nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid model based on neural networks for biomedical relation extraction
Venue: J. Biomed. Informatics
Year: 2018

Biomedical relation extraction can automatically extract high quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein protein interaction) PPI corpora and a (drug drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance.
Transformers as Soft Reasoners over Language
Venue: IJCAI
Year: 2020

Beginning with McCarthy's Advice Taker (1959) AI has pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge. However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research. This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation. We train transformers to reason (or emulate reasoning) over these sentences using synthetically generated data. Our models, that we call RuleTakers, provide the first empirical demonstration that this kind of soft reasoning over language is learnable, can achieve high (99% accuracy, and generalizes to test data requiring substantially deeper chaining than seen during training (95% scores) We also demonstrate that the models transfer well to two hand authored rulebases, and to rulebases paraphrased into more natural language. These findings are significant as it suggests a new role for transformers, namely as limited "soft theorem provers" operating over explicit theories in language. This in turn suggests new possibilities for explainability, correctability, and counterfactual reasoning in question answering.
New hybrid system of fuel cell power plant and wind turbine for household consumption
Venue: 2013 3rd International Conference on Electric Power and Energy Conversion Systems
Year: 2013

This paper has been suggested a hybrid system of fuel cell power plant and wind turbine. The proposed system connected to grid for home power consumption. The operational cost of the system in wind energy has different effects. In fuel cell power plant (FCP) Genetic algorithm is used to find optimum output power. FCP can produce both electrical energy and thermal energy at the same time, Therefore it tried to supply thermal load and electrical load. Combination of wind turbine and FCP with local grid results in lower operational cost than that of individual units. For analyze the operational cost a Cost function consist of cost of energy, electrical power from wind energy, power trade with the local grid, thermal recovery from the FCP, Setup Fee of FCP and maintenance cost are used. The tariff for electricity is variable and loads have changed in every time. A wind energy unit is used to commute in Mashhad, Iran. The operation of the fuel cell power plant system is planned to optimize cost according to thermal load and electrical load demand, available wind power and different tariffs for buy and selling electricity of local grid. The obtained results confirm the capability of the proposed system.
Local Word Vectors Guide Keyphrase Extraction
Venue: ArXiv
Year: 2017

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors) i.e. embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and can therefore help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state of the art unsupervised keyphrase extraction methods.
Squeak and rattle noise classification using radial basis function neural networks
Year: 2020

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary classification) and complex ones (multi class classification) (c) 2020 Institute of Noise Control Engineering.
The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps
Venue: Remote. Sens.
Year: 2017

Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of error using a Monte Carlo simulation for two Agriculture Statistic Districts (ASD) in the U.S.A. The results of the simulations were used as base maps for subsequent upscaling, utilizing the majority rule based aggregation method. The results show that increasing error level resulted in higher proportional errors for each crop in both study areas. As a result of increasing error level, landscape characteristics of the base map also changed greatly resulting in higher proportional error in the upscaled maps. Furthermore, the proportional error is sensitive to the crop area proportion in the base map and decreases as the crop proportion increases. These findings indicate that three factors, the error level of the thematic map, the change in landscape pattern/characteristics of the thematic map, and the objective of the project, should be considered before performing any upscaling. The first two factors can be estimated by using pre existing land cover maps with relatively high accuracy. The third factor is dependent on the project requirements (e.g. landscape characteristics, proportions of cover types, and use of the upscaled map) Overall, improving our understanding of the impacts of land cover mapping error is necessary to the proper design for upscaling and obtaining the optimal upscaled map.
Hardware and Software Functions of Standalone Field Data Acquisition Devices for the Low Voltage Power Distribution Grid
Year: 2013

This paper is dealing with the data acquisition instruments, which are necessary nowadays for the optimal operation of low voltage power distribution networks. It introduces the requirements of data acquisition instruments, the proper choice of data acquisition time step and the solutions for real time placement of the acquired data. It describes the most common hardware architectures and the calculations that are fundamental for the usual software solutions of the data acquisition instruments. At last, it gives some practical advice to the application of data acquisition instruments. Keywords data acquisition, data processing, low voltage grid, microcontroller, communication
The Chinese Dream, Neoliberalism, and International Legal Ideology
Year: 2018

