were extracted, including consensual accuracy, amount, and range of emotion perceived. These scores were compared with other aspects of emotional intelligence such as empathy and related to constructs such as alexithymia and neuroticism. A general ability to perceive consensual emotional content in visual stimuli was found, and it was most strongly associated with the ability to respond empathically to others.