nils-herrmann/clean_scirepeval huggingface dataset:
A Humanoid Robot Companion for Wheelchair Users

In this paper we integrate a humanoid robot with a powered wheelchair with the aim of lowering the cognitive requirements needed for powered mobility. We propose two roles for this companion: pointing out obstacles and giving directions. We show that children enjoyed driving with the humanoid companion by their side during a field-trial in an uncontrolled environment. Moreover, we present the results of a driving experiment for adults where the companion acted as a driving aid and conclude that participants preferred the humanoid companion to a simulated companion. Our results suggest that people will welcome a humanoid companion for their wheelchairs.
Cultural Representations of Gender and Science Portrayals of Female Scientists and Engineers in Popular Films

Images of female scientists and engineers in popular$lms convey cultural and social assumptions about the role of women in science, engineering, and technology (SET). This study analyzed cultural representations of gender conveyed through images offemale scientists andengineers in popularjilms from 1991 to 2001. While many of these depictions of female scientists and engineers emphasized their appearance and focused on romance, most depictions also presented female scientists and engineers in professional positions of high status. Other images that showed the fernale scientists and engineers' interactions with male colleagues, ho~vevel; reinforced traditional social and cultural assumptions about the role of women in SET through overt and subtle forms of stereotyping. This article explores the sign$cance of thesejindings fordevelopingprograms to change girls'perceptions of scientists and engineers and attitudes toward SET careers.
Mining anchor text for query refinement

When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.
A Bayesian LDA-based model for semi-supervised part-of-speech tagging

We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that words’ distributions over tags, p(t|w), are sparse. In addition we introduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outperforms the best previously proposed model for this task on a standard dataset.
Measuring and avoiding side effects using relative reachability

How can we design reinforcement learning agents that avoid causing unnecessary disruptions to their environment? We argue that current approaches to penalizing side effects can introduce bad incentives in tasks that require irreversible actions, and in environments that contain sources of change other than the agent. For example, some approaches give the agent an incentive to prevent any irreversible changes in the environment, including the actions of other agents. We introduce a general definition of side effects, based on relative reachability of states compared to a default state, that avoids these undesirable incentives. Using a set of gridworld experiments illustrating relevant scenarios, we empirically compare relative reachability to penalties based on existing definitions and show that it is the only penalty among those tested that produces the desired behavior in all the scenarios.
Level-of-detail visualization of clustered graph layouts

The level-of-detail techniques presented in this paper enable a comprehensible interactive visualization of large and complex clustered graph layouts either in 2D or 3D. Implicit surfaces are used for the visually simplified representation of vertex clusters, and so-called edge bundles are formed for the simplification of edges. Additionally, dedicated transition techniques are provided for continuously adaptive and adjustable views of graphs that range from very abstract to very detailed representations.
Creating a Probabilistic Graph for WordNet using Markov Logic Network

The paper shows how to create a probabilistic graph for WordNet. A node is created for every word and phrase in WordNet. An edge between two nodes is labeled with the probability that a user that is interested in the source concept will also be interested in the destination concept. For example, an edge with weight 0.3 between "canine" and "dog" indicates that there is a 30% probability that a user who searches for "canine" will be interested in results that contain the word "dog". We refer to the graph as probabilistic because we enforce the constraint that the sum of the weights of all the edges that go out of a node add up to one. Structural (e.g., the word "canine" is a hypernym (i.e., kind of) of the word "dog") and textual (e.g., the word "canine" appears in the textual definition of the word "dog") data from WordNet is used to create a Markov logic network, that is, a set of first order formulas with probabilities. The Markov logic network is then used to compute the weights of the edges in the probabilistic graph. We experimentally validate the quality of the data in the probabilistic graph on two independent benchmarks: Miller and Charles and WordSimilarity-353.
A 1-to-6Gb/s phase-interpolator-based burst-mode CDR in 65nm CMOS

