nils-herrmann/clean_scirepeval huggingface dataset:
Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition

How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.
From impressionism to expressionism: Automatically identifying van Gogh's paintings
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

Curators, art historians, and connoisseurs are often interested in determining the authorship of paintings. Machine learning and image processing techniques can assist in this task by providing non invasive, automatic, and objective methods. In this work, we study the automatic identification of Vincent van Gogh's paintings using a Convolutional Neural Network that extracts discriminative visual patterns of a painter directly from images, and a machine learning classifier allied with a fusion method in the final decision process. We divide each painting into non overlapping patches, classify them individually, and then aggregate the outcomes for the final response. We find out that using the patch with highest confidence score leads to the best result, outperforming the traditional voting scheme. We also contribute with a new and public dataset for van Gogh painting identification.
Distribution function based efficient secure group communication using key tree
Venue: 2016 International Conference on Recent Trends in Information Technology (ICRTIT)

There are many applications such as a messaging application, teleconferencing, information service etc. which use a group communication model that provides confidentiality, integrity and authenticity of the messages sent over the group. Thus, it results in a secure group communication. The most important part in deploying a secure group communication model is to reduce the number of encryption and rekeying operations. Most of the known algorithm uses LKH (logical key hierarchy) based protocol in which a key tree or graph is created in hierarchical manner that will be used to multicast the messages at a cost of O(log n) where n is the number of members in the group. Almost all the methods introduced till now are not taking full advantage of the key tree they were mainly concentrated on minimizing the rekeying operations after a leave or a join in the group. We have proposed a simple and elegant method that is based on a distribution function F(S) to multicast the messages to a subset of n members of the group G. Our method takes the full advantage of the key tree, It minimizes the rekey operations with minimum time complexity and also maintain the security requirements for the secure group communication.
Kontribusi Prestasi Belajar Prakarya Kewirausahaan dan Peran Guru sebagai Agen Perubahan dalam Proses Pembelajaran Terhadap Minat Berwirausaha Siswa Kelas XII Jurusan Teknik Bangunan SMK Negrei 1 Seyegan Tahun ajaran 2015/2016

Penelitian ini bertujuan untuk mengetahui: (1) prestasi belajar prakarya kewirausahaan; (2) peran guru sebagai agen perubahan dalam proses pembelajaran; (3) minat berwirausaha; (4) kontribusi efektif prestasi belajar prakarya kewirausahaan; (5) kontribusi efektif peran guru sebagai agen perubahan dalam proses pembelajaran; (6) kontribusi efektif prestasi belajar prakarya kewirausahaan dan peran guru sebagai agen perubahan dalam proses pembelajaran secara bersama sama terhadap minat berwirausaha siswa. Penelitian ini merupakan jenis penelitian expose facto dengan pendekatan kuantitatif. Populasi penelitian ini siswa kelas XII jurusan teknik bangunan SMK N 1 Seyegan jumlah 83 siswa. Jumlah sampel ditentukan melalui tabel Cochran, Cohen menjadi 68 siswa pengambilan sampel dilakukan secara acak. Teknik pengumpulan data menggunakan dokumentasi berupa nilai ujian tengah semester 1 untuk prestasi belajar mata pelajaran prakarya kewirausahaan, angket untuk peran guru sebagai agen perubahan dalam proses pembelajaran dan minat berwirausaha. Angket disajikan dalam bentuk skala Likert empat alternatif jawaban. validitas instrumen diuji dengan uji korelasi product moment dan uji reliabilitas instrument dengan metode Cronbach's Alpha sedangkan uji regresi digunakan untuk menguji hipotesis dengan taraf signifikan 5% Hasil penelitian menunjukan bahwa: (1) prestasi mata pelajaran prakarya kewirausahaan yang lolos kriteria ketuntasan minmal adalah 51 siswa (73% dan 17 siswa (27% masih dibawah batas ketuntasan minimal, (2) peran guru sebagai agen perubahan dalam proses pembelajaran termasuk dalam kategori tinggi yang dicapai 44 siswa (65% (3) minat berwirausaha termasuk dalam kategori sangat tinggi yang dicapai 36 siswa (53% (4) prestasi belajar memberikan kontribusi efektif terhadap minat berwirausaha sebesar 15,52% (5) peran guru sebagai agen perubahan dalam proses pembelajaran memberikan kontribusi efektif terhadap minat berwirausaha sebesar 10,50% (6) prestasi belajar dan peran guru sebagai agen perubahan dalam proses pembelajaran secara bersama sama memberikan kontribusi efektif terhadap minat berwirausaha sebesar 26,2%
Sentiment Analysis and Ontology Engineering An Environment of Computational Intelligence
Venue: Sentiment Analysis and Ontology Engineering

This edited volume provides the reader with a fully updated, in depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.
ICT in EMI Programmes at Tertiary Level in Spain: A Holistic Model.

The European Higher Education Area (EHEA) in Spain has increased the number of degrees taught through English, although secondary schools do not ensure an appropriate set of linguistic skills for bilingual degrees. A holistic, accountable model for Information and Communications Technology (ICT) supported learning can give students the adequate scaffolding to perform better in their module related tasks. Using Content and Language Integrated Learning (CLIL) blended with pre and post lecture online tasks, social networks and micro blogging as tools for further practice as well as integrating these into in class practices, student performance improves. Contrasting the impact of these interventions reveals the need to cater for mixed learning styles and abilities.
INTEGRATING ICT IN ENGLISH LANGUAGE TEACHING AND LEARNING IN INDONESIA

Indonesian ELT is complex for numerous reasons, and the level of students" outcome has been regarded unsatisfactory by a number of researchers and academics. This paper considers ICT as one of possible alternatives to deal with the complexity of Indonesian ELT and to improve its outcomes. It widely explores ICT integration in English LTL, especially on how ICT has been used in this field. It further investigates the benefits and challenges of integrating ICT in LTL. The paper argues that the integration of ICT is promising for changing and improving the effectiveness of the current Indonesian ELT condition when it is carried out in line with the effective LTL principles. The integration of ICT will enable teachers to vary teaching and learning activities, to gradually change the teaching style to be more student centred, to train students to have more active role in learning, and to access a huge range of authentic learning materials. The paper also acknowledges the contraints that will emerge in an effort of integrating ICT in Indonesian English LTL. Hence, some recommedations for action are proposed at the end.  English LTL, Indonesian ELT, ICT, benefit, challenge
DYNAMIC BEHAVIOR OF PH IN FRESH URINE PUDDLES OF DAIRY COWS

Modern livestock farming is an important contributor to ammonia (NH 3 emissions. In the Netherlands, 94% of NH 3 emissions originate from agriculture, of which 34% is emitted from commercial dairy cow barns. From current mechanistic modeling, it is known that the pH of urine puddles from cows is one of the most important variables in estimating NH 3 emissions. However, little pH data are available from commercial cow barns. Therefore, the objective of this study was to investigate pH values and to study their dynamic behavior in fresh, on floor urine puddles in these barns. To do this, the pH of urine puddles was measured for 4 h per puddle, and a model was developed to describe the pH behavior. In total, 26 fresh puddles were measured from cows at three commercial dairy farms in summer and winter. At farm level, we found initial pH values of 8.1 through 8.4, which increased to 8.9 through 9.4 after 4 h. The pH difference between summer and winter was 0.3 (p 3 emission modeling than the current situation of using a static pH value. Based on this study, we recommend using the mean measured pH curve as input for puddle simulation during NH 3 emission modeling of dairy cow barns.
Predictive functional profiling using marker gene sequences and community diversity analyses of microbes in full scale anaerobic sludge digesters
Venue: Bioprocess and Biosystems Engineering

Anaerobic digestion (AD) is widely used in treating the sewage sludge, as it can reduce the amount of sludge, eliminate pathogens and produce biofuel. To enhance the operational performance and stability of anaerobic bioreactors, operational and conventional chemical data from full scale sludge anaerobic digesters were collected over a 2 year period and summarized, and the microbial community diversity of the sludge sample was investigated at various stages of the AD process. For the purpose of distinguishing between the functional and community diversity of the microbes, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) software was used to impute the prevalence of 16S rDNA marker gene sequences in the difference in various sludge samples. Meanwhile, a taxa analysis was also carried out to investigate the different sludge samples. The microbial community diversity analysis of one AD sludge sample showed that the most dominant bacterial genera were Saccharicrinis, Syntrophus, Anaerotruncus and Thermanaerothrix. Among archaea, acetoclastic Methanosaeta represented 56.0 and hydrogenotrophic Methanospirillum, Methanoculleus, Methanothermus and Methanolinea accounted for 41.3 of all methanogens. The taxa, genetic and functional prediction analyses of the feedstock and AD sludge samples suggested great community diversity differences between them. The taxa of bacteria in two AD sludge samples were considerably different, but the abundances of the functional KEGG pathways took on similar levels. The numbers of identified pathogens were significantly lower in the digested sludge than in the feedstock, but the PICRUSt results showed the difference in "human diseases" abundances in the level 1 pathway between the two sludge samples was small.
Influence of melt mixing processing sequence on electrical conductivity of polyethylene/polypropylene blends filled with graphene
Venue: Polymer Bulletin

In this paper, we produced composites of high density polyethylene (PE)/polypropylene (PP) filled with graphene by melt compounding. Comparing composites produced in three processing sequences, we explored whether the sequence improved the composites' electrical conductivity. The (graphene/PE)/PP composite, prepared by simultaneous compounding, exhibited an electrical percolation threshold of 1.25 vol. In contrast, the (graphene/PP)/PE composite, prepared by blending the graphene with PP first and then blending the graphene/PP with PE, had a much lower electrical percolation threshold at less than 0.83 vol. At its percolation threshold, the (graphene/PP)/PE composite had a conductivity about two orders of magnitude higher than the (graphene/PE)/PP composite. We attribute this difference in conductivity to differences in the graphene distributions in the composites. In the (graphene/PE)/PP composite, the graphene sheets were selectively dispersed in the PE phase; in the (graphene/PP)/PE composite, some of the graphene was localized at the interface of the PE/PP blend. We also showed how the different processing sequences affected the composites' measured rheological and mechanical properties.
Affordances, Appropriation and Experience in Museum Exhibitions: Visitors' (Eye) Movement Patterns and the Influence of Digital Guides

This thesis provides new research about visitor (eye) movement patterns and accompanying cognitive processes surrounding the particular affordances of exhibition design and particular appropriation strategies. New methods in visitor studies and socio cultural anthropology were applied, in particular mobile eye tracking (MET) Previous visitor movement behaviour research has mainly been conducted by unaided observation. By using MET, new insights can be gained concerning the micro level of visitor movement patterns. Furthermore this thesis suggests a new cognitive science approach in visitor studies by combining anthropological and psychological theories and methods. The research settings were two different museum exhibitions in Germany: an ethnographic exhibition providing an audio guide and a literature exhibition providing a tablet like medium. A subsidiary aim was to investigate the differences between experts and novices. In sum, 26 movement patterns and sub patterns were found. It was shown that visitors' movement patterns are more complex than just stopping and attending to particular exhibits. These movement patterns serve the exploration of exhibits in a detailed manner from the visitors' own perspective creating a unique experience. The usage of digital guides leads to increasingly broad ranges of different movement patterns. Visitors using digital media tend to be more actively engaged visually and cognitively when visiting museums than visitors who were not using digital guides. The main difference between audio guides and tablet like guides is that audio guides allow a parallel way of appropriation by viewing exhibits and listening to the audio guide at the same time. The comparison between experts and novices showed that both groups behave and process similarly when viewing exhibitions, although experts are more active visitors than novices. Finally, the difference between visitors that were found are primarily determined by digital guides rather than by expert status. Findings from this research can help to support exhibition makers to create visitor centered exhibition designs. Finally, this thesis also helps to enable visitor researchers and socio cultural anthropologists to decide whether MET could be advantageous and applicable for their field research.
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Venue: CoNLL

We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping) fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state of the art approach leveraging an extensive set of carefully crafted features.
A reduced feature set for driver head pose estimation
Venue: Appl. Soft Comput.

Graphical abstractDisplay Omitted HighlightsWe present a new automatic approach for head yaw angle estimation of the driver.We rely on a set of geometric features computed from just three representative facial keypoints.The method has a confidence mechanism to decide the reliability of a sample label.The results are comparable to the state of the art techniques.The method can be easily integrated in massive consume devices. Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state of the art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.
Rapid and complete degradation of sulfur mustard adsorbed on M/zeolite 13X supported (M 5 wt% Mn, Fe, Co) metal oxide catalysts with ozone

Degradation of sulfur mustard (HD) adsorbed on zeolite 13X as well as M/zeolite 13X metal oxide materials (M 5 wt% Mn, Fe, Co) was studied with and without gaseous ozone as the oxidizing agent under ambient reaction conditions. In the absence of ozone gas HD degradation was very slow over bare zeolite 13X as well as on the metal oxide impregnated zeolite 13X catalysts. Introduction of ozone gas remarkably enhanced the rate of degradation of HD and complete oxidation of HD was achieved on M/zeolite 13X materials in less than 10 minutes of reaction time. The influence of metal content, and catalyst quantity on the distribution of HD degradation products was also investigated. The results revealed that slightly longer reaction times >5 minutes) and higher catalyst quantity favor the complete oxidation of HD. Carbon oxides were identified as the major degradation products of HD in the presence of ozone. GC MS techniques were used for the analysis of the gas phase, condensed and surface extracted products. The HD degradation mechanism in the presence and absence of ozone was proposed based on GC MS analysis results. The M/zeolite 13X catalysts were prepared using a wet impregnation method and characterized by different characterization techniques.
Enhanced thermal degradation of 2,2' dichlorodiethyl sulfide (sulfur mustard, HD) with the presence of metal oxides

Thermal degradation of sulfur mustard (2,2' dichlorodiethyl sulfide, HD) in the presence of metal oxide adsorbents was investigated by thermal desorption in conjunction with gas chromatography mass spectrometry (GC MS) Zr(OH)4, Al2O3, Al2CoO4, MgO, CeO2, and V2O5 were used as metal oxide adsorbents. Neat HD was spiked onto the metal oxides packed in glass tubes, which were kept at room temperature and then heated at moderately elevated temperatures of 100degC by a thermal desorption system. The products of thermal degradation were directly transferred and analyzed by GC MS. 1,4 Dithiane and 1,4 oxathiane were characterized as the major products of the thermal degradation of HD in the presence of Zr(OH)4, Al2O3, Al2CoO4, and CeO2 adsorbents. No effective degradation was observed with MgO and V2O5. Of particular note is Zr(OH)4, which extremely enhanced the thermal degradation of HD.
Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data :A Case of Iowa State

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine) RF (Random Forest) ERT (Extremely Randomized Trees) and DL (Deep Learning) Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September) (2) JA (July and August) and (3) OC (optimal combination of month) In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6 8 which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.
Corporate Governance, The Firm and Investor Capitalism

The shift from managerial capitalism to investor capitalism, dominated by the finance industry and finance capital accumulation, is jointly caused by a variety of institutional, legal, political, and ideological changes, beginning with the 1970s' downturn of the global economy. This book traces how the incorporation of businesses within the realm of the state leads to both certain benefits, characteristic of competitive capitalism, and to the emergence of new corporate governance problems emerges. Contrasting economic, legal, and managerial views of corporate governance practices in contemporary capitalism, the author examines how corporate governance has been understood and advocated differently during the New Deal era, the post World War II economic boom, and after 1980 in the era of free market advocacy.