as phones. Today smart phones are available in the market with different applications and hardware which can be implemented without any further development or en-hancement. With the help of the GSM network, a mobile can be used to implement a smart home by controlling devices and getting alerts on robbery and burglary THE GUARANTEE OF PRODUCTS’ UNIQUENESS. Our writers (experts, masters, bachelor, and doctorate) write all the papers from scratch and always follow the instructions of the client to the blogger.com the order is completed, it is verified that each copy that does not present plagiarism with the Smart Home Master Thesis latest software to ensure that it is % unique The research findings documented into a thesis paper for secure IoT-based smart home systems and the resulted list and recommendations will be some support morally during this masters thesis project. Particularly, to Joakim Lewin
Smart Home Thesis Final Presentation by Prina Shah
The Smart Data Analytics group is always looking for good students to write theses. The topics can be in one of the following broad areas:. For Students: Thesis Registration Workflow with Prof. Lehmann as First Supervisor during the COVID crisis. Please note that the defense of your thesis and the final submission of the thesis should be in the same semester to avoid that the student has to register for another semester.
Please contact Shimaa Ibrahim for scheduling a defense date. Please note that the list below is only a small sample of possible topics and ideas. Please contact us to discuss further, to find new topics, or to suggest a topic of your own. Skip to content. Blog Twitter Contact Privacy Policy. The topics can be in one of the following broad areas: Distributed Semantic Analytics Semantic Question Answering Structured Machine Learning Deep Learning Software Engineering for Data Science Semantic Data Management Knowledge Extraction and Validation For Students: Thesis Registration Workflow with Prof.
Lehmann smart home master thesis First Supervisor during the COVID crisis Please find a thesis topic see below including a mentor please contact those indicated there directly.
The mentor needs to get approval from Prof. Please contact Martina Doelp for the thesis registration form. Fill and sign the form and sent it to Martina Doelp via regular mail, not electronically, smart home master thesis. The date of registration needs to be added by Prof, smart home master thesis.
Lehmann will then sign it on paper and Martina Doelp will send it to the second examiner via mail and forward it to the exam office afterwards on paper. You can then verify in BASIS that the registration was successful. Open Theses Topic Level Contact Person Knowledge Graph Embeddings Benchmarking Knowledge Graph embedding methods KGE have become the standard toolkit for analyzing and learning from data on knowlede graphs.
They have been successfully applied to many domains including chemistry, physics, social sciences and bioinformatics. As the field grows, it becomes critical to identify the architectures and key mechanisms which generalize across knowledge graphs sizes, enabling us to tackle larger, more complex datasets and domains. In this thesis you should study and apply the main tests, metrics and testing models. Technology to use: Machine Learning, Knoweldge Graphs M Afshin SadeghiDr.
Diego Esteves Solving Mini Chess via Distributed Deep Reinforcement Learning and Proof Number Search Ina chess program beat a novice human opponent for the first time in a chess variant called Los Alamos chess. While chess engines have improved dramatically over the past decades, the game theoretic value either a draw smart home master thesis a win for White can be forced of Los Alamos Chess is still unknown.
In this thesis, the goal is to prove the game theoretic value of the game using a combination of a deep reinforcement learning techniques for determining the best move in a particular position, b position solvers based on proof number search, c endgame table bases generated for Los Alamos Chess and d a distributed proof tree manager allowing to execute the approach over a cluster.
If the student is capable of implementing the approach on a small cluster, we will apply for an execution within a large-scale cluster consisting of hundreds of computing nodes. Given that substantial resources may be invested for this thesis, students applying for it should have excellent grades, excellent programming skills and enthusiasm for game solving or chess.
M Prof. One of the key elements required for addressing its consequences is to provide objective information to citizens, smart home master thesis. Conversational AI methods aka chatbots, speech assistants, dialogue systems, … have become increasingly important over the past few years, smart home master thesis. In this master thesis, students can contribute to building a climate change chatbot by addressing one of the main challenges in deep learning and natural language processing below: Improving reading comprehension techniques by transfer learning from large text corpora such as SquAD to text documents describing climate change Improving the translation of natural language questions to queries against climate knowledge graphs Interactive question answering techniques for capturing user feedback M Dr.
Ricardo UsbeckProf. Jens Lehmann Applying Knowledge graph embeddings for Context-aware Question Answering The task for Question Answering faces new challenges when applied in scenarios with frequently changing information sets, such as a driving car. Current semantic parsing approaches rely on the extraction of named entities and smart home master thesis predicates from the input to match these with patterns in static Knowledge Bases.
So far, there is little to no effort to include knowledge about the environment i, smart home master thesis. context into the QA pipeline. To improve the performance for the so-called Context-aware QA, you will work on solutions to adopt different Graph embeddings approaches into the QA process. Please refer to the job description for further information. M Jewgeni Rose Smart Home — Akquise von Individualwissen im Kundendienst-Umfeld Refer to the Miele job description for further information.
M Giulio Napolitano IoT Data Catalogues While platforms and tools such as Hadoop and Apache Spark allow for efficient processing of Big Data sets, it becomes increasingly challenging to organize and structure these data sets. Data sets have various forms ranging from unstructured data in files to structured data in databases.
Often the data sets reside smart home master thesis different storage systems ranging from traditional file systems, over Big Data files systems HDFS to heterogeneous storage systems S3, RDBMS, MongoDB, Elastic Search, …. At AGT International, we are dealing primarily with IoT data sets, i. data sets that have been collected from sensors and that are processed using Machine Learning-based ML analytic pipelines.
The number of these data sets is rapidly growing increasing the importance of generating metadata that captures both technical e. storage location, size and domain metadata and correlates the data sets with each other, e. by storing provenance data set x is a processed version of data set y and domain relationships, smart home master thesis.
Martin StrohbachProf. Jens Lehmann Work at AGT International in Darmstadt Named Entity Recognition for Short-Text Named Entity Recognition NER models play an important role in the Information Extraction IE pipeline, smart home master thesis. This thesis will thoroughly investigate NER in microblogs and propose new algorithms to overcome current state-of-the-art models in this research area.
Diego Esteves Multilingual Fact Validation Algorithms DeFacto Deep Fact Validation is an algorithm able to validate smart home master thesis by finding trustworthy sources for them on the Web.
Currently, it supports 3 main languages en, de and fr. The goal of this thesis is to explore and implement alternative information retrieval IR methods to minimize the dependency of external tools on verbalizing natural language patterns.
As a result, we expect to enhance the algorithm smart home master thesis by expanding its coverage. Diego Esteves An Approach for Big Product Matching Consider comparing the same product data from thousands of e-shops, smart home master thesis. However, there are two main challenges that make the comparison difficult. First, the completeness of the product specifications used for organizing the products differs across different e-shops, smart home master thesis.
Second, the ability to represent information about product data and their taxonomy is very diverse. To improve the consumer experience, e. The main focus of this work is on data modeling smart home master thesis semantic enrichment of product data in order to obtain an effective and efficient product matching result. Giulio NapolitanoDebanjan Chaudhuri Learning word representations for out-of-vocabulary words using their contexts.
Natural language processing NLP research has recently witnessed a significant boost, following the introduction smart home master thesis word embeddings as proposed by Mikolov et. However, one of the biggest challenges of using word embeddings using the vanilla neural net architecture with words as input and context as outputs is the handling of out-of-vocabulary oov words, as the model fails badly on unseen words, smart home master thesis.
In this project we are suggesting an architecture using the proposed word2vec model only. Here, given an unseen word, smart home master thesis, we would predict a distributed embedding for it using the contexts it is being used in using the matrix that has learned to predict context given the word. More details M Dr. In the context of this thesis, the student will evaluate and make the necessary extensions to the MINTE integration framework in a Big Data scenario. Datasets: We are going to work with Biomedical Dataset Programming Language: Scala Frameworks: Ideally integrated in SANSA platform, smart home master thesis, but this is not a must.
The goal of this thesis topic is to evaluate the performance of the semantic similarity metrics we have develop in a Big Data scenario. We are going to work with the following metrics: GADES, GARUM, FCA New to be develop See references. Datasets: Smart home master thesis are going to work with Biomedical Dataset. Programming Language: Scala, Java Frameworks: Ideally integrated in SANSA platform, but this is not a must.
Currently there are the same efforts in the Knowledge Graphs community. Several approaches such as TransE, RDF2Vec, etc… propose models to create embeddings out of the RDF molecules.
The goal of this thesis is to extend the similarity metric MateTee see references with the state-of-the-art-approaches to create embedding from Knowledge Graph Entities, smart home master thesis. Datasets: We are going to work with Knowledge Graphs such as DBpedia y Drugbank. This more a foundational research. Programming Language: Python References: No references for the moment, part of the work is to find some related literature, smart home master thesis.
Programming Language: ReactJS References: A Faceted Reactive Browsing Interface for Multi RDF Knowledge Graph Exploration A Serendipity-Fostering Faceted Browser for Linked Data Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser M Diego Collarana Completed Theses Query Decomposer and Optimizer for querying scientific datasets Supervisor: Dr, smart home master thesis.
