nils-herrmann/clean_scirepeval huggingface dataset:
Subsolidus phase equilibria in the GdFeO3 SrFeO3 system in air
Year: 2020

The paper considers phase equilibria in the subsolidus region of the GdFeO3 SrFeO3 d system in air. By using X ray powder diffraction, SEM/EDS, and Mossbauer spectroscopy, a GdFeO3 SrFeO3 d phase diagram was constructed in the 1300 1500 degC temperature range. The samples of Gd1 xSrxFeO3 a for this study were synthesized by solid phase chemical reactions. The study of phase equilibria in the GdFeO3 SrFeO3 d system showed the presence of three areas, that is, of solid solutions based on the strontium poor GdFeO3 orthorhombic phase, of the strontium rich SrFeO3 d cubic phase, as well as of an intermediate region in the mixture of these phases. The miscibility gap width (Dx) decreases from 0.54 to 0.35 with an increase in temperature from 1300 to 1500 degC.
A review of the ecosystem functions in oil palm plantations, using forests as a reference system
Venue: Biological reviews of the Cambridge Philosophical Society
Year: 2017

Oil palm plantations have expanded rapidly in recent decades. This large scale land use change has had great ecological, economic, and social impacts on both the areas converted to oil palm and their surroundings. However, research on the impacts of oil palm cultivation is scattered and patchy, and no clear overview exists. We address this gap through a systematic and comprehensive literature review of all ecosystem functions in oil palm plantations, including several (genetic, medicinal and ornamental resources, information functions) not included in previous systematic reviews. We compare ecosystem functions in oil palm plantations to those in forests, as the conversion of forest to oil palm is prevalent in the tropics. We find that oil palm plantations generally have reduced ecosystem functioning compared to forests: 11 out of 14 ecosystem functions show a net decrease in level of function. Some functions show decreases with potentially irreversible global impacts (e.g. reductions in gas and climate regulation, habitat and nursery functions, genetic resources, medicinal resources, and information functions) The most serious impacts occur when forest is cleared to establish new plantations, and immediately afterwards, especially on peat soils. To variable degrees, specific plantation management measures can prevent or reduce losses of some ecosystem functions (e.g. avoid illegal land clearing via fire, avoid draining of peat, use of integrated pest management, use of cover crops, mulch, and compost) and we highlight synergistic mitigation measures that can improve multiple ecosystem functions simultaneously. The only ecosystem function which increases in oil palm plantations is, unsurprisingly, the production of marketable goods. Our review highlights numerous research gaps. In particular, there are significant gaps with respect to socio cultural information functions. Further, there is a need for more empirical data on the importance of spatial and temporal scales, such as differences among plantations in different environments, of different sizes, and of different ages, as our review has identified examples where ecosystem functions vary spatially and temporally. Finally, more research is needed on developing management practices that can offset the losses of ecosystem functions. Our findings should stimulate research to address the identified gaps, and provide a foundation for more systematic research and discussion on ways to minimize the negative impacts and maximize the positive impacts of oil palm cultivation.
A Persian Treebank with Stanford Typed Dependencies
Venue: LREC
Year: 2014

We present the Uppsala Persian Dependency Treebank (UPDT) with a syntactic annotation scheme based on Stanford Typed Dependencies. The treebank consists of 6,000 sentences and 151,671 tokens with an average sentence length of 25 words. The data is from different genres, including newspaper articles and fiction, as well as technical descriptions and texts about culture and art, taken from the open source Uppsala Persian Corpus (UPC) The syntactic annotation scheme is extended for Persian to include all syntactic relations that could not be covered by the primary scheme developed for English. In addition, we present open source tools for automatic analysis of Persian containing a text normalizer, a sentence segmenter and tokenizer, a part of speech tagger, and a parser. The treebank and the parser have been developed simultaneously in a bootstrapping procedure. The result of a parsing experiment shows an overall labeled attachment score of 82.05% and an unlabeled attachment score of 85.29% The treebank is freely available as an open source resource.
Saliency detection based on non local neural networks in low contrast images
Venue: International Conference on Graphic and Image Processing
Year: 2020

Saliency detection model has been widely used in many fields of computer vision. Currently, most models are not applicable to the foggy scenario. Because these models are highly dependent upon high level features extracted by deep learning and handcrafted features, these features cannot effectively highlight the significant targets in foggy images. In this paper, we present a saliency detection model for foggy images. This model extracts non local feature, i.e. jointly learned with local features under a unified deep learning framework. The key idea of the proposed model is to hierarchically introduce non local module with local contrast processing blocks, aiming to provide robust representation of saliency information towards foggy images with low signal to noise ratio property. Experiments have been conducted on three challenging datasets and our foggy image dataset consisting of dynamic object images. By comparing with the state of the art models, our model gets better performance.
DoReFa Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
Venue: ArXiv
Year: 2016

