Fairness, Copyright, and Video Games: Hate the Game, Not the Player

Creative communities often rely on social norms to regulate the production of creative content. Yet while an emerging body of literature has focused on isolated accounts of social norms operating in discrete, small-scale creative industries, no research to date has explored the social norms that pervade the world’s largest content microcosm—the sprawling video game community. Now a veritable global phenomenon, the video game industry has recently grown to eclipse the music and motion picture industries. But despite its meteoric rise, the video game industry has provoked little attention from copyright scholars. This Article is the first to explore the shifting role of copyright law in the gaming community, where game developers are increasingly using a complex amalgam of legal and nonlegal tools to regulate creative output. Based on an in-depth analysis of the extralegal norms that govern creative content in the video game industry, this Article distills a richly detailed account of the relationship between video game creators and consumers. It maps the intricate web of interests underpinning the relationship between game developers and consumers; identifies a rich cadre of fairness-driven social norms that permeate the gaming community; and considers the implications of these findings for copyright law. The Article ultimately concludes that strong copyright protection is largely (though not entirely) inessential in areas where norms of fairness drive the production of creative content

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

Recommendations for designing conversational companion robots with older adults through foundation models

Companion robots are aimed to mitigate loneliness and social isolation among older adults by providing social and emotional support in their everyday lives. However, older adults’ expectations of conversational companionship might substantially differ from what current technologies can achieve, as well as from other age groups like young adults. Thus, it is crucial to involve older adults in the development of conversational companion robots to ensure that these devices align with their unique expectations and experiences. The recent advancement in foundation models, such as large language models, has taken a significant stride toward fulfilling those expectations, in contrast to the prior literature that relied on humans controlling robots (i.e., Wizard of Oz) or limited rule-based architectures that are not feasible to apply in the daily lives of older adults. Consequently, we conducted a participatory design (co-design) study with 28 older adults, demonstrating a companion robot using a large language model (LLM), and design scenarios that represent situations from everyday life. The thematic analysis of the discussions around these scenarios shows that older adults expect a conversational companion robot to engage in conversation actively in isolation and passively in social settings, remember previous conversations and personalize, protect privacy and provide control over learned data, give information and daily reminders, foster social skills and connections, and express empathy and emotions. Based on these findings, this article provides actionable recommendations for designing conversational companion robots for older adults with foundation models, such as LLMs and vision-language models, which can also be applied to conversational robots in other domains

Neural approaches to dialog modeling

Cette thèse par article se compose de quatre articles qui contribuent au domaine de l’apprentissage profond, en particulier dans la compréhension et l’apprentissage des ap- proches neuronales des systèmes de dialogue. Le premier article fait un pas vers la compréhension si les architectures de dialogue neuronal couramment utilisées capturent efficacement les informations présentes dans l’historique des conversations. Grâce à une série d’expériences de perturbation sur des ensembles de données de dialogue populaires, nous constatons que les architectures de dialogue neuronal couramment utilisées comme les modèles seq2seq récurrents et basés sur des transformateurs sont rarement sensibles à la plupart des perturbations du contexte d’entrée telles que les énoncés manquants ou réorganisés, les mots mélangés, etc. Le deuxième article propose d’améliorer la qualité de génération de réponse dans les systèmes de dialogue de domaine ouvert en modélisant conjointement les énoncés avec les attributs de dialogue de chaque énoncé. Les attributs de dialogue d’un énoncé se réfèrent à des caractéristiques ou des aspects discrets associés à un énoncé comme les actes de dialogue, le sentiment, l’émotion, l’identité du locuteur, la personnalité du locuteur, etc. Le troisième article présente un moyen simple et économique de collecter des ensembles de données à grande échelle pour modéliser des systèmes de dialogue orientés tâche. Cette approche évite l’exigence d’un schéma d’annotation d’arguments complexes. La version initiale de l’ensemble de données comprend 13 215 dialogues basés sur des tâches comprenant six domaines et environ 8 000 entités nommées uniques, presque 8 fois plus que l’ensemble de données MultiWOZ populaire.This thesis by article consists of four articles which contribute to the field of deep learning, specifically in understanding and learning neural approaches to dialog systems. The first article takes a step towards understanding if commonly used neural dialog architectures effectively capture the information present in the conversation history. Through a series of perturbation experiments on popular dialog datasets, wefindthatcommonly used neural dialog architectures like recurrent and transformer-based seq2seq models are rarely sensitive to most input context perturbations such as missing or reordering utterances, shuffling words, etc. The second article introduces a simple and cost-effective way to collect large scale datasets for modeling task-oriented dialog systems. This approach avoids the requirement of a com-plex argument annotation schema. The initial release of the dataset includes 13,215 task-based dialogs comprising six domains and around 8k unique named entities, almost 8 times more than the popular MultiWOZ dataset. The third article proposes to improve response generation quality in open domain dialog systems by jointly modeling the utterances with the dialog attributes of each utterance. Dialog attributes of an utterance refer to discrete features or aspects associated with an utterance like dialog-acts, sentiment, emotion, speaker identity, speaker personality, etc. The final article introduces an embedding-free method to compute word representations on-the-fly. This approach significantly reduces the memory footprint which facilitates de-ployment in on-device (memory constraints) devices. Apart from being independent of the vocabulary size, we find this approach to be inherently resilient to common misspellings

