Exploiting multimodality and structure in world representations

An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can encode various types of links (temporal, causal, spatial) which can be essential for understanding and acting in the real world. This thesis presents three research works that study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of an existing one to the visual reasoning problem. By extending popular visual question answering (VQA) paradigms, I also designed several models that were evaluated on the novel dataset. This produced initial performance estimates for environment understanding, through the lens of a more challenging VQA downstream task. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by the best-performing method at the time, or incorporated theoretical properties via the use of topological data analysis algorithms. Both approaches competed with contemporary state-of-the-art graph classification methods, even outside social domains in the second case, where the inductive bias was PageRank-driven. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot, multi-task learning and scarce-labelled data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains, especially in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline. This type of framework may eventually contribute to developing lifelong-learning systems, due to its ability to adapt to novel tasks and distributions. The benchmark, methods and frameworks that I have devised during my doctoral studies suggest important future directions for embodied and graph representation learning research. These areas have increasingly proved their relevance to designing intelligent and collaborative agents, which we may interact with in the near future. By addressing several challenges in this problem space, my contributions therefore take a few steps towards building machine learning systems to be deployed in real-life settings.DREAM CD

AN ANALYSIS OF INFORMATION SEARCH AND RETRIEVAL TECHNIQUES

The informational growth dramatically changed the standard form of publication, access, and use of scientific information. Due to Internet, scientific publications migrated from the traditional to a digital configuration. Common users, as well as highly trained professionals from various fields, aim to retrieve information in the shortest possible time, but in the context of an unprecedented growth of data volume, big difficulties occur in finding and accessing the available information, resulting in stress and important delays in decision making. Therefore, training courses in information culture are organized in university libraries, particularly for a superior efficiency of scientific data bases usage. The paper presents an introduction to the architecture of a retrieval information system and a study of the most popular search engines and of the indexing process. Hereinafter, a Page Rank algorithm implementation is performed and a comparative study relative to different search engines is conducted, emphasizing the specific advantages and disadvantages

SECURING DATA AND APPLICATIONS IN CLOUD. CASE STUDY

Security is a principal concern when entrusting an organization’s critical information to geographically dispersed cloud platforms. In addition to the conventional IT system security procedures, designing security into cloud during the software development life cycle can greatly reduce the cloud attack surface. The paper focuses on presenting IBM Cloud security policies and their architecture and conducts a study of the security implementation steps, beginning with user's authentication on the Cloud platform. Hereinafter, the developed experimental application - a monitoring environment system - retrieves temperature, humidity, and pressure values from dedicated sensors, thus using the facilities offered by IBM Cloud platform

Intelligent Management of the Cryptographic Keys

With the continuous development of the computers networks, new problems have been posed in the process of keys management in the cryptographic systems. The main element in the cryptographic technologies is the keys management, as the cryptographic algorithms are known, while the keys have to be either secret (for unauthorized users that do not need them), or public (for users that need them). With an efficient cryptographic keys management system and the existing encryption techniques, there may be implemented a proper security system in the informational systems of the organizations. The process of cryptographic keys management consists in the following operations: keys generation, distribution, update, revocation, storage, backup/ recovery, import and export, usage control, expiration, and destruction. The cryptographic keys management techniques depend on the type of the keys, i.e. symmetric or public. Nowadays, the efforts of the researches in the cryptographic keys management are focused on the standardization and interoperability of the keys management. In this paper, the authors analyze the existing keys management systems and standards available for the keys management techniques, emphasizing the advantages and disadvantages of different systems. They also propose a cryptographic keys management model based on the ideas and principles of the INTERRAP architecture (a conceptual model developed by Jőrg Műller for intelligent agents). Also, there are incorporated some intelligent techniques to manage emergency situations, such as keys losing or their improper usage

STUDY AND DESIGN OF AN AUTOMATED PALLET SYSTEM

The need to increase efficiency, reliability and work capacity becomes a priority in small and large companies. High-level automation entails minimizing human labor and the human factor itself, but it still needs monitoring, surveillance, and inspection through human action. An automated system is much more efficient, its degree of repeatability being definitely a much higher one. The palletizing process used before the development of the automated pallet system implied in a 90% percentage the human factor through a human-machine cooperation, and the only automated mechanisms used were the conveyor belts and a mechanism for fixing the lid of the products transported on the belt. Thus, the industrial handling robot used in the automated pallet process becomes an essential resource, much faster