We propose DoReFa Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients. In particular, during backward pass, parameter gradients are stochastically quantized to low bitwidth numbers before being propagated to convolutional layers. As convolutions during forward/backward passes can now operate on low bitwidth weights and activations/gradients respectively, DoReFa Net can use bit convolution kernels to accelerate both training and inference. Moreover, as bit convolutions can be efficiently implemented on CPU, FPGA, ASIC and GPU, DoReFa Net opens the way to accelerate training of low bitwidth neural network on these hardware. Our experiments on SVHN and ImageNet datasets prove that DoReFa Net can achieve comparable prediction accuracy as 32 bit counterparts. For example, a DoReFa Net derived from AlexNet that has 1 bit weights, 2 bit activations, can be trained from scratch using 6 bit gradients to get 46.1\ top 1 accuracy on ImageNet validation set. The DoReFa Net AlexNet model is released publicly.
Tweet specific extractive summarization framework towards trending topic analysis
Venue: 2016 International Conference on Information Communication and Embedded Systems (ICICES)
Year: 2016

Twitter establishes itself as a critical element to any social networking based applications serving itself as a rich information delivering platform. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronted with overwhelming and unorganized tweets. Previously, there has been attempts to provide a short snippet to summarize a topic but, this does not scale up to user's expectation as it does not provide any analyzed summary of tweets. This work aims to develop a Tweet Specific Extractive Summarization system performing Trending Topic Analysis. This system analyzes trending topics through Topic based sentiment classification which summarizes the public views on selected trending topics and generates extractive sub summaries of topics over the time period using novel Tweet Feature Graph Model (TFGM) Tweet Specific Extraction Summarization framework differs from the traditional summarization in few aspects. First, conflicting summary generation could be avoided with sentiment classification enhanced by common and tweet specific feature extraction thereby sorting the data into separate sentiment corpus. Second, volume based followed by topic modelled approach of detecting sub topic in the corpus help detect subtopics under the trending topic more efficiently. Finally, Summary generation is accomplished using the Tweet Feature Graph Model (TFGM) which incorporates tweet specific salient features. This model increases relevancy of tweets in content selection phase which in turn contributes to the increase in quality of the summaries generated.
Enhanced Pattern Matching Performance Using Improved Boyer Moore Horspool Algorithm
Year: 2011

In computer science, the Boyer Moore Horspool algorithm is an algorithm for finding substrings in strings. A pattern matching problem can be classified into software and hardware based on implemental methods. It is important of enhance pattern matching performance. This paper proposes enhanced pattern matching performance using improved Boyer Moore Horspool Algorithm. It combines the deterministic finite state; the improved Boyer Moore Horspool Algorithm takes full use of the matching information to skip several characters. Experimental result shows that the proposed improved Boyer Moore Horspool Algorithm is effective.
A Look Inside the Black Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering
Venue: UMAP
Year: 2020

Deep learning based recommender systems are nowadays defining the state of the art. Unfortunately, their hard interpretability restrains their application in scenarios in which explainability is required/desirable. Many efforts have been devoted to injecting explainable information inside deep models. However, there is still a lot of work that needs to be done to fill this gap. In this paper, we take a step in this direction by providing an intuitive interpretation of the inner representation of a conditioned variational autoencoder (C VAE) for collaborative filtering. The interpretation is visually performed by plotting the principal components of the latent space learned by the model on MovieLens. We show that in the latent space conditions on correlated genres map users in close clusters. This characteristic enables the model to be used for profiling purposes.
Effect of aspect ratio and surface defects on the photocatalytic activity of ZnO nanorods
Venue: Scientific reports
Year: 2014

ZnO, aside from TiO2, has been considered as a promising material for purification and disinfection of water and air, and remediation of hazardous waste, owing to its high activity, environment friendly feature and lower cost. However, their poor visible light utilization greatly limited their practical applications. Herein, we demonstrate the fabrication of different aspect ratios of the ZnO nanorods with surface defects by mechanical assisted thermal decomposition method. The experiments revealed that ZnO nanorods with higher aspect ratio and surface defects show significantly higher photocatalytic performances.
Modeling and Simulation of FLC based Navigation Algorithm for Small Gas Pipeline Inspection Robot
Venue: 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
Year: 2018

Pipeline robot are widely used since pipelines require to be inspected regularly for leakages caused by natural disaster, etc. Most robots which rely heavily on manual operation are incapable of self navigation in pipe. Moreover incorrect operations would degrade the efficiency, and sometimes damage the robots especially when they pass through elbows or junctions. Some robots can realize navigation based on multi sensor such as position sensitive detector and laser sensor, but navigation performance for such robots will be greatly influenced by the performance of these sensors, and space to install large number of sensors is limited. In this study, we propose an approach of pipeline robot's navigation based on fuzzy logic control (FLC) algorithm for passing through elbows or T junctions. A CCD camera installed on the robot is used for locating region of interest (ROI) in elbow or junction. Moreover, ROIs formed by reflection of robot's LED light and edge of pipe's dark hole are considered as input variables in the FLC system. By analyzing system outputs, we can control the robot's speed and yaw angle in real time. Compared with conventional studies on pipeline robot's navigation method, the proposed method can be more precise and faster by using FLC algorithm and analyzing ROI with fewer sensors. Finally, we conducted a simulation validation, and the results showed that the robot was capable of adapting to known pipe environments and realizing navigation in straight part, elbow, and junction of pipe.
Reclaiming the Black Personhood: the Power of the Hip Hop Narrative in Mainstream Rap
Year: 2018

Follow this and additional works at: https:/scholarsarchive.byu.edu/criterion Part of the African American Studies Commons, American Popular Culture Commons, Civil Rights and Discrimination Commons, English Language and Literature Commons, Law and Race Commons, Other Music Commons, Other Rhetoric and Composition Commons, Race and Ethnicity Commons, and the Social Influence and Political Communication Commons
Quantitative Ethnobotany of Medicinal Plants Used by Indigenous Communities in the Bandarban District of Bangladesh
Venue: Front. Pharmacol.
Year: 2018

This study documents information on significant ethnomedicinal plants, which was collected from the traditional healers of three indigenous communities of Bangladesh. The documented data were quantitatively analyzed for the first time in this area. The information was obtained through open ended, semi structured questionnaires. The benefits, importance and coverage of ethnomedicine were expressed through several quantitative indices including Informant Consensus Factor (ICF) Use Value (UV) Frequency of Citation (FC) Relative Frequency of Citation (RFC) and Relative Importance Index (RI) The agreement of homogeneity between the present and previous studies and among the indigenous communities was evaluated using the Jaccard Index (JI) A total of 159 ethnomedicinal plant species, which were distributed in 132 genera under 62 families, were documented from 174 informants. Of these, 128 plants were native and 31 were exotic. Of a majority of documented species, herbs and leaves were the most utilized plant parts for the preparation of ethnomedicines (45.28% whereas pastes (63.03% were the most popular formulations. Among the documented species, the dominant families were the Asteraceae (14 species) and the Lamiaceae (12 species) The highest ICF value was 0.77 for digestive system disorders. Based on UVs, the five most commonly used ethnomedicinal plant species in the study area were Duabanga grandiflora (0.43) Zingiber officinale (0.41) Congea tomentosa (0.40) Matricaria chamomilla (0.33) and Engelhardtia spicata (0.28) The highest RFC was recorded for Rauvolfia serpentina (0.25) The highest RI value was calculated for both Scoparia dulcis and Leucas aspera (0.83) Importantly, 16 species were reported with new therapeutic uses and to our knowledge, 7 species described herein have never been ethnobotanically and pharmacologically studied, viz: Agastache urticifolia, Asarum cordifolium, C. tomentosa, E. spicata, Hypserpa nitida, Merremia vitifolia and Smilax odoratissima. The present study showed that traditional treatment using medicinal plants is still widespread in the study area. Documentation of new ethnomedicinal species with their therapeutic uses shall promote further phytochemical and pharmacological investigations and possibly, lead to the development of new drugs.
Predicting the Volatility of Cryptocurrency Time Series
Year: 2018

Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being "efficient" require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies' volatility has been found to exhibit at the same time differences and similarities with other financial time series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investigate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: 1 day, 1 and 2 weeks volatility levels.
Efficacy of chlorine sanitizer spray bottles exposed to various temperatures and ambient light
Year: 2017

Gastroenteritis is largely under reported across Canada. It is estimated that one reported case represents on average 313 cases. In addition, improper cleaning is one of the top ten reported causes of food borne illness. Sanitization is important to reduce the number of pathogenic microorganisms present on food contact surfaces to a safe level. Correct concentrations of sodium hypochlorite are to be prepared and used within the range of 100ppm 200ppm on food contact surfaces. The purpose of this study was to evaluate the rate of degradation of sodium hypochlorite in spray bottles to assess if these solutions need to be prepared fresh daily to achieve efficacy. Two variables seen within a food service establishment were chosen to evaluate the rate of degradation, temperature and ambient light.  Room temperature (20 C) 35 C and ambient light exposure were tested to evaluate their effect on the degradation of free chlorine in spray bottles over time in days. The experiment was preformed by setting up 3 individual spray bottles at 20 C with no light, 20 C with ambient light and 35 C with no light. The sodium hypochlorite was then sampled and recorded periodically three times a week over a 15 day period to determine the stability of the chorine solutions prepared at around 200ppm.  In the order of spray bottles tested, 20 C no light, 20 C ambient light and 35 C no light, a correlation coefficient of 0.3027, 0.8235 and 0.8169 were recorded. In addition, the following test spray bottles held a r squared value of 0.0916, 0.6781 and 0.6674. A p value of 0.5094, 0.0249 and 0.0249 were also assessed, with a corresponding power of 8.99% 73.74% and 71.75%  By calculating the linear regression formula, it was concluded that chlorine solution in spray bottles do not need to be prepared fresh daily. For 200ppm 20 C no light, 200ppm 20 C ambient light and 200ppm 35 C no light, at days 128, 67 and 45, the estimated concentration of sodium hypochlorite will be at the minimum requirement of 100ppm respectively.
Moment Matching Graph Networks for Causal Inference
Venue: ArXiv
Year: 2020

