nils-herrmann/clean_scirepeval huggingface dataset:
A high-precision time-to-digital converter for pulsed time-of-flight laser radar applications

A time-to-digital converter (TDC) has been designed, and six units have been constructed and tested. It consists of integrated digital time-interval measurement electronics with a 10-ns resolution that can be improved to 10 ps by an analog interpolation method. Identical construction of the interpolation electronics leads to stable performance of the TDC. The drifts of all six TDC units remain within 10 ps over a temperature range from 10 C to+50 C. The stability can be improved further by a real-time calibration procedure developed here. The single-shot precision of the TDC is better than 15 ps (standard deviation), but precision can be improved to below 0.1 ps by averaging about 10 000 measurements at the maximum measurement speed of 100 kHz. The time range of the TDC is currently 2.55 s, but this can be increased by adding an external digital counter. The TDC suffers from a periodic nonlinearity over the measured time range from 0 to 1.3 s, the amplitude and period of the nonlinearity being 20 ps and 40 ns, respectively. The reason lies in the integrated digital part of the electronics. The linearity of interpolation in the 10-ns time range improves, however, as more results are averaged.
SeiSIM : Structural Similarity Evaluation for Seismic Data Retrieval

Structural similarity evaluation is a critical step for retrieving existing databases to find matching records for a given seismic data set. The objective is to enable the re-use of historical findings to assist exploration with new survey data. Currently there are very few structural similarity metrics specifically designed for seismic data, especially seismic survey maps. In this paper, we propose a metric that combines texture similarity and geological similarity, which is derived from discontinuity maps. We test the seismic similarity metric in a retrieval application. The successful results indicate that our approach is promising.
Real-time object detection, localization and verification for fast robotic depalletizing

Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times. In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).
Control and path planning of a walk-assist robot using differential flatness

With the growth of elderly population in our society, technology will play an important role in providing functional mobility to humans. In this paper, we propose a robot walking helper with both passive and active control modes of guidance. From the perspective of human safety, the passive mode adopts the braking control law on the wheels to differentially steer the vehicle. The active mode can guide the user efficiently when the passive control with user-applied force is not adequate for guidance. The theory of differential flatness is used to plan the trajectory of control gains within the proposed scheme of the controller. Since the user input force is not known a-priori, the theory of model predictive control is used to periodically compute the trajectory of these control gains. The simulation results show that the walking assist robot, along with the structure of this proposed control scheme, can guide the user to a goal effectively.
Automated Collaborative Filtering Applications for Online Recruitment Services

Online recruitment services suffer from shortcomin gs due to traditional search techniques. Most users fail to c onstruct queries that provide an adequate and accurate description of their (job) requirements, leading to imprecise search results. We investigate one potent ial solution that combines implicit profiling methods and automated collaborat ive filtering (ACF) techniques to build personalised query-less job rec ommendations. Two ACF strategies are implemented and evaluated in the Job Finder domain.
A Categorization Scheme for Semantic Web Search Engines

Semantic web search engines are evolving and many prototype systems and some implementation have been developed. However, there are some different views on what a semantic search engine should do. In this paper, a categorization scheme for semantic web search engines are introduced and elaborated. For each category, its components are described according to a proposed general architecture and various approaches employed in these components are discussed. We also propose some factors to evaluate systems in each category.
Modeling and Predicting Students' Academic Performance Using Data Mining Techniques

The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation. Educational Data Mining (EDM) is an emerging tool for academic intervention. The educational institutions can use EDU for extensive analysis of students’ characteristics. In this study, we have collected students’ data from two undergraduate courses. Three different data mining classification algorithms (Naïve Bayes, Neural Network, and Decision Tree) were used on the dataset. The prediction performance of three classifiers are measured and compared. It was observed that Naïve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%. This study will help teachers to improve student academic performance.
Principles and Models for Organizing the IT Function

How should contemporary firms organize their IT function? Despite more than twenty years of experience and insights, this question continues to dominate the attention and interest of CIOs and senior business executives. During the 1970s and 1980s, firms alternated between centralized models (where authority for the majority of IT decisions was located in the corporate IT group) and decentralized models (where the authority for most IT decisions was located in the divisional or functional IT units).
Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs

Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques (image segmentation and image classification) and fractal analysis. The metrics that have been estimated were: area, perimeter, number of trees, distance between trees, and a missing tree check. This methodology was tested on three different plantations: olive, eucalyptus, and vineyard. The application developed is free, open source and takes advantage of QGIS integration with external software. Several tools available from Orfeo Toolbox and Geographic Resources Analysis Support System (GRASS) GIS were employed to generate a classified raster image which allows calculating the metrics referred before. The application was developed in the Python 2.7 language. Also, some functions, modules, and classes from the QGIS Application Programming Interface (API) and PyQt4 API were used. This new plugin is a valuable tool, which allowed for automatizing several parameters and metrics on tree crown using GIS analysis tools, while considering data acquired by UAV.
How the physical similarity of avatars can influence the learning of emotion regulation strategies in teenagers

