nils-herrmann/clean_scirepeval huggingface dataset:
"Bird Song Metronomics" Isochronous Organization of Zebra Finch Song Rhythm
Venue: Front. Neurosci.

The human capacity for speech and vocal music depends on vocal imitation. Songbirds, in contrast to non human primates, share this vocal production learning with humans. The process through which birds and humans learn many of their vocalizations as well as the underlying neural system exhibit a number of striking parallels and have been widely researched. In contrast, rhythm, a key feature of language, and music, has received surprisingly little attention in songbirds. Investigating temporal periodicity in bird song has the potential to inform the relationship between neural mechanisms and behavioral output and can also provide insight into the biology and evolution of musicality. Here we present a method to analyze birdsong for an underlying rhythmic regularity. Using the intervals from one note onset to the next as input, we found for each bird an isochronous sequence of time stamps, a "signal derived pulse," or pulseS, of which a subset aligned with all note onsets of the bird's song. Fourier analysis corroborated these results. To determine whether this finding was just a byproduct of the duration of notes and intervals typical for zebra finches but not dependent on the individual duration of elements and the sequence in which they are sung, we compared natural songs to models of artificial songs. Note onsets of natural song deviated from the pulseS significantly less than those of artificial songs with randomized note and gap durations. Thus, male zebra finch song has the regularity required for a listener to extract a perceived pulse (pulseP) as yet untested. Strikingly, in our study, pulsesS that best fit note onsets often also coincided with the transitions between sub note elements within complex notes, corresponding to neuromuscular gestures. Gesture durations often equaled one or more pulseS periods. This suggests that gesture duration constitutes the basic element of the temporal hierarchy of zebra finch song rhythm, an interesting parallel to the hierarchically structured components of regular rhythms in human music.
Convolutional Neural Networks for Large Scale Bird Song Classification in Noisy Environment
Venue: CLEF

This paper describes a convolutional neural network based deep learning approach for bird song classification that was used in an audio record based bird identification challenge, called BirdCLEF 2016. The training and test set contained about 24k and 8.5k recordings, belonging to 999 bird species. The recorded waveforms were very diverse in terms of length and content. We converted the waveforms into frequency domain and splitted into equal segments. The segments were fed into a convolutional neural network for feature learning, which was followed by fully connected layers for classification. In the official scores our solution reached a MAP score of over 40% for main species, and MAP score of over 33% for main species mixed with background species.
'Out of tune' consequences of inbreeding on bird song
Venue: Proceedings of the Royal Society B: Biological Sciences

The expression of bird song is expected to signal male quality to females. 'Quality' is determined by genetic and environmental factors, but, surprisingly, there is very limited evidence if and how genetic aspects of male quality are reflected in song. Here, we manipulated the genetic make up of canaries (Serinus canaria) via inbreeding, and studied its effects upon song output, complexity, phonetics and, for the first time, song learning. To this end, we created weight matched inbred and outbred pairs of male fledglings, which were subsequently exposed to the same tutor male during song learning. Inbreeding strongly affected syllable phonetics, but there were little or no effects on other song features. Nonetheless, females discriminated among inbred and outbred males, as they produced heavier clutches when mated with an outbred male. Our study highlights the importance of song phonetics, which has hitherto often been overlooked.
Whose Choice Is It? The Role of Attitude in Determining Customer's Intention to Use the Courier

Numerous researchers have centered their attention on attitude towards an intention to use services, but only a few ha ve focused on attitude towards customer's intention to use the courier. Th is paper aims to better understand the product quality, price reasonableness as well as brand image that influence the attitude and lastly the role of attitude as a mediator towards consumer's intention to use a courier in Indonesia. A total of 100 questionnarires were obtained from customers through random sampling. Using an Ordinary Least Squares (OLS) approach which was intended to test the hypotheses; with a significance level p 0.05. The findings show that product quality, and brand image are influencing the attitude of courier usage. The different results indicated that price reasonableness does not influence the attitude of courier usage whereas the attitude ables to act as mediator to product quality, price reasonableness, and brand image towards the intention to use a courier Concerning intention to use the courier our findings support an attitude dimensions that represents a commitment to improving a product quality standard and positive perception of the brand image. The evidence is also found for the multidimensional attitude model of attitudes: that attitude predicted by the product quality has followed by an adoption common standard. Moreover, our results state that the brand image is stronger predictors of attitude than product quality and moreover price reasonableness that the dimensions of attitude toward a differential impact on intention to use a courier with affective attitude appearing are more strongly related to intention. In summary, this study suggests that it is considerably more likely that attitudes will be closely related to actions. There's a need for future studies to extend the model of this research by adding other independent variables that are able to describe consumer demand.
Lubricant free deep drawing process by macro structured tools

