nils-herrmann/clean_scirepeval huggingface dataset:
Maze Solving Algorithms for Micro Mouse

The problem of micro-mouse is 30 years old but its importance in the field of robotics is unparalleled, as it requires a complete analysis & proper planning to be solved. This paper covers one of the most important areas of robot, ldquodecision making algorithmrdquo or in lay-manpsilas language, ldquorobot intelligencerdquo. For starting in the field of micro-mouse it is very difficult to begin with highly sophisticated algorithms. This paper begins with very basic wall follower logic to solve the maze. And gradually improves the algorithm to accurately solve the maze in shortest time with some more intelligence. The Algorithm is developed up to some sophisticated level as flood-fill algorithm. The paper would help all the beginners in this fascinating field, as they proceed towards development of the ldquobrain of the systemrdquo, particularly for robots concerned with path planning and navigation.
A high frequency high voltage power supply

Novel compact high frequency high voltage (HV) power supply is introduced for small volume, lightweight, ultra fast high voltage output application. The two key factors high frequency HV transformer and voltage multiplier diodes are evaluated in this paper. The HV resonant tank size is only 1L for 400kHz∼500kHz 35kV 2kW output, the rise time for output HV is around 100us by lab prototype experiment results.
Forecasting emerging technologies : Use of bibliometrics and patent analysis

It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies.
Quantum Computation and Quantum Information

This book provides a thorough introduction to quantum information theory and quantum computation in general, especially covering the theoretical and computational, rather than experimental, aspects of these fields. The particular strengths of the volume are: the completeness of both basic and more advanced aspects of quantum computation and theory (including simple topics such as qubits and qubit gates, but also the more advanced quantum Fourier transform and several quantum search algorithms), the well-chosen and broad spectrum of application fields (from more classical optical experiments to Shor’s algorithm, NMR, and ion traps), and the thorough assessment of quantum information theory, in particular with respect to noise, error correction, and cryptographic algorithms. Another important quality of this book is that it provides new information to almost any reader, whether their background is computer science, mathematics, or physics (i.e. the three “typical” sub-backgrounds that meet in the study of quantum information theory).
Recurrent neural network based recommendation for time heterogeneous feedback

In recommender systems, several kinds of user feedback with time stamps are collected and used for recommendation, which is called the time heterogeneous feedback recommendation problem. The existing recommendation methods can handle only one kind of feedback or ignore the time stamps of the feedback. To solve the time heterogeneous feedback recommendation problem, in this paper, we propose a recurrent neural network model to predict the probability that the user will access an item given the time heterogeneous feedback of this user. To our best knowledge, it is the first time to solve the time heterogeneous feedback recommendation problem by deep neural network model. The proposed model is learned by back propagation algorithm and back propagation through time algorithm. The comparison results on four real-life datasets indicate that the proposed method outperforms the compared state-ofthe-art approaches.
Sentence Extraction by tf/idf and Position Weighting from Newspaper Articles

Recently lots of researchers are focusing their interests on the development of summarization systems from large volume sources combined with knowledge acquisition techniques such as infor mation extraction text mining or information re trieval Some of these techniques are implemented according to the speci c knowledge in the domain or the genre from the source document In this pa per we will discuss Japanese Newspaper Domain Knowledge in order to make a summary My sys tem is implemented with the sentence extraction approach and weighting strategy to mine from a number of documents
To increase data capacity of QR code using multiplexing with color coding: An example of embedding speech signal in QR code

QR codes have become widely popular along with the increased usage and popularity of smart phones. In many areas, QR codes have overtaken the place of linear barcodes because of the obvious advantage of large data capacity and ease of data retrieval. QR code specifications offers many more advantages like reduced space, durability against soil and damage, high data capacity, supported languages are more than other barcodes, supports 360 degree reading, etc over linear barcodes which makes QR codes worth opting. The structural flexibility of QR code architecture opens many more possibilities to stretch the limits of data capacity further away which includes data hiding techniques, multiplexing techniques, use of color QR codes, use of data compression techniques, etc. Proposed technique attempts to increase data capacity by multiplexing several QR codes and generating a color QR code. Experimental results show that this technique offers increase of the data capacity upto 24 times as compared to a standard QR code of same size. To get quicker results while demultiplexing, multiplexing of 12 or less QR codes is advisable with proposed technique. Due to such large capacity offered by proposed technique, embedding of speech signal into a QR code has made possible.
Alternative c-means clustering algorithms

