nils-herrmann/clean_scirepeval huggingface dataset:
Online monitoring and control of the biogas process

The thesis is organised in two parts. The first part is a dissertation providing backgrounds for understanding the important aspects of the biogas process, and also an updated knowledge about monitoring and control of the biogas process. Moreover, an application of a novel online VFA monitoring system is presented, as well as the methods for improving biogas production. The second part consists of the following papers. An innovative online VFA monitoring system for the anaerobic process, based on headspace gas chromatography. Submitted. Effect of operating conditions and reactor configuration on efficiency of full-scale biogas plants. Effect of post digestion temperature on serial CSTR biogas reactors. Manuscript. ii Acknowledgements I wish to express my gratitude to those who have contributed to the completion of this thesis work for one way or another:  Associate professor Irini Angelidaki, my main supervisor, for giving me the opportunity to work at the Institute of Environment & Resources, and for supervising and supporting me through the whole study both during Master program and this Ph.D. project.  Dr. Damien John Batstone, my co-supervisor, for the technical supervision and great inspiration, especially for the great help during instrument design and construction.  Associate professor Hans Mosbaek, for the help with the GC setup.  Dr. Jean-Phillippe Steyer, for the supervision and the help with control implementation in the experiment. other technicians, for the technical help both during system construction and during the experiments. for an excellent secretarial assistance during the whole project period.  All my former and present colleagues and students for supporting, collaborating and sharing expertise, especially Christopher, for making me laugh even when facing problems; Henrik, for helping with the documents and danish language; Prasad, Larisa, Montira, Vilailak, Dawei and many other Ph.D. students, for sharing the pleasant working atmosphere.  All my friends in Denmark, for always being friendly and helpful, especially Lars-Geog for helping me with the danish summary.  The demand for online monitoring and control of biogas process is increasing, since better monitoring and control system can improve process stability and enhance process performance for better economy of the biogas plants. A number of parameters in both the liquid and the gas phase have been suggested as process indicators. These include gas production, pH, alkalinity, volatile fatty acids (VFA) and hydrogen. Of these, VFA is the most widely recognised as a direct, relevant measure of stability. The individual, rather …
Modeling and performance analysis of BitTorrent-like peer-to-peer networks

In this paper, we develop simple models to study the performance of BitTorrent, a second generation peer-to-peer (P2P) application. We first present a simple fluid model and study the scalability, performance and efficiency of such a file-sharing mechanism. We then consider the built-in incentive mechanism of BitTorrent and study its effect on network performance. We also provide numerical results based on both simulations and real traces obtained from the Internet.
Stochastic Expectation Propagation

Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable posterior distribution through a set of local approximations that are iteratively refined for each datapoint. EP can offer analytic and computational advantages over other approximations, such as Variational Inference (VI), and is the method of choice for a number of models. The local nature of EP appears to make it an ideal candidate for performing Bayesian learning on large models in large-scale dataset settings. However, EP has a crucial limitation in this context: the number of approximating factors needs to increase with the number of datapoints, N , which often entails a prohibitively large memory overhead. This paper presents an extension to EP, called stochastic expectation propagation (SEP), that maintains a global posterior approximation (like VI) but updates it in a local way (like EP). Experiments on a number of canonical learning problems using synthetic and real-world datasets indicate that SEP performs almost as well as full EP, but reduces the memory consumption by a factor of N . SEP is therefore ideally suited to performing approximate Bayesian learning in the large model, large dataset setting.
Adapting Sequence Models for Sentence Correction

In a controlled experiment of sequence-tosequence approaches for the task of sentence correction, we find that characterbased models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.
Location Estimation via Support Vector Regression

Location estimation using the global system for mobile communication (GSM) is an emerging application that infers the location of the mobile receiver from multiple signals measurements. While geometrical and signal propagation models have been deployed to tackle this estimation problem, the terrain factors and power fluctuations have confined the accuracy of such estimation. Using support vector regression, we investigate the missing value location estimation problem by providing theoretical and empirical analysis on existing and novel kernels. A novel synthetic experiment is designed to compare the performances of different location estimation approaches. The proposed support vector regression approach shows promising performances, especially in terrains with local variations in environmental factors
A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has been established in 1995 and became a very mature and most popular domain in SI. MultiObjective PSO (MOPSO) established in 1999, has become an emerging field for solving MOOs with a large number of extensive literature, software, variants, codes and applications. This paper reviews all the applications of MOPSO in miscellaneous areas followed by the study on MOPSO variants in our next publication. An introduction to the key concepts in MOO is followed by the main body of review containing survey of existing work, organized by application area along with their multiple objectives, variants and further categorized variants.
A Critical Review of Research on Formative Assessment : The Limited Scientific Evidence of the Impact of Formative Assessment in Education

The existence of a plethora of empirical evidence documenting the improvement of educational outcomes through the use of formative assessment is conventional wisdom within education. In reality, a limited body of scientifically based empirical evidence exists to support that formative assessment directly contributes to positive educational outcomes. The use of formative assessments, or other diagnostic efforts within classrooms, provides information that should help facilitate improved pedagogical practices and instructional outcomes. However, a review of the formative assessment literature revealed that there is no agreed upon lexicon with regard to formative assessment and suspect methodological approaches in the efforts to demonstrate positive effects that could be attributed to formative assessments. Thus, the purpose of this article was two-fold. First, the authors set out to clarify the terminology related to formative assessment and its usage. Finally, the article provides a critical analysis of the seminal literature on formative assessment, beginning with Black and Wiliam (1998), and extending through current published materials.
Grasp quality evaluation and planning for objects with negative curvature

We consider the problem of grasping concave objects, i.e., objects whose surface includes regions with negative curvature. When a multifingered hand is used to restrain these objects, these areas can be advantageously used to determine grasps capable of more robustly resisting to external disturbance wrenches. We propose a new grasp quality metric specifically suited for this case, and we use it to inform a grasp planner searching the space of possible grasps. Our findings are validated both in simulation and on a real robot system executing a bin picking task. Experimental validation shows that our method is more effective than those not explicitly considering negative curvature.
Community Aware Random Walk for Network Embedding

Social network analysis provides meaningful information about behavior of network members that can be used for diverse applications such as classification, link prediction. However, network analysis is computationally expensive because of feature learning for different applications. In recent years, many researches have focused on feature learning methods in social networks. Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network. In this paper, we introduce a novel algorithm named “CARE” for network embedding that can be used for different types of networks including weighted, directed and complex. Current methods try to preserve local neighborhood information of nodes, whereas the proposed method utilizes local neighborhood and community information of network nodes to cover both local and global structure of social networks. CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes. Subsequently, stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes. Our method can be scalable when new nodes are appended to network without information loss. Parallelize generation of customized random walks is also used for speeding up CARE. We evaluate the performance of CARE on multi label classification and link prediction tasks. Experimental results on various networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.
Navigation Techniques in Augmented and Mixed Reality: Crossing the Virtuality Continuum

Exploring and surveying the world has been an important goal of humankind for thousands of years. Entering the 21st century, the Earth has almost been fully digitally mapped. Widespread deployment of GIS (Geographic Information Systems) technology and a tremendous increase of both satellite and street-level mapping over the last decade enables the public to view large portions of the world using computer applications such as Bing Maps or Google Earth.
Using boosted trees for click-through rate prediction for sponsored search

We describe a new approach to solving the click-through rate (CTR) prediction problem in sponsored search by means of MatrixNet, the proprietary implementation of boosted trees. This problem is of special importance for the search engine, because choosing the ads to display substantially depends on the predicted CTR and greatly affects the revenue of the search engine and user experience. We discuss different issues such as evaluating and tuning MatrixNet algorithm, feature importance, performance, accuracy and training data set size. Finally, we compare MatrixNet with several other methods and present experimental results from the production system.
An Ensemble Approach to Link Prediction

A network with <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq1-2730207.gif"/></alternatives></inline-formula> nodes contains <inline-formula> <tex-math notation="LaTeX">$O(n^2)$</tex-math><alternatives><inline-graphic xlink:href="ma-ieq2-2730207.gif"/> </alternatives></inline-formula> possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Further, even though link prediction is closely related to missing value estimation problems, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity on large networks. Hence, most known link prediction methods are designed for <italic>evaluating</italic> the link propensity on a <italic>specified</italic> subset of links, rather than on the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this article, we propose an ensemble enabled approach to scaling up link prediction, by decomposing traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with latent factor models, which can be effectively implemented on networks of modest size. By incorporating with the characteristics of link prediction, the ensemble approach further reduces the sizes of subproblems without sacrificing its prediction accuracy. The ensemble enabled approach has several advantages in terms of performance, and our experimental results demonstrate the effectiveness and scalability of our approach.
Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems

In a Role-Playing Game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (online, batch or simulated) and the consumed resources in decision making (e.g. execution time, memory) will influence, in mayor degree, the game performance. When classical search algorithms such as A∗ can be used, they are the very first option. Nevertheless, such methods rely on precise and complete models of the search space, and there are many interesting scenarios where their application is not possible. Then, model free methods for sequential decision making under uncertainty are the best choice. In this paper, we propose a heuristic planning strategy to incorporate the ability of heuristic-search in path-finding into a Dyna agent. The proposed Dyna-H algorithm, as A∗ does, selects branches more likely to produce outcomes than other branches. Besides, it has the advantages of being a modelfree online reinforcement learning algorithm. The proposal was evaluated against the one-step Q-Learning and Dyna-Q algorithms obtaining excellent experimental results: Dyna-H significatively overcomes both methods in all experiments. We suggest also, a functional analogy between the proposed sampling from worst trajectories heuristic and the role of dreams (e.g. nightmares) in human behavior.
An Evaluation on Belbin’s Team Roles Theory (The Case of Sakarya Anatol an Profess on H gh School, Profess on H gh School and Vocat onal H gh School for Industry)

This study took place in Sakarya Anatolian High School, Profession High School and Vocational High School for Industry (SAPHPHVHfI) where a flexible and nonroutine organising style was tried to be realized. The management style was initiated on a study group at first, but then it helped the group to come out as natural team spontaneously. The main purpose of the study is to make an evaluation on five teams within the school where team (based) management has been experienced in accordance with Belbin (1981)’s team roles theory [9]. The study group of the research consists of 28 people. The data was obtained from observations, interviews and the answers given to the questions in Belbin Team Roles Self Perception Inventory (BTRSPI). Some of the findings of the study are; (1) There was no paralellism between the team and functional roles of the members of the mentioned five team, (2) The team roles were distributed equaly balanced but it was also found that most of the roles were played by the members who were less inclined to play it, (3) The there were very few members who played plant role within the teams and there were nearly no one who were inclined to play leader role.
The master key: a private authentication approach for pervasive computing environments

We propose a novel entity authentication approach for pervasive computing environments. A person uses a single device, the master key, which aggregates all his digital forms of access tokens for entity authentication. The master key discovers and selects proper tokens for its owner. With an emphasis on usability, the master key secures authentication, protects privacy information from outsiders and insiders, and supports various claimant-verifier relations. We analyze privacy and security properties of our approach and protocols, and we investigate the overhead. Performance measurements show that our protocols are efficient
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering

We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong nonadversarial system.
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Fisher vector (FV) classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered 'black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 and ILSVRC 2012 data sets.
Antibacterial and Antioxidant Activity of Essential Oil Terpenes against Pathogenic and Spoilage-Forming Bacteria and Cell Structure-Activity Relationships Evaluated by SEM Microscopy

The antibacterial activity and antioxidant effect of the compounds α-terpineol, linalool, eucalyptol and α-pinene obtained from essential oils (EOs), against pathogenic and spoilage forming bacteria were determined. The antibacterial activities of these compounds were observed in vitro on four Gram-negative and three Gram-positive strains. S. putrefaciens was the most resistant bacteria to all tested components, with MIC values of 2% or higher, whereas E. coli O157:H7 was the most sensitive strain among the tested bacteria. Eucalyptol extended the lag phase of S. Typhimurium, E. coli O157:H7 and S. aureus at the concentrations of 0.7%, 0.6% and 1%, respectively. In vitro cell growth experiments showed the tested compounds had toxic effects on all bacterial species with different level of potency. Synergistic and additive effects were observed at least one dose pair of combination against S. Typhimurium, E. coli O157:H7 and S. aureus, however antagonistic effects were not found in these combinations. The results of this first study are encouraging for further investigations on mechanisms of antimicrobial activity of these EO components.
A Survey of Prediction Using Social Media

Social media comprises interactive applications and platforms for creating, sharing and exchange of user-generated contents. The past ten years have brought huge growth in social media, especially online social networking services, and it is changing our ways to organize and communicate. It aggregates opinions and feelings of diverse groups of people at low cost. Mining the attributes and contents of social media gives us an opportunity to discover social structure characteristics, analyze action patterns qualitatively and quantitatively, and sometimes the ability to predict future human related events. In this paper, we firstly discuss the realms which can be predicted with current social media, then overview available predictors and techniques of prediction, and finally discuss challenges and possible future directions.
Combining Recurrent and Convolutional Neural Networks for Relation Classification

This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.
A Robust Approach to Aligning Heterogeneous Lexical Resources

Lexical resource alignment has been an active field of research over the last decade. However, prior methods for aligning lexical resources have been either specific to a particular pair of resources, or heavily dependent on the availability of hand-crafted alignment data for the pair of resources to be aligned. Here we present a unified approach that can be applied to an arbitrary pair of lexical resources, including machine-readable dictionaries with no network structure. Our approach leverages a similarity measure that enables the structural comparison of senses across lexical resources, achieving state-of-the-art performance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.
POSTER: Cyber Attack Prediction of Threats from Unconventional Resources (CAPTURE)

This paper outlines the design, implementation and evaluation of CAPTURE - a novel automated, continuously working cyber attack forecast system. It uses a broad range of unconventional signals from various public and private data sources and a set of signals forecasted via the Auto-Regressive Integrated Moving Average (ARIMA) model. While generating signals, auto cross correlation is used to find out the optimum signal aggregation and lead times. Generated signals are used to train a Bayesian classifier against the ground truth of each attack type. We show that it is possible to forecast future cyber incidents using CAPTURE and the consideration of the lead time could improve forecast performance.