nils-herrmann/clean_scirepeval huggingface dataset:
Detection of Surface Crack in Concrete Using Measurement Technique With Laser Displacement Sensor

Detection of crack in concrete is an important issue as concrete is one of the principal material used in civil infrastructures. Cracks often occur first on the surface of concrete structure under load and provide an indication for further degradation. In this brief, an efficient noncontact method of the detection of such cracks in concrete using a measurement system with a laser displacement sensor (LDS) is presented. The proposed system consists of an LDS mounted on the scanner performing a raster scan. During the scanning process, the system gives the reading of displacement value from the sensor head to the laser spot on the target surface of the specimen. The proof of concept is given by testing with two different concrete specimens, namely, a concrete slab with a 0.7-mm-width through crack and a cylindrical concrete specimen with multiple cracks on the surface caused by loading effect. It is shown that a characteristic response of crack as a sharp distortion of the displacement reading occurs when the laser spot crosses the crack. The negligible standard deviation proves the repeatability and accuracy of the measurement and shows that this technique can be applied in a real-life scenario. A root-mean-square deviation in the displacement reading of the LDS is used as an index of crack. The effect of the shape of the specimen is investigated by detecting same-width cracks at different points along the circumference of a cylindrical specimen with different incident angles.
Merging thermal and visual images by a contrast pyramid

J. M. Valeton I nstitute for Perception TNO Kampweg 5 3769 DE Soesterberg, The Netherlands Abstract. Integration of images from different sensing modalities can produce information that cannot be obtained by viewing the sensor outputs separately and consecutively. This paper introduces a hierarchical i mage merging scheme based on a multiresolution contrast decomposition (the ratio of a low-pass pyramid). The composite images produced by this scheme preserve those details from the input images that are most relevant to visual perception. The method is tested by merging parallel registered thermal and visual images. The results show that the fused images present a more detailed representation of the depicted scene. Detection, recognition, and search tasks may therefore benefit from this new image representation.
A distributed algorithm for 2D shape duplication with smart pebble robots

We present our digital fabrication technique for manufacturing active objects in 2D from a collection of smart particles. Given a passive model of the object to be formed, we envision submerging this original in a vat of smart particles, executing the new shape duplication algorithm described in this paper, and then brushing aside any extra modules to reveal both the original object and an exact copy, side-by-side. Extensions to the duplication algorithm can be used to create a magnified version of the original or multiple copies of the model object. Our novel duplication algorithm uses a distributed approach to identify the geometric specification of the object being duplicated and then forms the duplicate from spare modules in the vicinity of the original. This paper details the duplication algorithm and the features that make it robust to (1) an imperfect packing of the modules around the original object; (2) missing communication links between neighboring modules; and (3) missing modules in the vicinity of the duplicate object(s). We show that the algorithm requires O(1) storage space per module and that the algorithm exchanges O(n) messages per module. Finally, we present experimental results from 60 hardware trials and 150 simulations. These experiments demonstrate the algorithm working correctly and reliably despite broken communication links and missing modules.
Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

Place name disambiguation is an important task for improving the accuracy of geographic information retrieval. This task becomes more challenging when the input texts are short. Wikipedia provides information about places and has often been employed for named entity recognition. However, the natural language representation of Wikipedia articles limits more effective use of this rich knowledge base. DBpedia is the Semantic Web version of Wikipedia, which provides structured and machine-understandable knowledge mined from Wikipedia articles. This paper presents an approach for combining Wikipedia and DBpedia to disambiguate place names in short texts. We discuss the pros and cons of the two knowledge bases, and argue that a combination of both performs better than each of them alone. We evaluate our proposed method by conducting experiments against baselines of three established methods. The result indicates that our method has a generally higher precision and recall. While our study employs DBpedia, the proposed method is generic and can be extended to other structured Linked Datasets such as Freebase or Wikidata.
Wireless Optical Communication for Underwater Applications

Wireless optical communication can be presented as an alternative to acoustic modems for scenarios where high speed, moderate distances, lower power and less complex communication systems are desired. Wireless communication is much more feasible solution to the problem of communicating with robotic vehicles. In this paper, wireless optical communication for the underwater criteria is designed and analysed. The focus of the paper is to construct light emitting diode based links at low cost with faster data rate of 1Mbps, wirelessly under the water. The choice of using LEDs instead of Lasers was largely economic. However, the underwater environment can be very challenging optically and many of the advantages that lasers have in terms of beam quality can be rapidly degraded by scattering and turbulence. Keyword: Moderate distance, Wireless optical, Super bright blue LED.
A 10-bit 50-MS/s SAR ADC with techniques for relaxing the requirement on driving capability of reference voltage buffers