In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the in1uence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means (AFCM) clustering algorithms. These alternative types of c-means clustering have more robustness than c-means clustering. Numerical results show that AHCM has better performance than HCM and AFCM is better than FCM. We recommend AFCM for use in cluster analysis. Recently, this AFCM algorithm has successfully been used in segmenting the magnetic resonance image of Ophthalmology to di9erentiate the abnormal tissues from the normal tissues. ? 2002 Pattern Recognition Society.
Video Object Segmentation with Language Referring Expressions

Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensive and time-consuming. In this work we explore an alternative way of identifying a target object, namely by employing language referring expressions. Besides being a more practical and natural way of pointing out a target object, using language specifications can help to avoid drift as well as make the system more robust to complex dynamics and appearance variations. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our approach we augment the popular video object segmentation benchmarks, DAVIS16 and DAVIS17 with language descriptions of target objects. We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS16 and is competitive to methods using scribbles on the challenging DAVIS17 dataset.  "A man in a red sweatshirt performing breakdance"  Examples of the proposed approach. Classical semi-supervised video object segmentation relies on an expensive pixel-level mask annotation of a target object in the first frame of a video. We explore a more natural and more practical way of pointing out a target object by providing a language referring expression.
Gamification solutions for software acceptance: A comparative study of Requirements Engineering and Organizational Behavior techniques

Gamification is a powerful paradigm and a set of best practices used to motivate people carrying out a variety of ICT-mediated tasks. Designing gamification solutions and applying them to a given ICT system is a complex and expensive process (in time, competences and money) as software engineers have to cope with heterogeneous stakeholder requirements on one hand, and Acceptance Requirements on the other, that together ensure effective user participation and a high level of system utilization. As such, gamification solutions require significant analysis and design as well as suitable supporting tools and techniques. In this work, we compare concepts, tools and techniques for gamification design drawn from Software Engineering and Human and Organizational Behaviors. We conduct a comparison by applying both techniques to the specific Meeting Scheduling exemplar used extensively in the Requirements Engineering literature.
Static Path Planning for Mobile Beacons to Localize Sensor Networks

In this paper, we study the static path planning problem with wireless sensor network localization as the primary objective. We consider a model in which sensors are assumed to be uniformly deployed to a predefined deployment area. We then deploy a robot to serve as a mobile beacon to enable the localization of the sensor nodes. The robot follows a pre-determined static path while periodically broadcasting its current location coordinates to the nearby sensors. The static path planning problem looks for good paths that result in better localization accuracy and coverage of the sensor network while keeping the path length bounded. We propose two new path types, CIRCLES and S-CURVES, that are specifically designed to reduce the collinearity during localization. We compare our solution with existing ones using the Cramer Rao bound (CRB) as the evaluation tool, which gives an unbiased evaluation regardless of localization algorithm used. The evaluation shows that our solutions cope with collinearity in a more effective manner than previous solutions. Our solutions provide significantly better localization accuracy and coverage in the cases where collinearity is the greatest problem
Projector Calibration by "Inverse Camera Calibration"

The accuracy of 3-D reconstructions depends substantially on the accuracy of active vision system calibration. In this work, the problem of video projector calibration is solved by inverting the standard camera calibration work flow. The calibration procedure requires a single camera, which does not need to be calibrated and which is used as the sensor whether projected dots and calibration pattern landmarks, such as the checkerboard corners, coincide. The method iteratively adjusts the projected dots to coincide with the landmarks and the final coordinates are used as inputs to a camera calibration method. The otherwise slow iterative adjustment is accelerated by estimating a plane homography between the detected landmarks and the projected dots, which makes the calibration method fast.
Aspect based sentiment analysis

In this fast paced and social media frenzy world, decision making has been revolutionized as there are lots of opinions floating on the internet in the form of blogs, social media updates, forums etc. This paper focuses on how banks can utilize these reviews to improve their services. The reviews are scraped from the internet and the sentiment analysis is done on an aspect level to have a much clear idea of where the banks are performing bad according to the customers or users.
Do multiview X-ray systems improve X-ray image interpretation in airport security screening ?

