nils-herrmann/clean_scirepeval huggingface dataset:
6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions
Venue: IEEE Open Journal of the Communications Society
Year: 2020

The demand for wireless connectivity has grown exponentially over the last few decades. Fifth generation (5G) communications, with far more features than fourth generation communications, will soon be deployed worldwide. A new paradigm of wireless communication, the sixth generation (6G) system, with the full support of artificial intelligence, is expected to be implemented between 2027 and 2030. Beyond 5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security, and improved quality of service (QoS) compared to the 5G system. This paper presents the vision of future 6G wireless communication and its network architecture. This article describes emerging technologies such as artificial intelligence, terahertz communications, wireless optical technology, free space optical network, blockchain, three dimensional networking, quantum communications, unmanned aerial vehicles, cell free communications, integration of wireless information and energy transfer, integrated sensing and communication, integrated access backhaul networks, dynamic network slicing, holographic beamforming, backscatter communication, intelligent reflecting surface, proactive caching, and big data analytics that can assist the 6G architecture development in guaranteeing the QoS. Besides, expected applications with 6G communication requirements and possible technologies are presented. We also describe potential challenges and research directions for achieving this goal.
Histogram based Outlier Score (HBOS) A fast Unsupervised Anomaly Detection Algorithm
Year: 2012

Unsupervised anomaly detection is the process of nding outliers in data sets without prior training. In this paper, a histogrambased outlier detection (HBOS) algorithm is presented, which scores records in linear time. It assumes independence of the features making it much faster than multivariate approaches at the cost of less precision. A comparative evaluation on three UCI data sets and 10 standard algorithms show, that it can detect global outliers as reliable as state of theart algorithms, but it performs poor on local outlier problems. HBOS is in our experiments up to 5 times faster than clustering based algorithms and up to 7 times faster than nearest neighbor based methods.
Gender roles, sex and the expression of driving anger.
Venue: Accident; analysis and prevention
Year: 2017

The present study investigated the validity of the 25 item Driving Anger Expression Inventory (DAX) as well as the role of sex and gender roles in relation to the expression of driving anger in a sample of 378 French drivers (males=38% M=32.9years old) Confirmatory Factor Analysis supported the four factor structure of the 25 item DAX (Adaptive/Constructive Expression; Use of the Vehicle to Express Anger; Verbal Aggressive Expression and Personal Physical Aggressive Expression) and two of the three aggressive factors were found to have significant positive relationships with driving anger, while adaptive/constructive expression was negatively related to driving anger. Use of the vehicle to express anger was not significantly related to crash involvement, but was significantly related to all other crash related conditions (traffic tickets, loss of concentration, loss of control of the vehicle, near crash) The presence of feminine traits, but not sex, was predictive of adaptive/constructive behaviours, while masculine traits predicted more frequent verbal aggressive expression, use of the vehicle to express anger, personal physical aggressive expression and total aggressive expression. This finding may account for the inconsistent relationship found between driving anger and sex in previous research. This research also found that the 25 item DAX is a valid tool to measure the expression of driving anger and that the endorsement of masculine traits are related to more aggressive forms of driving anger expression.
Exploring N gram, Word Embedding and Topic Models for Content based Fake News Detection in FakeNewsNet Evaluation
Year: 2020

FakeNewsNet is a repository of two novel datasets, PolitiFact and GossipCop, which are employed for evaluation of fake news detection techniques. Unlike other extensively studied benchmark fake news datasets, the FakeNewsNet datasets incorporate news content, social context, and dynamic information, which could be used to study fake news propagation, detection, and mitigation. Existing works on FakeNewsNet have focused on one hot encoding, social contexts such as user based models, and dynamic information such as news propagation model. However, n gram, word embeddings, and topic models of news contents, which have been impressive in other contexts have not been explored. This paper therefore explores n gram, word embeddings, and topic models of news contents for the evaluation of FakeNewsNet datasets. Unigram based n gram model, skipgram word2vec based word embeddings model and Latent Dirichlet Allocation based topic model are extracted after preprocessing the datasets. The features are weighted by TFIDF to overcome the shortcomings of the individual models and analyzed using Logistic Regression. The evaluation of the models and their hybrids shows that n gram model outperforms word embedding and topic models. Specifically, n gram model records accuracy, precision, recall and F1 score of 0.80, 0.79, 0.78 and 0.79, respectively for PolitiFact and records 0.82, 0.75, 0.79 and 0.77, respectively for GossipCop. The comparison with benchmarks also shows that the performance of n gram model is better. General Terms Machine Learning, Computational Linguistics
Group ICA for Identifying Biomarkers in Schizophrenia: 'Adaptive' Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio temporal Regression
Year: 2018

