Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being

Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each: limited use, business use, power use, and personality- & externally induced problematic use. We provide evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.Comment: 10 pages, 6 figures, conference pape

Affective feedback: an investigation into the role of emotions in the information seeking process

User feedback is considered to be a critical element in the information seeking process, especially in relation to relevance assessment. Current feedback techniques determine content relevance with respect to the cognitive and situational levels of interaction that occurs between the user and the retrieval system. However, apart from real-life problems and information objects, users interact with intentions, motivations and feelings, which can be seen as critical aspects of cognition and decision-making. The study presented in this paper serves as a starting point to the exploration of the role of emotions in the information seeking process. Results show that the latter not only interweave with different physiological, psychological and cognitive processes, but also form distinctive patterns, according to specific task, and according to specific user

Ranking News-Quality Multimedia

News editors need to find the photos that best illustrate a news piece and fulfill news-media quality standards, while being pressed to also find the most recent photos of live events. Recently, it became common to use social-media content in the context of news media for its unique value in terms of immediacy and quality. Consequently, the amount of images to be considered and filtered through is now too much to be handled by a person. To aid the news editor in this process, we propose a framework designed to deliver high-quality, news-press type photos to the user. The framework, composed of two parts, is based on a ranking algorithm tuned to rank professional media highly and a visual SPAM detection module designed to filter-out low-quality media. The core ranking algorithm is leveraged by aesthetic, social and deep-learning semantic features. Evaluation showed that the proposed framework is effective at finding high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and a classification precision of 70%.Comment: To appear in ICMR'1

Affect-based information retrieval

One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap

Essays in the Economics of Aging

This thesis is made up of three main essays that aim to develop a deeper understanding of issues involving the public insurance programs for the elderly, and the risks they insure against. In the first essay (Chapter 2), using data from the Health and Retirement Study linked to administrative Medicare and Medicaid records, along with the Medical Expenditure Panel Survey, we estimate the stochastic process for total and out-of-pocket medical spending. By focussing on dynamics, we consider not only the risk of catastrophic expenses in a single year, but also the risk of moderate but persistent expenses that accumulate into a catastrophic lifetime cost. We also assess the reduction in out-of-pocket medical spending provided by public insurance schemes such as Medicare or Medicaid. We find that although Medicare and Medicaid pay the majority of medical expenses, households at age 65 will on average incur $59,000 in out-of-pocket costs with 10 percent of households incurring more than$121,000 in out-of-pocket expenses over their remaining lives. In the second essay (Chapter 3), we compare dementia prevalence and how it varies by socioeconomic status (SES) in the United States and England. We compare between country differences in age-gender standardized dementia prevalence, across the SES gradient. Dementia prevalence was estimated in each country using an algorithm based on an identical battery of demographic, cognitive, and functional measures. Dementia prevalence is higher among the disadvantaged in both countries, with the United States being more unequal according to four measures of SES. Once past health factors and education were controlled for, most of the within country inequalities disappeared; however, the cross-country difference in prevalence for those in the lowest income decile remained disproportionately high. This provides evidence that disadvantage in the United States is a disproportionately high risk factor for dementia. In the final essay (Chapter 4), we assess the optimal structure the U.S. Social Security system, taking into account the current system’s unfunded liabilities, transition dynamics and political feasibility constraints. We base the assessment on an estimated overlapping generations general equilibrium model that features both aggregate and idiosyncratic uncertainty. The quantitative analysis establishes that although transition costs greatly restrict the U.S. government’s ability to move away from the current Social Security system, ignoring the political feasibility constraints allows the government to increase welfare by transitioning to a more progressive and less costly to operate system. However, taking into account the political feasibility constraints overturns this result, as no reform is simultaneously welfare increasing and politically feasible