The aim of this study is to evaluate the influence of the physical similarity of avatars with the user on emotion regulation strategy training. In this study twenty-four teenagers observed an avatar (either physically similar to the participant or neutral) that gets frustrated with his/her computer, after which he/she applies an emotion regulation strategy (slow breathing). The intensity of the emotional induction and regulation processes was measured using questionnaires and electroencephalogram data. The results show that observing an avatar that is physically similar to the participant has a significantly greater impact on emotional valence and arousal in participants and also induces emotional states that are significantly more intense than when observing a neutral avatar. The results seem to indicate significantly greater activation of specific brain regions that are related to these processes and greater identification with the avatar in terms of both subjective and objective measures in participants that observed an avatar that was physically similar to them. However, there were no significant differences in the sense of presence or the appeal (i.e., satisfaction) to participants regarding the virtual environment. The use of avatars in mental health applications is relatively new and its specific influence is still unknown. We consider this study to be a first step forward in better understanding the use of avatars in mental health applications for youth. This research brings new guidelines to the design of different types of applications in this field in order to achieve greater behavioral changes in youth.
A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons

Efforts to achieve the long-standing dream of realizing scalable learning algorithms for networks of spiking neurons in silicon have been hampered by (a) the limited scalability of analog neuron circuits; (b) the enormous area overhead of learning circuits, which grows with the number of synapses; and (c) the need to implement all inter-neuron communication via off-chip address-events. In this work, a new architecture is proposed to overcome these challenges by combining innovations in computation, memory, and communication, respectively, to leverage (a) robust digital neuron circuits; (b) novel transposable SRAM arrays that share learning circuits, which grow only with the number of neurons; and (c) crossbar fan-out for efficient on-chip inter-neuron communication. Through tight integration of memory (synapses) and computation (neurons), a highly configurable chip comprising 256 neurons and 64K binary synapses with on-chip learning based on spike-timing dependent plasticity is demonstrated in 45nm SOI-CMOS. Near-threshold, event-driven operation at 0.53V is demonstrated to maximize power efficiency for real-time pattern classification, recognition, and associative memory tasks. Future scalable systems built from the foundation provided by this work will open up possibilities for ubiquitous ultra-dense, ultra-low power brain-like cognitive computers.
SEGMENTATION OF OPTIC DISK AND OPTIC CUP FROM RETINAL IMAGE

Retinal image analysis is becoming a prominent tool for detection of eye disease. Glaucoma is one of the serious eye disease. Presently manual inspections of optic disk and cup is a standard procedure which has its own limits for mass screening .In this paper we present an automatic optic disk segmentation and cup segmentation technique for glaucoma detection from retinal images. Detection of glaucoma from retinal image is a difficult task because of occlusions of blood vessel. Optic disk and cup segmentation method which is based on pixel intensities which are considered as a primary evidences of optic disk and cup boundaries. The result shows effectiveness in segmentation of optic disk and cup from retinal image.  Cup, Neuroretinal Rim, Optic Disk (OD), Retinal Images, Segmentation.
Beyond data warehousing: what's next in business intelligence?

During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.
Digital Democracy : Reimagining Pathways to Political Participation

Recently, research revolving around blogs has flourished. Usually, academics illustrate what blogs are, motivations to blog, and, only to some extent, their role in politics. Along these lines, we examine the impact of digital politics by looking specifically at blog readers. Although blog readers might be considered at the forefront of a new technological revolution, and people have speculated about their participatory habits both online and off, little research has specifically looked at this growing proportion of the population. This article models factors that predict traditional and online forms of participation, presenting a portrait of a new type of political advocate.
Can Scientific Impact Be Predicted?

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors' future h-indices based on their current scientific impact. Second, we examine the factors that drive papers-either previously or newly published-to increase their authors' predicted future h-indices. By leveraging relevant factors, we can predict an author's h-index in five years with an R2 value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an F1 score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants”.
Spatial augmented reality — A tool for 3D data visualization

This paper presents a proposal for the use of Spatial Augmented Reality as a tool for 3D data visualizations. The use of traditional display technologies such as LCD monitors provides a fish tank metaphor for the user, i.e. the information is behind a plane of glass. The use of VR technologies allows the user to be immersed in the 3D volume and remove this fish tank problem, but can limit the set of techniques that allow users to interact with spatial data. Spatial Augmented Reality employed in conjunction with a highresolution monitor provides an elegant blend of spatial reasoning, tangible interaction, and detailed information viewing. This paper purposes a range of usages for SAR in 3D visualizations.
Dynamic binding in a neural network for shape recognition.

Given a single view of an object, humans can readily recognize that object from other views that preserve the parts in the original view. Empirical evidence suggests that this capacity reflects the activation of a viewpoint-invariant structural description specifying the object's parts and the relations among them. This article presents a neural network that generates such a description. Structural description is made possible through a solution to the dynamic binding problem: Temporary conjunctions of attributes (parts and relations) are represented by synchronized oscillatory activity among independent units representing those attributes. Specifically, the model uses synchrony (a) to parse images into their constituent parts, (b) to bind together the attributes of a part, and (c) to bind the relations to the parts to which they apply. Because it conjoins independent units temporarily, dynamic binding allows tremendous economy of representation and permits the representation to reflect the attribute structure of the shapes represented.
Pre-processing very noisy text

Existing techniques for tokenisation and sentence boundary identification are extremely accurate when the data is perfectly clean (Mikheev, 2002), and have been applied successfully to corpora of news feeds and other post-edited corpora. Informal written texts are readily available, and with the growth of other informal text modalities (IRC, ICQ, SMS etc.) are becoming an interesting alternative, perhaps better suited as a source for lexical resources and language models for studies of dialogue and spontaneous speech. However, the high degree of spelling errors, irregularities and idiosyncrasies in the use of punctuation, white space and capitalisation require specialised tools. In this paper we study the design and implementation of a tool for pre-processing and normalisation of noisy corpora. We argue that rather than having separate tools for tokenisation, segmentation and spelling correction organised in a pipeline, a unified tool is appropriate because of certain specific sorts of errors. We describe how a noisy channel model can be used at the character level to perform this. We describe how the sequence of tokens needs to be divided into various types depending on their characteristics, and also how the modelling of white-space needs to be conditioned on the type of the preceding and following tokens. We use trainable stochastic transducers to model typographical errors, and other orthographic changes and a variety of sequence models for white space and the different sorts of tokens. We discuss the training of the models and various efficiency issues related to the decoding algorithm, and illustrate this with examples from a 100 million word corpus of Usenet news.
Which techniques does your application use?: An information extraction framework for scientific articles

Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas. With the exponential growth in research volumes, it has become difficult to keep track of the ever-growing number of application areas as well as the corresponding problem solving techniques. In this paper, we consider the computational linguistics domain and present a novel information extraction system that automatically constructs a pool of all application areas in this domain and appropriately links them with corresponding problem solving techniques. Further, we categorize individual research articles based on their application area and the techniques proposed/used in the article. k-gram based discounting method along with handwritten rules and bootstrapped pattern learning is employed to extract application areas. Subsequently, a language modelling approach is proposed to characterize each article based on its application area. Similarly, regular expressions and high-scoring noun phrases are used for the extraction of the problem solving techniques. We propose a greedy approach to characterize each article based on the techniques. Towards the end, we present a table representing the most frequent techniques adopted for a particular application area. Finally, we propose three use cases presenting an extensive temporal analysis of the usage of techniques and application areas.
Product type and consumers' perception of online consumer reviews

Consumers hesitate to buy experience products online because it is hard to get enough information about experience products via the Internet. Online consumer reviews may change that, as they offer consumers indirect experiences about dominant attributes of experience products, transforming them into search products. When consumers are exposed to an online consumer review, it should be noted that there are different kinds of review sources. This study investigates the effects of review source and product type on consumers’ perception of a review. The result of the online experiment suggests that product type canmoderate consumers’ perceived credibility of a review from different review sources, and the major findings are: (1) consumers are more influenced by a review for an experience product than for a search product when the review comes from consumer-developed review sites, and (2) a review from an online community is perceived to be the most credible for consumers seeking information about an experience product. The findings provide managerial implications for marketers as to how they can better manage online consumer reviews.
On Real-Time LIDAR Data Segmentation and Classification

We present algorithms for fast segmentation and classification of sparse 3D point clouds from rotating LIDAR sensors used for real-time applications such as autonomous mobile systems. Such systems must continuously process large amounts of data with update rates as high as 10 frames per second which makes complexity and performance of the algorithms very critical. Our approach to the segmentation of large and sparse point clouds is efficient and accurate which frees system resources for implementing other more demanding tasks such as classification. Segmentation is the emphasis of this paper as a necessary important first step for subsequent classification and further processing. We propose methods for segmenting sparse point clouds from rotating LIDAR sensors such as the Velodyne HDL-64E using rectangular and radial grids. The main part of the segmentation is performed on small grid images instead of large point clouds which makes the process computationally fast, simple, and very efficient. The proposed algorithms do not require flat horizontal structure of the ground and they demonstrate stable performance in different urban conditions and scenes for detection of different street objects including pedestrians, cars, and bicyclists.