Burst-mode clock and data recovery circuits (BMCDR) are widely used in passive optical networks (PON)  and as a replacement for conventional CDRs in clock-forwarding links to reduce power [2]. In PON, a single CDR performs the task of clock and data recovery for several burst sequences, each originating from a different source. As a result, the BMCDR is required to lock to an incoming data stream within tens of UIs (for example 40ns in GPON). Previous works use either injection locking [3, 4] or gated VCO [5, 6] to achieve this fast locking. In both cases, the control voltage of the CDR's VCO is generated by a reference PLL with a matching VCO to guarantee accurate frequency locking. However, any component mismatch between the two VCO's results in a frequency offset between the reference PLL frequency and the CDR's VCO frequency, and hence in a reduction of the CDR's tolerance for consecutive identical digits (CID). For example, [7] reports a frequency offset of over 20MHz (2000ppm) for 10Gb/s operation. We present a BMCDR that is based on phase interpolation (PI), eliminating the possibility of local frequency offset between the reference and recovered clock. We demonstrate 1 to 6Gb/s operation in 65nm CMOS with a locking time of less than 1UI.
Synaptic density in human frontal cortex - developmental changes and effects of aging.

Density of synaptic profiles in layer 3 of middle frontal gyrus was quantitated in 21 normal human brains ranging from newborn to age 90 years. Synaptic profiles could be reliably demonstrated by the phosphotungstic acid method (Bloom and Aghajanian) in tissue fixed up to 36 h postmortem. Synaptic density was constant throughout adult life (ages 16--72 years) with a mean of 11.05 X 10(8) synapses/cu.mm +/- 0.41 S.E.M. There was a slight decline in synaptic density in brains of the aged (ages 74--90 years) with a mean of 9.56 X 10(8) synapses/cu.mm +/- 0.28 S.E.M. in 4 samples (P less than 0.05). Synaptic density in neonatal brains was already high--in the range seen in adults. However, synaptic morphology differed; immature profiles had an irregular presynaptic dense band instead of the separate presynaptic projections seen in mature synapses. Synaptic density increased during infancy, reaching a maximum at age 1--2 years which was about 50% above the adult mean. The decline in synaptic density observed between ages 2--16 years was accompanied by a slight decrease in neuronal density. Human cerebral cortex is one of a number of neuronal systems in which loss of neurons and synapses appears to occur as a late developmental event.
Towards Crowdsourcing Clickbait Labels for YouTube Videos

Clickbait is increasingly used by publishers on social media platforms to spark their users’ natural curiosity and to elicit clicks on their content. Every click earns them display advertisement revenue. Social media users who are tricked into clicking may experience a sense of disappointment or agitation, and social media operators have been observing growing amounts of clickbait on their platforms. As largest video-sharing platform on the web, YouTube, too, suffers from clickbait. Many users and YouTubers alike have complained about this development. In this paper, we lay the foundation for crowdsourcing the first YouTube clickbait corpus by (1) augmenting the YouTube 8M dataset with meta data to obtain a large-scale base population of videos, and by (2) studying the task design suitable to manual clickbait identification.
Boosting and Differential Privacy

Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\em base synopsis generator} that takes a distribution on $\Q$ and produces a ``weak'' synopsis that yields ``good'' answers for a majority of the weight in $\Q$, our {\em Boosting for Queries} algorithm obtains a synopsis that is good for all of~$\Q$. We ensure privacy for the rows of the database, but the boosting is performed on the {\em queries}. We also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, {\it i.e.}, queries whose answers do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss, we obtain an $O(\eps^2)$ bound on the {\em expected} privacy loss from a single $\eps$-\dfp{} mechanism. Combining this with evolution of confidence arguments from the literature, we get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides $\eps$-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.
Histogram refinement for content-based image retrieval

Color histograms are widely used for content-based image retrieval. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very different appearances can have similar histograms. We describe a technique for comparing images called histogram refinement, which imposes additional constraints on histogram based matching. Histogram refinement splits the pixels in a given bucket into several classes, based upon some local property. Within a given bucket, only pixels in the same class are compared. We describe a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried using CCV’s in under 2 seconds. We demonstrate that histogram refinement can be used to distinguish images whose color histograms are indistinguishable.
Current and lifetime comorbidity of the DSM-IV anxiety and mood disorders in a large clinical sample.

