nils-herrmann/clean_scirepeval huggingface dataset:
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Venue: LREC
Year: 2020

Universal Dependencies is an open community effort to create cross linguistically consistent treebank annotation for many languages within a dependency based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part of speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the universal guidelines (UD v2) discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.
OSMANLI DEVRI DENIZLI'DE SIGIR VEBASI: VEBA YI BAKARI
Year: 2020

Sigir vebasi, tarih boyunca hayvan hastaliklari arasinda en onemli kayiplara sebebiyet vermis hastaliklarin basinda gelmistir. Buyukbas hayvanlarda gorulen bu hastalik virus araciligiyla hizli bir bicimde genisleyerek cok sayida hayvanin olumune neden olmustur. Asya merkezli sigir vebasi salgini ilk once Avrupa'da etkili olmus ve genis capta manda ile sigirin telef olmasina sebebiyet vermistir. Bu durum Avrupa'da hayvan hastaliklari konusunda uzmanlarin yetismesini amaclayan veterinerlik okullarinin kurulmasinin onunu acmistir. Ayrica milletlerarasi kongre ve toplantilar duzenlenerek salginin ortaya cikma ve yayilma sebepleri hakkinda gorusmeler yapilmistir. Osmanli Devleti'nde sigir vebasi ise farkli donemlerde etkili olmakla birlikte ozellikle 1877 1878 Osmanli Rus Savasi'nin ardindan Balkanlar uzerinden Anadolu'ya yayilmistir. 19. yuzyilin son ceyreginden itibaren Osmanli cografyasinin pek cok bolgesinde etkili olan veba salgini devletin ekonomisine ve hayvanciligina buyuk darbe vurmustur. Bu calismada, Denizli'de meydana gelen sigir vebasi salginlari ve Osmanli Devleti'nin salginlara karsi aldigi koruyucu onlemler hakkinda bilgi verilmesi amaclanmaktadir.
Musical Composition Style Transfer via Disentangled Timbre Representations
Venue: IJCAI
Year: 2019

Music creation involves not only composing the different parts (e.g. melody, chords) of a musical work but also arranging/selecting the instruments to play the different parts. While the former has received increasing attention, the latter has not been much investigated. This paper presents, to the best of our knowledge, the first deep learning models for rearranging music of arbitrary genres. Specifically, we build encoders and decoders that take a piece of polyphonic musical audio as input, and predict as output its musical score. We investigate disentanglement techniques such as adversarial training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation (timbre) of the parts per short time segment. By disentangling pitch and timbre, our models have an idea of how each piece was composed and arranged. Moreover, the models can realize "composition style transfer" by rearranging a musical piece without much affecting its pitch content. We validate the effectiveness of the models by experiments on instrument activity detection and composition style transfer. To facilitate follow up research, we open source our code at https:/github.com/biboamy/instrument disentangle.
Study of thermal and hydrodynamic characteristics of water nano encapsulated phase change particles suspension in an annulus of a porous eccentric horizontal cylinder
Year: 2020

In this paper, the thermal and hydrodynamic characteristics of a suspension with water Nano Encapsulated Phase Change Material (NEPCM) in an annulus of a porous eccentric horizontal cylinder are investigated. The NEPCM particles have a core shell structure and stability suspended in water. Hence, the particles, along with the liquid, could freely circulate inside the annuli of the horizontal cylinder due to the buoyancy forces. The cores of these particles are made from a Phase Change Material (PCM) Moreover, such cores are in a continuous exchange of heat transfer between the solid and liquid phases. The heat transfer is acting in a combination of absorption, storage, and release mechanisms. The governing equations for the fluid motions and conservation of energy could be written in partial differential forms and by using the appropriate non dimensional variables converted into non dimensional ones. Then, the numerical approach is applied by implementing the finite element method (FEM) to solve such equations iteratively. The impact of various non dimensional parameters including the fusion temperature, Stefan number, Rayleigh number, Darcy number, the volume fraction of nanoparticles, and eccentricity of the inner cylinder is addressed on the flow and heat transfer. It is observed that the most favourable fusion temperature ranges for the maximum heat transfer rate vary as a function of the Rayleigh number. In addition, the heat transfer rate can be enhanced by applying the phase change core of nanoparticles.
Single image HDR reconstruction using a CNN with masked features and perceptual loss
Venue: ACM Trans. Graph.
Year: 2020