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject specific networks from the group level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject specific networks using these two methods separately from resting state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs) We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45+ 8.9% sensitivity 0.74+ 0.11, specificity 0.79+ 0.11) than STR (accuracy 67.45+ 8.13% sensitivity 0.65+ 0.11, specificity 0.71+ 0.11) Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG ICA are more sensitive to group differences, and GIG ICA is promising for identifying image derived biomarkers of brain disease.
Creating space for Indigenous healing practices in patient care plans
Venue: Canadian medical education journal
Year: 2020

The Truth and Reconciliation Commission of Canada's Calls to Action ask that those who can effect change within the Canadian healthcare system recognize the value of Indigenous healing practices and support them in the treatment of Indigenous patients.  We distributed a survey to the Canadian Rheumatology Association membership to assess awareness of Indigenous healing practices, and attitudes informing their acceptance in patient care plans.  We received responses from 77/514 members (15% with most (73% being unclear or unaware of what Indigenous healing practices were. Nearly all (93% expressed interest in the concept of creating space for Indigenous healing practices in rheumatology care plans. The majority of support was for the use in preventive or symptom management strategies, and less as adjuncts to disease activity control. Themes identified through qualitative analysis of free text responses included a desire for patient centered care and support for reconciliation in medicine, but with a colonial construct of medicine, demonstration of an evidence bias, and hierarchy of medicines.  Overall, respondents were open to the idea of inclusion of Indigenous healing practices in patient's car plans, emphasizing importance for patient empowerment and patient centered care. However, they cited concerns that provide the indication for further learning and reconciliation in medicine.
Ruido en un canal de comunicaciones a traves de lineas electricas de potencia PLC
Year: 2017

La comunicacion por lineas de potencia PLC (Power Line Communication) es una de las tecnologias que ha permitido evolucionar la comunicacion de datos. Sin embargo, debido a los diversos tipos de ruido que se manejan en este canal, no ha alcanzado un amplio auge comparado con otras tecnologias como las comunicaciones por fibra optica o inalambrica. Si bien se ha mitigado en alguna medida el ruido de fondo, de banda estrecha y el impulsivo, hay un tipo de ruido impulsivo aperiodico que aun no se ha logrado mitigar, al punto que es el causante de las caidas de las comunicaciones haciendo que esta tecnologia no sea la mas atractiva para los usuarios. En este articulo se realiza una revision sobre el ruidopresente en el canal de comunicacion por lineas de potencia, haciendo mayor enfasis sobre el ruido aperiodico, debido a que es el que mas dificultades tiene para su deteccion y caracterizacion, dado que su presencia es muy aleatoria y fuertemente ligada a conmutacion de cargas de tipo lineal y no lineal sobre la red electrica, y por esta misma razon, hasta el momento no se ha establecido una estrategia lo suficientemente satisfactoria que permita su mitigacion. Este documento es un aporte para que futuros trabajos planteen estrategias para detectar, caracterizar, modelar y plantear un proceso para mitigar el ruido aperiodico.
The The Effect of Destination Image and Risk Perception toward Decision to Visit Kampung Coklat Tourism During New Normal Covid 19
Year: 2021