The comorbidity of current and lifetime DSM-IV anxiety and mood disorders was examined in 1,127 outpatients who were assessed with the Anxiety Disorders Interview Schedule for DSM-IV: Lifetime version (ADIS-IV-L). The current and lifetime prevalence of additional Axis I disorders in principal anxiety and mood disorders was found to be 57% and 81%, respectively. The principal diagnostic categories associated with the highest comorbidity rates were mood disorders, posttraumatic stress disorder (PTSD), and generalized anxiety disorder (GAD). A high rate of lifetime comorbidity was found between the anxiety and mood disorders; the lifetime association with mood disorders was particularly strong for PTSD, GAD, obsessive-compulsive disorder, and social phobia. The findings are discussed in regard to their implications for the classification of emotional disorders.
A behavior based malware detection scheme for avoiding false positive

The number of malware is increasing rapidly and a lot of malware use stealth techniques such as encryption to evade pattern matching detection by anti-virus software. To resolve the problem, behavior based detection method which focuses on malicious behaviors of malware have been researched. Although they can detect unknown and encrypted malware, they suffer a serious problem of false positives against benign programs. For example, creating files and executing them are common behaviors performed by malware, however, they are also likely performed by benign programs thus it causes false positives. In this paper, we propose a malware detection method based on evaluation of suspicious process behaviors on Windows OS. To avoid false positives, our proposal focuses on not only malware specific behaviors but also normal behavior that malware would usually not do. Moreover, we implement a prototype of our proposal to effectively analyze behaviors of programs. Our evaluation experiments using our malware and benign program datasets show that our malware detection rate is about 60% and it does not cause any false positives. Furthermore, we compare our proposal with completely behavior-based anti-virus software. Our results show that our proposal puts few burdens on users and reduces false positives.
A 10–100-GHz Double-Ridged Horn Antenna and Coax Launcher

The design, fabrication, and measurement of a coax to double-ridged waveguide launcher and horn antenna is presented. The novel launcher design employs two symmetric field probes across the ridge gap to minimize spreading inductance in the transition, and achieves better than 15 dB return loss over a 10:1 bandwidth. The aperture-matched horn uses a half-cosine transition into a linear taper for the outer waveguide dimensions and ridge width, and a power-law scaled gap to realize monotonically varying cutoff frequencies, thus avoiding the appearance of trapped mode resonances. It achieves a nearly constant beamwidth in both E- and H-planes for an overall directivity of about 16.5 dB from 10-100 GHz.
Impossibility Theorems for Domain Adaptation

The domain adaptation problem in machine learning occurs when the test data generating distribution differs from the one that generates the training data. It is clear that the success of learning under such circumstances depends on similarities between the two data distributions. We study assumptions about the relationship between the two distributions that one needed for domain adaptation learning to succeed. We analyze the assumptions in an agnostic PAC-style learning model for a the setting in which the learner can access a labeled training data sample and an unlabeled sample generated by the test data distribution. We focus on three assumptions: (i) similarity between the unlabeled distributions, (ii) existence of a classifier in the hypothesis class with low error on both training and testing distributions, and (iii) the covariate shift assumption. I.e., the assumption that the conditioned label distribution (for each data point) is the same for both the training and test distributions. We show that without either assumption (i) or (ii), the combination of the remaining assumptions is not sufficient to guarantee successful learning. Our negative results hold with respect to any domain adaptation learning algorithm, as long as it does not have access to target labeled examples. In particular, we provide formal proofs that the popular covariate shift assumption is rather weak and does not relieve the necessity of the other assumptions. We also discuss the intuitively appealing Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: W&CP 9.
Body part segmentation of noisy human silhouette images

In this paper we propose a solution to the problem of body part segmentation in noisy silhouette images. In developing this solution we revisit the issue of insufficient labeled training data, by investigating how synthetically generated data can be used to train general statistical models for shape classification. In our proposed solution we produce sequences of synthetically generated images, using three dimensional rendering and motion capture information. Each image in these sequences is labeled automatically as it is generated and this labeling is based on the hand labeling of a single initial image.We use shape context features and Hidden Markov Models trained based on this labeled synthetic data. This model is then used to segment silhouettes into four body parts; arms, legs, body and head. Importantly, in all the experiments we conducted the same model is employed with no modification of any parameters after initial training.
Using a model to compute the optimal schedule of practice.

By balancing the spacing effect against the effects of recency and frequency, this paper explains how practice may be scheduled to maximize learning and retention. In an experiment, an optimized condition using an algorithm determined with this method was compared with other conditions. The optimized condition showed significant benefits with large effect sizes for both improved recall and recall latency. The optimization method achieved these benefits by using a modeling approach to develop a quantitative algorithm, which dynamically maximizes learning by determining for each item when the balance between increasing temporal spacing (that causes better long-term recall) and decreasing temporal spacing (that reduces the failure related time cost of each practice) means that the item is at the spacing interval where long-term gain per unit of practice time is maximal. As practice repetitions accumulate for each item, items become stable in memory and this optimal interval increases.
Affective touch gesture recognition for a furry zoomorphic machine

Over the last decade, the surprising fact has emerged that machines can possess therapeutic power. Due to the many healing qualities of touch, one route to such power is through haptic emotional interaction, which requires sophisticated touch sensing and interpretation. We explore the development of touch recognition technologies in the context of a furry artificial lap-pet, with the ultimate goal of creating therapeutic interactions by sensing human emotion through touch. In this work, we build upon a previous design for a new type of fur-based touch sensor. Here, we integrate our fur sensor with a piezoresistive fabric location/pressure sensor, and adapt the combined design to cover a curved creature-like object. We then use this interface to collect synchronized time-series data from the two sensors, and perform machine learning analysis to recognize 9 key affective touch gestures. In a study of 16 participants, our model averages 94% recognition accuracy when trained on individuals, and 86% when applied to the combined set of all participants. The model can also recognize which participant is touching the prototype with 79% accuracy. These results promise a new generation of emotionally intelligent machines, enabled by affective touch gesture recognition.
Towards Dataset Dynamics: Change Frequency of Linked Open Data Sources

Datasets in the LOD cloud are far from being static in their nature and how they are exposed. As resources are added and new links are set, applications consuming the data should be able to deal with these changes. In this paper we investigate how LOD datasets change and what sensible measures there are to accommodate dataset dynamics. We compare our findings with traditional, document-centric studies concerning the “freshness” of the document collections and propose metrics for LOD datasets.
Photometric Model of a Rain Drop

The visual appearance of rain is highly complex. Unlike the particles that cause other weather conditions such as haze and fog, rain drops are large and visible to the naked eye. Each drop refracts and reflects both scene radiance and environmental illumination towards an observer. As a result, a spatially distributed ensemble of drops moving at high velocities (rain) produces complex spatial and temporal intensity fluctuations in images and videos. To analyze the effects of rain, it is essential to understand the visual appearance of a single rain drop. In this paper, we develop geometric and photometric models for the refraction through, and reflection (both specular and internal) from, a rain drop. Our geometric and photometric models show that each rain drop behaves like a wide-angle lens that redirects light from a large field of view towards the observer. From this, we observe that in spite of being a transparent object, the brightness of the drop does not depend strongly on the brightness of the background. Our models provide the fundamental tools to analyze the complex effects of rain. Thus, we believe our work has implications for vision in bad weather as well as for efficient rendering of rain in computer graphics.
An ultra low-power rail-to-rail comparator for ADC designs

This paper presents a novel ultra low-power rail-to-rail comparator which can be suitably used in low-to-medium speed A/D converters. This comparator adopts a preamplifier followed by a dynamic latch structure to achieve fast-decision, high-resolution as well as reduced kick-back noise. A new adaptive power control technique is used to reduce the power consumption of the preamplifier. The circuit is designed and simulated in Global Foundries 65nm CMOS technology. The simulation results have shown that the power consumption of 12-bit comparator is 191.2 nW at the clock frequency of 15 MHz and 0.8 V supply voltage with a delay of 1.17nS.
Global Correlation Based Ground Plane Estimation Using V-Disparity Image

This paper presents the estimation of the position of the ground plane for navigation of on-road or off-road vehicles, in particular for obstacle detection using stereo vision. Ground plane estimation plays an important role in stereo vision based obstacle detection tasks. V-disparity image is widely used for ground plane estimation. However, it heavily relies on distinct road features which may not exist. In here, we introduce a global correlation method to extract the position of the ground plane in V-disparity image even without distinct road features.
Clarifying the definition of redundancy as used in robotics

Several descriptions of redundancy are presented in the literature , often from widely dif ferent perspectives . Therefore , a discussion of these various definitions and the salient points would be appropriate . In particular , any definition and redundancy needs to cover the following issues ; the dif ference between multiple solutions and an infinite number of solutions ; degenerate solutions to inverse kinematics ; task redundancy ; and the distinction between non-redundant , redundant and highly redundant manipulators .