nils-herrmann/clean_scirepeval huggingface dataset:
EVALUATION OF GIS AND BIM ROLES FOR THE INFORMATION MANAGEMENT OF HISTORICAL BUILDINGS

An architectural heritage object carries heterogeneous and multi-layered information beyond physical characteristics. It requires an integrated representation of various types of information for understanding and management prior to the decision-making process of conservation. This requirement is a twofold manner consisting of representation and management processes. There exists a variety of approaches for representation of heritage objects in digital three-dimensional (3D) environment, but the selection of the appropriate one according to the needs is crucial. On one hand, there have been recently great attempts to adopt Building Information Modeling (BIM) for historical buildings. Nevertheless, the related works in the topic focus mainly on pre-processing of data, such as the integration of born-digital material into a BIM environment and the creation of parametric objects according to historical building characteristics. As the information management of a historical building requires enhanced attribute management and integration of different datasets, further investigation on the BIM capabilities in management terms is crucial. On the other hand, Geographical Information Systems (GIS) have great potentials in exploring spatial relationships, but their potential in 3D representation is still somehow limited. The paper reviews and evaluates the roles of BIM and GIS, highlighting their advantages and disadvantages for integration, retrieval and management of heterogeneous data in the context of historical buildings.
Support vector machines for collaborative filtering

Support Vector Machines (SVMs) have successfully shown efficiencies in many areas such as text categorization. Although recommendation systems share many similarities with text categorization, the performance of SVMs in recommendation systems is not acceptable due to the sparsity of the user-item matrix. In this paper, we propose a heuristic method to improve the predictive accuracy of SVMs by repeatedly correcting the missing values in the user-item matrix. The performance comparison to other algorithms has been conducted. The experimental studies show that the accurate rates of our heuristic method are the highest.
Automatically Generating Models for Botnet Detection

A botnet is a network of compromised hosts that is under the control of a single, malicious entity, often called the botmaster. We present a system that aims to detect bot-infected machines, independent of any prior information about the command and control channels or propagation vectors, and without requiring multiple infections for correlation. Our system relies on detection models that target the characteristic fact that every bot receives commands from the botmaster to which it responds in a specific way. These detection models are generated automatically from network traffic traces recorded from actual bot instances. We have implemented the proposed approach and demonstrate that it can extract effective detection models for a variety of different bot families. These models are precise in describing the activity of bots and raise very few false positives.
Prediction of weather-induced airline delays based on machine learning algorithms

The primary goal of the model proposed in this paper is to predict airline delays caused by inclement weather conditions using data mining and supervised machine learning algorithms. US domestic flight data and the weather data from 2005 to 2015 were extracted and used to train the model. To overcome the effects of imbalanced training data, sampling techniques are applied. Decision trees, random forest, the AdaBoost and the k-Nearest-Neighbors were implemented to build models which can predict delays of individual flights. Then, each of the algorithms' prediction accuracy and the receiver operating characteristic (ROC) curve were compared. In the prediction step, flight schedule and weather forecast were gathered and fed into the model. Using those data, the trained model performed a binary classification to predicted whether a scheduled flight will be delayed or on-time.
Mining the Situation: Spatiotemporal Traffic Prediction With Big Data

With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions.
Visuo-haptic object-related activation in the ventral visual pathway

The ventral pathway is involved in primate visual object recognition. In humans, a central stage in this pathway is an occipito–temporal region termed the lateral occipital complex (LOC), which is preferentially activated by visual objects compared to scrambled images or textures. However, objects have characteristic attributes (such as three-dimensional shape) that can be perceived both visually and haptically. Therefore, object-related brain areas may hold a representation of objects in both modalities. Using fMRI to map object-related brain regions, we found robust and consistent somatosensory activation in the occipito–temporal cortex. This region showed clear preference for objects compared to textures in both modalities. Most somatosensory object-selective voxels overlapped a part of the visual object-related region LOC. Thus, we suggest that neuronal populations in the occipito–temporal cortex may constitute a multimodal object-related network.
Catch Me if You Can: An Abnormality Detection Approach for Collaborative Spectrum Sensing in Cognitive Radio Networks

Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical cognitive radio systems the data fusion center typically does not know the attacker's strategy. To alleviate the problem of the unknown strategy of attacker(s), an abnormality-detection approach, based on the abnormality detection in data mining, is proposed. The performance of the attacker detection in the single-attacker scenario is analyzed explicitly. For the case in which the attacker does not know the reports of honest secondary users (called independent attack), it is shown that the attacker can always be detected as the number of spectrum sensing rounds tends to infinity. For the case in which the attacker knows all the reports of other secondary users, based on which the attacker sends its report (called dependent attack), an approach for the attacker to perfectly avoid being detected is found, provided that the attacker has perfect information about the miss-detection and false-alarm probabilities. This motivates cognitive radio networks to protect the reports of secondary users. The performance of attacker detection in the general case of multiple attackers is demonstrated using numerical simulations.
Dynamics of rapid vertical climbing in cockroaches reveals a template.