X-ray screening of passenger bags is one of the core elements in aviation security in order to prevent terrorist attacks. Large investments have been made into new technologies, for example in multi-view X-ray systems. Because of several X-rays, multi-view systems provide more than one X-ray image of the same passenger bag and hence present the security screener multiple perspectives of that bag. In this study, we evaluated the benefit of multi-view X-ray systems compared with state-of-the-art singleview X-ray systems. Singleand multi-view X-ray images of passenger bags were presented to 32 novices who had to decide if the bag contained a prohibited item or not. The results show that multiview X-ray systems lead to a higher detection performance of prohibited items in difficult conditions, such as when it is rotated in a non-canonical manner or superimposed by other objects. Additionally, the results indicate an increase of the reaction time for performing the screener’s task with multi-view in comparison with single-view X-ray systems. A specific training for airport’s security screeners might increase the advantages and reduce the disadvantages of multi-view X-ray systems.
The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions

This paper presents large, accurately calibrated and timesynchronised data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a colour camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.
What Actually Wins Soccer Matches : Prediction of the 2011-2012 Premier League for Fun and Profit

Sports analytics is a fascinating problem area in which to apply statistical learning techniques. This thesis brings new data to bear on the problem of predicting the outcome of a soccer match. We use frequency counts of in-game events, sourced from the Manchester City Analytics program, to predict the 380 matches of the 2011-2012 Premier League season. We generate prediction models with multinomial regression and rigorously test them with betting simulations. An extensive review of prior efforts is presented, as well as a novel theoretically optimal betting strategy. We measure performance different feature sets and betting strategies. Accuracy and simulated profit far exceeding those of all earlier efforts are achieved.
A 22.5-to-32-Gb/s 3.2-pJ/b Referenceless Baud-Rate Digital CDR With DFE and CTLE in 28-nm CMOS

This paper presents a referenceless baud-rate clock and data recovery (CDR) incorporated with a continuous-time linear equalizer (CTLE) and one-tap decision feedback equalizer (DFE) to achieve data rates from 22.5 to 32 Gb/s across a channel with Nyquist loss ranging from 10.1 to 14.8 dB. The referenceless CDR includes a proposed frequency acquisition scheme that consists of two parts: frequency detection and frequency correction. Frequency detection is achieved by examining rising and falling data waveforms to detect discrepancies between the data rate and the locally recovered clock frequency. Frequency correction uses digitally adjustable asymmetry of the proposed adjustable baud-rate phase detector to correct any frequency error. The receiver is implemented in the TSMC 28-nm CMOS process with an analog front end consisting of a CTLE, sampling comparators, a digitally controlled oscillator, and a digital back end consisting of synthesized digital CDR logic. The open-loop frequency detector range is measured to be 39%. The closed-loop CDR capture range is measured to be 34%, limited by test equipment. The proposed frequency acquisition scheme improves the measured CDR capture range by up to  $227\times $ . At 32 Gb/s, the entire receiver consumes 102.04 mW, achieving energy consumption below 3.19 pJ/b.
Stem-based PoS tagging for agglutinative languages

Words are made up of morphemes being glued together in agglutinative languages. This makes it difficult to perform part-of-speech tagging for these languages due to sparsity. In this paper, we present two Hidden Markov Model based Bayesian PoS tagging models for agglutinative languages. Our first model is word-based and the second model is stem-based where the stems of the words are obtained from other two unsupervised stemmers: HPS stemmer and Morfessor FlatCat. The results show that stemming improves the accuracy in PoS tagging. We present the results for Turkish as an agglutinative language and English as a morphologically poor language.
Research of Decision Tree on YARN Using MapReduce and Spark