The covid 19 pandemic has had such a huge impact on various sectors, especially the tourism sector, restrictions imposed in various regions have made tourism continue to decline. This research aimedto study the relationship between destination image, risk perception, and tourists' decision to visit. The research is focused on tourists who have visited Kampung Coklat Tourism Destinations assample using nonprobability sampling technique. Questionnaires were distributed to 200 respondents directly to tourists. The data analysis technique used Multiple Linear Regression with validity and reliability tests.classical assumptions, and hypothesis testing (t test) The results showed that the destination image was positively affectedby the decision to visit, and also the perception of risk was positively affected by the decision of tourists to visit.The implications in this research can be used as input for the Management of Kampung Coklat Tourism in developing destination images to increase tourists' decisions to visit.
Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten.
Venue: Child development
Year: 2007

This study examined the role of self regulation in emerging academic ability in one hundred and forty one 3 to 5 year old children from low income homes. Measures of effortful control, false belief understanding, and the inhibitory control and attention shifting aspects of executive function in preschool were related to measures of math and literacy ability in kindergarten. Results indicated that the various aspects of child self regulation accounted for unique variance in the academic outcomes independent of general intelligence and that the inhibitory control aspect of executive function was a prominent correlate of both early math and reading ability. Findings suggest that curricula designed to improve self regulation skills as well as enhance early academic abilities may be most effective in helping children succeed in school.
Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short term but normal long term object recognition memory
Venue: Behavioural Brain Research
Year: 2016

Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2 Cre flox/flox) mice would display normal short term (5 or 15min retention delay) and long term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2 Cre flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2 Cre flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short term memory for object features, but not spatial location.
A DDPG based Approach for Energy aware UAV Navigation in Obstacle constrained Environment
Venue: 2020 IEEE 6th World Forum on Internet of Things (WF IoT)
Year: 2020

In this paper, we propose a three dimensional autonomous UAV navigation framework using Deep Deterministic Policy Gradient (DDPG) learning approach. The objective is to employ a self trained UAV as an airborne Internet of Things (IoT) unit to navigate obstacles and reach a destination point, where it can communicate with a ground sensor node with sufficiently high data rate. We develop a customized reward function which aims to minimize the distance separating the UAV and its destination while penalizing collisions. A dynamic energy threshold is also set to redirect the UAV towards the charging station in case of battery depletion. We numerically simulate the behavior of the UAV when learning the environmental obstacles, and autonomously selecting trajectories for selected scenarios. Finally, we show that our learning approach achieves close performance to the one of the graph based Dijkstra's algorithm.
Data Stream Processing at Network Edges
Venue: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Year: 2018

This paper studies the problem of finding an assignment of data stream processing components onto servers under the objective to minimize both energy consumption and average delay experienced by end users within the system. The aforementioned problem is studied under the context of internet of things (IoT) where servers belonging to micro datacenters are placed at network edges and thus close to data. We propose two algorithms to tackle the aforementioned problem taking into account limited server and network resources at micro datacenters. The first algorithm is called Delay Aware Algorithm (DA) and results in a delay efficient assignment without taking into account energy consumption. The second algorithm is called Energy Efficient and Delay Aware Algorithm (EFDA) and results in a both energy and delay efficient assignment of application processing components onto servers within the system. We provide an experimental evaluation to show the behavior of the proposed algorithms against algorithms in the literature.
Curating convergence: interpreting museum objects in integrated collecting institutions
Year: 2018

This article is concerned with the phenomenon of convergence (institutional integration) of museums, libraries, archives and other cultural facilities. It investigates how such amalgamations impact the capacity of curatorial and collections staff to carry out interpretive practices, including object research, documentation and exhibition development. Through thematic content analysis of staff interviews conducted at five case studies of local government run converged collecting institutions in Australia and New Zealand, the paper suggests that administrative restructuring often predicated on the dual imperatives of financial efficiency and improved cultural provision produces institutional frameworks that undermine the capacity of collections staff to interrogate the meanings of objects. In turn, impediments to interpretive practice limit the extent to which collections are rendered intellectually accessible to visitors.
Low Resource Sequence Tagging with Weak Labels
Venue: AAAI
Year: 2020

