nils-herrmann/clean_scirepeval huggingface dataset:
Wearable Bits: Scaffolding Creativity with a Prototyping Toolkit for Wearable E textiles
Venue: Tangible and Embedded Interaction
Year: 2020

Smart garment and wearable e textile prototypes are difficult to co design because of the variety of expertise needed (garment design, sewing skills, hardware prototyping, and software programming) To help with this, we developed a toolkit for prototyping wearable e textiles, named Wearable Bits, which enables co design with non expert users without demanding sewing, hardware or software skills. We developed a low fidelity and medium fidelity experience prototype of the toolkit and ran a series of workshops where non expert users designed their own e textile wearables using Wearable Bits. In this paper, we discuss the ideas they developed, their construction techniques, the roles individuals took on while building, and suggestions for future toolkits.
Intra annual reflectance composites from Sentinel 2 and Landsat for national scale crop and land cover mapping
Year: 2019

Many applications that target dynamic land surface processes require a temporal observation frequency that is not easily satisfied using data from a single optical sensor. Sentinel 2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time series temporal frequency at high spatial resolution. Multi sensor image compositing is one way for performing pixel level data integration and has many advantages for processing frameworks, especially if analyses over larger areas are targeted. Our compositing approach is optimized for narrow temporal intervals and allows the derivation of time series of consistent reflectance composites that capture field level phenologies. We processed more than a year's worth of imagery acquired by Sentinel 2A MSI and Landsat 8 OLI as available from the NASA Harmonized Landsat Sentinel dataset. We used all data acquired over Germany and integrated observations into composites for three defined temporal intervals (10 day, monthly and seasonal) Our processing approach includes generation of proxy values for OLI in the MSI red edge bands and temporal gap filling on the 10 day time series. We then derive a national scale crop type and land cover map and compare our results to spatially explicit agricultural reference data available for three federal states and to the results of a recent agricultural census for the entire country. The resulting map successfully captures the crop type distribution across Germany at 30 m resolution and achieves 81% overall accuracy for 12 classes in three states for which reference data was available. The mapping performance for most classes was highest for the 10 day composites and many classes are discriminated with class specific accuracies >80% For several crops, such as cereals, maize and rapeseed our mapped acreages compare very well with the official census data with average differences between mapped and census area of 11% 2% and 3% respectively. Other classes (grapevine and forest classes) perform slightly less well, likely, because the available reference data does not fully capture the variability of these classes across Germany. The inclusion of the red edge bands slightly improved overall accuracies in all cases and improved class specific accuracies for most crop classes. Similarly, our gap filling procedure led to improved mapping accuracies when compared to nongap filled 10 day features. Overall, our results demonstrate the valuable potential of approaches that utilize data from Sentinel 2 and Landsat which allows for detailed assessments of agricultural and other land uses over large areas.
Deep Learning Based Masonry Wall Image Analysis
Venue: Remote. Sens.
Year: 2020

In this paper we introduce a novel machine learning based fully automatic approach for the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For this purpose, we propose a four stage algorithm which comprises three interacting deep neural networks and a watershed transform based brick outline extraction step. At the beginning, a U Net based sub network performs initial wall segmentation into brick, mortar and occluded regions, which is followed by a two stage adversarial inpainting model. The first adversarial network predicts the schematic mortar brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. The second adversarial network predicts the pixels' color values yielding a realistic visual experience for the observer. Finally, using the neural network outputs as markers in a watershed based segmentation process, we generate the accurate contours of the individual bricks, both in the originally visible and in the artificially inpainted wall regions. Note that while the first three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state of the art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types.
Predictors of reduction loss in tibial plateau fracture surgery: Focusing on posterior coronal fractures.
Venue: Injury
Year: 2016