Rapid, vertically climbing cockroaches produced climbing dynamics similar to geckos, despite differences in attachment mechanism, ;foot or toe' morphology and leg number. Given the common pattern in such diverse species, we propose the first template for the dynamics of rapid, legged climbing analogous to the spring-loaded, inverted pendulum used to characterize level running in a diversity of pedestrians. We measured single leg wall reaction forces and center of mass dynamics in death-head cockroaches Blaberus discoidalis, as they ascended a three-axis force plate oriented vertically and coated with glass beads to aid attachment. Cockroaches used an alternating tripod gait during climbs at 19.5+/-4.2 cm s(-1), approximately 5 body lengths s(-1). Single-leg force patterns differed significantly from level running. During vertical climbing, all legs generated forces to pull the animal up the plate. Front and middle legs pulled laterally toward the midline. Front legs pulled the head toward the wall, while hind legs pushed the abdomen away. These single-leg force patterns summed to generate dynamics of the whole animal in the frontal plane such that the center of mass cyclically accelerated up the wall in synchrony with cyclical side-to-side motion that resulted from alternating net lateral pulling forces. The general force patterns used by cockroaches and geckos have provided biological inspiration for the design of a climbing robot named RiSE (Robots in Scansorial Environments).
Active Learning for Crowd-Sourced Databases

Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are more accurate than computers, such as image tagging, entity resolution, or sentiment analysis. However, due to the time and cost of human labor, solutions that solely rely on crowd-sourcing are often limited to small datasets (i.e., a few thousand items). This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling with the speed and cost-effectiveness of machine learning classifiers. By using active learning as our optimization strategy for labeling tasks in crowdsourced databases, we can minimize the number of questions asked to the crowd, allowing crowd-sourced applications to scale (i.e, label much larger datasets at lower costs). Designing active learning algorithms for a crowd-sourced database poses many practical challenges: such algorithms need to be generic, scalable, and easy-to-use for a broad range of practitioners, even those who are not machine learning experts. We draw on the theory of nonparametric bootstrap to design, to the best of our knowledge, the first active learning algorithms that meet all these requirements. Our results, on 3 real-world datasets collected with Amazon’s Mechanical Turk, and on 15 UCI datasets, show that our methods on average ask 1–2 orders of magnitude fewer questions than the baseline, and 4.5–44× fewer than existing active learning algorithms.
The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality

We illustrate the usefulness of an Ontology-Based Data Management (OBDM) approach to develop an open information system, allowing for a deep level of interoperability among different databases, and accounting for additional dimensions of data quality compared to the standard dimensions of the OECD (Quality framework and guidelines for OECD statistical activities, OECD Publishing, Paris, 2011) Quality Framework. Recent advances in engineering in computer science provide promising tools to solve some of the crucial issues in data integration for Research and Innovation.
Internet and mobile phone text-messaging dependency: Factor structure and correlation with dysphoric mood among Japanese adults

Unhealthy use of the Internet and mobile phones is a health issue in Japan. We solicited participation in this questionnaire-based study from the employees of a city office in Kumamoto. A total of 92 men and 54 women filled in the Internet Addiction Questionnaire (IAQ), the Self-perception of Text-message Dependency Scale (STDS), and the Hospital Anxiety and Depression Scale (HADS). The prevalence of ‘‘light Internet addiction’’ and ‘‘severe Internet addiction’’ were 33.7% and 6.1% for men whereas they were 24.6% and 1.8% for women. The prevalence of ‘‘light mobile phone text-message addiction’’ was 3.1% for men and 5.4% for women. There were no cases of ‘‘sever text-message addiction’’. We found a two-factor structure for the IAQ and a three-factor structure for the STDS. We also performed an EFA of the IAQ and STDS subscales, and this revealed a two-factor structure – Internet Dependency and Text-message Dependency. An STDS subscale, Relationship Maintenance, showed a moderate factor loading of the factor that reflected unhealthy Internet use. In a path analysis, Depression was associated with both Internet Dependency and Text-message Dependency whereas Anxiety was associated negatively with Text-message Dependency. These results suggest applicability of the IAQ and STDS and that Internet and Text-message Dependences are factorially distinct.
A control architecture for quadruped locomotion over rough terrain

Legged robots have the potential to navigate a much larger variety of terrain than their wheeled counterparts. In this paper we present a hierarchical control architecture that enables a quadruped, the "LittleDog" robot, to walk over rough terrain. The controller consists of a high-level planner that plans a set of footsteps across the terrain, a low-level planner that plans trajectories for the robot's feet and center of gravity (COG), and a low-level controller that tracks these desired trajectories using a set of closed-loop mechanisms. We conduct extensive experiments to verify that the controller is able to robustly cross a wide variety of challenging terrains, climbing over obstacles nearly as tall as the robot's legs. In addition, we highlight several elements of the controller that we found to be particularly crucial for robust locomotion, and which are applicable to quadruped robots in general. In such cases we conduct empirical evaluations to test the usefulness of these elements.
All for one: feature combination for highly channel-degraded speech activity detection

