National security and social media monitoring: a presentation of the emotive and related systems

Today social media streams, such as Twitter, represent vast amounts of 'real-time' daily streaming data. Topics on these streams cover every range of human communication, ranging from banal banter, to serious reactions to events and information sharing regarding any imaginable product, item or entity. It has now become the norm for publicly visible events to break news over social media streams first, and only then followed by main stream media picking up on the news. It has been suggested in literature that social-media are a valid, valuable and effective real-time tool for gauging public subjective reactions to events and entities. Due to the vast big-data that is generated on a daily basis on social media streams, monitoring and gauging public reactions has to be automated and most of all scalable - i.e. human, expert monitoring is generally unfeasible. In this paper the EMOTIVE system, a project funded jointly by the DSTL (Defence Science and Technology Laboratory) and EPSRC, which focuses on monitoring fine-grained emotional responses relating to events of national security importance, will be presented. Similar systems for monitoring national security events are also presented and the primary traits of such national security social media monitoring systems are introduced and discussed

Enhancing fan experience during live sports broadcasts through second screen applications

When sports fans attend live sports events, they usually engage in social experiences with friends, family members and other fans at the venue sharing the same affiliation. However, fans watching the same event through a live television broadcast end up not feeling so emotionally connected with the athletes and other fans as they would if they were watching it live, together with thousands of other fans. With this in mind, we seek to create mobile applications that deliver engaging social experiences involving remote fans watching live broadcasted sports events. Taking into account the growing use of mobile devices when watching TV broadcasts, these mobile applications explore the second screen concept, which allows users to interact with content that complements the TV broadcast. Within this context, we present a set of second screen application prototypes developed to test our concepts, the corresponding user studies and results, as well as suggestions on how to apply the prototypes’ concepts not only in different sports, but also during TV shows and electronic sports. Finally, we also present the challenges we faced and the guidelines we followed during the development and evaluation phases, which may give a considerable contribution to the development of future second screen applications for live broadcasted events

