Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we requirea quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature  selection method in a hybrid way. Vehicle logo images are first characterized by histograms of oriented gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

Second-Person Surveillance: Politics of User Implication in Digital Documentaries

This dissertation analyzes digital documentaries that utilize second-person address and roleplay to make users feel implicated in contemporary refugee crises, mass incarceration in the U.S., and state and corporate surveillances. Digital documentaries are seemingly more interactive and participatory than linear film and video documentary as they are comprised of a variety of auditory, visual, and written media, utilize networked technologies, and turn the documentary audience into a documentary user. I draw on scholarship from documentary, game, new media, and surveillance studies to analyze how second-person address in digital documentaries is configured through user positioning and direct address within the works themselves, in how organizations and creators frame their productions, and in how users and players respond in reviews, discussion forums, and Let’s Plays. I build on Michael Rothberg’s theorization of the implicated subject to explore how these digital documentaries bring the user into complicated relationality with national and international crises. Visually and experientially implying that users bear responsibility to the subjects and subject matter, these works can, on the one hand, replicate modes of liberal empathy for suffering, distant “others” and, on the other, simulate one’s own surveillant modes of observation or behavior to mirror it back to users and open up one’s offline thoughts and actions as a site of critique. This dissertation charts how second-person address shapes and limits the political potentialities of documentary projects and connects them to a lineage of direct address from educational and propaganda films, museum exhibits, and serious games. By centralizing the user’s individual experience, the interventions that second-person digital documentaries can make into social discourse change from public, institution-based education to more privatized forms of sentimental education geared toward personal edification and self-realization. Unless tied to larger initiatives or movements, I argue that digital documentaries reaffirm a neoliberal politics of individual self-regulation and governance instead of public education or collective, social intervention. Chapter one focuses on 360-degree virtual reality (VR) documentaries that utilize the feeling of presence to position users as if among refugees and as witnesses to refugee experiences in camps outside of Europe and various dwellings in European cities. My analysis of Clouds Over Sidra (Gabo Arora and Chris Milk 2015) and The Displaced (Imraan Ismail and Ben C. Solomon 2015) shows how these VR documentaries utilize observational realism to make believable and immersive their representations of already empathetic refugees. The empathetic refugee is often young, vulnerable, depoliticized and dehistoricized and is a well-known trope in other forms of humanitarian media that continues into VR documentaries. Forced to Flee (Zahra Rasool 2017), I am Rohingya (Zahra Rasool 2017), So Leben Flüchtlinge in Berlin (Berliner Morgenpost 2017), and Limbo: A Virtual Experience of Waiting for Asylum (Shehani Fernando 2017) disrupt easy immersions into realistic-looking VR experiences of stereotyped representations and user identifications and, instead, can reflect back the user’s political inaction and surveillant modes of looking. Chapter two analyzes web- and social media messenger-based documentaries that position users as outsiders to U.S. mass incarceration. Users are noir-style co-investigators into the crime of the prison-industrial complex in Fremont County, Colorado in Prison Valley: The Prison Industry (David Dufresne and Philippe Brault 2009) and co-riders on a bus transporting prison inmates’ loved ones for visitations to correctional facilities in Upstate New York in A Temporary Contact (Nirit Peled and Sara Kolster 2017). Both projects construct an experience of carceral constraint for users to reinscribe seeming “outside” places, people, and experiences as within the continuation of the racialized and classed politics of state control through mass incarceration. These projects utilize interfaces that create a tension between replicating an exploitative hierarchy between non-incarcerated users and those subject to mass incarceration while also de-immersing users in these experiences to mirror back the user’s supposed distance from this mode of state regulation. Chapter three investigates a type of digital game I term dataveillance simulation games, which position users as surveillance agents in ambiguously dystopian nation-states and force users to use their own critical thinking and judgment to construct the criminality of state-sanctioned surveillance targets. Project Perfect Citizen (Bad Cop Studios 2016), Orwell: Keeping an Eye on You (Osmotic Studios 2016), and Papers, Please (Lucas Pope 2013) all create a dual empathy: players empathize with bureaucratic surveillance agents while empathizing with surveillance targets whose emails, text messages, documents, and social media profiles reveal them to be “normal” people. I argue that while these games show criminality to be a construct, they also utilize a racialized fear of the loss of one’s individual privacy to make players feel like they too could be surveillance targets. Chapter four examines personalized digital documentaries that turn users and their data into the subject matter. Do Not Track (Brett Gaylor 2015), A Week with Wanda (Joe Derry Hall 2019), Stealing Ur Feelings (Noah Levenson 2019), Alfred Premium (Joël Ronez, Pierre Corbinais, and Émilie F. Grenier 2019), How They Watch You (Nick Briz 2021), and Fairly Intelligent™ (A.M. Darke 2021) track, monitor, and confront users with their own online behavior to reflect back a corporate surveillance that collects, analyzes, and exploits user data for profit. These digital documentaries utilize emotional fear- and humor-based appeals to persuade users that these technologies are controlling them, shaping their desires and needs, and dehumanizing them through algorithmic surveillance

Young people’s experiences of abuse and conflict within their intimate partner relationships

Research shows Adolescent Intimate Partner Abuse (AIPA) is a widespread problem with potential to impact significantly upon wellbeing. This thesis aimed to further the existing body of knowledge from a psychologically orientated perspective. Section one presents a meta-synthesis of qualitative studies exploring young people’s experiences of the intersection between AIPA and new technologies. This followed the seven-step meta-ethnographic approach of Noblit and Hare (1988). Twelve eligible papers were identified that yielded three themes relating to technology as a platform for creating jealousy, and enabling the subsequent monitoring, and control of partners. Production of an overarching theme referring to technology and protection of ‘self-interests’ was enabled. Findings suggest technology represents a motivator and means for carrying out AIPA and that this occurs against a backdrop of adolescent development, including acquisition of gendered roles. Section two presents a descriptive study, employing semi-structured interviews, that set out to explore young people’s experiences of psychological wellbeing in relation to AIPA. Participants were sixteen young people, aged 13 to 17 years, who had encountered self- defined ‘difficulties’ within their relationships. Data gathered were analysed using thematic analysis, resulting in the emergence of three themes that, when viewed as a whole, suggested events surrounding abusive acts cause considerable negative emotional impact of an anxious nature, and technology is regarded as integral to how problems manifest, contributing a significant burden for mental health. Section three is an extended critique of the research paper, considering the role of personal positionality in shaping the research process and concluding that reflexivity in this area is of particular relevance to those working within caring professions and undertaking research. Taken together, the thesis offers insights relevant to practice, policy, and future research, including furthering of theory. Clinical psychologists are well positioned to implement and further these findings within their practice and wider activities

Digital writing technologies in higher education : theory, research, and practice

This open access book serves as a comprehensive guide to digital writing technology, featuring contributions from over 20 renowned researchers from various disciplines around the world. The book is designed to provide a state-of-the-art synthesis of the developments in digital writing in higher education, making it an essential resource for anyone interested in this rapidly evolving field. In the first part of the book, the authors offer an overview of the impact that digitalization has had on writing, covering more than 25 key technological innovations and their implications for writing practices and pedagogical uses. Drawing on these chapters, the second part of the book explores the theoretical underpinnings of digital writing technology such as writing and learning, writing quality, formulation support, writing and thinking, and writing processes. The authors provide insightful analysis on the impact of these developments and offer valuable insights into the future of writing. Overall, this book provides a cohesive and consistent theoretical view of the new realities of digital writing, complementing existing literature on the digitalization of writing. It is an essential resource for scholars, educators, and practitioners interested in the intersection of technology and writing

Security and Privacy Threats on Mobile Devices through Side-Channels Analysis

In recent years, mobile devices (such as smartphones and tablets) have become essential tools in everyday life for billions of people all around the world. Users continuously carry such devices with them and use them for daily communication activities and social network interactions. Hence, such devices contain a huge amount of private and sensitive information. For this reason, mobile devices become popular targets of attacks. In most attack settings, the adversary aims to take local or remote control of a device to access user sensitive information. However, such violations are not easy to carry out since they need to leverage a vulnerability of the system or a careless user (i.e., install a malware app from an unreliable source). A different approach that does not have these shortcomings is the side-channels analysis. In fact, side-channels are physical phenomenon that can be measured from both inside or outside a device. They are mostly due to the user interaction with a mobile device, but also to the context in which the device is used, hence they can reveal sensitive user information such as identity and habits, environment, and operating system itself. Hence, this approach consists of inferring private information that is leaked by a mobile device through a side-channel. Besides, side-channel information is also extremely valuable to enforce security mechanisms such as user authentication, intrusion and information leaks detection. This dissertation investigates novel security and privacy challenges on the analysis of side-channels of mobile devices. This thesis is composed of three parts, each focused on a different side-channel: (i) the usage of network traffic analysis to infer user private information; (ii) the energy consumption of mobile devices during battery recharge as a way to identify a user and as a covert channel to exfiltrate data; and (iii) the possible security application of data collected from built-in sensors in mobile devices to authenticate the user and to evade sandbox detection by malware. In the first part of this dissertation, we consider an adversary who is able to eavesdrop the network traffic of the device on the network side (e.g., controlling a WiFi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. Our work proves that it is possible to leverage machine learning techniques to identify user activity and apps installed on mobile devices analyzing the encrypted network traffic they produce. Such insights are becoming a very attractive data gathering technique for adversaries, network administrators, investigators and marketing agencies. In the second part of this thesis, we investigate the analysis of electric energy consumption. In this case, an adversary is able to measure with a power monitor the amount of energy supplied to a mobile device. In fact, we observed that the usage of mobile device resources (e.g., CPU, network capabilities) directly impacts the amount of energy retrieved from the supplier, i.e., USB port for smartphones, wall-socket for laptops. Leveraging energy traces, we are able to recognize a specific laptop user among a group and detect intruders (i.e., user not belonging to the group). Moreover, we show the feasibility of a covert channel to exfiltrate user data which relies on temporized energy consumption bursts. In the last part of this dissertation, we present a side-channel that can be measured within the mobile device itself. Such channel consists of data collected from the sensors a mobile device is equipped with (e.g., accelerometer, gyroscope). First, we present DELTA, a novel tool that collects data from such sensors, and logs user and operating system events. Then, we develop MIRAGE, a framework that relies on sensors data to enhance sandboxes against malware analysis evasion