The present study analyses a key contemporary transformation in international law: the ascent of China and the decline of the hegemony of the United States. I shall here construct a two stage theoretical picture of China's mission to establish a regional order with Beijing at the centre. First, I present the argument that international law is a vehicle for hegemonic ideology and practice, taking my orientation from Gramscian theories of hegemony. Second, I shall unfold the Chinese Dream a Confucian variant of neoliberalism and the One Belt One Road Initiative, to demonstrate that China's evolution into a core production zone in the neoliberal world economy may be achieved through international legal structures.
The Role of Source Code Vocabulary in Programming Teaching and Learning
Venue: 2020 IEEE Frontiers in Education Conference (FIE)
Year: 2020

This full paper, categorized as Research to Practice, presents an investigation on the role of source code vocabulary in introductory programming learning practices. According to software engineering literature, source code vocabulary is a fundamental aspect of software quality as it contributes to software readability and eases maintenance and evolution tasks. Furthermore, some studies show that identifier naming and comments in the code tend to use terms that reflect the software problem domain specification and can be used, for instance, to localize feature. In this paper, we build upon this knowledge of software engineering and investigate how we can take advantage of it in computer science education, specially on the teaching of programming. We conducted a twofold empirical study in an introductory programming course to investigate: (1) How to give automated feedback about identifier naming, aiming to improve software readability, and (2) To what extent source code vocabulary reflects problem specification comprehension, which is crucial to effective programming problem solving. In the first study, we found that 51.7% of the students improved their source code vocabulary, after receiving such feedback. In the second study, the results showed that students tend to manage to better comprehend the programming problem being solved when their code identifier names are connected to the related description. It is a promising indicative that we can use this information to assess the problem requirements comprehension, which is a fundamental step in the programming problem solving cycle. The main contribution of this paper is to shed light on the source code vocabulary and promote its role in the programming teaching and learning scenario. We present some evidence that students' source code vocabulary is a rich information source about their understanding and we can use it to produce formative feedback.
The cholinergic system and spatial learning
Venue: Behavioural Brain Research
Year: 2011

Acetlylcholine (ACh) in the central nervous system is critical for a multitude of functions. Here, we concentrate on declarative memory in humans, and its equivalent episodic like memory in rodents and highlight current understanding of cholinergic system in these processes. Spatial memory formation represents a simple form of episodic like memory in rodents that engages the basal forebrain cholinergic system and its target structures. In these, ACh exerts numerous functions. (1) During spatial acquisition learning, ACh efflux into the extracellular space is immediate in hippocampus and cortex; during consolidation of spatial reference memory, ACh levels are low. These requirements explain why ACh receptor blockade during acquisition blocks memory formation, and it is also consonant with the notion that an unspecific enhancement of cholinergic activity during consolidation is detrimental to memory formation. (2) Working and short term memory for spatial locations engages the nucleus basalis prefrontal cortex ACh system. ACh activity is trial related and maintained for some time post training. (3) Striatal cholinergic activity is increased during stimulus response learning and behavioural flexibility (reversal learning, extinction) providing a possible switch between different behavioural strategies. (4) At present, there is no clear difference between muscarinic and nicotinergic systems with respect to spatial learning. Antagonists of the respective receptors impair memory formation, agonists can reverse these deficits or may, under specific conditions act more like a general cognitive enhancers by way of improving attention. (5) Data reviewed here do not provide conclusive evidence for muscarinic or nicotinic receptors presenting as novel therapeutic targets, and there is no clear indication for ACh derived novel biomarkers for translational medicine. Unresolved and contradictory results are highlighted and discussed.
Exploring the Multitude of Real Time Multi GPU Configurations
Venue: 2014 IEEE Real Time Systems Symposium
Year: 2014