Affect Conveying Instant Messaging

Instant messaging applications cannot convey non-verbal communication through text-based messages. That can lead to an unpleasant misunderstanding between dyads when the discussion is held on a computer or smartphone. This study aims to determine if the affect conveying instant messaging applications has any usage within users who are daily users of instant messaging applications. Furthermore, does the application benefit the test users in the real variant group compared with the control group users? The tests were conducted with an instant messaging prototype application developed just for this experiment. To test the affect conveying instant messaging prototype, we gathered a test group, which were randomly divided into two different groups, those that tested the correct version and the control group. Both test groups tested the same application but with different affect conveying module or variant. The real group tested the real variant, and the random or control group tested the variant, which randomly chooses the conveyed affect or emotion. The affect is conveyed with emojis in both of the variants. After the tests were done, testers had to answer nine different interview questions. Finally, for three interview questions, testers give a grade on how satisfied they were with that particular function. The grades were analyzed with descriptive statistical methods, and the verbal interview answers were analyzed by gathering recurring themes across the answers. The study results show that the real variant of the affect conveying instant messaging prototype performed better overall than the random variant. Test users also think that the prototype and its affect conveying functionality was fun. However, they did not see any exact situations where they would use the affect conveying functionality in an instant messaging application. Testers thought that they would use it with friends and family rather than in professional life. Generally, the way emotions were conveyed in the prototype was well-received. Test users did not see any significant issues in it or if the same functionality would be used in applications like games.Pikaviestintäsovellukset eivät oletusarvoisesti välitä sanatonta viestintää tekstimuodossa olevien viestien mukana. Se on omiaan aiheuttamaan epämiellyttäviä väärinymmärryksiä keskusteluosapuolien välillä, kun keskustelu käydään tietokoneiden tai älypuhelimien avulla. Tämän tutkielman tavoitteena on selvittää, onko tunteita automaattisesti välittävällä pikaviestintäsovelluksella käyttöä niiden käyttäjien keskuudessa, jotka käyttävät pikaviestintäsovelluksia päivittäin omassa elämässä. Lisäksi selvitämme, tuoko testisovellus hyötyä sovelluksen oikeaa versiota testanneille verrattuna kontrolliryhmään. Testit ovat suoritettu sille erikseen luodulla pikaviestintäsovelluksella. Testatakseen testiin erikseen tehtyä pikaviestintäsovellus prototyyppiä, kokosimme testiryhmän, joka satunnaisesti jaettiin kahteen eri ryhmään, oikeaa versiota testanneisiin ja kontrolliryhmään. Molemmat testiryhmät testasivat samaa sovellusta, mutta joissa olivat kuitenkin eri affektiivinen moduuli. Todellisen testin suorittaneet testasivat niin sanottua oikeaa versiota, jossa tunteiden välitys toimi kuten sen oli tarkoitus toimia ja kontrolliryhmä testasi sovelluksen versiota, jossa tunteet, joita välitettiin, olivat sovelluksen satunnaisesti valitsemia tunteita, eivätkä oikeasti sovelluksen analysoimia tunteita käyttäjästä. Sovellusprototyypin testin jälkeen testaajat vastasivat yhdeksään haastattelukysymykseen, joista kolme kysymystä olivat sellaisia, joihin testaajan tuli antaa numeerinen arvosana siten kuinka tyytyväisiä he olivat kyseiseen toiminnallisuuteen. Numeeriset arvosanat analysoitiin kuvailevia tilastointimenetelmiä käyttäen ja sanalliset haastatteluvastaukset analysointiin teemoittamalla haastattelut ja löytämällä sieltä toistuvia teemoja. Tutkimustulokset osoittavat, että pikaviestintäsovellus prototyypin niin sanottu oikea versio toimi yleisesti paremmin mitä satunnaistettu tunteiden välitys. Testaajat myös ajattelivat, että prototyyppi ja sen automaattinen tunteidenvälitys oli hauskaa ja mielekästä seurattavaa keskustelun aikana. Testaajat eivät kuitenkaan haastattelun aikana löytäneet mitään tarkkaa reaalielämän käyttöä sovellukselle. Testaajat käyttäisivät sovellusta mieluimmin läheisten ja jo tuntemien ihmisten kanssa kuin esimerkiksi työelämässä. Testaajat yleisesti pitivät pikaviestintäsovelluksen tunteiden välityksestä, ja he eivät nähneet mitään suurta ongelmaa sen toiminnan kanssa tai että samanlainen toiminto olisi vapaaehtoisesti käytössä joissain muissa sovelluksissa, kuten videopeleissä

Combining mouse and keyboard events with higher level desktop actions to detect mild cognitive impairment

We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users? daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants? computers

National Security and Social Media Monitoring: A Presentation of the EMOTIVE and Related Systems

Today social media streams, such as Twitter, represent vast amounts of 'real-time' daily streaming data. Topics on these streams cover every range of human communication, ranging from banal banter, to serious reactions to events and information sharing regarding any imaginable product, item or entity. It has now become the norm for publicly visible events to break news over social media streams first, and only then followed by main stream media picking up on the news. It has been suggested in literature that social-media are a valid, valuable and effective real-time tool for gauging public subjective reactions to events and entities. Due to the vast big-data that is generated on a daily basis on social media streams, monitoring and gauging public reactions has to be automated and most of all scalable - i.e. human, expert monitoring is generally unfeasible. In this paper the EMOTIVE system, a project funded jointly by the DSTL (Defence Science and Technology Laboratory) and EPSRC, which focuses on monitoring fine-grained emotional responses relating to events of national security importance, will be presented. Similar systems for monitoring national security events are also presented and the primary traits of such national security social media monitoring systems are introduced and discussed

Social media analytics with applications in disaster management and COVID-19 events

Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data --Abstract, page iv

YODA – Your Only Design Assistant

Converting user interface designs created by graphic designers into computer code is a typical job of a front end engineer in order to develop functional web and mobile applications. This conversion process can often be extremely tedious, slow and prone to human error. In this project, deep learning based object detection along with optical character recognition is used to generate platform ready prototypes directly from design sketches. Also, a new design language is introduced to facilitate expressive prototyping and allowing the creation of more expressive and functional designs. It is observed that the AI powered application along with modern web technology can significantly help streamline and automate the overall product development routine and eliminate hurdles from the product development process

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200