Digital cameras can only capture a limited range of real world scenes' luminance, producing images with saturated pixels. Existing single image high dynamic range (HDR) reconstruction methods attempt to expand the range of luminance, but are not able to hallucinate plausible textures, producing results with artifacts in the saturated areas. In this paper, we present a novel learning based approach to reconstruct an HDR image by recovering the saturated pixels of an input LDR image in a visually pleasing way. Previous deep learning based methods apply the same convolutional filters on wellexposed and saturated pixels, creating ambiguity during training and leading to checkerboard and halo artifacts. To overcome this problem, we propose a feature masking mechanism that reduces the contribution of the features from the saturated areas. Moreover, we adapt the VGG based perceptual loss function to our application to be able to synthesize visually pleasing textures. Since the number of HDR images for training is limited, we propose to train our system in two stages. Specifically, we first train our system on a large number of images for image inpainting task and then fine tune it on HDR reconstruction. Since most of the HDR examples contain smooth regions that are simple to reconstruct, we propose a sampling strategy to select challenging training patches during the HDR fine tuning stage. We demonstrate through experimental results that our approach can reconstruct visually pleasing HDR results, better than the current state of the art on a wide range of scenes.
Graph Signal Processing Part II: Processing and Analyzing Signals on Graphs
Venue: ArXiv
Year: 2019

The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet illustrative and general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Discrete Fourier Transform (GDFT) The spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev polynomial approximation series. Ideas related to the sampling of graph signals are presented and further linked with compressive sensing. Localized graph signal analysis in the joint vertex spectral domain is referred to as the vertex frequency analysis, since it can be considered as an extension of classical time frequency analysis to the graph domain of a signal. Important topics related to the local graph Fourier transform (LGFT) are covered, together with its various forms including the graph spectral and vertex domain windows and the inversion conditions and relations. A link between the LGFT with spectral varying window and the spectral graph wavelet transform (SGWT) is also established. Realizations of the LGFT and SGWT using polynomial (Chebyshev) approximations of the spectral functions are further considered. Finally, energy versions of the vertex frequency representations are introduced.
Microclimatic resilience of subtropical woodlands and urban forest benefits
Venue: Urban Forestry Urban Greening
Year: 2019

With compact development mode and subtropical climate, urban Hong Kong suffers from the urban heat island exacerbated by climate change. In recent years, record high summer air temperatures (Ta) and heat stress were recorded, which discourage outdoor exercise and nature based enjoyment. The thermal overload could be mitigated by urban woodlands that bring effective cooling through evapotranspiration and shading by diverse species composition, growth forms and biomass structure. This study measured microclimatic parameters over 12 months at four experimental plots in a peri urban golf course, including simple woodland with limited species and single layer structure, complex woodland with diverse species and multiple tier structure, unshaded lawn, and bare concrete serving as control. Woodland cooling was most prominent on summer and sunny days, and weaker in winter and cloudy conditions. Daytime Ta in woodlands was cooler, whereas nighttime warming was observed mainly at the simple woodland. Despite seasonal variations of cooling strengths, Ta was consistently suppressed on cloudy day vis a vis sunny day. On summer sunny day, concrete and lawn plots registered scorching air and surface temperatures (Ts) whereas woodland plots demonstrated summer cooling amplification with maximum 4.1 degC Ta depression versus concrete and 3.9 degC versus lawn. Comprehensive evaluations and comparisons were made between the magnitude and variations of the woodland cooling effect in different seasons and weather conditions. The benefits and feasibility of creating urban woodland patches in Hong Kong to improve urban climate and thermal comfort were discussed.
Resident Perceptions of a Proposed Environmental Education Center and Demonstration Farm.
Year: 2018

To gauge community support for a proposed environmental education center and demonstration farm, we surveyed 514 local residents. Our intent was to assess community members' support for the project and relevant programming interests and to determine the roles that level of community satisfaction, perceived economic impact, and demographics played with regard to project support. We found that most community members supported the development and that levels of community satisfaction, perceptions of economic impact, background, gender, and age were significantly associated with level of support. Our research also revealed that community members were most interested in programs about nature and growing and preserving food and were not interested in technologybased programming.
The impact of expanding advanced level secondary school students' awareness and use of metacognitive learning strategies on confidence and proficiency in foreign language speaking skills
Year: 2018

