nils-herrmann/clean_scirepeval huggingface dataset:
Wireless Sensor Network and Internet of Things in Precision Agriculture

Internet of Things is one of the most popular subjects nowadays where sensors and smart devices facilitate the provision of information and communication. In IoT, one of the main concepts is wireless sensor networks in which data is collected from all the sensors in a network characterized by low power consumption and a wide range of communication. In this study, an architecture to monitor soil moisture, temperature and humidity on small farms is provided. The main motivation for this study is to decrease water consumption whilst increasing productivity on small agricultural farms and precisions on them. This motivation is further propelled by the fact that agriculture is the backbone of some towns and most villages in most of the countries. Furthermore, some countries depend on farming as the main source of income. Putting the above-mentioned factors into consideration, the farm is divided into regions; the proposed system monitors soil moisture, humidity and temperature in the respective regions using wireless sensor networks, internet of things and sends a report to the end user. The report contains, as part of the information, a 10-day weather forecast. We believe that with the above information, the end user (farmer) can more efficiently schedule farm cultivation, harvesting, irrigation, and
Real-time detection of planar regions in unorganized point clouds

Automatic detection of planar regions in point clouds is an important step for many graphics, image processing, and computer vision applications. While laser scanners and digital photography have allowed us to capture increasingly larger datasets, previous techniques are computationally expensive, being unable to achieve real-time performance for datasets containing tens of thousands of points, even when detection is performed in a non-deterministic way. We present a deterministic technique for plane detection in unorganized point clouds whose cost is O(n log n) in the number of input samples. It is based on an efficient Hough-transform voting scheme and works by clustering approximately co-planar points and by casting votes for these clusters on a spherical accumulator using a trivariate Gaussian kernel. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones, being the first practical solution for deterministic plane detection in large unorganized point clouds.
Efficient lazy elimination for averaged one-dependence estimators

Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by relaxing the attribute independence assumption. Backwards Sequential Elimination (BSE) is a wrapper technique for attribute elimination that has proved effective at this task. We explore a new technique, Lazy Elimination (LE), which eliminates highly related attribute-values at classification time without the computational overheads inherent in wrapper techniques. We analyze the effect of LE and BSE on a state-of-the-art semi-naive Bayesian algorithm Averaged One-Dependence Estimators (AODE). Our experiments show that LE significantly reduces bias and error without undue computation, while BSE significantly reduces bias but not error, with high training time complexity. In the context of AODE, LE has a significant advantage over BSE in both computational efficiency and error.
How Effective Are School Bullying Intervention Programs ?

Research on effectiveness of school bullying interventions has lagged behind descriptive studies on this topic. The literature on bullying intervention research has only recently expanded to a point that allows for synthesis of findings across studies. The authors conducted a meta-analytic study of school bullying intervention research across the 25-year period from 1980 through 2004, identifying 16 studies that met our inclusion criteria. These studies included 15,386 K through 12 student participants from European nations and the United States. Applying standard meta-analysis techniques to obtain averaged effect size estimates across similar outcomes, the authors found that the intervention studies produced meaningful and clinically important positive effects for about one-third of the variables. The majority of outcomes evidenced no meaningful change, positive or negative. The authors conclude that school bullying interventions may produce modest positive outcomes, that they are more likely to influence knowledge, attitudes, and self-perceptions rather than actual bullying behaviors; and that the majority of outcome variables in intervention studies are not meaningfully impacted.
Rational Choice Theory : Toward a Psychological , Social , and Material Contextualization of Human Choice Behavior

