Data-Efficient Machine Learning with Focus on Transfer Learning

Machine learning (ML) has attracted a significant amount of attention from the artifi- cial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain dis- crepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) has been considered as an effective solution to address the three shortcomings of conventional ML. Furthermore, TL is one of the most active areas in the DEML. Over the past ten years, significant progress has been made in TL. In this dissertation, I propose to address the three problems by developing a software- oriented framework and TL algorithms. Firstly, I introduce a DEML framework and a evaluation system. Moreover, I present two novel TL algorithms and applications on real-world problems. Furthermore, I will first present the first well-defined DEML framework and introduce how it can address the challenges in ML. After that, I will give an updated overview of the state-of-the-art and open challenges in the TL. I will then introduce two novel algorithms for two of the most challenging TL topics: distant domain TL and cross-modality TL (image-text). A detailed algorithm introduction and preliminary results on real-world applications (Covid-19 diagnosis and image clas- sification) will be presented. Then, I will discuss the current trends in TL algorithms and real-world applications. Lastly, I will present the conclusion and future research directions

Multimodal Video Analysis and Modeling

From recalling long forgotten experiences based on a familiar scent or on a piece of music, to lip reading aided conversation in noisy environments or travel sickness caused by mismatch of the signals from vision and the vestibular system, the human perception manifests countless examples of subtle and effortless joint adoption of the multiple senses provided to us by evolution. Emulating such multisensory (or multimodal, i.e., comprising multiple types of input modes or modalities) processing computationally offers tools for more effective, efficient, or robust accomplishment of many multimedia tasks using evidence from the multiple input modalities. Information from the modalities can also be analyzed for patterns and connections across them, opening up interesting applications not feasible with a single modality, such as prediction of some aspects of one modality based on another. In this dissertation, multimodal analysis techniques are applied to selected video tasks with accompanying modalities. More specifically, all the tasks involve some type of analysis of videos recorded by non-professional videographers using mobile devices.Fusion of information from multiple modalities is applied to recording environment classification from video and audio as well as to sport type classification from a set of multi-device videos, corresponding audio, and recording device motion sensor data. The environment classification combines support vector machine (SVM) classifiers trained on various global visual low-level features with audio event histogram based environment classification using k nearest neighbors (k-NN). Rule-based fusion schemes with genetic algorithm (GA)-optimized modality weights are compared to training a SVM classifier to perform the multimodal fusion. A comprehensive selection of fusion strategies is compared for the task of classifying the sport type of a set of recordings from a common event. These include fusion prior to, simultaneously with, and after classification; various approaches for using modality quality estimates; and fusing soft confidence scores as well as crisp single-class predictions. Additionally, different strategies are examined for aggregating the decisions of single videos to a collective prediction from the set of videos recorded concurrently with multiple devices. In both tasks multimodal analysis shows clear advantage over separate classification of the modalities.Another part of the work investigates cross-modal pattern analysis and audio-based video editing. This study examines the feasibility of automatically timing shot cuts of multi-camera concert recordings according to music-related cutting patterns learnt from professional concert videos. Cut timing is a crucial part of automated creation of multicamera mashups, where shots from multiple recording devices from a common event are alternated with the aim at mimicing a professionally produced video. In the framework, separate statistical models are formed for typical patterns of beat-quantized cuts in short segments, differences in beats between consecutive cuts, and relative deviation of cuts from exact beat times. Based on music meter and audio change point analysis of a new recording, the models can be used for synthesizing cut times. In a user study the proposed framework clearly outperforms a baseline automatic method with comparably advanced audio analysis and wins 48.2 % of comparisons against hand-edited videos

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Exploiting Social Media Sources for Search, Fusion and Evaluation

The web contains heterogeneous information that is generated with different characteristics and is presented via different media. Social media, as one of the largest content carriers, has generated information from millions of users worldwide, creating material rapidly in all types of forms such as comments, images, tags, videos and ratings, etc. In social applications, the formation of online communities contributes to conversations of substantially broader aspects, as well as unfiltered opinions about subjects that are rarely covered in public media. Information accrued on social platforms, therefore, presents a unique opportunity to augment web sources such as Wikipedia or news pages, which are usually characterized as being more formal. The goal of this dissertation is to investigate in depth how social data can be exploited and applied in the context of three fundamental information retrieval (IR) tasks: search, fusion, and evaluation. Improving search performance has consistently been a major focus in the IR community. Given the in-depth discussions and active interactions contained in social media, we present approaches to incorporating this type of data to improve search on general web corpora. In particular, we propose two graph-based frameworks, social anchor and information network, to associate related web and social content, where information sources of diverse characteristics can be used to complement each other in a unified manner. We investigate how the enriched representation can potentially reduce vocabulary mismatch and improve retrieval effectiveness. Presenting social media content to users is valuable particularly for queries intended for time-sensitive events or community opinions. Current major search engines commonly blend results from different search services (or verticals) into core web results. Motivated by this real-world need, we explore ways to merge results from different web and social services into a single ranked list. We present an optimization framework for fusion, where impact of documents, ranked lists, and verticals can be modeled simultaneously to maximize performance. Evaluating search system performance has largely relied on creating reusable test collections in IR. Traditional ways to creating evaluation sets can require substantial manual effort. To reduce such effort, we explore an approach to automating the process of collecting pairs of queries and relevance judgments, using high quality social media, Community Question Answering (CQA). Our approach is based on the idea that CQA services support platforms for users to raise questions and to share answers, therefore encoding the associations between real user information needs and real user assessments. To demonstrate the effectiveness of our approaches, we conduct extensive retrieval and fusion experiments, as well as verify the reliability of the new, CQA-based evaluation test sets

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018