Current methods for sequence tagging depend on large quantities of domain specific training data, limiting their use in new, user defined tasks with few or no annotations. While crowdsourcing can be a cheap source of labels, it often introduces errors that degrade the performance of models trained on such crowdsourced data. Another solution is to use transfer learning to tackle low resource sequence labelling, but current approaches rely heavily on similar high resource datasets in different languages. In this paper, we propose a domain adaptation method using Bayesian sequence combination to exploit pre trained models and unreliable crowdsourced data that does not require high resource data in a different language. Our method boosts performance by learning the relationship between each labeller and the target task and trains a sequence labeller on the target domain with little or no gold standard data. We apply our approach to labelling diagnostic classes in medical and educational case studies, showing that the model achieves strong performance though zero shot transfer learning and is more effective than alternative ensemble methods. Using NER and information extraction tasks, we show how our approach can train a model directly from crowdsourced labels, outperforming pipeline approaches that first aggregate the crowdsourced data, then train on the aggregated labels.
Impact of Agricultural Investments on World Wheat Market under Climate Change: Effects of Agricultural Knowledge and Innovation System, and Development and Maintenance of Infrastructure
Venue: Japan Agricultural Research Quarterly: JARQ
Year: 2019

The role of agricultural investment growth in alleviating climate risks for wheat production systems and markets was examined using a partial equilibrium model, the Wheat Economy Climate Change (WECC) model, which covers the wheat markets of 10 countries and two regions. This study examines how future agricultural investments will affect the world wheat market. The volatility of international wheat prices at baseline is expected to increase in 2014 2016 and until 2040 because of climate change. However, a constant increase in agricultural investments in major producing countries will contribute to international wheat price stability. In particular, agricultural investments in Russia are crucial for stabilizing international wheat prices in mid long term under future climate change conditions.  Agricultural economics Additional key words: partial equilibrium model, wheat price, price stability in mid long term, Russia and Ukraine * February 2018; accepted 18 July 2018. JARQ 53 (2) 109 125 (2019) https:/www.jircas.go.jp
Short Term Results of Trial of Topical Insulin for Treatment of Dry Eyes in Diabetics.
Venue: Eye contact lens
Year: 2019

To assess the short term effects of topical insulin (TI) 1 unit/drop 4 times per day for 4 weeks on the symptoms and signs of diabetic with dry eye disease (DED) METHODS In this randomized, double blind interventional study, diabetics with DED aged 18 to 60 years were randomly assigned to TI or standard artificial tears (SAT) Baseline Ocular Surface Disease Index (OSDI) Schirmer I test (ST) tear break up time (TBUT) and ocular Sjogren's International Collaborative Clinical Alliance (SICCA) score were compared 4 weeks after treatment.  A total of 160 participants (involving 320 eyes) received either TI (n=80) or SAT (n=80) After 4 weeks of treatment, a significant number of participants in both TI and SAT groups showed improvement in their OSDI score, 66% and 63% respectively (P=0.0001) but were not significantly better than each other (P=0.453) However, most participants in both groups showed worsening of ST and TBUT (P>0.05) Most of the participants in both groups showed no change in their ocular SICCA score (P>0.05)  The study has shown a significant and similar improvement in the OSDI score for TI 1 unit/drop four times daily and SAT in treating diabetics with DED. Further research is required to understand the long term effects of TI on the ocular surface.
Tropologia em Hayden White: apontamentos historiograficos
Year: 2019

O presente texto pretende elaborar uma investigacao sobre a tese tropologica de Hayden White na area de teoria da historia, discutindo em um primeiro momento seus elementos constitutivos internos, procurando expor suas contradicoes e afinidades com uma nocao de ciencia. A seguir e feita uma exposicao das leituras que White foi recebendo ao longo do tempo, onde enfoca se autores de varios matizes historiograficos e as criticas que escreveram sobre sua tese. Por fim elabora se algumas propostas de superacao desse arcabouco teorico, dando atencao aos questionamentos que envolvem arte e pesquisa historiografica no que diz respeito a producao da verdade em confronto a ideia da historia como mero discurso. Objetiva se, assim, contribuir para os debates que amplificam os campos teoricos e metodologicos da historia, trazendo para o centro da discussao um historiador central para as ciencias humanas contemporaneas.