Some studies have reported that fracture pattern was associated with reduction loss after surgery. The purpose of this study was to evaluate various factors that can influence reduction loss, including fracture patterns in unicondylar and bicondylar tibial plateau fractures.  A total of 138 tibial plateau fractures that underwent open reduction and internal fixation using plates were retrospectively reviewed. The OTA/AO classification, fracture pattern, degree of comminution, and existence of reduction loss were evaluated based on simple radiographs and computed tomography. Patient information, including age, gender, and occupation, were acquired through chart reviews. The effect of each variable on reduction loss was evaluated through multiple logistic regression analysis.  Of 138 knees, reduction loss was observed in 40 knees (29.0% Reduction loss was found in 11 (20.4% of the type B knees (54 knees) and 29 (34.5% of the type C knees (84 knees) according to the OTA/AO classification. The multiple logistic regression analysis for all cases revealed that the existence of comminution and coronal fracture influenced the occurrence of reduction loss, with odds ratios of 9.148 and 4.823, respectively (P<0.001 and P=0.001, respectively) In type B and type C, according to the OTA/AO classification, the existence of comminution and coronal fracture had causal relationships with the occurrence of reduction loss. The odds ratios of comminution and coronal fracture for reduction loss for type B were 9.114 and 9.117, respectively (P=0.019 and P=0.031, respectively) and the odds ratios for type C were 8.490 and 4.782, respectively (P=0.001 and 0.009, respectively) CONCLUSIONS When a tibial plateau fracture has a coronal fracture, if it is difficult to fix its fragments rigidly with medial or lateral plate fixation; therefore, buttress plating or direct fixation of fragments through the posteromedial, posterolateral, or posterior approach should be considered.
Spoken language development in children following cochlear implantation.
Venue: JAMA
Year: 2010

Cochlear implantation is a surgical alternative to traditional amplification (hearing aids) that can facilitate spoken language development in young children with severe to profound sensorineural hearing loss (SNHL)  To prospectively assess spoken language acquisition following cochlear implantation in young children.  Prospective, longitudinal, and multidimensional assessment of spoken language development over a 3 year period in children who underwent cochlear implantation before 5 years of age (n 188) from 6 US centers and hearing children of similar ages (n 97) from 2 preschools recruited between November 2002 and December 2004. Follow up completed between November 2005 and May 2008.  Performance on measures of spoken language comprehension and expression (Reynell Developmental Language Scales)  Children undergoing cochlear implantation showed greater improvement in spoken language performance (10.4; 95% confidence interval [CI] 9.6 11.2 points per year in comprehension; 8.4; 95% CI, 7.8 9.0 in expression) than would be predicted by their preimplantation baseline scores (5.4; 95% CI, 4.1 6.7, comprehension; 5.8; 95% CI, 4.6 7.0, expression) although mean scores were not restored to age appropriate levels after 3 years. Younger age at cochlear implantation was associated with significantly steeper rate increases in comprehension (1.1; 95% CI, 0.5 1.7 points per year younger) and expression (1.0; 95% CI, 0.6 1.5 points per year younger) Similarly, each 1 year shorter history of hearing deficit was associated with steeper rate increases in comprehension (0.8; 95% CI, 0.2 1.2 points per year shorter) and expression (0.6; 95% CI, 0.2 1.0 points per year shorter) In multivariable analyses, greater residual hearing prior to cochlear implantation, higher ratings of parent child interactions, and higher socioeconomic status were associated with greater rates of improvement in comprehension and expression.  The use of cochlear implants in young children was associated with better spoken language learning than would be predicted from their preimplantation scores.
Effect of low dose bortezomib on bone formation in smouldering multiple myeloma
Venue: British journal of haematology
Year: 2019

A. Pulido, S.A. Saf, R. Zangger, K. Gruber, K. Macheroux, P. (2014) Collapse of the native structure caused by a single amino acid exchange in human NAD(P)H:quinone oxidoreductase(1. FEBS Journal, 281, 4691 4704. Pileggi, C. Di Sanzo, M. Mascaro, V. Marafioti, M.G. Costanzo, F.S. Pavia, M. (2017) Role of serum ferritin level on overall survival in patients with myelodysplastic syndromes: results of a meta analysis of observational studies. PLoS ONE, 12, e0179016. Sanchez, R.I. Mesia Vela, S. Kauffman, F.C. (2003) Induction of NAD(P)H quinone oxidoreductase and glutathione S transferase activities in livers of female August Copenhagen Irish rats treated chronically with estradiol: comparison with the Sprague Dawley rat. Journal of Steroid Biochemistry and Molecular Biology, 87, 199 206. Schanz,  Sole, F. Mallo, M. Luno, E. Cervera, J. Granada, I. Hildebrandt, B. Slovak, M.L. Ohyashiki, K. Steidl, C. Fonatsch, C. Pfeilstocker, M. Nosslinger, T. Valent, P. Giagounidis, A. Aul, C. Lubbert, M. Stauder, R. Krieger, O. Garcia Manero, G. Faderl, S. Pierce, S. Le Beau, M.M. Bennett, J.M. Greenberg, P. Germing, U. Haase, D. (2012) New comprehensive cytogenetic scoring system for primary myelodysplastic syndromes (MDS) and oligoblastic acute myeloid leukemia after MDS derived from an international database merge.  (2016) Targeting epigenetic pathways in acute myeloid leukemia and myelodysplastic syndrome: a systematic review of hypomethylating agents trials.  Epigenetics, 8, 68.
Protein Targets of Frankincense: A Reverse Docking Analysis of Terpenoids from Boswellia Oleo Gum Resins
Venue: Medicines
Year: 2018

