nils-herrmann/clean_scirepeval huggingface dataset:
Building IoT-Based Applications for Smart Cities: How Can Ontology Catalogs Help?

The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) LOVs for IoT (LOV4IoT). To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey-based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.
Interpretation as Abduction

Abduction is inference to the best explanation In the TACITUS project at SRI we have developed an approach to abductive inference called weighted abduction that has resulted in a signi cant simpli cation of how the problem of interpreting texts is conceptualized The interpretation of a text is the minimal explanation of why the text would be true More precisely to interpret a text one must prove the logical form of the text from what is already mutually known allowing for coercions merging redundancies where possible and making assumptions where necessary It is shown how such local pragmatics problems as reference resolution the interpretation of compound nominals the resolution of syntactic ambiguity and metonymy and schema recognition can be solved in this manner Moreover this approach of interpretation as abduction can be combined with the older view of parsing as deduction to produce an elegant and thorough integration of syntax semantics and pragmatics one that spans the range of linguistic phenomena from phonology to discourse structure Finally we discuss means for making the abduction process e cient possibilities for extending the approach to other pragmatics phenomena and the semantics of the weights and costs in the abduction scheme
A Crowdfunding Model for Green Energy Investment

This paper studies a new renewable energy investment model through crowdfunding, which is motivated by emerging community solar farms. In this paper we develop a sequential game theory model to capture the interactions among crowdfunders, the solar farm owner, and an electricity company who purchases renewable energy generated by the solar farm in a multi-period framework. By characterizing a unique subgame-perfect equilibrium, and comparing it with a benchmark model without crowdfunding, we find that under crowdfunding although the farm owner reduces its investment level, the overall green energy investment level is increased due to the contribution of crowdfunders. We also find that crowdfunding can increase the penetration of green energy in consumption and thus reduce the energy procurement cost of the electricity company. Finally, the numerical results based on real data indicates crowdfunding is a simple but effective way to boost green generation.
The relationship between nomophobia and the distraction associated with smartphone use among nursing students in their clinical practicum

The increasing concern about the adverse effects of overuse of smartphones during clinical practicum implies the need for policies restricting smartphone use while attending to patients. It is important to educate health personnel about the potential risks that can arise from the associated distraction.



The aim of this study was to analyze the relationship between the level of nomophobia and the distraction associated with smartphone use among nursing students during their clinical practicum.



A cross-sectional study was carried out on 304 nursing students. The nomophobia questionnaire (NMP-Q) and a questionnaire about smartphone use, the distraction associated with it, and opinions about phone restriction policies in hospitals were used.



A positive correlation between the use of smartphones and the total score of nomophobia was found. In the same way, there was a positive correlation between opinion about smartphone restriction polices with each of the dimensions of nomophobia and the total score of the questionnaire.



Nursing students who show high levels of nomophobia also regularly use their smartphones during their clinical practicum, although they also believe that the implementation of policies restricting smartphone use while working is necessary.
Active project based learning pedagogies: Learning hardware, software design and wireless sensor instrumentation

"Chalk and Talk" is an ineffective pedagogy for engineering education, but still widely used in various part of the world. In recent days, Project Based Learning (PBL) is one of the modern pedagogy in which a project, based on certain problems, is given to students. Teacher carefully breaks downs this project into some driving questions and gets involve students into group based or individual activities to answer/solve these questions. This paper discusses four stage processes to implement PBL pedagogy to teach electronics hardware, software and wireless sensor instrumentation to a group of electronics technology undergraduate students. Further division of project into different tasks/driving questions is also discussed in this paper. The observations based on students' performance showed that they take more interest in their work and enjoy more on individual or group based learning to solve certain problems.
The King-Kong Effects: Improving sensation of walking in VR with visual and tactile vibrations at each step

In this paper we present novel sensory feedbacks named ”King-Kong Effects” to enhance the sensation of walking in virtual environments. King Kong Effects are inspired by special effects in movies in which the incoming of a gigantic creature is suggested by adding visual vibrations/pulses to the camera at each of its steps. In this paper, we propose to add artificial visual or tactile vibrations (King-Kong Effects or KKE) at each footstep detected (or simulated) during the virtual walk of the user. The user can be seated, and our system proposes to use vibrotactile tiles located under his/her feet for tactile rendering, in addition to the visual display. We have designed different kinds of KKE based on vertical or lateral oscillations, physical or metaphorical patterns, and one or two peaks for heal-toe contacts simulation. We have conducted different experiments to evaluate the preferences of users navigating with or without the various KKE. Taken together, our results identify the best choices for future uses of visual and tactile KKE, and they suggest a preference for multisensory combinations. Our King-Kong effects could be used in a variety of VR applications targeting the immersion of a user walking in a 3D virtual scene.
Semilobar Holoprosencephaly Associated with Multiple Malformations in a Foal.

