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See detailMachbarkeitsstudie "Betreuungsatlas Schweiz": Die Geographie betreuter Kindheit
Neumann, Sascha UL; Tinguely, Luzia; Hekel, Nicole UL et al

Report (2015)

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See detailMachine Argumentation. Can We Replace Taxi Drivers by Robots?
Gabbay, Dov M. UL; Cramer, Marcos; Dauphin, Jérémie UL et al

in Natural Argument, A tribute to John Woods (2019)

We need ethical non-monotonic action logics to control machines which interact heavily with humans. Such logics face special problems and require features which we need to recognise and to address. We ... [more ▼]

We need ethical non-monotonic action logics to control machines which interact heavily with humans. Such logics face special problems and require features which we need to recognise and to address. We believe that injecting argumentation methods into action pre-conditions is possibly the way to proceed to model what is needed. To get an idea of what is needed we must investigate a typical problem of replacing a human with a robot operating in a highly interactive environment. This paper focuses on replacing a human taxi driver by a robot. Robot driven cars are already under production and so there is an urgent need for modelling the kind of Artificial Intelligence/Logic/Norms/Ethics which is to be involved and installed in the mind of the Robot. This is research in progress. [less ▲]

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See detailMachine learning and natural language processing on the patent corpus: data, tools, and new measures
Balsmeier, Benjamin UL; Li, Guan-Cheng; Assaf, Mohamad et al

in Journal of Economics & Management Strategy (2018), 27

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See detailMachine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

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See detailMachine Learning for Data-Driven Smart Grid Applications
Glauner, Patrick UL; Meira, Jorge Augusto UL; State, Radu UL

Scientific Conference (2018)

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns ... [more ▼]

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. We deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car. In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has lead to adaptable, more generic methods, that require less expert knowledge and that are easier to deploy in a number of use cases. This is an introductory level course to discuss what machine learning is and how to apply it to data-driven smart grid applications. Practical case studies on real data sets, such as load forecasting, detection of irregular power usage and visualization of customer data, will be included. Therefore, attendees will not only understand, but rather experience, how to apply machine learning methods to smart grid data. [less ▲]

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See detailMachine learning for physical-layer security: Attacks and SLP Countermeasures for Multiantenna Downlink Systems
Mayouche, Abderrahmane UL; Spano, Danilo; Tsinos, Christos UL et al

in 2019 IEEE Global Communications Conference (2019)

Most physical-layer security (PLS) work employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on ... [more ▼]

Most physical-layer security (PLS) work employ information theoretic metrics for performance analysis. In this paper, however, we investigate PLS from a signal processing point of view, where we rely on bit-error rate (BER) at the eavesdropper (Eve) as a metric for information leakage. Meanwhile, recently, symbol-level precoding (SLP) has been shown to provide PLS gains as a new way for security. However, in this work, we introduce a machine learning (ML) based attack, where we show that even SLP schemes can be vulnerable to such attacks. Namely, this attack manifests when an eavesdropper (Eve) utilizes ML in order to learn the precoding pattern when precoded pilots are sent. With this ability, an Eve can decode data with favorable accuracy. As a countermeasure to this attack, we propose a novel precoding design. The proposed countermeasure yields high BER at the Eve, which makes symbol detection practically infeasible for the latter, thus providing physical-layer security between the base station (BS) and the users. In the numerical results, we validate both the attack and the countermeasure, and show that this gain in security can be achieved at the expense of only a small additional power consumption at the transmitter. [less ▲]

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See detailMachine Learning for Reliable Network Attack Detection in SCADA Systems
Lopez Perez, Rocio; Adamsky, Florian UL; Soua, Ridha UL et al

in 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications (IEEE TrustCom-18) (2018)

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See detailMachine learning in nanoscience: big data at small scales
Brown, Keith A.; Brittman, Sarah; Maccaferri, Nicolò UL et al

in Nano Letters (2020), 20(1), 2-10

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with ... [more ▼]

Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML’s advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this mini-review, which is not able to be comprehensive, we highlight some recent efforts to connect the ML and nanoscience communities focusing on three types of interaction: (1) using ML to analyze and extract new information from large nanoscience data sets, (2) applying ML to accelerate materials discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers. [less ▲]

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See detailMachine learning of accurate energy-conserving molecular force fields
Chmiela, Stefan; Tkatchenko, Alexandre UL; Sauceda, Huziel et al

in Science Advances (2017), 3

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems— we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate ... [more ▼]

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems— we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. [less ▲]

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See detailMachine learning of molecular electronic properties in chemical compound space
Montavon, Gregoire; Rupp, Matthias; Gobre, Vivekanand et al

in NEW JOURNAL OF PHYSICS (2013), 15

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful ... [more ▼]

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy polarizability, frontier orbital eigenvalues, ionization potential electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a `quantum machine' is similar, and sometimes superior, to modern quantum-chemical methods-at negligible computational cost. [less ▲]

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See detailMachine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
Hansen, K.; Biegler, F.; Ramakrishnan, R. et al

in Journal of Physical Chemistry Letters (2015), 6(12), 2326-2331

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical ... [more ▼]

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the "holy grail" of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. © 2015 American Chemical Society. [less ▲]

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See detailMachine learning techniques for atmospheric pollutant monitoring
Sainlez, Matthieu UL; Heyen, Georges

Poster (2012, January 27)

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See detailMachine Learning Techniques for Passive Network Inventory
François, Jérôme UL; Abdelnur, Humberto J.; State, Radu UL et al

in IEEE Transactions on Network and Service Management (2010), 7(4), 244-257

Being able to fingerprint devices and services, i.e., remotely identify running code, is a powerful service for both security assessment and inventory management. This paper describes two novel ... [more ▼]

Being able to fingerprint devices and services, i.e., remotely identify running code, is a powerful service for both security assessment and inventory management. This paper describes two novel fingerprinting techniques supported by isomorphic based distances which are adapted for measuring the similarity between two syntactic trees. The first method leverages the support vector machines paradigm and requires a learning stage. The second method operates in an unsupervised manner thanks to a new classification algorithm derived from the ROCK and QROCK algorithms. It provides an efficient and accurate classification. We highlight the use of such classification techniques for identifying the remote running applications. The approaches are validated through extensive experimentations on SIP (Session Initiation Protocol) for evaluating the impact of the different parameters and identifying the best configuration before applying the techniques to network traces collected by a real operator. [less ▲]

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See detailMachine learning techniques for semantic analysis of dysarthric speech: An experimental study
Despotovic, Vladimir UL; Walter, Oliver; Haeb-Umbach, Reinhold

in Speech Communication (2018), 99

We present an experimental comparison of seven state-of-the-art machine learning algorithms for the task of semantic analysis of spoken input, with a special emphasis on applications for dysarthric speech ... [more ▼]

We present an experimental comparison of seven state-of-the-art machine learning algorithms for the task of semantic analysis of spoken input, with a special emphasis on applications for dysarthric speech. Dysarthria is a motor speech disorder, which is characterized by poor articulation of phonemes. In order to cater for these non- canonical phoneme realizations, we employed an unsupervised learning approach to estimate the acoustic models for speech recognition, which does not require a literal transcription of the training data. Even for the subsequent task of semantic analysis, only weak supervision is employed, whereby the training utterance is accompanied by a semantic label only, rather than a literal transcription. Results on two databases, one of them containing dysarthric speech, are presented showing that Markov logic networks and conditional random fields substantially outperform other machine learning approaches. Markov logic networks have proved to be espe- cially robust to recognition errors, which are caused by imprecise articulation in dysarthric speech. [less ▲]

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See detailMachine Learning to Geographically Enrich Understudied Sources: A Conceptual Approach
Viola, Lorella UL; Verheul, Jaap

in Rocha, Ana; Steels, Luc; van den Herik, Jaap (Eds.) Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH (2020)

This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets ... [more ▼]

This paper discusses the added value of applying machine learning (ML) to contextually enrich digital collections. In this study, we employed ML as a method to geographically enrich historical datasets. Specifically, we used a sequence tagging tool (Riedl and Padó 2018) which implements TensorFlow to perform NER on a corpus of historical immigrant newspapers. Afterwards, the entities were extracted and geocoded. The aim was to prepare large quantities of unstructured data for a conceptual historical analysis of geographical references. The intention was to develop a method that would assist researchers working in spatial humanities, a recently emerged interdisciplinary field focused on geographic and conceptual space. Here we describe the ML methodology and the geocoding phase of the project, focussing on the advantages and challenges of this approach, particularly for humanities scholars. We also argue that, by choosing to use largely neglected sources such as immigrant newspapers (a lso known as ethnic newspapers), this study contributes to the debate about diversity representation and archival biases in digital practices. [less ▲]

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See detailMachine Learning to Support the Presentation of Complex Pathway Graphs.
Nielsen, Sune Steinbjorn UL; Ostaszewski, Marek UL; McGee, Fintan et al

in IEEE/ACM transactions on computational biology and bioinformatics (2019)

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult ... [more ▼]

Visualization of biological mechanisms by means of pathway graphs is necessary to better understand the often complex underlying system. Manual layout of such pathways or maps of knowledge is a difficult and time consuming process. Node duplication is a technique that makes layouts with improved readability possible by reducing edge crossings and shortening edge lengths in drawn diagrams. In this article we propose an approach using Machine Learning (ML) to facilitate parts of this task by training a Support Vector Machine (SVM) with actions taken during manual biocuration. Our training input is a series of incremental snapshots of a diagram describing mechanisms of a disease, progressively curated by a human expert employing node duplication in the process. As a test of the trained SVM models, they are applied to a single large instance and 25 medium-sized instances of hand-curated biological pathways. Finally, in a user validation study, we compare the model predictions to the outcome of a node duplication questionnaire answered by users of biological pathways with varying experience. We successfully predicted nodes for duplication and emulated human choices, demonstrating that our approach can effectively learn human-like node duplication preferences to support curation of pathway diagrams in various contexts. [less ▲]

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See detailMachine learning-assisted neurotoxicity prediction in human midbrain organoids
Monzel, Anna Sophia UL; Hemmer, K; Smits, Lisa UL et al

in Parkinsonism and Related Disorders (2020)

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See detailA Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications
Abualhaija, Sallam UL; Arora, Chetan UL; Sabetzadeh, Mehrdad UL et al

in 27th IEEE International Requirements Engineering Conference (RE'19) (2019)

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable ... [more ▼]

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 30 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. [less ▲]

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See detailMachine Learning-Based Malware Detection for Android Applications: History Matters!
Allix, Kevin UL; Bissyande, Tegawendé François D Assise UL; Klein, Jacques UL et al

Report (2014)

Machine Learning-based malware detection is a promis- ing scalable method for identifying suspicious applica- tions. In particular, in today’s mobile computing realm where thousands of applications are ... [more ▼]

Machine Learning-based malware detection is a promis- ing scalable method for identifying suspicious applica- tions. In particular, in today’s mobile computing realm where thousands of applications are daily poured into markets, such a technique could be valuable to guaran- tee a strong filtering of malicious apps. The success of machine-learning approaches however is highly de- pendent on (1) the quality of the datasets that are used for training and of (2) the appropriateness of the tested datasets with regards to the built classifiers. Unfortu- nately, there is scarce mention of these aspects in the evaluation of existing state-of-the-art approaches in the literature. In this paper, we consider the relevance of history in the construction of datasets, to highlight its impact on the performance of the malware detection scheme. Typ- ically, we show that simply picking a random set of known malware to train a malware detector, as it is done in most assessment scenarios from the literature, yields significantly biased results. In the process of assessing the extent of this impact through various experiments, we were also able to confirm a number of intuitive assump- tions about Android malware. For instance, we discuss the existence of Android malware lineages and how they could impact the performance of malware detection in the wild. [less ▲]

Detailed reference viewed: 620 (36 UL)