Speech activity detection (SAD) on channel transmissions is a critical preprocessing task for speech, speaker and language recognition or for further human analysis. This paper presents a feature combination approach to improve SAD on highly channel degraded speech as part of the Defense Advanced Research Projects Agency’s (DARPA) Robust Automatic Transcription of Speech (RATS) program. The key contribution is the feature combination exploration of different novel SAD features based on pitch and spectro-temporal processing and the standard Mel Frequency Cepstral Coefficients (MFCC) acoustic feature. The SAD features are: (1) a GABOR feature representation, followed by a multilayer perceptron (MLP); (2) a feature that combines multiple voicing features and spectral flux measures (Combo); (3) a feature based on subband autocorrelation (SAcC) and MLP postprocessing and (4) a multiband comb-filter F0 (MBCombF0) voicing measure. We present single, pairwise and all feature combinations, show high error reductions from pairwise feature level combination over the MFCC baseline and show that the best performance is achieved by the combination of all features.
Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics

Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.
GSM-based distribution transformer monitoring system

This paper presents design and implementation of a mobile embedded system to monitor and record key operation indicators of a distribution transformer like load currents, transformer oil and ambient temperatures. The proposed on-line monitoring system integrates a global service mobile (GSM) Modem, with stand alone single chip microcontroller and sensor packages. It is installed at the distribution transformer site and the above mentioned parameters are recorded using the built-in S-channel analog to digital converter (ADC) of the embedded system. The acquired parameters are processed and recorded in the system memory. If there is any abnormality or an emergency situation the system sends SMS (short message service) messages to designated mobile telephones containing information about the abnormality according to some predefined instructions and policies that are stored on the embedded system EEPROM. Also, it sends SMS to a central database via the GSM modem for further processing. This mobile system will help the utilities to optimally utilize transformers and identify problems before any catastrophic failure.
Sentiment Analysis of Suicide Notes: A Shared Task

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large set of labels. The number of participants was larger than in any previous biomedical challenge task. We describe the data production process and the evaluation measures, and give a preliminary analysis of the results. Many systems performed at levels approaching the inter-coder agreement, suggesting that human-like performance on this task is within the reach of currently available technologies.
Mining point-of-interest data from social networks for urban land use classification and disaggregation

Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate a complete process that comprises the collection, unification, classification and validation of a type of VGI—online point-of-interest (POI) data—and develop methods to utilize such POI data to estimate disaggregated land use (i.e., employment size by category) at a very high spatial resolution (census block level) using part of the Boston metropolitan area as an example. With recent advances in activity-based land use, transportation, and environment (LUTE) models, such disaggregated land use data become important to allow LUTE models to analyze and simulate a person’s choices of work location and activity destinations and to understand policy impacts on future cities. These data can also be used as alternatives to explore economic activities at the local level, especially as government-published census-based disaggregated employment data have become less available in the recent decade. Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold. The automatic classification of POI can also be utilized for other types of analyses on cities.
A hybrid model for IT project with Scrum

Many organizations are gradually catching up with the tide of adopting agile practices at workplace, but they seem to be struggling with how to choose the agile practices and mix them into their IT software project development and management. These organizations have already had their own development styles, many of which have adhered to the traditional plan-driven methods such as waterfall. The inherent corporate culture of resisting to change or hesitation to abandon what they have established for a whole new methodology hampers the process change. In this paper, we will review the current state of agile adoption in business organizations and propose a new approach to IT project development and management by blending Scrum, an agile method, into traditional plan-driven project development and management. The management activity involved in Scrum is discussed, the team and meeting composing of Scrum are investigated, the challenges and benefits of applying Scrum in traditional IT project development and management are analyzed, the blending structure is illustrated and discussed, and the iterative process with Scrum and planned process without Scrum are compared.
Robust solutions of Linear Programming problems contaminated with uncertain data

Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the well-known NETLIB collection. We then apply the Robust Optimization methodology (Ben-Tal and Nemirovski [1–3]; El Ghaoui et al. ) to produce “robust” solutions of the above LPs which are in a sense immuned against uncertainty. Surprisingly, for the NETLIB problems these robust solutions nearly lose nothing in optimality.
Influence of Word Normalization on Text Classification

In this paper we focus our attention on the comparison of various lemmatization and stemming algorithms, which are often used in nature language processing (NLP). Sometimes these two techniques are considered to be identical, but there is an important difference. Lemmatization is generally more utilizable, because it produces the basic word form which is required in many application areas (i.e. cross-language processing and machine translation). However, lemmatization is a difficult task especially for highly inflected natural languages having a lot of words for the same normalized word form. We present a novel lemmatization algorithm which utilizes the multilingual semantic thesaurus Eurowordnet (EWN). We describe the algorithm in detail and compare it with other widely used algorithms for word normalization on two different corpora. We present promising results obtained by our EWN-based lemmatization approach in comparison to other techniques. We also discuss the influence of the word normalization on classification task in general. In overall, the performance of our method is good and it achieves similar precision and recall in comparison with other word normalization methods. However, our experiments indicate that word normalization does not affect the text classification task significantly.