The main purpose of this paper is to provide a brief overview of the rational choice approach, followed by an identification of several of the major criticisms of RCT and its conceptual and empirical limitations. It goes on to present a few key initiatives to develop alternative, more realistic approaches which transcend some of the limitations of Rational Choice Theory (RCT). Finally, the article presents a few concluding reflections and a table comparing similarities and differences between the mainstream RCT and some of the initial components of an emerging choice theory. Our method has been to conduct a brief selective review of rational choice theoretical formulations and applications as well as a review of diverse critical literature in the social sciences where rational choice has been systematically criticized. We have focused on a number of leading contributors (among others, several Nobel Prize Recipients in economics, who have addressed rational choice issues). So this article makes no claim for completeness. The review maps a few key concepts and assumptions underpinning the conceptual model and empirical applications of RCT. It reviews also a range of critical arguments and evidence of limitations. It identifies selected emerging concepts and theoretical revisions and adaptations to choice theory and what they entail. The results obtained, based on our literature reviews and analyses, are the identification of several major limitations of RCT as well as selected modifications and adaptations of choice theory which overcome or promise to overcome some of the RCT limitations. Thus, the article with Table 1 in hand provides a point of departure for follow-up systematic reviews and more precise questions for future theory development. The criticisms and adaptations of RCT have contributed to greater realism, empirical relevance, and increased moral considerations. The developments entail, among other things: the now well-known cognitive limitations (“bounded rationality”) and, for instance, the role of satisficing rather than maximizing in decision-making to deal with cognitive T. Burns, E. Roszkowska 196 complexity and the uncertainties of multiple values; choice situations are re-contextualized with psychology, sociology, economic, and material conditions and factors which are taken into account explicitly and insightfully in empirical and theoretical work. Part of the contextualization concerns the place of multiple values, role and norm contradictions, and moral dilemmas in much choice behavior. In concluding, the article suggests that the adaptations and modifications made in choice theory have led to substantial fragmentation of choice theory and as of yet no integrated approach has appeared to simply displace RCT.
Extractivism: Extracting Activists Events from News Articles Using Existing NLP Tools and Services

Activists have a significant role in shaping social views and opinions. Social scientists study the events activists are involved in order to find out how activists shape our views. Unfortunately, individual sources may present incomplete, incorrect, or biased event descriptions. We present a method where we automatically extract event mentions from different news sources that could complement, contradict, or verify each other. The method makes use of off-the-shelf NLP tools. It is therefore easy to setup and can also be applied to extract events that are not related to activism.
An improved k-medoids clustering algorithm

In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This algorithm improves obviously the drawbacks of the k-medoids algorithm, such as the time complexity, scalability on large dataset, and can't find the clusters of sizes different very much and the convex shapes. Experiments show that this algorithm enhances the quality and scalability of clustering.
Evaluating Entity Linking with Wikipedia

Named Entity Linking (nel) grounds entity mentions to their corresponding node in a Knowledge Base (kb). Recently, a number of systems have been proposed for linking entity mentions in text to Wikipedia pages. Such systems typically search for candidate entities and then disambiguate them, returning either the best candidate or nil. However, comparison has focused on disambiguation accuracy, making it difficult to determine how search impacts performance. Furthermore, important approaches from the literature have not been systematically compared on standard data sets. We reimplement three seminal nel systems and present a detailed evaluation of search strategies. Our experiments find that coreference and acronym handling lead to substantial improvement, and search strategies account for much of the variation between systems. This is an interesting finding, because these aspects of the problem have often been neglected in the literature, which has focused largely on complex candidate ranking algorithms.
Leveraging Tokens in a Natural Language Query for