Decision tree is one of the most widely used classification methods. For massive data processing, MapReduce is a good choice. Whereas, MapReduce is not suitable for iterative algorithms. The programming model of Spark is proposed as a memory-based framework that is fit for iterative algorithms and interactive data mining. In this paper, C4.5 is implemented on both MapReduce and Spark. The result of each layer of the decision tree can be kept in memory in the implementation on Spark. Through the experiments of C4.5, we observed an improvement of 950% on Spark than on MapReduce when the dataset is small. When the number of lines reached 50 million, Spark still kept an improvement of 73%. We concluded the algorithms and applications applicable for MapReduce and Spark. In the discussion section further experiments were performed to confirm our conclusions.
Parent – School Involvement and School Performance : Mediated Pathways Among Socioeconomically Comparable African American and Euro-American Families

Children’s academic and social competencies were examined as mediators to explain the often positive relation between parent–school involvement and achievement. Ethnic variations in the relation between parent–school involvement and early achievement and the mediated pathways were examined. Because much of the comparative research confounds ethnicity with socioeconomic status, the relations were examined among socioeconomically comparable samples of African American and Euro-American kindergarten children and their mothers. For reading achievement, academic skills mediated the relation between involvement and achievement for African Americans and Euro-Americans. For math achievement, the underlying process differed across ethnic groups. For African Americans, academic skills mediated the relation between school involvement and math performance. For Euro-Americans, social competence mediated the impact of home involvement on school achievement.
Generative Event Schema Induction with Entity Disambiguation

This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.
K-means clustering for efficient and robust registration of multi-view point sets

Efficiency and robustness are the important performance for the registration of multi-view point sets. To address these two issues, this paper casts the multi-view registration into a clustering problem, which can be solved by the extended K-means clustering algorithm. Before the clustering, all the centroids are uniformly sampled from the initially aligned point sets involved in the multi-view registration. Then, two standard K-means steps are utilized to assign all points to one special cluster and update each clustering centroid. Subsequently, the shape comprised by all cluster centroids can be used to sequentially estimate the rigid transformation for each point set. These two standard K-means steps and the step of transformation estimation constitute the extended K-means clustering algorithm, which can achieve the clustering as well as the multi-view registration by iterations. To show its superiority, the proposed approach has tested on some public data sets and compared with the-state-of-art algorithms. Experimental results illustrate its good efficiency and robustness for the registration of multi-view point sets.
Lexical-functional grammar: a formal system for grammatical representation

In learning their native language children develop a remarkable set of capabilities They acquire knowledge and skills that enable them to pro duce and comprehend an inde nite number of novel utterances and to make quite subtle judgments about certain of their properties The ma jor goal of psycholinguistic research is to devise an explanatory account of the mental operations that underlie these linguistic abilities In pursuing this goal we have adopted what we call the Competence Hypothesis as a methodological principle We assume that an explana tory model of human language performance will incorporate a theoreti cally justi ed representation of the native speaker s linguistic knowledge a grammar as a component separate both from the computational mech anisms that operate on it a processor and from other nongrammatical processing parameters that might in uence the processor s behavior To a certain extent the various components that we postulate can be studied independently guided where appropriate by the well established methods and evaluation standards of linguistics computer science and experimen tal psychology However the requirement that the various components ultimately must t together in a consistent and coherent model imposes even stronger constraints on their structure and operation
Beyond information seeking: towards a general model of information behaviour

The aim of the paper is to propose new models of information behaviour that extend the concept beyond simply information seeking to consider other modes of behaviour. The models chiefly explored are those of Wilson and Dervin. Argument A shortcoming of some models of information behaviour is that they present a sequence of stages where it is evident that actual behaviour is not always sequential. In addition, information behaviour models tend to confine themselves to depictions of information seeking.  A model of 'multi-directionality' is explored, to overcome the notion of sequential stages. Inspired by authors such as Chatman, Krikelas, and Savolainen, modes of information behaviour such as creating, destroying and avoiding information are included.  New models of information behaviour are presented that replace the notion of 'barriers' with the concept of 'gap', as a means of integrating the views of Wilson and Dervin. The proposed models incorporate the notion of multi-directionality and identify ways in which an individual may navigate 'gap' using modes of information behaviour beyond information seeking.