A high speed successive approximation (SAR) ADC requires reference voltage buffers with high driving capability. Moreover, the power consumption of the reference buffers is usually several times larger than that of the SAR ADC itself. Three techniques are adopted to mitigate the requirement on driving capability of reference voltage buffers for SAR ADCs. A 10b 50MS/s ADC based on the proposed techniques is presented. The prototype ADC was fabricated in 40nm LP 1P7M CMOS technology. It consumes 0.47 mW at 50 MS/s from 1.1V supply voltage and achieves ENOB of 9.18-bit and figure of merit (FoM) of 16 fJ/conversion-step. The active area is 0.0114 mm2.
Machine Learning Paradigms for Speech Recognition: An Overview

Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem - for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.
Necessary Conditions for Existence of Some Designs in Polynomial Metric Spaces

Let M be a metric space with a finite diameter D and a finite normalized measure μM. Let the Hilbert space L2(M) of complex-valued functions be decomposed into a countable (whenM is infinite) or finite (with D+ 1 members whenM is finite) direct sum of mutually orthogonal subspaces L2(M) = V0 ⊕ V1 ⊕ · · · (V0 is the space of constant functions). We denote N = D + 1 ifM is finite and N = ∞ otherwise. In definition and description of the notion of polynomial metric spaces we follow [26, 27].
Robust ground plane detection for obstacle avoidance of mobile robots using a monocular camera

This paper presents a vision-based obstacle avoidance design using a monocular camera onboard a mobile robot. An image processing procedure is developed to estimate distances between the robot and obstacles based-on inverse perspective transformation (IPT) in image plane. A robust image processing solution is proposed to detect and segment navigatable ground plane area within the camera view. The proposed method integrate robust feature matching with adaptive color segmentation for plane estimation and tracking to cope with variations in illumination and camera view. After IPT and ground region segmentation, a result similar to the occupancy grid map is obtained for mobile robot obstacle avoidance and navigation. Practical experimental results of a wheeled mobile robot show that the proposed imaging system successfully estimates distance of objects and avoid obstacles in an indoor environment.
Semantic exploitation of implicit patent information

In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.
A Bayesian Perspective of Statistical Machine Learning for Big Data

Statistical Machine Learning (SML) refers to a body of algorithms and methods by which computers are allowed to discover important features of input data sets which are often very large in size. The very task of feature discovery from data is essentially the meaning of the keyword ‘learning’ in SML. Theoretical justifications for the effectiveness of the SML algorithms are underpinned by sound principles from different disciplines, such as Computer Science and Statistics. The theoretical underpinnings particularly justified by statistical inference methods are together termed as statistical learning theory. This paper provides a review of SML from a Bayesian decision theoretic point of view – where we argue that many SML techniques are closely connected to making inference by using the so called Bayesian paradigm. We discuss many important SML techniques such as supervised and unsupervised learning, deep learning, online learning and Gaussian processes especially in the context of very large data sets where these are often employed. We present a dictionary which maps the key concepts of SML from Computer Science and Statistics. We illustrate the SML techniques with three moderately large data sets where we also discuss many practical implementation issues. Thus the review is especially targeted at statisticians and computer scientists who are aspiring to understand and apply SML for moderately large to big data sets.
How Online Basic Psychological Need Satisfaction Influences Self-Disclosure Online among Chinese Adolescents: Moderated Mediation Effect of Exhibitionism and Narcissism

Under the basic framework of self-determination theory, the present study examined a moderated mediation model in which exhibitionism mediated the relationship between online basic psychological need satisfaction and self-disclosure on the mobile Internet, and this mediation effect was moderated by narcissism. A total of 296 Chinese middle school students participated in this research. The results revealed that exhibitionism fully mediated the association between online competence need satisfaction and self-disclosure on the mobile net, and partly mediated the association between online relatedness need satisfaction and self-disclosure on the mobile net. The mediating path from online basic psychological need satisfaction (competence and relatedness) to exhibitionism was moderated by narcissism. Compared to the low level of narcissism, online competence need satisfaction had a stronger predictive power on exhibitionism under the high level of narcissism condition. In contrast, online relatedness need satisfaction had a weaker predictive power on exhibitionism.
A Merged Interleaved Flyback PFC Converter with Active Clamp and ZVZCS

The design and analysis of a compact flyback AC to DC power supply is described and simulation results are presented. Of particular interest is merging two interleaved power converters such that the main switch in one converter behaves as the clamp switch of the other while ripple cancellation is accomplished. Also, PFC and ZVZCS are achieved with no extra components. Using these approaches, the total number of active and passive components used and consequently the associated power loss, complexity and cost is reduced, which makes the design appealable for low-power high-volume applications. The proposed circuit converts 110VAC to 230VDC at 2.2A load current with an overall simulated efficiency of around 93%. Merged, interleaved, flyback, PFC, active clamp, ZVZCS.
A Sparse Coding Neural Network ASIC With On-Chip Learning for Feature Extraction and Encoding

