nils-herrmann/clean_scirepeval huggingface dataset:
Procedural approach to volumetric terrain generation

The recent outbreak of indie games has popularized volumetric terrains to a new level, although video games have used them for decades. These terrains contain geological data, such as materials or cave systems. To improve the exploration experience and due to the large amount of data needed to construct volumetric terrains, industry uses procedural methods to generate them. However, they use their own methods, which are focused on their specific problem domains, lacking customization features. Besides, the evaluation of the procedural terrain generators remains an open issue in this field since no standard metrics have been established yet. In this paper, we propose a new approach to procedural volumetric terrains. It generates completely customizable volumetric terrains with layered materials and other features (e.g., mineral veins, underground caves, material mixtures and underground material flow). The method allows the designer to specify the characteristics of the terrain using intuitive parameters. Additionally, it uses a specific representation for the terrain based on stacked material structures, reducing memory requirements. To overcome the problem in the evaluation of the generators, we propose a new set of metrics for the generated content.
Exploration and exploitation in evolutionary algorithms: A survey

“Exploration and exploitation are the two cornerstones of problem solving by search.” For more than a decade, Eiben and Schippers' advocacy for balancing between these two antagonistic cornerstones still greatly influences the research directions of evolutionary algorithms (EAs) [1998]. This article revisits nearly 100 existing works and surveys how such works have answered the advocacy. The article introduces a fresh treatment that classifies and discusses existing work within three rational aspects: (1) what and how EA components contribute to exploration and exploitation; (2) when and how exploration and exploitation are controlled; and (3) how balance between exploration and exploitation is achieved. With a more comprehensive and systematic understanding of exploration and exploitation, more research in this direction may be motivated and refined.
Smart farm using wireless sensor network for data acquisition and power control distribution

Smart Farming makes a tremendous contribution for food sustainability for 21st century. Using wireless sensor network in farming from; independent power source distribution, monitoring valves and switches operation, and remote area control will efficiently produce excellent quality farm products in all season. In order to control farm power distribution and irrigation system, this paper proposes a communication methodology of the wireless sensor network for collecting environment data and sending control command to turn on/off irrigation system and manipulate power distribution. The simulation results shows that the proposed system developed is accurate robust and reliable.
Classification of breast lesions based on laws' feature extraction techniques

Breast lesions are characterized into three classes which include primary benign, primary malignant and secondary malignant. In the present work Laws' mask texture features are computed from the ultrasound images of the breast lesions. These Laws' masks of various resolutions i.e., of length 3,5, 7 and 9 have been used to extract the statistical features (Mean, Standard Deviation, Kurtosis, Skewness and Energy) from Laws' texture images. In the present work using the SVM classifier, an overall classification accuracy of 88.3% and the individual classification accuracy values of 95.2%, 88.6% and 91.6% have been obtained for primary benign, primary malignant and secondary malignant classes respectively.
Handwriting and perceptual-motor skills in clumsy, dysgraphic, and 'normal' children.

Among various perceptual-motor tests, only visuomotor integration was significant in predicting accuracy of handwriting performance for the total sample of 59 children consisting of 19 clumsy children, 22 nonclumsy dysgraphic children, and 18 'normal' children. They were selected from a sample of 360 fourth-graders (10-yr.-olds). For groups of clumsy and 'normal' children, the prediction of handwriting performance is difficult. However, correlations among scores on 6 measures showed that handwriting was significantly related to visuomotor integration, visual form perception, and tracing in the total group and to visuomotor integration and visual form perception in the clumsy group. The weakest correlations occurred between tests measuring simple psychomotor functions and handwriting. Moreover, clumsy children were expected to do poorly on tests measuring aiming, tracing, and visuomotor integration, but not on tests measuring visual form perception and finger tapping. Dysgraphic children were expected to do poorly on visuomotor integration only.
Learning Global Term Weights for Content-based Recommender Systems

Recommender systems typically leverage two types of signals to effectively recommend items to users: user activities and content matching between user and item profiles, and recommendation models in literature are usually categorized into collaborative filtering models, content-based models and hybrid models. In practice, when rich profiles about users and items are available, and user activities are sparse (cold-start), effective content matching signals become much more important in the relevance of the recommendation. The de-facto method to measure similarity between two pieces of text is computing the cosine similarity of the two bags of words, and each word is weighted by TF (term frequency within the document) × IDF (inverted document frequency of the word within the corpus). In general sense, TF can represent any local weighting scheme of the word within each document, and IDF can represent any global weighting scheme of the word across the corpus. In this paper, we focus on the latter, i.e., optimizing the global term weights, for a particular recommendation domain by leveraging supervised approaches. The intuition is that some frequent words (lower IDF, e.g. “database”) can be essential and predictive for relevant recommendation, while some rare words (higher IDF, e.g. the name of a small company) could have less predictive power. Given plenty of observed activities between users and items as training data, we should be able to learn better domain-specific global term weights, which can further improve the relevance of recommendation. We propose a unified method that can simultaneously learn the weights of multiple content matching signals, as well as global term weights for specific recommendation tasks. Our method is efficient to handle large-scale training data ∗This work was conducted during an internship at LinkedIn.  IW3C2 reserves the right to provide a hyperlink to the author’s site if the Material is used in electronic media. WWW 2016, April 11–15, 2016, Montréal, Québec, Canada. ACM 978-1-4503-4143-1/16/04. http://dx.doi.org/10.1145/2872427.2883069 . generated by production recommender systems. And experiments on LinkedIn job recommendation data justify the effectiveness of our approach.
A Survey Paper on Crime Prediction Technique Using Data Mining

Crime prediction is an attempt to identify and reducing the future crime. Crime prediction uses past data and after analyzing data, predict the future crime with location and time. In present days serial criminal cases rapidly occur so it is an challenging task to predict future crime accurately with better performance. Data mining technique are very useful to solving Crime detection problem. So the aim of this paper to study various computational techniques used to predict future crime. This paper provides comparative analysis of Data mining Techniques for detection and prediction of future crime.  Data, Crime Prediction, Data Mining Technique, Predictive Accuracy.
Towards life-long visual localization using an efficient matching of binary sequences from images

Life-long visual localization is one of the most challenging topics in robotics over the last few years. The difficulty of this task is in the strong appearance changes that a place suffers due to dynamic elements, illumination, weather or seasons. In this paper, we propose a novel method (ABLE-M) to cope with the main problems of carrying out a robust visual topological localization along time. The novelty of our approach resides in the description of sequences of monocular images as binary codes, which are extracted from a global LDB descriptor and efficiently matched using FLANN for fast nearest neighbor search. Besides, an illumination invariant technique is applied. The usage of the proposed binary description and matching method provides a reduction of memory and computational costs, which is necessary for long-term performance. Our proposal is evaluated in different life-long navigation scenarios, where ABLE-M outperforms some of the main state-of-the-art algorithms, such as WI-SURF, BRIEF-Gist, FAB-MAP or SeqSLAM. Tests are presented for four public datasets where a same route is traversed at different times of day or night, along the months or across all four seasons.
Control of Variable Speed Wind Turbines

Uncontrolled wind turbine configuration, such as stall-regulation captures, energy relative to the amount of wind speed. This configuration requires constant turbine speed because the generator that is being directly coupled is also connected to a fixed-frequency utility grid. In extremely strong wind conditions, only a fraction of available energy is captured. Plants designed with such a configuration are economically unfeasible to run in these circumstances. Thus, wind turbines operating at variable speed are better alternatives. This paper focuses on a controller design methodology applied to a variable-speed, horizontal axis wind turbine. A simple but rigid wind turbine model was used and linearised to some operating points to meet the desired objectives. By using blade pitch control, the deviation of the actual rotor speed from a reference value is minimised. The performances of PI and PID controllers were compared relative to a step wind disturbance. Results show comparative responses between these two controllers. The paper also concludes that with the present methodology, despite the erratic wind data, the wind turbine still manages to operate most of the time at 88% in the stable region.
The use of remote sensing and landscape metrics to describe structures and changes in urban land uses

Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial formölandscape metricsöto describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed `landscape metric signatures' for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.
Modeling urban growth in Atlanta using logistic regression

This study applied logistic regression to model urban growth in the Atlanta Metropolitan Area of Georgia in a GIS environment and to discover the relationship between urban growth and the driving forces. Historical land use/cover data of Atlanta were extracted from the 1987 and 1997 Landsat TM images. Multi-resolution calibration of a series of logistic regression models was conducted from 50 m to 300 m at intervals of 25 m. A fractal analysis pointed to 225 m as the optimal resolution of modeling. The following two groups of factors were found to affect urban growth in different degrees as indicated by odd ratios: (1) population density, distances to nearest urban clusters, activity centers and roads, and high/low density urban uses (all with odds ratios < 1); and (2) distance to the CBD, number of urban cells within a 7 · 7 cell window, bare land, crop/grass land, forest, and UTM northing coordinate (all with odds ratios > 1). A map of urban growth probability was calculated and used to predict future urban patterns. Relative operating characteristic (ROC) value of 0.85 indicates that the probability map is valid. It was concluded that despite logistic regression’s lack of temporal dynamics, it was spatially explicit and suitable for multi-scale analysis, and most importantly, allowed much deeper understanding of the forces driving the growth and the formation of the urban spatial pattern.
Kin2. A Kinect 2 toolbox for MATLAB

This paper introduces Kin2, a Kinect 2 toolbox for MATLAB. This toolbox encapsulates most of the Kinect for Windows SDK 2.0 functionality in a single class with high-level methods. The toolbox is written mostly in C++ with MATLAB Mex functions providing access to color, depth, infrared, and body index frames; coordinate mapping capabilities; real-time six-bodies tracking with 25 joints and hands states; face and high-definition face processing; and real-time 3D reconstruction. We showed that the performance of a Kin2 application decreases 30% on average with respect to a native C++ application, however, Kin reduces in one order of magnitude the average code length yielding a significant reduction in development time for prototyping and research.
CS10K Teachers by 2017?: Try CS1K+ students NOW! Coping with the Largest CS1 Courses in History

"Be careful what you wish for, you just might get it." - Proverb In 2005, computing education was experiencing a crisis. Enrollments had "fallen to such an extent that some academic computing programs were facing significant reductions in staffing levels or even elimination". The community responded, with panels to investigate and highlight ways to infuse "passion, beauty, joy and awe" into the introductory experiences, the CS10K project to bring computing to 10,000 teachers and 100,000 students, and better messaging of career opportunities, to name a few of the initiatives to bring students back into our seats.
 Well, by golly, it worked! It certainly didn't hurt our cause that Wall Street almost collapsed, young whiz kids were becoming TECH billionaires, an inspiring video and an interactive website led millions of people to code for an hour every December, or smart devices put computing into the hands of young people, and social media became the killer app. Whatever it was, CS became hot again. And we mean HOT. There are now several institutions around the world that have well over a thousand students taking CS1 in the Fall of 2015. There's just so much lemonade one can make before the seams start to burst, and the wheels come off the bus, as many shared at SIGCSE 2015 at the Birds of the Feather session.
 The goal of this panel is to bring together educators who were charged with delivering face-to-face CS1 on the grandest scale the field has ever seen. How did they cope? Does it become all people management with an army of Teaching Assistants? What were the differences and common themes in their survival plans? What is working? What mistakes were made? How are they supporting differential learning for the students who don't have the same experience as others? How is diversity being affected? Finally, what advice would they have for others interested in venturing into the tsunami, and broaden participation at a massive scale?
Comparative analysis of five machine learning algorithms for IP traffic classification

With rapid increase in internet traffic over last few years due to the use of variety of internet applications, the area of IP traffic classification becomes very significant from the point of view of various internet service providers and other governmental and private organizations. Now days, traditional IP traffic classification techniques such as port number based and payload based direct packet inspection techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various encryption techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for this classification. In this research paper, real time internet traffic dataset has been developed using packet capturing tool and then using attribute selection algorithms, a reduced feature dataset has been developed. After that, five ML algorithms MLP, RBF, C4.5, Bayes Net and Naïve Bayes are used for IP traffic classification with these datasets. This experimental analysis shows that Bayes Net and C4.5 are effective ML techniques for IP traffic classification with accuracy in the range of 94 %.
Flux-Weakening Characteristics of Trapezoidal Back-EMF Machines in Brushless DC and AC Modes

The performance of a brushless motor which has a surface-mounted magnet rotor and a trapezoidal back-emf waveform when it is operated in BLDC and BLAC modes is evaluated, in both constant torque and flux-weakening regions, assuming the same torque, the same peak current, and the same rms current. It is shown that although the motor has an essentially trapezoidal back-emf waveform, the output power and torque when operated in the BLAC mode in the flux-weakening region are significantly higher than that can be achieved when operated in the BLDC mode due to the influence of the winding inductance and back-emf harmonics
Use of Attack Graphs in Security Systems

Attack graphs have been used tomodel the vulnerabilities of the systems and their potential exploits.The successful exploits leading to the partial/total failure of the systems are subject of keen security interest. Considerable effort has been expended in exhaustive modeling, analyses, detection, and mitigation of attacks. One prominent methodology involves constructing attack graphs of the pertinent system for analysis and response strategies.This not only gives the simplified representation of the system, but also allows prioritizing the security properties whose violations are of greater concern, for both detection and repair. We present a survey and critical study of state-of-the-art technologies in attack graph generation and use in security system. Based on our research, we identify the potential, challenges, and direction of the current research in using attack graphs.
Enrichment of OntoSenseNet: Adding a sense-annotated Telugu lexicon

The paper describes the enrichment of OntoSenseNeta verbcentric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because native speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation procedure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Additionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives.
Selective deficit in personal moral judgment following damage to ventromedial prefrontal cortex.

Recent fMRI evidence has detected increased medial prefrontal activation during contemplation of personal moral dilemmas compared to impersonal ones, which suggests that this cortical region plays a role in personal moral judgment. However, functional imaging results cannot definitively establish that a brain area is necessary for a particular cognitive process. This requires evidence from lesion techniques, such as studies of human patients with focal brain damage. Here, we tested 7 patients with lesions in the ventromedial prefrontal cortex and 12 healthy individuals in personal moral dilemmas, impersonal moral dilemmas and non-moral dilemmas. Compared to normal controls, patients were more willing to judge personal moral violations as acceptable behaviors in personal moral dilemmas, and they did so more quickly. In contrast, their performance in impersonal and non-moral dilemmas was comparable to that of controls. These results indicate that the ventromedial prefrontal cortex is necessary to oppose personal moral violations, possibly by mediating anticipatory, self-focused, emotional reactions that may exert strong influence on moral choice and behavior.
Stacked inductors and transformers in CMOS technology

A modification of stacked spiral inductors increases the self-resonance frequency by 100% with no additional processing steps, yielding values of 5 to 266 nH and self-resonance frequencies of 11.2 to 0.5 GHz. Closed-form expressions predicting the self-resonance frequency with less than 5% error have also been developed. Stacked transformers are also introduced that achieve voltage gains of 1.8 to 3 at multigigahertz frequencies. The structures have been fabricated in standard digital CMOS technologies with four and five metal layers.
iBOAT: Isolation-Based Online Anomalous Trajectory Detection

Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.