Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory

The Pepper robot has become a widely recognised face for the perceived potential of social robots to enter our homes and businesses. However, to date, commercial and research applications of the Pepper have been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result of a number of technical limitations, including limited sensing capabilities, and have as a result, reduced the number of roles in which use of the robot can be explored. In this paper, we present our approach to solving these problems, with the intention of opening up new research applications for the robot. To demonstrate the applicability of our approach, we have framed this work within the context of providing interactive tours of an open-plan robotics laboratory.Comment: 8 pages, Submitted to IROS 2018 (2018 IEEE/RSJ International Conference on Intelligent Robots and Systems), see https://bitbucket.org/pepper_qut/ for access to the softwar

Developing a Formal Navy Knowledge Management Process

Prepared for: Chief of Naval Operations, N1Organization tacit and explicit knowledge are required for high performance, and it is imperative for such knowledge to be managed to ensure that it flows rapidly, reliably and energetically. The Navy N1 organization has yet to develop a formal process for knowledge management (KM). This places N1 in a position of competitive disadvantage, particularly as thousands of people change jobs every day, often taking their hard earned job knowledge out the door with them and leaving their replacements with the need to learn such knowledge anew. Building upon initial efforts to engage with industry and conceptualize a Navy KM strategy, the research described in this study employs a combination of Congruence Model analysis, Knowledge Flow Theory, and qualitative methods to outline an approach for embedding a formal Navy KM process. This work involves surveying best tools and practices in the industry, government and nonprofit sectors, augmented by in depth field research to examine two specific Navy organizations in detail. Results are highly promising, and they serve to illuminate a path toward improving Navy knowledge flows as well as continued research along these lines.Chief of Naval Operations, N1Chief of Naval Operations, N1.Approved for public release; distribution is unlimited

Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation

AI Tool is a large language model (LLM) designed to generate human-like responses in natural language conversations. It is trained on a massive corpus of text from the internet, which allows it to leverage a broad understanding of language, general knowledge, and various domains. AI Tool can provide information, engage in conversations, assist with tasks, and even offer creative suggestions. The underlying technology behind AI Tool is a transformer neural network. Transformers excel at capturing long-range dependencies in text, making them well-suited for language-related tasks. AI Tool has 175 billion parameters, making it one of the largest and most powerful LLMs to date. This work presents an overview of AI Tool's responses on various sectors of industry. Further, the responses of AI Tool have been cross-verified with human experts in the corresponding fields. To validate the performance of AI Tool, a few explicit parameters have been considered and the evaluation has been done. This study will help the research community and other users to understand the uses of AI Tool and its interaction pattern. The results of this study show that AI Tool is able to generate human-like responses that are both informative and engaging. However, it is important to note that AI Tool can occasionally produce incorrect or nonsensical answers. It is therefore important to critically evaluate the information that AI Tool provides and to verify it from reliable sources when necessary. Overall, this study suggests that AI Tool is a promising new tool for natural language processing, and that it has the potential to be used in a wide variety of applications

Exploring Content Management Issues in Air Force On-Line Communities of Practice: A Multiple Case Study Approach

The practice of content management attempts regardless of platform to ensure that pertinent information is current, relevant, and presented in a usable manner. The Air Force Communities of Practice (CoPs) are hosted by AFMC/DRW, The purpose of these CoPs is to facilitate and promote an environment of capturing and sharing knowledge among members of a particular field task or common practice. As the host for these CoPs AFMC/DRW desires to increase CoP participation efficiency and effectiveness. Addressing existing or potential content management issues will help do so. This multiple-case study research observed and interviewed managers and members of eight active CoPs busted by AFMC/DRW. This research suggested that the interviewed CoPs currently use no formally documented content management processes. Some CoP members indicated developing formal content management processes and procedures establishing a good taxonomy and better defining roles and responsibilities of content owners may help solve future content management Issues

Mapping the repository landscape : harnessing similarity with RepoSim and RepoSnipy

The rapid growth of scientific software development has led to the emergence of large and complex codebases, making it challenging to search, find, and compare software repositories within the scientific research community. In this paper, we propose a solution by leveraging deep learning techniques to learn embeddings that capture semantic similarities among repositories. Our approach focuses on identifying repositories with similar semantics, even when their code fragments and documentation exhibit different syntax. To address this challenge, we introduce two complementary open-source tools: RepoSim and RepoSnipy. RepoSim is a command-line toolbox designed to represent repositories at both the source code and documentation levels. It utilizes the UniXcoder pre-trained language model, which has demonstrated remarkable performance in code-related understanding tasks. RepoSnipy is a web-based neural semantic search engine that utilizes the powerful capabilities of RepoSim and offers a user-friendly search interface, allowing researchers and practitioners to query public repositories hosted on GitHub and discover semantically similar repositories. RepoSim and RepoSnipy empower researchers, developers, and practitioners by facilitating the comparison and analysis of software repositories. They not only enable efficient collaboration and code reuse but also accelerate the development of scientific software.Postprin

How can startups make use of cloud services

The purpose of this project is to discuss the technical obstacles that small and medium-sized enterprises (SMEs) face, as well as how cloud computing can help to solve these issues. Cloud computing has the ability to radically change competitive environments by offering a new forum for generating and delivering business value and market development. Small and medium-sized enterprises (SMEs) also lack sufficient technological capital. They could either buy expensive software customized by large companies or develop their own low-cost applications with limited functionality. The research questions answered in this project are as follows: 1. How can a startup build a cost-effective website and host it on a cloud platform? 2. How can a startup make a website always available? 3. How can a startup resolve the low latency problem using cloud services? 4. How can a startup make use of different cloud services to reduce the use of infrastructure and technology resources. The methodology used for this project is to understand what is cloud computing, cloud implementation models, cloud computing services, and their benefits. The Implementation Solution is offered by evaluating various cloud vendors such as AWS and Microsoft Azure and the various services they provide. This study provides a realistic demonstration of designing and hosting a website in AWS and Microsoft Azure to support this approach. This study offers insights and suggestions to SMEs for implementing various cloud services to achieve economies of scale. The project concluded that cloud computing is simple to use, inexpensive, eliminates the need for physical office space, and eliminates the need to transport storage devices. Because of its scalability and faster content delivery, as well as various business models, the cloud is an excellent choice for startups

Nudging according to user’s preferences

Physical inactivity has been identified as a global pandemic, physical inactivity causes multiple health outcomes in different demographic group such as coronary heart disease, type 2 diabetes, colon, and breast cancer. A physical inactive person takes less than 5000 steps a day. To try to reduce physical inactivity along individual for healthy lifestyle, this thesis provides personalized digital nudge. Nudge means to guide someone to do something that is beneficial for the long-term benefit of the person being nudged and doing so using UI (user interface) in digital environment is known as digital nudging. As people are relying more on technology for their decision making, the information collected from the integration of the devices is used to provide personalized nudges. As people have access to smartphones and wearable devices, data is collected from these devices to provide tailored nudges to achieve minimum required steps to reduce inactivity. Personalized nudge is a smart nudge which predictably influence people's behaviour. It is a type of digital nudge. This kind of nudge takes user’s information into account before nudging a user. This thesis also provides recommendations (new activities) based on person’s preference. The presented system was also tested by real users, and the feedback suggested that the presented system indeed urged them to be more active

Localization of sources in electroencephalographic registers during working memory tasks

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Director: Albert Compte. Tutor: Santiago Marco.EEG source localization is a non-invasive imaging technique developed to locate the anatomical sources recorded at the scalp during an EEG recording. The reconstruction of the sources can be computed by solving the so-called inverse problem. This is an ill-posed problem which aims in estimating the sources that fit the recorded measurements. There exist several powerful commercial and academic software packages that cover multiple methods on data processing, source localization, and statistical analysis. In this work, the open-source MNE-Python package was selected as the working environment used to address the challenge of characterizing and locating neural activation. This study provides a pipeline with practical steps on the EEG source localization technique. The results obtained in this project have been validated by experts in the Theoretical Neurobiology and Computational Neuroscience fields In this project, the EEG source localization has been computed over a group of encephalitic patients and a control group. The two groups had shown differences regarding the electrical activity in a working memory trial. This study aimed in localizing the anatomical brain regions that were responsible of the electrical differences. It has been observed that instants before the stimulus, the activated sites between control groups and encephalitic groups differ. In the case of the control group, the activated region was located at the frontal lobe of the left hemisphere. Whereas, in the case of the encephalitic group the activated region was located at the temporal lobe of the right hemisphere