In this note we explore a fully unsupervised deep learning framework for simulating non linear structural equation models from observational training data. The main contribution of this note is an architecture for applying moment matching loss functions to the edges of a causal Bayesian graph, resulting in a generative conditional moment matching graph neural network. This framework thus enables automated sampling of latent space conditional probability distributions for various graphical interventions, and is capable of generating out of sample interventional probabilities that are often faithful to the ground truth distributions well beyond the range contained in the training set. These methods could in principle be used in conjunction with any existing autoencoder that produces a latent space representation containing causal graph structures.
Yelp Review Rating Prediction: Machine Learning and Deep Learning Models
Venue: ArXiv
Year: 2020

We predict restaurant ratings from Yelp reviews based on Yelp Open Dataset. Data distribution is presented, and one balanced training dataset is built. Two vectorizers are experimented for feature engineering. Four machine learning models including Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine are implemented. Four transformer based models containing BERT, DistilBERT, RoBERTa, and XLNet are also applied. Accuracy, weighted F1 score, and confusion matrix are used for model evaluation. XLNet achieves 70% accuracy for 5 star classification compared with Logistic Regression with 64% accuracy.
A Comparative Study of Work Life Balance and Job Satisfaction of the Employees Working in Business Process Outsourcing Sector
Year: 2018

This paper provides an insight into the Work life balance and job satisfaction of individuals working in business process outsourcing. In the present fast growing scenario, people don't have time for themselves or for their families. It is difficult to balance both work and personal life at a time, especially for the employees working in Business process outsourcing (BPO's).The Work life balance of an individual is one of the factors, which affect their satisfaction or happiness with life as a whole. Data was obtained from 200 employees, 120 male and 80 female. Work life balance was measured by the 15 item scale developed by Fisher McAuley (2003) consist of three dimensions work interference with personal life (WIPL) personal life interference with work (PLIW) work/personal life enhancement (WPLE) and job satisfaction was measured by using scale Khan and Sheeba (2002).Data comparison of work life balance, its three dimensions work interference with personal life, work/personal life enhancement, personal life interference with work and job satisfaction between male and female t test was found suitable. All the analysis was carried out with the help of the computer using SPSS 16.0 version.
Effects and mechanism of ultrasound pretreatment of protein on the Maillard reaction of protein hydrolysate from grass carp (Ctenopharyngodon idella)
Venue: Ultrasonics sonochemistry
Year: 2020

The effects of two types (energy divergent/gathered) of ultrasound pretreatment of protein on the Maillard reaction of protein hydrolysate from grass carp (Ctenopharyngodon idella) were studied. The test and analysis of Fourier transform infrared spectroscopy, surface hydrophobicity and atomic force microscopy of protein, peptide concentration, molecular weight distribution and free amino acid content of protein hydrolysate were performed to reveal the mechanism. Also, the sensory characteristics of Maillard reaction products were evaluated. Results showed that Maillard reaction products presented higher absorbance value at 294 and 420 nm after pretreated by two types of ultrasound compared to that of control. The grafting degree value of products pretreated by energy divergent ultrasound increased by 13.87% Both of these two types of ultrasound pretreatment showed higher (p 0.05) value of grafting degree compared to that of positive control (thermal denaturation) The random coil content and surface hydrophobicity of protein improved significantly (p 0.05) and the depth distribution of protein molecules narrowed down after pretreated by ultrasound, especially energy divergent type ultrasound. The change of protein structure increased small molecular peptide/amino acid content in protein hydrolysate, so that it promoted the Maillard reaction process of protein hydrolysate and glucose. The mouthfulness and overall acceptance of Maillard reaction products increased after pretreated by two types of ultrasound. Results indicated that ultrasound, especially energy divergent type ultrasound pretreatment of protein was an effective method to promote Maillard reaction evolution of protein hydrolysate from grass carp protein and improved the flavor of Maillard reaction products.