In an increasingly multilingual world, the question of how to improve foreign language speaking skills of pupils in British schools is of paramount importance to language teachers and policy makers today. This paper examines how an explicit focus on metacognitive strategy use within secondary school foreign language lessons impacts pupils' confidence and proficiency in speaking. The small scale action research study was conducted with a class of five Year 12 (age 16 17) Advanced Level learners of French in a secondary school in England. While all of the pupils generally achieved well in the reading, writing and listening aspects of the course, they performed considerably less well in speaking tasks. The primary aim of this study was therefore to introduce the students to a range of metacognitive learning strategies with a view to improving their confidence and proficiency in speaking. Data were collected from questionnaires, interviews, strategy checklists and assessment marks collected both before and after a six week period of strategy instruction. The findings indicate that the use of learning strategies seems to have had a positive impact on pupils' confidence and proficiency in speaking and after the intervention the participants reported an increase in how much they both valued and used a range of metacognitive strategies.
Which conservatism? The identity of the Polish Law and Justice party
Year: 2020

This article deals with Poland's Law and Justice (PiS) considered a conservative party in the scholarly literature. Drawing largely on party manifestos, the article demonstrates the character, the specificities and the evolution of the party's identity and ideology. A theoretical basis for the undertaking is provided by Klaus von Beyme's concept of party families, Arend Lijphart's seven ideological dimensions and classic texts on conservatism. The analysis finds that the most important components in PiS's current identity are Catholicism itself and the great emphasis the party places on the role of the Catholic Church. Also important for the party's identity are visions of a nation conceived on ethnic principle, a strong and active state able to form society with a national spirit, anti communism and a negation of developments in Poland since 1989. A substantial role is played by the quasi religiously conceived legacy of the party's co founder, Lech Kaczynski, who tragically perished in an aircraft crash. With its Catholic nationalist profile, PiS is close to the Christian current within the conservative New Right, and to Polish National Democracy in the interwar period.
The physical properties of fruits and the physiological quality of seeds of selected crambe genotypes
Year: 2020

Crambe fruits are abundant in oil that contains more than 50% of erucic acid which has many applications in chemical, cosmetic and food processing industries. Selected physical properties of fruits and the physiological quality of seeds of ten crambe genotypes, including eight new breeding lines and two cultivars, were evaluated. The analyzed traits varied significantly subject to weather conditions during the growing cycle. The results were used to identify three lines (Elst2007 2; Elst2007 3 and PRI 9104 71) with large and heavy fruits. Crambe seeds were characterized by high viability, but dormant seeds were also detected, and their proportions were determined by weather conditions during the growing cycle. Breeding line Elst2007 16 was characterized by a high proportion of dormant seeds in all years of the study, and it was least influenced by environmental conditions. The germination capacity of the evaluated crambe genotypes was determined by the number of fresh non germinating seeds. Genotypes Elst2007 2; Elst2007 3 and PRI 9104 71 were characterized by large and heavy fruits as well as the highest seed vigor. Evaluations of the physical properties of crambe fruits and the physiological quality of crambe seeds contribute to the effectiveness of breeding programs by selecting genotypes with large and high quality seeds.
Benefits of flooding induced aquatic adventitious roots depend on the duration of submergence: linking plant performance to root functioning
Venue: Annals of botany
Year: 2017

Temporal flooding is a common environmental stress for terrestrial plants. Aquatic adventitious roots (aquatic roots) are commonly formed in flooding tolerant plant species and are generally assumed to be beneficial for plant growth by supporting water and nutrient uptake during partial flooding. However, the actual contribution of these roots to plant performance under flooding has hardly been quantified. As the investment into aquatic root development in terms of carbohydrates may be costly, these costs may depending on the specific environmental conditions offset the beneficial effects of aquatic roots. This study tested the hypothesis that the balance between potential costs and benefits depends on the duration of flooding, as the benefits are expected to outweigh the costs in long term but not in short term flooding.  The contribution of aquatic roots to plant performance was tested in Solanum dulcamara during 1 4 weeks of partial submergence and by experimentally manipulating root production. Nutrient uptake by aquatic roots, transpiration and photosynthesis were measured in plants differing in aquatic root development to assess the specific function of these roots.  As predicted, flooded plants benefited from the presence of aquatic roots. The results showed that this was probably due to the contribution of roots to resource uptake. However, these beneficial effects were only present in long term but not in short term flooding. This relationship could be explained by the correlation between nutrient uptake and the flooding duration dependent size of the aquatic root system.  The results indicate that aquatic root formation is likely to be selected for in habitats characterized by long term flooding. This study also revealed only limited costs associated with adventitious root formation, which may explain the maintenance of the ability to produce aquatic roots in habitats characterized by very rare or short flooding events.
SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON BASED AND MACHINE LEARNING APPROACHES
Year: 2020