Natural Language Interface to Database (NLIDB) systems convert a Natural Language (NL) query into a Structured Query Language (SQL) and then use the SQL query to retrieve information from a database. The main advantage with this function of an NLIDB system is that it makes information retrieval much easier and more importantly, it also allows non-technical users to query a database. In this work, we present an effective usage of tokens in an NL query to address various problems in an NLIDB system. The conversion of an NL query to SQL query is framed as a token classification problem, using which we unveil a novel method of mapping an NL query to an SQL query. Our approach reasonably addresses domain dependency which is one of the major drawbacks of NLIDB systems. Concepts Identification is a major component of NLIDB systems (Gupta et al., 2012; Srirampur et al., 2014). We improve Concepts Identification (Srirampur et al., 2014) by making use of Stanford Parser Dependencies. Our approach is more robust than previously proposed methods. In addition, we also discuss how Concepts Identification can be applied to address Ellipsis in a dialogue process. In addition to providing results to a user, it is essential to provide a relevant and a compact set of results. We propose a new problem of generating a compact set of results to a user query. At a higher level, user-system interactions are modeled based on patterns frequently observed between a user’s current query and his previous queries, while interacting with a system. Using these models, we propose a novel method of system prompting to help a user obtain a smaller and a relevant set of results. In addition to providing a compact and a relevant set of results, it is imperative that answers of an NLIDB system are comprehensible even by the people who are less familiar with a common language like English. NLIDB systems use Natural Language Generation (NLG) modules to provide answers in the form of sentences. It is important to make sure that an answer generated by an NLG module is very simple to understand. This brings in the problem of text simplification, wherein, one of the most crucial and initial sub-problems is Complex Word Identification (CWI). We address the problem of CWI by distinguishing words as simple and complex. A plethora of classifiers were explored to identify the complex words. This information helps us in improving the simplification of an NLIDB system’s final output to a user. To summarize, in addition to addressing problems within an NLIDB system, this work touches postNLIDB problems like results processing. All the proposed issues are tackled using tokens in an NL query as the basic and a driving unit of force.
Multi-camera activity correlation analysis

We propose a novel approach for modelling correlations between activities in a busy public space captured by multiple non-overlapping and uncalibrated cameras. In our approach, each camera view is automatically decomposed into semantic regions, across which different spatio-temporal activity patterns are observed. A novel Cross Canonical Correlation Analysis (xCCA) framework is formulated to detect and quantify temporal and causal relationships between regional activities within and across camera views. The approach accomplishes three tasks: (1) estimate the spatial and temporal topology of the camera network; (2) facilitate more robust and accurate person re-identification; (3) perform global activity modelling and video temporal segmentation by linking visual evidence collected across camera views. Our approach differs from the state of the art in that it does not rely on either intra or inter camera tracking. It therefore can be applied to even the most challenging video surveillance settings featured with severe occlusions and extremely low spatial and temporal resolutions. Its effectiveness is demonstrated using 153 hours of videos from 8 cameras installed in a busy underground station.
Sego: Pervasive Trusted Metadata for Efficiently Verified Untrusted System Services

Sego is a hypervisor-based system that gives strong privacy and integrity guarantees to trusted applications, even when the guest operating system is compromised or hostile. Sego verifies operating system services, like the file system, instead of replacing them. By associating trusted metadata with user data across all system devices, Sego verifies system services more efficiently than previous systems, especially services that depend on data contents. We extensively evaluate Sego's performance on real workloads and implement a kernel fault injector to validate Sego's file system-agnostic crash consistency and recovery protocol.
Automated modeling and robotic grasping of unknown three-dimensional objects

This paper describes the development of a novel vision-based modeling and grasping system for three-dimensional (3D) objects whose shape and location are unknown a priori. Our approach integrates online computer vision-based 3D object modeling with online 3D grasp planning and execution. A single wrist-mounted video camera is moved around the stationary object to obtain images from multiple viewpoints. Object silhouettes are extracted from these images and used to form a 3D solid model of the object. To refine the model, the object's top surface is modeled by scanning with a wrist-mounted line laser while recording images. The laser line in each image is used to form a 3D surface model that is combined with the silhouette result. The grasp planning algorithm is designed for the parallel-jaw grippers that are commonly used in industry. The algorithm analyses the solid model, generates a robust force closure grasp, and outputs the required gripper position and orientation for grasping the object. The robot then automatically picks up the object. Experiments are performed with two real-world 3D objects, a metal bracket and a hex nut. The shape, position and orientation of the objects are not known by the system a priori. The time required to compute an object model and plan a grasp was less than 4 s for each object. The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table.
Visual and Infrared Sensor Data-Based Obstacle Detection for the Visually Impaired Using the Google Project Tango Tablet Development Kit and the Unity Engine

