nils-herrmann/clean_scirepeval huggingface dataset:
Content analysis of e petitions with topic modeling: How to train and evaluate LDA models?
Venue: Inf. Process. Manag.
Year: 2018

E petitions have become a popular vehicle for political activism, but studying them has been difficult because efficient methods for analyzing their content are currently lacking. Researchers have used topic modeling for content analysis, but current practices carry some serious limitations. While modeling may be more efficient than manually reading each petition, it generally relies on unsupervised machine learning and so requires a dependable training and validation process. And so this paper describes a framework to train and validate Latent Dirichlet Allocation (LDA) the simplest and most popular topic modeling algorithm, using e petition data. With rigorous training and evaluation, 87% of LDA generated topics made sense to human judges. Topics also aligned well with results from an independent content analysis by the Pew Research Center, and were strongly associated with corresponding social events. Computer assisted content analysts can benefit from our guidelines to supervise every process of training and evaluation of LDA. Software developers can benefit from learning the demands of social scientists when using LDA for content analysis. These findings have significant implications for developing LDA tools and assuring validity and interpretability of LDA content analysis. In addition, LDA topics can have some advantages over subjects extracted by manual content analysis by reflecting multiple themes expressed in texts, by extracting new themes that are not highlighted by human coders, and by being less prone to human bias.
Negative Stereotypes of Ethnic Out groups: A Longitudinal Examination Among Palestinian, Israeli Jewish, and Israeli Arab Youth.
Venue: Journal of research on adolescence the official journal of the Society for Research on Adolescence
Year: 2016

Ethno political conflict impacts thousands of youth globally and has been associated with a number of negative psychological outcomes. Extant literature has mostly addressed the adverse emotional and behavioral outcomes of exposure while failing to examine change over time in social cognitive factors in contexts of ethno political conflict. Using cohort sequential longitudinal data, the present study examines ethnic variation in the development of negative stereotypes about ethnic out groups among Palestinian (n=600) Israeli Jewish (n=451) and Israeli Arab (n=450) youth over three years. Age and exposure to ethno political violence were included as covariates for these trajectories. Findings indicate important ethnic differences in trajectories of negative stereotypes about ethnic out groups, as well as variation in how such trajectories are shaped by prolonged ethno political conflict.
Non volatile ferroelastic switching of the Verwey transition and resistivity of epitaxial Fe3O4/PMN PT (011)
Venue: Scientific reports
Year: 2013

A central goal of electronics based on correlated materials or 'Mottronics' is the ability to switch between distinct collective states with a control voltage. Small changes in structure and charge density near a transition can tip the balance between competing phases, leading to dramatic changes in electronic and magnetic properties. In this work, we demonstrate that an electric field induced two step ferroelastic switching pathway in (011) oriented 0.71Pb(Mg1/3Nb2/3)O3 0.29PbTiO3 (PMN PT) substrates can be used to tune the Verwey metal insulator transition in epitaxial Fe3O4 films in a stable and reversible manner. We also observe robust non volatile resistance switching in Fe3O4 up to room temperature, driven by ferroelastic strain. These results provides a framework for realizing non volatile and reversible tuning of order parameters coupled to lattice strain in epitaxial oxide heterostructures over a broad range of temperatures, with potential device applications.
Dual leucine zipper kinase is required for mechanical allodynia and microgliosis after nerve injury
Venue: eLife
Year: 2018

Neuropathic pain resulting from nerve injury can become persistent and difficult to treat but the molecular signaling responsible for its development remains poorly described. Here, we identify the neuronal stress sensor dual leucine zipper kinase (DLK; Map3k12) as a key molecule controlling the maladaptive pathways that lead to pain following injury. Genetic or pharmacological inhibition of DLK reduces mechanical hypersensitivity in a mouse model of neuropathic pain. Furthermore, DLK inhibition also prevents the spinal cord microgliosis that results from nerve injury and arises distant from the injury site. These striking phenotypes result from the control by DLK of a transcriptional program in somatosensory neurons regulating the expression of numerous genes implicated in pain pathogenesis, including the immune gene Csf1. Thus, activation of DLK is an early event, or even the master regulator, controlling a wide variety of pathways downstream of nerve injury that ultimately lead to chronic pain.
Innovating with Pedagogy Space Technology (PST) Framework: The Online Moot Court
Year: 2015

The Pedagogy Space Technology (PST) Framework (Radcliffe, 2009) is a framework that has been used to guide the creation of new and modern teaching spaces. This paper highlights the journey of an Innovation@CDU Grant Project which uses the PST Framework to build an online moot court which is able to ensure that both internal students on campus and external students online are able to moot effectively. The project uses Blackboard Collaborate(tm) and the PST Framework. The technology has been implemented in Charles Darwin University (CDU) to create the new teaching spaces in CDU. The same technology in the new teaching spaces is then used to build an online moot court. The paper explains the PST Framework and discusses how the framework has been used and applied to further innovations such as an online moot court. It also explains the project's journey and the challenges and successes of the project. The research also includes the experiences and observations of the author who is the Project Leader of the Innovation@CDU Grant Project.
On the Production of He+ of Solar Origin in the Solar Wind
Year: 2020

Solar wind measurements in the heliosphere are predominantly comprised of protons, alphas, and minor elements in a highly ionized state. The majority of low charge states, such as He$ measured in situ are often attributed to pick up ions of non solar origin. However, through inspection of the velocity distribution functions of near Earth measurements, we find a small but significant population of He$ ions in the normal solar wind whose properties indicate that it originated from the Sun and has evolved as part of the normal solar wind. Current ionization models, largely governed by electron impact and radiative ionization and recombination processes, underestimate this population by several orders of magnitude. Therefore, to reconcile the singly ionized He observed, we investigate recombination of solar He${2+ through charge exchange with neutrals from circumsolar dust as a possible formation mechanism of solar He$ We present an empirical profile of neutrals necessary for charge exchange to become an effective vehicle to recombine He${2+ to He$ such that it meets observational He$ values. We find the formation of He$ is not only sensitive to the density of neutrals but also to the inner boundary of the neutral distribution encountered along the solar wind path. However, further observational constraints are necessary to confirm that the interaction between solar \alpha$ particles and dust neutrals is the primary source of the He$ observations.
Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data
Year: 2018

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4% respectively. The overall accuracy of the model was validated using a cross validation test and revealed a median accuracy of 94.4% indicating high reliability of the DCNN based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.
SCREENING FOR HEAVY MOLECULAR WEIGHT HYDROCARBON UTILIZING BACTERIA FROM OIL IMPACTED, NON OIL IMPACTED SOIL AND NATURAL DEPOSITS
Year: 2016

Control and treatment of hazardous effects of heavy molecular weight oil (bitumen) pollution are essential in contaminated soil. This study involved the isolation and screening of microorganisms capable of utilizing heavy molecular weight hydrocarbon from oil impacted, non oil impacted soil and natural deposits of bitumen. Total heterotrophic bacterial counts in the samples ranged from 1.4 aa? 10 5 CFU/g to 2.0 aa? 10 6 CFU/g. Total oil utilizing bacterial counts varied from 1.5 aa? 10 4 CFU/g to 3.6 aa? 10 5 CFU/g. Isolates were identified using API 20E kit. They belong to the genera Burkholderia, Pseudomonas, and Serratia. Degradation efficiency of the isolates on Premium Motor Spirit (PMS) Dual Purpose Kerosene (DPK) and Low Pour Point Fuel Oil (LPFO) were carried out by a colorimetric rapid screen test using 2, 6 dichlorophenol indophenol (DCPIP) reduction test which was monitored by measuring absorbance at 600 nm at every 24 hrs for 120 hrs. Order of ability of the isolates to degrade PMS: P. aeruginosa P. mendocina P. borbori S. rubidae P. cichorii B. cepacia while for DPK is P. cichorii P. borbori S. rubidae P. mendocina B. cepacia P. aeruginosa. Ability to degrade LPFO: P. cichorii P. borbori P. aeruginosa P. mendocina B. cepacia S. rubidae.
STDR: A Deep Learning Method for Travel Time Estimation
Venue: DASFAA
Year: 2019

With the booming traffic developments, estimating the travel time for a trip on road network has become an important issue, which can be used for driving navigation, traffic monitoring, route planning, and ride sharing, etc. However, it is a challenging problem mainly due to the complicate spatial temporal dependencies, external weather conditions, road types and so on. Most traditional approaches mainly fall into the sub segments or sub paths categories, in other words, divide a path into a sequence of segments or sub paths and then sum up the sub time, yet which don't fit the real world situations such as the continuously dynamical changing route or the waiting time at the intersections. To address these issues, in this paper, we propose an end to end Spatial Temporal Deep learning network with Road type named STDR to estimate the travel time based on historical trajectories and external factors. The model jointly leverages CNN and LSTM to capture the complex nonlinear spatial temporal characteristics, more specifically, the convolutional layer extracts the spatial characteristics and the LSTM with attention mechanism extracts the time series characteristics. In addition, to better discover the influence of the road type, we introduce a road segmentation approach which is capable of dividing the trajectory based on the shape of trajectory. We conduct extensive verification experiments for different settings, and the results demonstrate the superiority of our method.
Tracking dynamic community evolution in social networks
Venue: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)
Year: 2014

Healthcare social networking has known a prominent popularity. Nowadays, millions of healthcare communities are appeared. Community detection methods in social networks have essentially focused on static graphs neglecting the temporal characteristics of networks. Recently, motivated by the dynamic nature of real world projected on virtual social networks, an increasing number of evolutionary properties of communities issues are considered by research community. In this work, we aim to present a study on social networks across the time axis i.e. temporal social networks and we propose an algorithm classifying changes and based on indicators, and also we integrate data warehouse layer in order to have an over view of all possible changes helpful for future analysis. The experimental results include an example of changes related to the apparition of diabetes in our country Tunisia.
Hierarchical deep semantic hashing for fast image retrieval
Venue: Multimedia Tools and Applications
Year: 2016

Content Based large scale image retrieval has recently attracted considerable attention because of the explosive increase of online images. Inspired by recent advances in convolutional neural networks, we propose a hierarchical deep semantic method for learning similarity function that solves the problems of precision and speed of retrieval in the setting of large scale environments. The distinctive contribution of our work is a novel approach that can utilize previous knowledge of the semantic hierarchy. When semantic information and a related hierarchy structure are available, significant improvements can be attained. Exploiting hierarchical relationships is the most important thing for large scale issues. Binary code can be learned from deep neural network for representing the latent concepts that dominate the semantic labels. Different from other supervised methods that require learning an explicit hashing function to map the binary code features from the images, our method learns Hierarchical Deep Semantic Hashing code (HDSH code) and image representations in an implicit manner, making it suitable for large scale datasets. An additional contribution is a novel hashing scheme (generated at the same time with semantic information) that is able to reduce the computational cost of retrieval. Comprehensive experiments were conducted on Holidays, Oxford5k/105k, Caltech256 retrieval datasets, our HDSH performs competitively even when the convolutional neural network has been pre trained for a surrogate unrelated task. We further demonstrates its efficacy and scalability on a large scale dataset Imagenet with millions of images. With deep hierarchical semantic hashing, we report retrieval times are 0.15ms and 53.92ms on Holidays and Imagenet dataset, respectively.
A survey on ensemble learning
Venue: Frontiers of Computer Science
Year: 2019

Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc.
Relational inductive biases, deep learning, and graph networks
Venue: ArXiv
Year: 2018

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences a hallmark of human intelligence from infancy remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand engineering" and "end to end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias the graph network which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open source software library for building graph networks, with demonstrations of how to use them in practice.
Effectiveness of a Binocular Video Game vs Placebo Video Game for Improving Visual Functions in Older Children, Teenagers, and Adults With Amblyopia: A Randomized Clinical Trial
Venue: JAMA ophthalmology
Year: 2018

Importance Binocular amblyopia treatment using contrast rebalanced stimuli showed promise in laboratory studies and requires clinical trial investigation in a home based setting.  To compare the effectiveness of a binocular video game with a placebo video game for improving visual functions in older children and adults.  The Binocular Treatment of Amblyopia Using Videogames clinical trial was a multicenter, double masked, randomized clinical trial. Between March 2014 and June 2016, 115 participants 7 years and older with unilateral amblyopia (amblyopic eye visual acuity, 0.30 1.00 logMAR; Snellen equivalent, 20/40 20/200) due to anisometropia, strabismus, or both were recruited. Eligible participants were allocated with equal chance to receive either the active or the placebo video game, with minimization stratified by age group (child, age 7 to 12 years; teenager, age 13 to 17 years; and adult, 18 years and older) Interventions Falling blocks video games played at home on an iPod Touch for 1 hour per day for 6 weeks. The active video game had game elements split between eyes with a dichoptic contrast offset (mean [SD] initial fellow eye contrast, 0.23 [0.14] The placebo video game presented identical images to both eyes.  Change in amblyopic eye visual acuity at 6 weeks. Secondary outcomes included compliance, stereoacuity, and interocular suppression. Participants and clinicians who measured outcomes were masked to treatment allocation.  Of the 115 included participants, 65 (56.5% were male and 83 (72.2% were white, and the mean (SD) age at randomization was 21.5 (13.6) years. There were 89 participants (77.4% who had prior occlusion. The mean (SD) amblyopic eye visual acuity improved 0.06 (0.12) logMAR from baseline in the active group (n 56) and 0.07 (0.10) logMAR in the placebo group (n 59) The mean treatment difference between groups, adjusted for baseline visual acuity and age group, was 0.02 logMAR (95% CI, 0.06 to 0.02; P .25) Compliance with more than 25% of prescribed game play was achieved by 36 participants (64% in the active group and by 49 (83% in the placebo group. At 6 weeks, 36 participants (64% in the active group achieved fellow eye contrast greater than 0.9 in the binocular video game. No group differences were observed for any secondary outcomes. Adverse effects included 3 reports of transient asthenopia.  The specific home based binocular falling blocks video game used in this clinical trial did not improve visual outcomes more than the placebo video game despite increases in fellow eye contrast during game play. More engaging video games with considerations for compliance may improve effectiveness.
Aspecte noi privind contabilitatea imobilizarilor necorporale si corporale
Year: 2019

NEDERITA, Alexandru, POPOVICI, Angela. Aspecte noi privind contabilitatea imobilizarilor necorporale si corporale.  Competitivitate si inovare in economia cunoasterii [online] conf. st. intern. ed. a 21 a: Lucrari stiintifice, 27 28 sept. 2019.  ASEM, 2019, pp. 531 539. ISBN 978 9975 75 968 7.
Analysis on Variation of Major Evaluating Indexes for Journal of Shanxi Agriculture Sciences during 2006 2011
Year: 2013

Based on the data from Chinese Journal Citation Reports and periodical statistics and evaluation by Wanfang Database,which were co issued by Institute of Scientific Technology Information of China and Wanfang Data Corporation Ltd.,analysis on the major evaluating indexes for the Journal of Shanxi Agriculture Sciences during 2006 2011 was conducted through source index, citation index and highly quoted index.The results showed that indexes of the published article volume, average quotation number, overseas or fund thesis ratios, total citation frequency, impact factor,number of citing magazine, subject diffusion index, cited frequency in 5 years, impact factor in 5 years, article cited 50% cumulation index all showed a rising tendency. It suggests that for the Journal of Shanxi Agriculture Sciences the impact factor is rising year by year, the degree of inheritance by the scientists increases, and the inner or inter disciplinary citation is becoming more and more frequent.
FasterSeg: Searching for Faster Real time Semantic Segmentation
Venue: ICLR
Year: 2020

We present FasterSeg, an automatically designed semantic segmentation network with not only state of the art performance but also faster speed than current methods. Utilizing neural architecture search (NAS) FasterSeg is discovered from a novel and broader search space integrating multi resolution branches, that has been recently found to be vital in manually designed segmentation models. To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to "collapsing" to low latency yet poor accuracy models. Moreover, we seamlessly extend FasterSeg to a new collaborative search (co searching) framework, simultaneously searching for a teacher and a student network in the same single run. The teacher student distillation further boosts the student model's accuracy. Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg. For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.