Avoiding lubricants in forming processes like the deep drawing process is an important way to save resources in today's industry. In this paper, a method will be presented for the elimination of lubrication in the deep drawing process by means of a new macro structured tool design. This structuring enables the control of friction forces as well as the material flow. The paper will present the basic process principle, an analytical model for the process design and experimental results. Starting with symmetrical geometries, the outlook will be given for three dimensional, complex parts.
Integrating roots into a whole plant network of flowering time genes in Arabidopsis thaliana

Molecular data concerning the involvement of the roots in the genetic pathways regulating floral transition are lacking. In this study, we performed global analyses of root transcriptome in Arabidopsis in order to identify flowering time genes that are expressed in the roots and genes that are differentially expressed in the roots during the induction of flowering. Data mining of public microarray experiments uncovered that about 200 genes whose mutation was reported to alter flowering time are expressed in the roots but only few flowering integrators were found. Transcriptomic analysis of the roots during synchronized induction of flowering by a single 22 h long day revealed that 595 genes were differentially expressed. A delay in clock gene expression was observed upon extension of the photoperiod. Enrichment analyses of differentially expressed genes in root tissues, gene ontology categories and cis regulatory elements converged towards sugar signaling. We inferred that roots are integrated in systemic signaling whereby carbon supply coordinates growth at the whole plant level during the induction of flowering.
Case Studies and Derivation of Course Profile in accordance with NCS based Video Graphics Job

This study analyzed with the case analysis of a series of processes from job analysis survey. And results analysis, and academic achievement in order to transform the curriculum of existing courses into the curriculum of NCS based courses. Also this study analysed of the existing curriculum. Also analyzed the trend of workforce trends and needs of the broadcasting content industry. Through a needs analysis for the industry and alumni and students, video graphics, video editing and video directing were selected. In this paper, it dealt mainly with respect to the video graphics in a dual job. Modeling capability into the unit through a job analysis, animation, effects and lighting were chosen accordingly based introduction of 3D Graphics. Application of 3D Graphics were derived two courses and selected profiles and performance criteria. This training according to the NCS curriculum for students was evaluated based on the student`s job was to investigate the learning ability. Academic achievement were the result satisfaction.
A Novel Technique for Generation and Optimization of Test Cases Using Use Case, Sequence, Activity Diagram and Genetic Algorithm
Venue: J. Softw.

This paper presents a novel approach for generation of test cases from UML design diagrams. In this new generation scheme, we have considered use case diagram, activity diagram and sequence diagram. Our approach consists of converting the use case diagram into use case diagram graph (UDG) activity diagram into activity diagram graph (ADG) and sequence diagram into sequence diagram graph (SDG) After that three graphs UDG, ADG and SDG are integrated to form System Graph (SYTG) The System Graph is then traversed to generate test cases also optimized using Genetic Algorithm. The generated test cases are suitable to detect maximum number of faults like use case dependency, interaction, scenario, pre post condition faults and error handling.
Highly nonlinear stress relaxation response of articular cartilage in indentation: Importance of collagen nonlinearity.
Venue: Journal of biomechanics

Modern fibril reinforced computational models of articular cartilage can include inhomogeneous tissue composition and structure, and nonlinear mechanical behavior of collagen, proteoglycans and fluid. These models can capture well experimental single step creep and stress relaxation tests or measurements under small strains in unconfined and confined compression. Yet, it is known that in indentation, especially at high strain velocities, cartilage can express highly nonlinear response. Different fibril reinforced poroelastic and poroviscoelastic models were used to assess measured highly nonlinear stress relaxation response of rabbit articular cartilage in indentation. Experimentally measured depth dependent volume fractions of different tissue constituents and their mechanical nonlinearities were taken into account in the models. In particular, the collagen fibril network was modeled using eight separate models that implemented five different constitutive equations to describe the nonlinearity. These consisted of linear elastic, nonlinear viscoelastic and multiple nonlinear elastic representations. The model incorporating the most nonlinearly increasing Young's modulus of collagen fibrils as a function of strain captured best the experimental data. Relative difference between the model and experiment was ~3% Surprisingly, the difference in the peak forces between the experiment and the model with viscoelastic collagen fibrils was almost 20% Implementation of the measured volume fractions did not improve the ability of the model to capture the measured mechanical data. These results suggest that a highly nonlinear formulation for collagen fibrils is needed to replicate multi step stress relaxation response of rabbit articular cartilage in indentation with high strain rates.
Linear Viscoelastic and Dielectric Relaxation Response of Unentangled UPy Based Supramolecular Networks

Supramolecular polymers possess versatile mechanical properties and a unique ability to respond to external stimuli. Understanding the rich dynamics of such associative polymers is essential for tailoring user defined properties in many products. Linear copolymers of 2 methoxyethyl acrylate (MEA) and varying amounts of 2 ureido 4[1H] pyrimidone (UPy) quadruple hydrogen bonding side units were synthesized via free radical polymerization. Their linear viscoelastic response was studied via small amplitude oscillatory shear (SAOS) The measured linear viscoelastic envelope (LVE) resembles that of a well entangled polymer melt with a distinct plateau modulus. Dielectric relaxation spectroscopy (DRS) was employed to independently examine the lifetime of hydrogen bond units. DRS reveals a high frequency a relaxation associated with the dynamic glass transition, followed by a slower a* relaxation attributed to the reversible UPy hydrogen bonds. This time scale is referred to as the bare lifetime of hydrogen bondi.
Human action recognition using genetic algorithms and convolutional neural networks
Venue: Pattern Recognit.

In this paper, an approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed. We demonstrate that initializing the weights of a convolutional neural network (CNN) classifier based on solutions generated by genetic algorithms (GA) minimizes the classification error. A gradient descent algorithm is used to train the CNN classifiers (to find a local minimum) during fitness evaluations of GA chromosomes. The global search capabilities of genetic algorithms and the local search ability of gradient descent algorithm are exploited to find a solution that is closer to global optimum. We show that combining the evidences of classifiers generated using genetic algorithms helps to improve the performance. We demonstrate the efficacy of the proposed classification system for human action recognition on UCF50 dataset. HighlightsAn approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed.The global and local search capabilities of genetic algorithms and gradient descent algorithms, respectively, are exploited by initializing the CNN classifier with the solutions generated by genetic algorithms and training the classifiers using gradient descent algorithm for fitness evaluation of GA chromosomes.Also, the evolution of candidate solutions explored by GA framework is examined.A near accurate recognition performance of 99.98
Hybrid deep neural network model for human action recognition
Venue: Appl. Soft Comput.

Graphical abstractDisplay Omitted A hybrid deep neural network model for human action recognition using action bank features is proposed.Classifier diversity is achieved by diversifying the input features and varying the initialization of the weights of the neural network.The performance of the proposed approach using different fusion functions is analyzed.We examine changes in output of the proposed fusion model with increasing base classifiers, for all the test cases. In this paper, we propose a hybrid deep neural network model for recognizing human actions in videos. A hybrid deep neural network model is designed by the fusion of homogeneous convolutional neural network (CNN) classifiers. The ensemble of classifiers is built by diversifying the input features and varying the initialization of the weights of the neural network. The convolutional neural network classifiers are trained to output a value of one, for the predicted class and a zero, for all the other classes. The outputs of the trained classifiers are considered as confidence value for prediction so that the predicted class will have a confidence value of approximately 1 and the rest of the classes will have a confidence value of approximately 0. The fusion function is computed as the maximum value of the outputs across all classifiers, to pick the correct class label during fusion. The effectiveness of the proposed approach is demonstrated on UCF50 dataset resulting in a high recognition accuracy of 99.68%
Classification of human actions using pose based features and stacked auto encoder
Venue: Pattern Recognit. Lett.

Human action recognition using pose based features is proposed.Use of key movements of actions for efficient input representation.Unsupervised pre training using stacked auto encoder is exploited. Display Omitted In this paper, we propose a method for classification of human actions using pose based features. We demonstrate that statistical information of key movements of actions can be utilized in designing an efficient input representation, using fuzzy membership functions. The ability of stacked auto encoder to learn the underlying features of input data is exploited to recognize human actions. The efficacy of the proposed approach is demonstrated on CMU MOCAP and Berkeley MHAD datasets.
Challenges in the operation of multimodal transport system: the case of ethiopian shipping and logistics services enterprise

This article investigates the key challenges in the operation of Multimodal Transport System. The 'Multimodal Transport' concept can be defined as the combination of various types of transport modes used in a national or international transport operation, which provides door to door services, under the responsibility of one single transport operator. The Multimodal Transport System is a concept aimed at improving trading efficiency. Following the introduction of Multimodal Transport System in Ethiopia since 2012, there have been symptoms which have stimulated substantial public debate and inefficiencies as studies shows. The study identifies the key challenges of Multimodal Transport System in the trade and investments during importation of goods and it is expected to help beneficiaries to know the gap for using Multimodal Transport System. Simple descriptive statistics and inferential statistical measures were used to analyze the data. The key identified challenges are network connectivity, lack of ICT usage at each level, lack of railway infrastructure, and lack of clear lows and regulations that support the involvement of privately owned service providers. The study suggests that in order to improve the services of Multimodal Transport System these key challenges should be solved. In order to encourage competition between private operators and the Ethiopian Shipping and Logistics Services Enterprise, the government or Ethiopian Maritime Affairs Authority is recommended to produce the possible new law or/and proclamation which requires private potential Multimodal Transport Operators to be established at the national level for developing and maintaining competitive advantage.
Simple Pricing Schemes For Consumers With Evolving Values
Venue: SODA

We consider a pricing problem where a buyer is interested in purchasing/using a good, such as an app or music or software, repeatedly over time. The consumer discovers his value for the good only as he uses it, and the value evolves with each use. Optimizing for the seller's revenue in such dynamic settings is a complex problem and requires assumptions about how the buyer behaves before learning his future value(s) and in particular, how he reacts to risk. We explore the performance of a class of pricing mechanisms that are extremely simple for both the buyer and the seller to use: the buyer reacts to prices myopically without worrying about how his value evolves in the future; the seller needs to optimize for revenue over a space of only two parameters, and can do so without knowing the buyer's risk profile or fine details of the value evolution process. We present simple versus optimal type results, namely that under certain assumptions, simple pricing mechanisms of the above form are approximately optimal regardless of the buyer's risk profile. Our results assume that the buyer's value per usage evolves as a martingale. For our main result, we consider pricing mechanisms in which the seller offers the product for free for a certain number of uses, and then charges an appropriate fixed price per usage. We assume that the buyer responds by buying the product for as long as his value exceeds the fixed price. Importantly, the buyer does not need to know anything about how his future value will evolve, only how much he wants to use the product right now. Regardless of the buyers' initial value, our pricing captures as revenue a constant fraction of the total value that the buyers accumulate in expectation over time.
The Significance of Consumer's Awareness about Organic Food Products in the United Arab Emirates

Awareness about negative externalities generated by conventional farming is gaining momentum with consumers around the world, opting for alternatively, namely organically, produced food products. Information about consumers' awareness is an essential element for farmers and marketing agencies to successfully plan production that can capture a greater market share. This study discusses effective factors influencing consumers' awareness about the benefits of organic food in the United Arab Emirates. Sample data and ordinary least square (OLS) regression techniques are applied to delineate factors influencing consumers' awareness about organic food. The results from this regression analysis highlight the importance of specific socioeconomic determinants that change awareness about organic food products in United Arab Emirates (UAE) households. This study finds that awareness about organic food is influenced more effective factors such as gender, nationality, and education as well as income, occupation and age. These research findings apply to other economies and societies that have an increasing per capita spending on organic food, but also where people are highly sensitive to information provided about organic food. Therefore, these results are important to these research beneficiaries including food marketing planners, researchers, and agricultural and food policy makers.
Responses of orchids to habitat change in Corsica over 27 years.
Venue: Annals of botany

Orchids are known to be particularly sensitive to environmental changes due to their narrow ranges of secondary successional habitats. Lack of data at the community level limits our ability to evaluate how traits of different species influence their responses to habitat change. Here, we used a diachronic survey of Mediterranean orchid communities in Corsica to examine this question.  Using data from two field surveys conducted 27 years apart (1982 84 and 2009 11) at the same 45 sites in Corsica, we evaluated the impact of increase in woody plant cover (WPC) on (i) the richness and composition and (ii) the local extinction/colonization dynamics of orchids. We applied a Bayesian multispecies site occupancy model to each of the 36 orchid species recorded at these sites to estimate the detection probability of each species, enabling us to account for under detection in estimating their dynamics.  Between 1982 and 2011, WPC changed at 82*3 of sites (increasing at 75*6 decreasing at 6*7 Despite marked changes in composition of orchid communities at the local scale, no significant change was detected in species richness at the regional scale. Canopy closure affected the probability of new colonization of sites, but had no significant influence on the probability of local extinction. However, the abundance of shade intolerant species declined more sharply than that of shade requiring species. Among orchid species, the detection probability was significantly and positively correlated with population density and plant height.  This study reveals contrasted dynamics of orchid communities between local and regional scales in Corsica. Although high turnover in communities was found at the local scale, regional species richness was maintained despite major land use changes. Conserving landscape mosaics could provide locally suitable habitats for orchids of different ecologies to maintain diversity at larger spatial scales.