A novel visual and infrared sensor data-based system to assist visually impaired users in detecting obstacles in their path while independently navigating indoors is presented. The system has been developed for the recently introduced Google Project Tango Tablet Development Kit equipped with a powerful graphics processor and several sensors which allow it to track its motion and orientation in 3-D space in real-time. It exploits the inbuilt functionalities of the Unity engine in the Tango SDK to create a 3-D reconstruction of the surrounding environment, then associates a Unity collider component with the user and utilizes it to determine his interaction with the reconstructed mesh in order to detect obstacles. The user is warned about any detected obstacles via audio alerts. An extensive empirical evaluation of the obstacle detection component has yielded favorable results, thus, confirming the potential of this system for future development work.
Curvature-based regularization for surface approximation

We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noisy) data point by minimizing the squared distances to the points and the irregularity of the surface implicitly defined by the tangent planes. In order to avoid the well-known ”shrinking” bias associated with first-order surface regularization, we choose a robust smoothing term that approximates curvature of the underlying surface. In contrast to a number of recent publications estimating curvature using discrete (e.g. binary) labellings with triple-cliques we use higher-dimensional labels that allows modeling curvature with only pair-wise interactions. Hence, many standard optimization algorithms (e.g. message passing, graph cut, etc) can minimize the proposed curvature-based regularization functional. The accuracy of our approach for representing curvature is demonstrated by theoretical and empirical results on synthetic and real data sets from multiview reconstruction and stereo.
Replicator neural networks for universal optimal source coding.

Replicator neural networks self-organize by using their inputs as desired outputs; they internally form a compressed representation for the input data. A theorem shows that a class of replicator networks can, through the minimization of mean squared reconstruction error (for instance, by training on raw data examples), carry out optimal data compression for arbitrary data vector sources. Data manifolds, a new general model of data sources, are then introduced and a second theorem shows that, in a practically important limiting case, optimal-compression replicator networks operate by creating an essentially unique natural coordinate system for the manifold.
Ontology Alignment for Linked Open Data

The Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.
Local sleep spindle modulations in relation to specific memory cues

Sleep spindles have been connected to memory processes in various ways. In addition, spindles appear to be modulated at the local cortical network level. We investigated whether cueing specific memories during sleep leads to localized spindle modulations in humans. During learning of word-location associations, words presented in the left and right visual hemifields were paired with different odors. By presenting a single odor during a subsequent nap, we aimed to selectively reactivate a subset of the studied material in sleeping subjects. During sleep, we observed topographically restricted spindle responses to memory cues, suggesting successful reactivation of specific memory traces. In particular, we found higher amplitude and greater incidence of fast spindles over posterior brain areas involved in visuospatial processing, contralateral to the visual field being cued. These results suggest that sleep spindles in different cortical areas reflect the reprocessing of specific memory traces.
Partially Dielectric-Loaded Ridged Horn Antenna Design for Ultrawideband Gain and Radiation Performance Enhancement

In this letter, a partially dielectric-loaded double-ridged horn antenna (PD-DRHA) that operates at 1-18-GHz frequency band is proposed for ultrawideband applications. The partially dielectric loading technique using a small lens inward the aperture has been implemented to enhance the gain of the standard double-ridged horn antenna (DRHA), without changing the physical dimensions. Approximately 5 dB gain increment level is achieved from 5 to 15 GHz with slight changes on return loss characteristics. Moreover, the pattern distortion of DRHA typically between 12-18-GHz band is improved. The antenna gain, radiation pattern, and input reflection performance measurements are presented with comparisons.
Demand Response as a Market Resource Under the Smart Grid Paradigm

Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ¿virtual power plant¿ is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms.