A matter of time: exploring survival analysis through cybersecurity

Despite the impact of employee behavior on organizational security, the topic of cybersecurity historically remains the responsibility of Information Security Management researchers and Information Technology professionals. However, the exponential increase in the prevalence and repercussions of cyber-related incidents invites collaboration between the fields of I-O Psychology and cybersecurity. The proposed presentation discusses the potential for I-O Psychology to contribute to cybersecurity efforts while demonstrating the fundamentals and applicability of survival analysis

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Literacies of Bilingual Youth: A Profile of Bilingual Academic, Social, and TXT Literacies

This dissertation identifies three types of language skills that urban Spanish/English bilingual youth possess (academic, social, and texting language), and reports on their relationship while documenting and analyzing the features of text messaging among this population. The participants in this study are Spanish-dominant bilingual young adults enrolled in a high school completion program in New York City. They are in the process of developing both Spanish and English academic literacy skills, and it is well known that they tend to perform below the grade they are enrolled in. For this reason, they are often referred to as being “language-less” (DeCapua & Marshall, 2011; Freeman, Freeman, & Mercuri, 2002) in an academic setting. Yet, little was previously known about their linguistic skills in other language forms such as social and Txt. This research seeks to understand and document their abilities across language forms and modalities, painting a composite picture of non-traditional bilinguals students’ linguistic skills. The aims of this dissertation are achieved through three different approaches. The first is a quantitative study into participants’ literacy skills through the use of assessments measuring academic literacy and social language awareness across written, aural, and digital modalities. The second is an in-depth analysis of the features participants use when texting (communicating via SMS and iMessage). Txt is a relatively new language form, and the analysis presented in this dissertation identifies the features and patterns that illustrate its systematic and constrained nature. The third approach is a case study focused on the texting behavior between two prolific texters. The theories developed based on the texting patterns of all participants (except those two texters) are applied to this one conversation for validation. This conversation constitutes more than half of the text messages that students contributed to the project, highlighting just how important this language form is in the daily life of young adults. A final component of this dissertation is the public availability of the text messages as an anonymized corpus along with the code and methods used to analyze the data. The text message corpus is available at www.byts.commons.gc.cuny.ed

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Využití tenchnologie GRID při zpracování medicínské informace

Práce se soustředí na vybrané oblasti biomedicínského výzkumu, které mohou profitovat ze současných výpočetních infrastruktur vybudovaných ve vědecké komunitě v evropském a světovém prostoru. Teorie výpočtu, paralelismu a distribuovaného počítání je stručně uvedena s ohledem na počítání v gridech a cloudech. Práce se zabývá oblastí výměny medicínských snímků a představuje propojení Gridového PACS systému s existujícími distribuovanými systémy pro sdílení DICOM snímků. Práce se dál zaměřuje na studium vědy týkající se lidského hlasu. Práce představuje vzdálený způsob přístupu k aplikaci pro analýzu hlasu v reálném čase pomocí úpravy protokolů pro vzdálenou plochu a pro přenos zvukových nahrávek. Tento dílčí výsledek ukazuje možnost využití stávajících aplikací na dálku specialisty na hlas. Oblast lidské fyziologie a patofyziologie byla studována pomocí přístupu tzv. systémové biologie. Práce přispívá v oblasti metodologie modelování lidské fyziologie pro tvorbu komplexních modelů založených na akauzálním a objektově orientovaném modelovacím přístupu. Metody pro studium parametrů byly představeny pomocí technologie počítání v gridech a v cloudech. Práce ukazuje, že proces identifikaci parametrů středně komplexních modelů kardiovasculárního systému a komplexního modelu lidské fyziologie lze významně zrychlit...This thesis focuses on selected areas of biomedical research in order to benefit from current computational infrastructures established in scientific community in european and global area. The theory of computation, parallelism and distributed computing, with focus on grid computing and cloud computing, is briefly introduced. Exchange of medical images was studied and a seamless integration of grid-based PACS system was established with the current distributed system in order to share DICOM medical images. Voice science was studied and access to real-time voice analysis application via remote desktop technology was introduced using customized protocol to transfer sound recording. This brings a possibility to access current legacy application remotely by voice specialists. The systems biology approach within domain of human physiology and pathophysiology was studied. Modeling methodology of human physiology was improved in order to build complex models based on acausal and object-oriented modeling techniques. Methods for conducting a parameter study (especially parameter estimation and parameter sweep) were introduced using grid computing and cloud computing technology. The identification of parameters gain substantial speedup by utilizing cloud computing deployment when performed on medium complex models of...nezařazení_neaktivníFirst Faculty of Medicine1. lékařská fakult

Final Workshop Proceedings of the Collaborative Project "Fast / Instant Release of Safety Relevant Radionuclides from Spent Nuclear Fuel" (7th EC FP CP FIRST-Nuclides), Karlsruhe 01 - 02 September 2014 (KIT Scientific Reports ; 7716)

The EURATOM FP7 Collaborative Project “Fast / Instant Release of Safety Relevant Radionuclides from Spent Nuclear Fuel (CP FIRST-Nuclides)” improved significantly the understanding of the mobilization of dose-dominating radionuclides from high burnup spent nuclear UO₂ fuels. The data are decisive for safety analysis for deep geological nuclear waste repositories

Automated Analysis of Synchronization in Human Full-body Expressive Movement

The research presented in this thesis is focused on the creation of computational models for the study of human full-body movement in order to investigate human behavior and non-verbal communication. In particular, the research concerns the analysis of synchronization of expressive movements and gestures. Synchronization can be computed both on a single user (intra-personal), e.g., to measure the degree of coordination between the joints\u2019 velocities of a dancer, and on multiple users (inter-personal), e.g., to detect the level of coordination between multiple users in a group. The thesis, through a set of experiments and results, contributes to the investigation of both intra-personal and inter-personal synchronization applied to support the study of movement expressivity, and improve the state-of-art of the available methods by presenting a new algorithm to perform the analysis of synchronization

Sustainable Transportation Planning During an Era of Technological Evolution

?Sustainable Transportation Planning During an Era of Technological Evolution? provides an in-depth analysis spanning three pivotal chapters, each focusing on the nexus of climate change, transportation, and metropolitan planning. Chapter 2 delves into climate change's role in transportation plans. The literature reviews transportation?s effects on climate change and the significance of Metropolitan Planning Organization (MPO) transportation planning. After identifying research gaps and ethical considerations, the chapter outlines a methodology employing Latent Dirichlet Allocation (LDA) model construction, text preprocessing, and evaluation metrics. Python?s Natural Language Processing (NLP) toolbox is utilized to analyze the contents of 42 long-range transportation plans. Semantic interpretations emphasize word frequencies, climate keywords, transportation plan similarities, and data visualizations, culminating in a discussion of the findings and research conclusions. Chapter 3 shifts to the modeling of autonomous vehicles (AV) concerning sustainable development. It reviews the implications of technological advancements on mobility, long-term planning, and urban expansion. The methodology presents land use forecasts, transportation modeling, and AV simulation parameters. Various scenarios, such as increased auto availability and decreased parking costs, are explored. A detailed discussion synthesizes these findings, leading to a research conclusion. Chapter 4 targets current MPO transportation planning activities, accentuating climate action. Data is collected via a PDF survey completed and returned by 13 of Wisconsin?s 14 MPOs. A description of the survey methodology is followed by an examination of the findings, offering key insights and latent thematic comparisons to the findings documented in Chapter 2 concerning climate action currently being taken by Wisconsin?s MPOs via their long-range transportation planning activities

Individual activity-travel analysis based on smartphone WiFi data

The mobility patterns of the population are the basis of most analyses in the transportation field. We aim to extract these patterns from smartphone traces. The following thesis proposes a Bayesian approach based on smartphone location records, land use information and schedule data to understand the activities that people daily perform. We investigate two alternatives. The prior is either based on schedule data or based on land use information. We test the algorithm on the smartphone WiFi traces provided by Nokia. They have been obtained from people who live around the Leman Lake, essentially in the region of Lausanne. The Swiss Federal Statistical Office (FSO) and OpenStreerMap (OSM) provide the schedule and the land use information. The results show that the prior based on schedule data is not convenient: the importance of the individual behavior decreases, and the activity Home comes up too often. The results are much better when the prior depends on the land use that surrounds a users’ location of interest. In addition, we have successfully extracted recognizable mobility patterns, particularly for the activities Home and Work