Self-Supervised Reinforcement Learning for Recommender Systems

In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clicks, purchases etc. The current state-of-the-art supervised approaches fail to model them appropriately. Casting sequential recommendation task as a reinforcement learning (RL) problem is a promising direction. A major component of RL approaches is to train the agent through interactions with the environment. However, it is often problematic to train a recommender in an on-line fashion due to the requirement to expose users to irrelevant recommendations. As a result, learning the policy from logged implicit feedback is of vital importance, which is challenging due to the pure off-policy setting and lack of negative rewards (feedback). In this paper, we propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. The RL part acts as a regularizer to drive the supervised layer focusing on specific rewards(e.g., recommending items which may lead to purchases rather than clicks) while the self-supervised layer with cross-entropy loss provides strong gradient signals for parameter updates. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC). We integrate the proposed frameworks with four state-of-the-art recommendation models. Experimental results on two real-world datasets demonstrate the effectiveness of our approach.Comment: SIGIR202

Εποπτικός έλεγχος μίας γραμμής συναρμολόγησης αναφοράς για την οροφή αυτοκινήτων με παρουσία σφαλμάτων

Στην παρούσα διπλωματική εργασία θα παρουσιαστεί το σύστημα της γραμμής συναρμολόγησης της οροφής του πλαισίου (σκελετού) ενός αυτοκινήτου. Θα παρουσιαστούν τα μαθηματικά μοντέλα των επιμέρους υποσυστημάτων του συστήματος συναρμολόγησης, δηλαδή ο μεταφορέας, ο τροφοδότης του πλαισίου, ο τροφοδότης της οροφής, η πρέσα και ο εκφορτωτής. Θα αναπτυχθούν τα μαθηματικά μοντέλα των επιμέρους υποσυστημάτων του συστήματος συναρμολόγησης με την παρουσία σφαλμάτων ενεργοποιητών/αισθητήρων. Θα αναπτυχθεί το συνολικό μοντέλο του συστήματος με παρουσία σφαλμάτων. Θα παρουσιαστούν οι επιθυμητές συμπεριφορές σε μορφή κανόνων και σε μορφή επιθυμητών γλωσσών. Θα αποδειχτεί η ελεγξιμότητα των επιθυμητών γλωσσών ως προς το συνολικό αυτόματο του συστήματος. Θα σχεδιαστεί ένα σύνολο δυναμικών εποπτών με βάση τις επιθυμητές γλώσσες και θα σχεδιαστεί μία δομοστοιχειωτή αρχιτεκτονική εποπτικού ελέγχου με ανοχή σε σφάλματα.In this diploma thesis the system of an assembly line of the roof (frame) of a car will be presented. The mathematical models of the individual subsystems of the assembly system will be presented, i.e., the transporter, the chassis feeder, the roof feeder, the press and the unloader. Mathematical models of the individual subsystems of the assembly system will be developed in the presence of actuator / sensor faults. The overall system model with faults will be developed. The desired behaviors will be presented in the form of rules and in the form of desired languages. The controllability of the desired languages regarding the overall automaton of the system will be proved. A set of dynamic supervisors will be designed based on the desired languages and a modular supervisory control architecture with fault tolerance will be designed

On the Role of Engagement in Information Seeking Contexts: From Research to Implementation

This workshop will provide a forum for researchers, practitioners and developers interested in user engagement and emotion in the context of information systems design and use. Specifically, we seek to address questions such as “How do we ensure that the measurement of subjective user experiences is robust and scalable?”, “How do we design for engaging and emotionally compelling experiences?”, and “How do we prevent disengagement?” The ability to answer these questions relies upon: a solid conceptual understanding of subjective experiences; robust, scalable approaches to measuring engagement; and the ability to utilize this knowledge in information systems design. This three-part workshop will include: talks by the organizers to ground the workshop’s themes; position paper presentations and design exemplars from attendees, and an interactive session focused on design scenarios and prototyping. The intersection of emotion and engagement with measurement and design in information seeking contexts is a timely issue for the iSchool community.ye

A Simple Convolutional Generative Network for Next Item Recommendation

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback