This paper aims to answer the question of how artificial intelligence could help humans maintain and/or improve situational awareness in search and rescue operations at sea, as well as where in such processes artificial intelligence could be incorporated to most efficiently compensate for human vulnerabilities and support human strengths. In order to answer this, a joint cognitive system perspective has been adopted whilst joining in search and rescue practice operations. These operations have been observed and persons participating in them have been interviewed, in order to gather insights about the process and the persons conducting it. The results from these insights coupled with experience with artificial intelligence and automation, show that artificial intelligence could help improve and/or maintain situational awareness by adopting functions which take up unnecessary time from man. According to the joint cognitive system view, these functions should never be solely performed by artificial intelligence however, but in coagency with man; allocated functions should overlap between man and machine. Suggestions have been given regarding which functions in particular an artificial intelligent agent could perform in terms of search and rescue and where these functions occur in the process. None of these suggestions are without man involvement, as they should not be. To summarise, these suggestions include; a UAV equipped with an infrared camera to search large areas quickly, a decision support system equipped with image recognition to analyse images gathered from the UAV, as well as a communication tool which allows for shared search patterns and hotspots between search and rescue units. / WARA-PS

Syftet med uppsatsen är att belysa möjligheter och begränsningar med olika tekniska system för att identifiera kontakter på stridsfältet, främst med avseende på att minska risken för vådabekämpningar. Uppsatsen skall också belysa om införande av tekniska system för identifiering av kontakter är den enskilt bästa metoden för att undvika vådabekämpningar. Syftet är att läsaren skall uppnå en förståelse för vad olika typer av system för identifiering kan bidra med för att minska risken för vådabekämpningar. Utöver detta belyses andra nackdelar och fördelar med de olika tekniska systemen förutom just inom området identifiering. Uppsatsen beskriver olika händelser där vådabekämpningar skett och kopplar dessa mot hur olika tekniska system eventuellt hade kunnat minska risken för att vådabekämpningen skulle ha skett. Uppsatsen beskriver också ett antal olika tekniska system för identifiering av kontakter på stridsfältet. / The purpose of this essay is to shed light on possibilities and limitations regarding different systems for identification of contacts on the battlefield, mainly for the purpose of reducing fratricide. The essay will also shed light on if the introduction of systems for identification on the battlefield is the best one single method that will reduce fratricide. The purpose is that the reader will achieve an understanding of how different systems for target identification will reduce the risk for fratricide. In addition the possibilities and limitations of other systems in the field of combat identification will also be addressed. The essay describes different events where fratricide has occurred and connects these events with the possibilities and limitations of the identification systems described and how these systems could have reduced the risk for fratricide. The essay also describes different systems for target identification and combat identification.

Detta är en kvalitativ intervjustudie som undersöker maskinpersonalens upplevelser av att använda headsetkommunikation i arbetet ombord. Totalt sex stycken maskinbesättningsmedlemmar intervjuades om deras upplevelser av hur headsetkommunikation har påverkat utförandet av arbetet, personlig säkerhet och tidseffektivitet. Resultaten som framkom av intervjuerna visar på att kommunikationen förbättrats med headsetkommunikation genom att talkommunikationen inte begränsas av höga bullernivåer vilket leder till minskad röstbelastning. De intervjuade vittnade om att den förbättrade kommunikationen underlättade i arbetet och ökade den personliga säkerheten genom att risken för missförstånd minskade samt att maskinbesättningens situationsmedvetenhet ökade. Förbättrad ergonomi vid svåra arbetsställningar i trånga utrymmen jämfört med handburna radioapparater upplevdes även det som en fördel med headsetkommunikation. Tidsåtgången i arbetet upplevdes minska när tillgängligheten till andra besättningsmedlemmar på kommunikationsradio ökade eftersom det minskade behovet av att förflytta sig för att kommunicera samtidigt som det kan leda till snabbare responstider. Den ökade tillgängligheten upplevdes även kunna leda till att olyckor inom maskinrummet upptäcks snabbare till följd av att ett uteblivet svar på radioanrop kan ses som ett avvikande beteende. / This is a qualitative interview study researching the experience engine room personnel have with using headset communication in their work onboard. A total of six crewmembers were interviewed about their experiences and how headset communication had affected their ability to perform their work, personal safety and time efficiency. The results showed that communication had improved with the use of headsets and that speech communication where no longer limited by high noise levels which in turn leads to less strain on the voice. The interviewed crew members testified that the improved communication facilitated their work and enhanced the personal safety by decreasing the risk for misunderstandings and increasing their situational awareness. Increased ergonomics in situations with difficult working postures and limited space in comparison with hand held radios were also observed as an advantage with headset communication. Time efficiency was perceived to improve since the availability of other crewmembers on the radio increased and decreased the need for moving to another area to communicate which at the same time lead to faster response times. Increased availability of crewmembers on the radio could also shorten the time before an accident in the engine room is detected since a failure to answer a radio call would be seen as irregular behavior.

Thesis (Master of Military Studies)-Marine Corps Command and Staff College, 2008. / Title from title page of PDF document (viewed on: Jan 11, 2010). Includes bibliographical references.

The general purpose of this study was to determine whether a relationship exists between emotional awareness and human error in aviation. A quantitative analysis approach was used to explore this by means of a cross-sectional survey design. The independent variable emotional awareness and the dependent variable human error were contextualised and operationalised. During the empirical phase, biographical information was collected and the Hartmann Emotional Boundary Questionnaire was administered to a purposive sample consisting of 173 aircrew members within the South African Air Force. Factor analysis revealed an eight-factor structure: involved; exactness; blend; openness; structured; unstructured; flexibility; and imagination. No differentiation was found between the mustering groups in relation to emotional awareness and human error. However, correlations differentiated between aircrew with zero human error and aircrew with “more than ten years’ aviation experience”. The test for differences between human error and the emotional awareness sub-construct "imagination" indicated a medium significance. From this relationship, the researcher deducted that “imaginative aircrew are prone to err”. / Industrial and Organisational Psychology / M. Com. (Industrial and organisational Psychology

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Intrusion Detection Systems (IDS) are designed to monitor the computer network infrastructure against possible attacks by generating security alerts. With the increase of components connected to computer networks, traditional IDS are not capable of effectively detecting malicious attacks. This occurs either by the distributed amount of data that traverses the network or the complexity of the attacks launched against the network. Therefore, the design of Internet Early Warning Systems (IEWS) enables the early detection of threats in the network, possibly avoiding eventual damages to the network resources. The IEWS works as a sink that collects alerts from different sources (for example, from different IDS), centralizing and correlating information in order to provide a holistic view of the network. This way, the current dissertation describes an IEWS architecture for correlating alerts from (geographically) spread out IDS using the Case-Based Reasoning (CBR) technique together with IP Georeferencing. The results obtained during experiments, which were executed over the implementation of the developed technique, showed the viability of the technique in reducing false-positives. This demonstrates the applicability of the proposal as the basis for developing advanced techniques inside the extended IEWS architecture. / Sistemas de Detecção de Instrução (Intrusion Detection Systems IDS) são projetados para monitorar possíveis ataques à infraestruturas da rede através da geração de alertas. Com a crescente quantidade de componentes conectados na rede, os IDS tradicionais não estão sendo suficientes para a efetiva detecção de ataques maliciosos, tanto pelo volume de dados como pela crescente complexidade de novos ataques. Nesse sentido, a construção de uma arquitetura Internet Early Warning Systems (IEWS) possibilita detectar precocemente as ameaças, antes de causar algum perigo para os recursos da rede. O IEWS funciona como um coletor de diferentes geradores de alertas, possivelmente IDS, centralizando e correlacionado informações afim de gerar uma visão holística da rede. Sendo assim, o trabalho tem como objetivo descrever uma arquitetura IEWS para a correlação de alertas gerados por IDS dispersos geograficamente utilizando a técnica Case-Based Reasoning (CBR) em conjunto com Georreferenciamento de endereços IP. Os resultados obtidos nos experimentos, realizados sobre a implementação da técnica desenvolvida, mostraram a viabilidade da técnica na redução de alertas classificados como falsos-positivos. Isso demonstra a aplicabilidade da proposta como base para o desenvolvimento de técnicas mais apuradas de detecção dentro da arquitetura de IEWS estendida.

Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-11T12:19:26Z No. of bitstreams: 1 TeseLCB.pdf: 19957803 bytes, checksum: 66c9854c5f0067734f1a81f62cc661b0 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-21T12:06:12Z (GMT) No. of bitstreams: 1 TeseLCB.pdf: 19957803 bytes, checksum: 66c9854c5f0067734f1a81f62cc661b0 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-21T12:06:22Z (GMT) No. of bitstreams: 1 TeseLCB.pdf: 19957803 bytes, checksum: 66c9854c5f0067734f1a81f62cc661b0 (MD5) / Made available in DSpace on 2016-10-21T12:06:31Z (GMT). No. of bitstreams: 1 TeseLCB.pdf: 19957803 bytes, checksum: 66c9854c5f0067734f1a81f62cc661b0 (MD5) Previous issue date: 2016-01-26 / Não recebi financiamento / Situational Awareness (SAW) is a cognitive process widely spread in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. In the emergency management domain, the situational information inferred by decision support systems affects the SAW of human operators, which is also influenced by the dynamicity and critical nature of the events. Failures in SAW, typically caused by high levels of stress, information overload and the inherent need to perform multiple tasks, can induce human operators to errors in decision-making, resulting in risks to life, assets or to the environment. Data fusion processes present opportunities to improve human operators’ SAW and enrich their knowledge on situations. However, problems related to the quality of information can lead to uncertainties, especially when human operators are also sources of information, requiring the restructuring of the fusion process. The state of the art of data and information fusion models presents approaches with limited participation of human operators, typically reactive, besides solutions that are restricted in mechanisms to manage the quality of information throughout the fusion process. Thus, the present work presents a new information fusion model, called Quantify (Quality-aware Human-driven Information Fusion Model), whose major differentials are the greater involvement of human operators and the use of the information quality management throughout the fusion process. In order to support the Quantify model, an innovative methodology was developed for the assessment and representation of data and information quality, called IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness) specialized in the context of emergency situational awareness and which also involves the human operator. In order to validate the model and the methodology, a service-oriented architecture and two emergency situation assessment systems were developed, one guided by the Quantify model and another driven by the state-of-the-art model (User-Fusion). In a case study, robbery events reported to the emergency response service of the S˜ao Paulo State Military Police (Pol´ıcia Militar do Estado de S˜ao Paulo - PMESP) were submitted to the systems and then evaluated by the PMESP operators, revealing higher rates of SAW by the application of the Quantify model. These positive results confirm the need of this new model and methodology, besides revealing an opportunity to enrich the current emergency response system used by PMESP. / Situational Awareness (SAW) is a cognitive process widely spread in areas that require critical decision-making and refers to the level of consciousness that an individual or team has about a situation. In the emergency management domain, the situational information inferred by decision support systems affects the SAW of human operators, which is also influenced by the dynamicity and critical nature of the events. Failures in SAW, typically caused by high levels of stress, information overload and the inherent need to perform multiple tasks, can induce human operators to errors in decision-making, resulting in risks to life, assets or to the environment. Data fusion processes present opportunities to improve human operators’ SAW and enrich their knowledge on situations. However, problems related to the quality of information can lead to uncertainties, especially when human operators are also sources of information, requiring the restructuring of the fusion process. The state of the art of data and information fusion models presents approaches with limited participation of human operators, typically reactive, besides solutions that are restricted in mechanisms to manage the quality of information throughout the fusion process. Thus, the present work presents a new information fusion model, called Quantify (Quality-aware Human-driven Information Fusion Model), whose major differentials are the greater involvement of human operators and the use of the information quality management throughout the fusion process. In order to support the Quantify model, an innovative methodology was developed for the assessment and representation of data and information quality, called IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness) specialized in the context of emergency situational awareness and which also involves the human operator. In order to validate the model and the methodology, a service-oriented architecture and two emergency situation assessment systems were developed, one guided by the Quantify model and another driven by the state-of-the-art model (User-Fusion). In a case study, robbery events reported to the emergency response service of the S˜ao Paulo State Military Police (Pol´ıcia Militar do Estado de S˜ao Paulo - PMESP) were submitted to the systems and then evaluated by the PMESP operators, revealing higher rates of SAW by the application of the Quantify model. These positive results confirm the need of this new model and methodology, besides revealing an opportunity to enrich the current emergency response system used by PMESP. / Consciência Situacional (Situational Awareness - SAW) é um processo cognitivo amplamente difundido em áreas que demandam a tomada de decisão critica e se refere ao nível de consciência que um indivíduo ou equipe detém sobre uma situação. No domínio de gerenciamento de emergências, a informação situacional inferida por sistemas de apoio à decisão afeta a SAW de operadores humanos, a qual é também influenciada pela dinamicidade e natureza crítica dos eventos. Falhas de SAW, tipicamente provocadas pelo alto nível de stress, sobrecarga de informação e pela inerente necessidade de realização de múltiplas tarefas, podem induzir operadores humanos a erros no processo decisório e acarretar riscos `a vida, ao patrimônio ou ao meio ambiente. Processos de fusão de dados apresentam oportunidades para aprimorar a SAW de operadores humanos e enriquecer o seu conhecimento sobre situações. Entretanto, problemas referentes `a qualidade da informação podem gerar incertezas, principalmente quando operadores humanos são também fontes de informação, demandando assim a reestruturação do processo de fusão. O estado da arte em modelos de fusão de dados e informações apresenta abordagens com limitada participação de humanos, tipicamente reativa, além das soluções serem restritas em mecanismos para gerir a qualidade da informação. Assim, este trabalho apresenta um novo modelo de fusão de informações, denominado Quantify (Quality-Aware Human-Driven Information Fusion Model), cujos principais diferenciais são a intensificação da participação humana e o emprego continuo da gestão da qualidade da informação ao longo do processo de fusão. Em suporte ao modelo Quantify, foi desenvolvida uma metodologia inovadora para a avaliação e representação da qualidade de dados e informações, denominada IQESA (Information Quality Assessment Methodology in the Context of Emergency Situation Awareness), especializada no contexto de consciência situacional de emergências e que também envolve o operador humano. Para validar o modelo e a metodologia, uma arquitetura orientada a serviços e dois sistemas de avaliação de situações de emergência foram desenvolvidos, um deles orientado pelo modelo Quantify e outro dirigido pelo modelo do estado da arte (User-Fusion). Em estudo de caso, eventos de roubo relatados ao serviço de atendimento a emergências da Polícia Militar do Estado de São Paulo (PMESP) foram submetidos aos sistemas e avaliados por operadores da PMESP, revelando índices superiores de SAW pelo emprego do modelo Quantify. Tais resultados positivos corroboram com a necessidade deste novo modelo e metodologia, além de revelar uma oportunidade de enriquecimento do sistema atual de atendimento a emergências utilizado pela PMES

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The popularization of the Internet has provided an increase in the number of web applications that work with critical information. Parallel to this, attacks that exploit the vulnerabilities of these applications has also grown. This scenario has stimulated companies to invest in tools to monitor their network infrastructure in order to detect malicious activity. One of the main tools used by companies to monitor their network infrastructures and identifying attacks are Intrusion Detection Systems. However, due to expansion of the volume of data in computer networks, these systems are becoming limited. In contrast, researchers have explored the construction of Internet Early Warning Systems to monitor malicious activities on the Internet. This work proposes a data model of a knowledge base for Internet EarlyWarning Systems. The model represents the data of different aspects of the network with a focus on events related to intrusion detection, such as data of alerts generated by intrusion detection systems, information on response measures, traffic statistics and signatures of known attacks. A case study on a real network infrastructure demonstrates the applicability of the data model of knowledge base and identifies the advantages of its use. Furthermore, the data stored in the knowledge base potentializes the construction of situational awareness of monitored environment, directing the activities of the security team and helping in the decision process responses to potential attacks. / A popularização da Internet tem proporcionado um aumento no número de aplicações web que trabalham com informações críticas. Em paralelo a isso, os ataques que exploram as vulnerabilidades dessas aplicações também tem crescido. Esse cenário tem estimulado as empresas a investir em ferramentas para monitorar sua infraestrutura de rede, visando a detecção de atividades mal-intencionadas. Uma das principais ferramentas utilizadas pelas empresas para o monitoramento de suas infraestruturas de redes e identificação de ataques são os Sistemas de Detecção de Intrusão. No entanto, devido a expansão do volume de dados que trafegam nas redes de computadores, estes sistemas estão tornando-se limitados. Em contrapartida, pesquisadores têm explorado a construção de Internet Early Warning Systems para o monitoramento de atividades maliciosas na Internet. Este trabalho propõe a modelagem de dados de uma base de conhecimento para Internet Early Warning Systems. O modelo representa os dados de diferentes aspectos da rede com foco em eventos relacionados a detecção de intrusão, tais como: dados de alertas gerados por sistemas de detecção de intrusão, informações sobre medidas de respostas, estatísticas do tráfego e assinaturas de ataques já conhecidos. Um estudo de caso em uma infraestrutura de rede real demonstra a aplicabilidade do modelo de dados da base de conhecimento e permite identificar as vantagens de sua utilização. Além disso, os dados armazenados na base de conhecimento potencializam a construção de uma consciência situacional do ambiente monitorado, direcionando as atividades da equipe de segurança e auxiliando no processo de decisão de respostas a ataques em potencial.

Enhanced vision and object detection could be useful in the aviation domain in situations of bad weather or cluttered environments. In particular, enhanced vision and object detection could improve situational awareness and aid the pilot in environment interpretation and detection of hazardous objects. The fundamental concept of object detection is to interpret what objects are present in an image with the aid of a prediction model or other feature extraction techniques. Constructing a comprehensive data set that can describe the operational environment and be robust for weather and lighting conditions is vital if the object detector is to be utilised in the avionics domain. Evaluating the accuracy and robustness of the constructed data set is crucial. Since erroneous detection, referring to the object detection algorithm failing to detect a potentially hazardous object or falsely detecting an object, is a major safety issue. Bayesian uncertainty estimations are evaluated to examine if they can be utilised to detect miss-classifications, enabling the use of a Bayesian Neural Network with the object detector to identify an erroneous detection. The object detector Faster RCNN with ResNet-50-FPN was utilised using the development framework Detectron2; the accuracy of the object detection algorithm was evaluated based on obtained MS-COCO metrics. The setup achieved a 50.327 % AP@[IoU=.5:.95] score. With an 18.1 % decrease when exposed to weather and lighting conditions. By inducing artificial artefacts and augmentations of luminance, motion, and weather to the images of the training set, the AP@[IoU=.5:.95] score increased by 15.6 %. The inducement improved the robustness necessary to maintain the accuracy when exposed to variations of environmental conditions, which resulted in just a 2.6 % decrease from the initial accuracy. To fully conclude that the augmentations provide the necessary robustness for variations in environmental conditions, the model needs to be subjected to actual image representations of the operational environment with different weather and lighting phenomena. Bayesian uncertainty estimations show great promise in providing additional information to interpret objects in the operational environment correctly. Further research is needed to conclude if uncertainty estimations can provide necessary information to detect erroneous predictions.

<div>Real-time social media data can provide useful information on evolving events and situations. In addition, various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. Informed by discussions with first responders and government officials, we focus on two major barriers limiting the widespread adoption of social media for situational awareness: the lack of geotagged data and the deluge of irrelevant information during events. Geotags are naturally useful, as they indicate the location of origin and provide geographic context. Only a small portion of social media is geotagged, however, limiting its practical use for situational awareness. The deluge of irrelevant data provides equal difficulties, impeding the effective identification of semantically relevant information. Existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time, limiting situational awareness. In this work, we first adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We then present a novel interactive learning framework in which users rapidly identify relevant data by iteratively correcting the relevance classification of tweets in real-time. We integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system adapted for real-time situational awareness.</div>