Frankincense, the oleo gum resin of Boswellia trees, has been used in traditional medicine since ancient times. Frankincense has been used to treat wounds and skin infections, inflammatory diseases, dementia, and various other conditions. However, in many cases, the biomolecular targets for frankincense components are not well established.  In this work, we have carried out a reverse docking study of Boswellia diterpenoids and triterpenoids with a library of 16034 potential druggable target proteins.  Boswellia diterpenoids showed selective docking to acetylcholinesterase, several bacterial target proteins, and HIV 1 reverse transcriptase. Boswellia triterpenoids targeted the cancer relevant proteins (poly(ADP ribose) polymerase 1, tankyrase, and folate receptor b) inflammation relevant proteins (phospholipase A2, epoxide hydrolase, and fibroblast collagenase) and the diabetes target 11b hydroxysteroid dehydrogenase.  The preferential docking of Boswellia terpenoids is consistent with the traditional uses and the established biological activities of frankincense.
Design of an Underwater Acoustic MIMO OFDM System Using NI LabView and CompactDAQ
Venue: Global Oceans 2020: Singapore U.S. Gulf Coast
Year: 2020

Recently, the orthogonal frequency division multiplexing (OFDM) technology is receiving increasing attention in underwater acoustic (UA) communications. This paper presents the design of a real time UA OFDM communication system with multiple transducers and hydrophones using the National Instruments CompactDAQ device and LabView software. The purpose of this system is to provide academic researchers a flexible and reconfigurable multiple input multiple output (MIMO) UA communication transceiver with an open architecture to conveniently test and validate the performance of their MIMO transceiver algorithms, which is missing in the literature. The system design, including both the transmitter and receiver, is discussed. The performance of this MIMO UA communication system is verified through a UA communication experiment conducted in the Canning River, Western Australia. Compared with existing modems, the proposed implementation simplifies the prototype design and supports MTMO functions.
Law and Norms: Empirical Evidence
Year: 2019

A large theoretical literature argues laws exert a causal effect on norms. This paper is the first to provide a clean empirical test of the proposition. Using an incentivized vignette experiment, we directly measure social norms relating to actions subject to legal thresholds. Results from three samples with around 800 subjects drawn from universities in the UK and China, and the UK general population, show laws often, but not always, influence norms. The strength of the effect varies across different scenarios, with some evidence that it is more powerful when law breaking is more likely to be intentional and accurately measurable.
The Menace of Vote Buying and Selling in Nigeria and Ways Forward
Year: 2020

This article presents an in depth analysis of the Menace of vote buying and selling in Nigeria and ways forward. It is a product of deep socio political and socio economic analysis based on personal experience, sample opinions across quarters and observation of the trend. The paper identified that the occurrence of vote buying is understood as incentives or gifts given to voters before elections in exchange of their votes. Vote buying is rarely an isolated action and it perpetuates corruption throughout the entire political system. One of the major consequences of vote buying and selling is that it pave ways for the wrong persons get the job and get the works done wrongly, causes series of loss, disruption of the economy and harmony of a community or country. The paper recommends that to nip this menace there must be strict rules and laws for offences of electoral malpractices. Voters' education and reforms in the infrastructure and resource in the election process.
An Augmented Reality Shared Mission Planning Scenario: Observations on Shared Experience
Venue: HCI
Year: 2019

The proliferation of immersive technologies has accentuated their potential utilization across a broad range of operational situations from strategic planning to the tactical edge. However, little is known about individual and team behavior associated with immersive technologies. We begin to address this challenge by conducting a user based experiment comparing a 3D augmented reality (AR) device (HoloLens) to a traditional 2D flat screen display. Participant dyads used both technologies to interactively plan a mission to retrieve a repository of intelligence documents located within enemy held territory. Survey and performance data were collected during the mission planning task. A survey instrument called the Shared Augmented Reality Experience (SARE) survey was developed to measure some aspects of "presence" as defined by factor categories from early virtual environment research. Tested during the mission planning task, a correlation analysis reveals several significant relationships between the survey items and the SARE total score. All relationships between the SARE total score and performance metrics are in the predicted direction; however, only one relationship is significant. Based on the limited data collected to date, experimental outcomes indicate that the level of shared AR experience should be associated with better performance and larger samples should be pursued.
Neural hemispheric organization in successful adult language learning: Is left always right?
Year: 2020

Learning a new language during adulthood is a markedly difficult and heterogeneous process. Whether language learning engages the same left lateralized language network as native language processing or a more distributed network is currently unknown. One approach to address this question is to evaluate the contributions of each cerebral hemisphere in successful language learning. The current review adopts an individual difference approach and provides a systematic overview of (1) the neural factors that predict various language learning outcomes and (2) neuroplastic effects of successful language learning. Our analysis shows that, prior to learning, the neural characteristics of the left hemisphere predominantly predict future speech sound learning. However, more higher level learning tends to be predicted by a more distributed network including the right hemisphere and bilateral brain structures. Over the course of language learning, both hemispheres show structural and functional malleability. We argue that a dynamic bilateral framework involving neural correlates both within and between the two hemispheres underlies the ultimate success of language learning. Learners' native language network (the leftward functional organization for language processing) is related to learning success at the speech sound and word levels. However, when learning involves greater complexity, the initial recruitment of the right hemisphere and the subsequent functional shift from right to left and bilateral hemispheres are essential to ensure successful attainment.
Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence based deep learning
Venue: Briefings Bioinform.
Year: 2020

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.
Where the Gaps Lie: Ten Years of Research into Collaboration on BIM Enabled Construction Projects
Year: 2017

A BIM enabled Construction Project (BIMCP) refers to a project involving relevant BIM tools to generate, exchange and manage project data between project participants. Success in delivering BIMCPs largely relies on how effective project members collaborate. As a result, collaboration on BIMCP has become a growing field of research while a review of studies on collaboration on BIMCPs is still missing. To address this gap, this paper presents the findings of a systematic review on studies devoted to collaboration on BIMCPs over the past 10 years (2006 2016) To this end, 208 studies published in 12 ICT oriented journals in the construction context are thoroughly reviewed. The findings bring to light that studies on collaboration on BIMCPs are sporadic, isolated and focus on narrowed, limited and disjointed areas associated with collaboration. The study contributes to the field through highlighting the gaps of the existing literature on the topic. This provides a stepping stone to direct future inquiries that target collaboration on BIMCPs.
Attention based BiGRU CNN for Chinese question classification
Venue: Journal of Ambient Intelligence and Humanized Computing
Year: 2019

Chinese question classification is one of the essential tasks in nature language processing (NLP) for Chinese language due to its distinctive characteristics. Methods presented in the literature are usually based on rules or traditional machine learning methods, which require manually created rules or features. Thus, the accuracy of the classification is constrained by inherent limitations of these methods. As deep learning based methods have been proved to be able to mine deep information of text, to alleviate the problem, this article proposes a novel deep neural network model, Attention Based BiGRU CNN network (ABBC) and applies it to Chinese question classification task. The model combines the characteristics and advantages of convolutional neural network, attention mechanism and recurrent neural network. Our model can not only extract the features of Chinese questions effectively, but also learn the context information of words to solve the problem that the Text CNN model can lose position feature. By comparing out model to four other classic models, the experimental results show that our model achieves the best performance in the Chinese question classification task.
"Liar, Liar Pants on Fire" A New Benchmark Dataset for Fake News Detection
Venue: ACL
Year: 2017

Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present liar: a new, publicly available dataset for fake news detection. We collected a decade long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta data with text. We show that this hybrid approach can improve a text only deep learning model.