Recently, Sentiment Analysis applied to social media data has gradually become one of the significant research interest in the data mining domain due to the large volume of data available on social media networks. Sentiment Analysis is concerned with analyzing text to identify opinions or emotions and categorizing them as positive, negative or neutral. Applying sentiment analysis to short texts such as Twitter messages is a challenging task because tweets might contain a combination of formal and informal language, special characters, emojis and symbols. Therefore, it is often difficult to understand the semantics of the text and it is complex to extract the proper emotions expressed by users. In this paper, sentiment analysis approaches, namely: lexicon based and machine learning approaches, are applied and evaluated on an Arabic tweets dataset (short texts) regarding the Syrian civil war and crises. The experimental results revealed that machine learning approaches outperformed the lexicon based in the context of predicting the subjectivity of tweets. In terms of machine learning, five popular machine learning algorithms were applied and evaluated. According to the experimental results, the Logistic Model Trees (LMT) algorithm achieved the highest performance results, followed by the simple logistic and the SVM algorithms, respectively. The results also showed that there are enhancements in performance when utilizing feature selection approaches. Based on all performance evaluation measures, the LMT algorithms reported the best performance results (Acc= 85.55, F1= 0.92 and AUC= 0.86)
Applying population based evolutionary algorithms and a neuro fuzzy system for modeling landslide susceptibility
Year: 2019

The main objective of the present study was to produce a novel ensemble data mining technique that involves an adaptive neuro fuzzy inference system (ANFIS) optimized by Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) for spatial modeling of landslide susceptibility. Step wise Assessment Ratio Analysis (SWARA) was utilized for the evaluation of the relation between landslides and landslide related factors providing ANFIS with the necessary weighting values. The developed methods were applied in Langao County, Shaanxi Province, China. Eighteen factors were selected based on the experience gained from studying landslide phenomena, the local geo environmental conditions as well as the availability of data, namely; elevation, slope aspect, slope angle, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, land use, normalized difference vegetation index, rainfall, lithology, distance to faults, fault density, distance to roads, road density, distance to rivers and river density. A total of 288 landslides were identified after analyzing previous technical surveys, airborne imagery and conducting field surveys. Also, 288 non landslide areas were identified with the usage of Google Earth imagery and the analysis of a digital elevation model. The two datasets were merged and later divided into two subsets, training and testing, based on a random selection scheme. The produced landslide susceptibility maps were evaluated by the receiving operating characteristic and the area under the success and predictive rate curves (AUC) The results showed that AUC based on the training and testing dataset was similar and equal to 0.89. However, the processing time during the training and implementation phase was considerable different. SWARA ANFIS PSO appeared six times faster in respect to the processing time achieved by SWARA ANFIS SFLA. The proposed novel approach, which combines expert knowledge, neuro fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility.
Training attention for conscious non REM sleep: The yogic practice of yoga nidra and its implications for neuroscience research.
Venue: Progress in brain research
Year: 2019

The study of consciousness within cognitive neuroscience has been dominated in recent years by investigations originating from collaborations between neuroscientific investigators and Buddhist meditation practitioners. The results have been remarkable, particularly when quantitative and qualitative research methods have been combined as they are in the neurophenomenological methodology originated by Francisco Varela. The addition of qualitative data about the experience of the subject greatly enriches the interpretive potential of quantitative data and honors the ultimate subjectivity of all phenomena, if we accept consciousness as the universal first principle as some quantum physicists now do. This remarkable progress, however, has dropped a thread of inquiry begun in the late 1960s by the Menninger Foundation in Topeka, Kansas (the United States) under the leadership of Elmer and Alyce Green. Their studies of the conscious control of involuntary processes drew on collaboration with an Indian master of yoga meditation, Swami Rama of the Himalayas, which opened a number of intriguing possibilities, which have yet to be followed up in detail with the most recent research tools and methodologies. Among these is the ability to enter the deepest, non REM delta wave sleep while maintaining awareness both internally and of one's surroundings (yoga nidra) The particular interest in this ability lies not only in the benefits that accrue from especially deep relaxation and an especially pure experience of mindful awareness, but also from the yogi's description of this as a way to gradually learn to enter the deepest states of meditation (samadhi) and remain there even when otherwise active in the world (turiya) This chapter is one of a series hoping to elucidate that state from both traditional and contemporary descriptions of the state of yoga nidra, draw measurable hypotheses from these descriptions and discuss the methodological problems of conducting these investigations with sufficiently competent samples of subjects. The focus of this chapter is on training subjects who can become capable of entering the state of yoga nidra.