Motivated by computational capacity and power efficiency, techniques for integrating graphics processing units (GPUs) into real time systems have become an active area of research. While much of this work has focused on single GPU systems, multiple GPUs may be used for further benefits. Similar to CPUs in multiprocessor systems, GPUs in multi GPU systems may be managed using partitioned, clustered, or global methods, independent of CPU organization. This gives rise to many combinations of CPU/GPU organizational methods that, when combined with additional GPU management options, results in thousands of "reasonable" configuration choices. In this paper, we explore real time schedulability of several categories of configurations for multiprocessor, multi GPU systems that are possible under GPUSync, a recently proposed highly configurable real time GPU management framework. Our analysis includes the careful consideration of GPU related overheads. We show system configuration strongly affects real time schedulability. We also identify which configurations offer the best schedulability in order to guide the implementation of GPU based real time systems and future research.
Digital signal processing for self vibration monitoring in grinding: A new approach based on the time frequency analysis of vibration signals
Venue: Measurement
Year: 2019

The occurrence of chatter can generate parts outside the dimensional and geometric tolerances or even cause irreversible damage, such as changes to the hardness and surface roughness of the ground part. The objective of this study is to propose a new vibration signal processing technique based on the short time Fourier transform (STFT) and the ratio of power (ROP) statistic for the detection of chatter during the tangential surface grinding of AISI 1045 steel with different grinding wheels. Vibration signals were recorded at 2 MHz in the grinding tests. The Vickers hardness, roughness and metallography of the ground workpiece surfaces were also evaluated. A digital processing technique based on the STFT and ROP was applied to the vibration signals to extract the characteristics of the chatter. The results show that this technique can be used to characterize over time the spectral patterns of a frequency band related to chatter.
Ecografia automatica de mama para la deteccion de lesiones mamarias: Comparacion con la ecografia mamaria convencional
Year: 2018

Resumen Objetivos Comparar la ecografia convencional frente a la ecografia automatica de mama (ABUS) y la mamografia con tomosintesis aislada en la deteccion y el diagnostico de lesiones mamarias. Sujetos y metodos Se incluyeron 155 mujeres sintomaticas y asintomaticas con mamas densas. Todas se realizaron mamografia y seguidamente ecografia manual y ABUS, que fueron interpretadas por 2 radiologos diferentes. Los estudios fueron categorizados segun el Breast Imaging Reporting and Data System (BI RADS) Resultados El indice kappa de concordancia fue de 0,83, p  ABUS demostro alta correlacion con la ecografia manual detectando mas lesiones benignas. El diagnostico de las malignas fue equivalente con ambas tecnicas, que fueron superiores a la mamografia de forma aislada. ABUS podria sustituir a la ecografia manual para complementar a la mamografia en la deteccion de cancer de mama en mujeres con mama densa.
Bringing next generation diagnostics to the clinic through synthetic biology
Venue: EMBO molecular medicine
Year: 2016

The promise for real precision medicine is contingent on innovative technological solutions to diagnosis and therapy. In the post genomic era, rational and systematic approaches to biological design could provide new ways to dynamically probe, monitor, and interface human pathophysiology. Emerging as a mature field increasingly transitioning to the clinics, synthetic biology integrates engineering principles to build sensors, control circuits, and actuators within the biological substrate according to clinical specifications. A particularly tantalizing goal is to develop novel versatile, programmable and autonomous diagnostic devices intertwined with therapy and personalized for the patient to get closest, finest, and most comprehensive diagnostic information and medical procedures. Here, we discuss how synthetic biology could be preparing the future of medicine, supporting and speeding up the development of diagnostics with novel capabilities to bring direct improvement from the clinical laboratory to the patient, while addressing healthcare evolution and global health concerns.
THE EFFECTS OF DIFFERENT MODIFIED STARCHES ON SOME PHYSICAL AND TEXTURE PROPERTIES OF MEAT EMULSION
Year: 2017

The effect of modified starch addition on the functional properties of meat emulsions was studied by using a model system. In this research, the effects of modified potato starches (native, acid, dextrinized and pregelatinized) and starch level (1% 2 and 4 w/w) on the emulsion capacity, stability, pH and texture properties were investigated in oil/water model emulsion systems. The highest emulsion capacity (181.42 ml oil/g protein) was determined for acid modified starch. As a result of texture analysis, hardness, gumminess and chewiness values of raw and cooked meat emulsions were significantly (P <0.01) affected by the type of starch and starch level. It was concluded in this study that 1% pre gelatinized modified starch is good for ensuring emulsion stability and can be used as stabilizer for meat emulsions in order to improve the some texture properties of meat emulsions due to higher emulsion stability and capacity than the other starch levels.
Blending the past and present in a retro branded music concert: the impact of nostalgia proneness
Year: 2018

Retro branding is gaining unprecedented momentum. This study aims to empirically examine the moderating impact of nostalgia proneness on the relationship between retro branding and consumer behavioral intentions in the music industry. Nostalgia and retro branding are two paramount elements conceptually discussed in literature but rarely investigated together empirically despite their interconnections.  An experiment including four different scenarios blending retro and contemporary stimuli was conducted on 181 subjects. Two rock band variables were manipulated: song set list (i.e. list of songs) and band lineup.  The findings suggest that mixing the past and present for a retro brand impacts consumer behavior. A more nuanced explanation is suggested by showing that a retro brand has a strong effect on consumers' intentions to attend and willingness to pay, but not on their WOM intentions, when these consumers are more prone to feeling nostalgia.  Nostalgia and retro branding appear to be interconnected concepts, but few studies have assessed how nostalgia proneness can impact consumers' intentions toward a retro brand. Fewer have investigated consumers' intentions toward an experiential, intangible retro brand.
Comparing RGB based vegetation indices from UAV imageries to estimate hops canopy area
Year: 2020

Remote estimation of hops plants in hop gardens is imperative in field of precision agriculture, because of precise imaging of hop garden structure. Monitoring of hop plant volume and area can help to predict the condition and yield of hops. In this study, two unmanned aerial vehicles (UAV) eBee X senseFly UAV equipped with Red Green Blue (RGB) S.O.D.A. camera and Vertical Take Off Landing (VTOL) UAV FireFly6 Pro by BirdsEyeView Aerobotics equipped with MicaSense RedEdge MX camera were used to acquire images of hop garden at phenology stage maturity of cones (24 th July) before harvest. Seven commonly used RGB vegetation indices (VI) were derived from these RGB and multispectral (MS) images after photogrammetric pre processing and orthophoto mosaic extraction using Pix4Dmapper software. Vegetation Indices as the Green Percentage Index (G% Excess of Green Index (ExGreen) Green Leaf Index (GLI) Visible Atmospherically Resistant Index (VARI) Red Green Blue Vegetation Index (RGBVI) Normalised Green Red Difference Index (NGRDI) and Triangular Greenness Index (TGI) were derived from both data sets. Binary model from each of VI was derived and threshold value for green vegetation was set. The results showed significant differences in hop plant area based on the specifications of cameras, especially wavelengths centres, and design and flight parameters of both UAV types. The comparison of various indices showed, that ExG and TGI indices has the highest congruity of estimated vegetation indices in hop garden canopy area for both used cameras. Further processing by Fuzzy Overlay tool proved high accuracy in green canopy area estimation for ExG and TGI vegetation indices.
Utilization of Kanawa (Chenopodium pallidicaule Aellen) Flour in Pasta Making
Venue: Journal of Chemistry
Year: 2019

Kanawa (Chenopodium pallidicaule Aellen) is an Andean crop harvested in Bolivia and Peru. Because of the characteristics and properties of kanawa seed, its flour can be used to replace partially wheat flour in pasta to increase its nutritional value. The objective of this study was to investigate the production of fiber and protein enriched pasta made with wheat and kanawa blends. The effect of the substitution level of wheat by kanawa flour on pasta quality was evaluated taking into account the influence of kanawa composition on starch behavior and gluten network formation. Proximal composition, thermal behavior, and pasting properties of kanawa and wheat flours were determined. Pasta was made from wheat flour (control) replacing 10, 20, and 30% of wheat by kanawa flour from L1 and L2 ecotypes. Water absorption and cooking loss were increased showing the deleterious effect of kanawa flour on pasta quality, but the L1 ecotype showed better performance in cooking properties. Kanawa pasta firmness and chewiness decreased with the kanawa content increase; however, the L1 ecotype did not modify the firmness when 10 and 20% were included in pasta. Both kanawa flours improved the nutritional quality of pasta, increasing the dietary fiber content and protein quality.