Ioanna Lytra ; Level: M; Year: Knowledge Data Containers with Access Control and Security Capabilities Supervisor: Dr. Ioanna Lytra ; Level: M; Year: RDF compression techniques Supervisor: Dr. Damien GrauxGezim Sejdiu ; Level: M; Year: Double Machine Learning Student: Justus Winkelmann; Supervisor: Prof.
Jens Lehmann a ; Level: B; Year: Automated Link Discovery for Data Harmonization in the Maritime Domain Student: Jaime Trillos ; Supervisor: Dr. Hajira JabeenDr, smart home master thesis.
Ioanna Lytra ; Level: M; Year: Reinforcement Learning Strategy for Firefighter Problem Student: Jing-Long Wu; Supervisor: Dr. Diego Esteves ; Level: M; Year: Detection of Duplicate Questions in Community based Question Answering Smart home master thesis Hao Cao; Supervisor: Mohnish DubeyDebanjan Chaudhuri ; Level: M; Year: Scalable Completeness Quality Check over Big RDF Data Student: Gulnar Khalilova ; Supervisor: Gezim Sejdiu ; Level: M; Year: An Efficient Recommendation System for RDF Partitioners over Large-Scale RDF Datasets Student: Pardeep Naik ; Supervisor: Gezim SejdiuDr.
Ioanna Lytra ; Level: M; Year: An Efficient Semantic-based Entity-Resolution over Big RDF Data with SANSA Framework Student: Mohammad Ghasemi ; Supervisor: Gezim Sejdiu ; Level: M; Year: RDF Data Compression Techniques in a Highly Distributed Context Student: Abakar Bouba ; Supervisor: Gezim SejdiuDr.
Damien Graux ; Level: M; Year: RDF Doctor: A Holistic Approach for Error Detection and Correction of RDF Data Student: Ahmad Hemid ; Supervisor: Dr. Lavdim Halilaj ; Level: M; Year: Predictive Uncertainty Quantification with Compound Density Networks Student: Agustinus Kristiadi ; Supervisor: Jun. Asja Fischer ; Level: M; Year: Deep Neural Network For Entity Smart home master thesis Using Background Knowledge Student: Akhilesh Vyas ; Supervisor: Dr.
Kuldeep Singh ; Level: M; Year: Solving Multistate Bar Exam Questions with Deep Bidirectional Transformers Student: Endri Kacupaj ; Supervisor: Dr. Maria Maleshkova; Level: M; Year: Distributed In-memory SPARQL Processing Student: Haziiev Eskender; Supervisor: Dr. Hajira Jabeen; Level: M; Year: Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs Student: Gaurav Maheshwari ; Supervisor: Prof. Jens Lehmann, smart home master thesis, Jun. Asja Fischer Level: M; Year: Efficient In-memory Graph Partitioning Algorithms and Query Engine for RDF Data Student: Wang Zhe ; Supervisor: Gezim SejdiuDr.
Ioanna Lytra ; Level: M; Year: Rule Mining on Distributed RDF Data Student: Kunal Jha ; Supervisor: Gezim SejdiuDr. Hajira Jabeen ; Level: M; Year: Scalable Deep Learning Technique for Sensitive Data Exposure Detection Student: Mohamad Denno ; Smart home master thesis Gezim Sejdiu ; Level: M; Year: Scalable Knowledge Graph Exploration for Sentiment Classification Student: Ali Denno ; Supervisor: Gezim Sejdiu ; Level: M; Year: Efficient and Scalable In-memory Semantic Partitioning for RDF Data Student: Imran Khan ; Supervisor: Gezim SejdiuDr.
Ioanna Lytra ; Level: M; Year: Distributed Data Parsing and Vandalism Detection on Large Knowledge Graphs using Apache Spark and Hadoop Ecosystem Student: Nayef Roqaya ; Supervisor: Gezim SejdiuDr.
The Perfect Defense: The Oral Defense of a Dissertation
, time: 22:00Master’s in Smart Electrical Networks and Systems
Master's Thesis Smart Home - Teiresias by Bc. Martin Hasaj Thesis supervisor: Ing. Pavel Kordík Study program: Electronics and Informatics Subject: Software Engineering May ii. Acknowledgement I would like to thank my relatives who stood by me during my studies and my friends who always This thesis aims to provide security and privacy for Internet of Things devices in a smart home setting. The first and core contribution is the development of a gateway which stands at the border of the smart home, between the home’s devices and outside users such as service providers First, a subject-matter expert will write your essay from scratch. Examine instructions and requirements, create Smart Home Master Thesis a structure, and write down a perfect and unique text. The final result Smart Home Master Thesis is guaranteed to meet your expectations and earn you the best grade.. Second, professional editors and proofreaders will double-check your essay to fix Smart
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