Hardware-based computer vision accelerators will be an essential part of future mobile devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to vision processing, which is an advantage over software-based solutions and general-purpose hardware. In this work, we present an ASIC that is designed to learn and extract features from images and videos. The ASIC contains 256 leaky integrate-and-fire neurons connected in a scalable two-layer network of 8 × 8 grids linked in a 4-stage ring. Sparse neuron activation and the relatively small grid keep the spike collision probability low to save access arbitration. The weight memory is divided into core memory and auxiliary memory, such that the auxiliary memory is only powered on for learning to save inference power. High-throughput inference is accomplished by the parallel operation of neurons. Efficient learning is implemented by passing parameter update messages, which is further simplified by an approximation technique. A 3.06 mm2 65 nm CMOS ASIC test chip is designed to achieve a maximum inference throughput of 1.24 Gpixel/s at 1.0 V and 310 MHz, and on-chip learning can be completed in seconds. To improve the power consumption and energy efficiency, core memory supply voltage can be reduced to 440 mV to take advantage of the error resilience of the algorithm, reducing the inference power to 6.67 mW for a 140 Mpixel/s throughput at 35 MHz.
Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid

This two-part paper discusses the analysis and control of legged locomotion in terms of N -step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. Part 1 introduced the N -step capturability framework and showed how to obtain capture regions and control sequences for simplified gait models. In Part 2, we describe an algorithm that uses these results as approximations to control a humanoid robot. The main contributions of this part are 1) step location adjustment using the 1-step capture region, 2) novel instantaneous capture point control strategies, and 3) an experimental evaluation of the 1-step capturability margin. The presented algorithm was tested using M2V2, a 3D force-controlled humanoid robot with 12 actuated degrees of freedom in the legs, both in simulation and in physical experiments. The physical ∗J. Pratt, T. Koolen, S. Cotton, J. Carff, M. Johnson, and P. Neuhaus are with the Institute for Human and Machine Cognition, Florida †T. Koolen and T. de Boer are with the Delft University of Technology, the Netherlands ‡J. Rebula is with the University of Michigan, This work was funded through the Army Tank and Automotive Research and Development Command (W56HZV04-C-0072), the Defense Advanced Research Projects Agency (FA8650-05-C-7265), the Office of Naval Research (N00014-091-0913-01), NASA (NNX10AG54A), and the Honda Research Institute. robot was able to recover from forward and sideways pushes of up to 21 Ns while balancing on one leg and stepping to regain balance. The simulated robot was able to recover from sideways pushes of up to 15 Ns while walking, and walked across randomly placed stepping stones.
Recommending Packages to Groups

The success of recommender systems has made them the focus of a massive research effort in both industry and academia. Recent work has generalized recommendations to suggest packages of items to single users, or single items to groups of users. However, to the best of our knowledge, the interesting problem of recommending a package to a group of users (P2G) has not been studied to date. This is a problem with several practical applications, such as recommending vacation packages to tourist groups, entertainment packages to groups of friends, or sets of courses to groups of students. In this paper, we formulate the P2G problem, and we propose probabilistic models that capture the preference of a group towards a package, incorporating factors such as user impact and package viability. We also investigate the issue of recommendation fairness. This is a novel consideration that arises in our setting, where we require that no user is consistently slighted by the item selection in the package. We present aggregation algorithms for finding the best packages and compare our suggested models with baseline approaches stemming from previous work. The results show that our models find packages of high quality which consider all special requirements of P2G recommendation.
Explain Black-box Image Classifications Using Superpixel-based Interpretation

How to best understand and interpret the decisions of deep neural networks is a crucial topic, as the impact of intelligent deep network systems is prevalent in many applications. We propose a superpixel based method to interpret and explain the results of black-box deep networks in the widely-applied image classification tasks. We perform probabilistic prediction difference analysis upon one or more superpixels clustered from image pixels. Our method generates a superpixel score map visualization that can provide rich interpretation regarding image components. Such interpretation provides supportive/unsupportive likelihood of image regions upon the decisions performed by the black-box classifier. We compare our method against state-of-art pixelwise interpretation methods over the latest deep neural network classifiers on the ImageNet dataset. Results show that our method produces more consistent interpretations in less computation time. Our method also supports interactive interpretation, where users can acquire explanations on specified regions through a convenient interface for a prompt reaction.
How Social Robots Will Help Us to Diagnose, Treat, and Understand Autism

Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of autism. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism.
Association between daily activities, process skills, and motor skills in
community-dwelling patients after left hemiparetic stroke

The purpose of this study was to evaluate the relationships between daily activities, information processing, and motor skills in individuals with hemineglect after having a left hemiparetic stroke.  The instrumental activities of daily living of 35 patients (22 male and 13 female; age: 57.1 ± 16.9 years) with hemineglect after having a left hemiparetic stroke were assessed by using three clinical measurement tools, including activity card sorting, assessment of motor and process skills, and the modified Barthel Index.  The results of the regression analysis indicated that the patients' processing skills in instrumental activities of daily living after having a left hemiparetic stroke were reduced. Participation in leisure and social activities was also affected as assessed by using the modified Barthel Index.  This study supports the clinical need for rehabilitation intervention after a left hemiparetic stroke to improve patients' processing skills and independence in performing activities of daily living.