A full-term male foal born in a farm holidays in Maremma (Tuscany, Italy) was euthanized shortly after birth due to the presence of several malformations. The rostral maxilla and the nasal septum were deviated to the right (wry nose), and a severe cervico-thoracic scoliosis and anus atresia were evident. Necropsy revealed ileum atresia and agenesis of the right kidney. The brain showed an incomplete separation of the hemispheres of the rostral third of the forebrain and the olfactory bulbs and tracts were absent (olfactory aplasia). A diagnosis of semilobar holoprosencephaly (HPE) was achieved. This is the first case of semilobar HPE associated with other organ anomalies in horses.
Embark: Securely Outsourcing Middleboxes to the Cloud

It is increasingly common for enterprises and other organizations to outsource network processing to the cloud. For example, enterprises may outsource firewalling, caching, and deep packet inspection, just as they outsource compute and storage. However, this poses a threat to enterprise confidentiality because the cloud provider gains access to the organization’s traffic. We design and build Embark, the first system that enables a cloud provider to support middlebox outsourcing while maintaining the client’s confidentiality. Embark encrypts the traffic that reaches the cloud and enables the cloud to process the encrypted traffic without decrypting it. Embark supports a wide-range of middleboxes such as firewalls, NATs, web proxies, load balancers, and data exfiltration systems. Our evaluation shows that Embark supports these applications with competitive performance.
Increasing Evolvability Considered as a Large-Scale Trend in Evolution

Evolvability is the capacity to evolve. This paper introduces a simple computational model of evolvability and demonstrates that, under certain conditions, evolvability can increase indefinitely, even when there is no direct selection for evolvability. The model shows that increasing evolvability implies an accelerating evolutionary pace. It is suggested that the conditions for indefinitely increasing evolvability are satisfied in biological and cultural evolution. We claim that increasing evolvability is a large-scale trend in evolution. This hypothesis leads to testable predictions about biological and cultural evolution.
Detection and classification of Eye Blink Artifact in electroencephalogram through Discrete Wavelet Transform and Neural Network

Electroencephalography (EEG) is the recording of electrical activity along the scalp of human brain. EEG is most often used to diagnose brain disorders i.e. epilepsy, sleep disorder, coma, brain death etc. EEG signals are frequently contaminated by Eye Blink Artifacts generated due to the opening and closing of eye lids during EEG recording. To analyse signal of EEG for diagnosis it is necessary that the EEG recording should be artifact free. This paper is based on the work to detect the presence of artifact and its actual position with extent in EEG recording. For the purpose of classification of artifact or non-artifact activity Artificial Neural Network (ANN) is used and for detection of contaminated zone the Discrete Wavelet Transform with level 6 Haar is used. The part of zone detection is necessary for further appropriate removal of artifactual activities from EEG recording without losing the background activity. The results demonstrated from the ANN classifier are very much promising such as- Sensitivity 98.21 %, Specificity 87.50 %, and Accuracy 95.83 %.
Skipping congestion-links for coflow scheduling

Data transfer duration accounts for a great proportion of job completion time in big-data systems. To reduce the time spent on data transfer, some traffic scheduling mechanisms at coflow-level are proposed recently. Most of them abstract datacenter networks as an ideal non-blocking big-switch, and the bottleneck is located at egress or ingress ports of end-hosts instead of in networks. Thus, they mainly focus on how to allocate port capacities of end-hosts to jobs without considering in-network congestion. However, link congestion frequently occurs in datacenter networks due to network oversubscription and load imbalance. When link congestion occurs, bottleneck locations will move from the ports of end-hosts to network links. In this paper, we design and implement SkipL, a congestion-aware coflow scheduler which could detect congestion and schedules coflows at end-hosts to effectively reduce coflow completion time. In addition, to be easily deployed in cloud environments, SkipL does not require to control flow routes. SkipL prototype system is implemented in Linux. The results of experiments conducted in a real small testbed and simulations conducted in the flow-level simulator show that SkipL reduces the average Coflow Completion Time(CCT) compared to the per-flow fair sharing scheduling method and Varys.
A Link Prediction Approach for Item Recommendation with Complex Number

Recommendation can be reduced to a sub-problem of link prediction, with specific nodes (users and items) and links (similar relations among users/items, and interactions between users and items). However, the previous link prediction algorithms need to be modified to suit the recommendation cases since they do not consider the separation of these two fundamental relations: similar or dissimilar and like or dislike. In this paper, we propose a novel and unified way to solve this problem, which models the relation duality using complex number. Under this representation, the previous works can directly reuse. In experiments with the Movie Lens dataset and the Android software website AppChina.com, the presented approach achieves significant performance improvement comparing with other popular recommendation algorithms both in accuracy and coverage. Besides, our results revealed some new findings. First, it is observed that the performance is improved when the user and item popularities are taken into account. Second, the item popularity plays a more important role than the user popularity does in final recommendation. Since its notable performance, we are working to apply it in a commercial setting, AppChina.com website, for application recommendation.
(Quasi)Periodicity Quantification in Video Data, Using Topology

This work introduces a novel framework for quantifying the presence and strength of recurrent dynamics in video data. Specifically, we provide continuous measures of periodicity (perfect repetition) and quasiperiodicity (superposition of periodic modes with non-commensurate periods), in a way which does not require segmentation, training, object tracking or 1-dimensional surrogate signals. Our methodology operates directly on video data. The approach combines ideas from nonlinear time series analysis (delay embeddings) and computational topology (persistent homology), by translating the problem of finding recurrent dynamics in video data, into the problem of determining the circularity or toroidality of an associated geometric space. Through extensive testing, we show the robustness of our scores with respect to several noise models/levels; we show that our periodicity score is superior to other methods when compared to human-generated periodicity rankings; and furthermore, we show that our quasiperiodicity score clearly indicates the presence of biphonation in videos of vibrating vocal folds, which has never before been accomplished end to end quantitatively.
Identifying community structures in dynamic networks

Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game-theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game-Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.
A comparison of social, learning, and financial strategies on crowd engagement and output quality

A significant challenge for crowdsourcing has been increasing worker engagement and output quality. We explore the effects of social, learning, and financial strategies, and their combinations, on increasing worker retention across tasks and change in the quality of worker output. Through three experiments, we show that 1) using these strategies together increased workers' engagement and the quality of their work; 2) a social strategy was most effective for increasing engagement; 3) a learning strategy was most effective in improving quality. The findings of this paper provide strategies for harnessing the crowd to perform complex tasks, as well as insight into crowd workers' motivation.
On Budgeted Influence Maximization in Social Networks

Given a fixed budget and an arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1-1/√e). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.
Never-Ending Learning

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a neverending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never-Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidenceweighted beliefs (e.g., servedWith(tea, biscuits)). NELL has also learned millions of features and parameters that enable it to read these beliefs from the web. Additionally, it has learned to reason over these beliefs to infer new beliefs, and is able to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http://rtw.ml.cmu.edu, and followed on Twitter at @CMUNELL.
PTEN/PI3K and MAPK signaling in protection and pathology following CNS injuries

Brain and spinal cord injuries initiate widespread temporal and spatial neurodegeneration, through both necrotic and programmed cell death mechanisms. Inflammation, reactive oxidation, excitotoxicity and cell-specific dysregulation of metabolic processes are instigated by traumatic insult and are main contributors to this cumulative damage. Successful treatments rely on prevention or reduction of the magnitude of disruption, and interfering with injurious cellular responses through modulation of signaling cascades is an effective approach. Two intracellular signaling pathways, the phosphatase and tensin homolog (PTEN)/phosphatidylinositol 3-kinase (PI3K) and mitogenactivated protein kinase (MAPK) signaling cascades play various cellular roles under normal and pathological conditions. Activation of both pathways can influence anatomical and functional outcomes in multiple CNS disorders. However, some mechanisms involve inhibiting or enhancing one pathway or the other, or both, in propagating specific downstream effects. Though many intracellular mechanisms contribute to cell responses to insult, this review examines the evidence exploring PTEN/PI3K and MAPK signaling influence on pathology, neuroprotection, and repair and how these pathways may be targeted for advancing knowledge and improving neurological outcome after injury to the brain and spinal cord.
Development and validation of a cancer awareness questionnaire for Malaysian undergraduate students of Chinese ethnicity.

To describe the development and validation of a cancer awareness questionnaire (CAQ) based on a literature review of previous studies, focusing on cancer awareness and prevention.



A total of 388 Chinese undergraduate students in a private university in Kuala Lumpur, Malaysia, were recruited to evaluate the developed self-administered questionnaire. The CAQ consisted of four sections: awareness of cancer warning signs and screening tests; knowledge of cancer risk factors; barriers in seeking medical advice; and attitudes towards cancer and cancer prevention. The questionnaire was evaluated for construct validity using principal component analysis and internal consistency using Cronbach's alpha (α) coefficient. Test-retest reliability was assessed with a 10-14 days interval and measured using Pearson product-moment correlation.



The initial 77-item CAQ was reduced to 63 items, with satisfactory construct validity, and a high total internal consistency (Cronbach's α=0.77). A total of 143 students completed the questionnaire for the test-retest reliability obtaining a correlation of 0.72 (p<0.001) overall.



The CAQ could provide a reliable and valid measure that can be used to assess cancer awareness among local Chinese undergraduate students. However, further studies among students from different backgrounds (e.g. ethnicity) are required in order to facilitate the use of the cancer awareness questionnaire among all university students.
Communication in open source software development mailing lists

Open source software (OSS) development teams use electronic means, such as emails, instant messaging, or forums, to conduct open and public discussions. Researchers investigated mailing lists considering them as a hub for project communication. Prior work focused on specific aspects of emails, for example the handling of patches, traceability concerns, or social networks. This led to insights pertaining to the investigated aspects, but not to a comprehensive view of what developers communicate about. Our objective is to increase the understanding of development mailing lists communication. We quantitatively and qualitatively analyzed a sample of 506 email threads from the development mailing list of a major OSS project, Lucene. Our investigation reveals that implementation details are discussed only in about 35% of the threads, and that a range of other topics is discussed. Moreover, core developers participate in less than 75% of the threads. We observed that the development mailing list is not the main player in OSS project communication, as it also includes other channels such as the issue repository.