Empathic voice assistants: Enhancing consumer responses in voice commerce

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions

A balance of benefits and burdens: academia in a digital copyright context

Most academics can agree that intellectual property warrants legal protection, especially in an educational context where their own publications are often traded for promotion and tenure. However, academics would also agree that they require a reliable exemption allowing them to use copyright protected work for educational purposes. Copyright law has historically satisfied both these needs by protecting academic publications from unauthorized use, and by providing an educational exemption that allows educators access to copyright protected work in their classes without first gaining permission or paying a royalty.;In attempting to update current copyright law to match technological advances and to harmonize with international copyright law, the United States Congress recently passed a body of legislation that weakens the educational exemption and impedes educational access to copyright protected work. Academic organizations protested the unfairness of this legislation. The reasons they cite relate directly to the erosion of the educational exemption, impeded access to creative works for teaching purposes, and a diminishing cultural commons. They share the view that recent legislation has ignored the educational stakeholder, insofar as this legislation seems to have increased burdens for classroom applications, while the benefits of copyright appear to remain few. If what the aformentioned organizations charge is true, then the balance of burdens and benefits has shifted for educators and students in the classroom environment. This shift in balance undermines Article 1, Section 8 of the United States Constitution, which implies that the reason for establishing copyright law is to benefit all stakeholders.;This work focuses on recent changes in copyright protection of digital intellectual property. To understand, more specifically, how digital copyright legislation burdens academic authors and audiences, this dissertation analyzes the 1998 Digital Millennium Copyright Act (DMCA) and selected text representing academic positions on recent digital copyright legislation

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences

Copyright Trust

Collaborative production of expressive content accounts for an ever growing number of copyrighted works. Indeed, in the age of content sharing and peer production, collaborative efforts may have become the paradigmatic form of authorship. Surprisingly, though, copyright law continues to view the single author model as the dominant model of peer production. Copyright law’s approach to authorship is currently based on a hodgepodge of rigid doctrines that conflate ownership and control. The result is a binary system under which a contributor to a collaborative work is either recognized as an author with a full control and management rights or a person who is deemed a non-author with no rights whatsoever. We argue that the doctrines and judicial precedents that govern the all-important issue of authorship are out of step with authorial reality. And the cost to the copyright system is enormous. As we show in this Article the misalignment between copyright law and authorial reality is both inefficient and unfair: it harms incentives to create, it denies reward to contributors, it leads to under-utilization of content and it creates excessive litigation. To remedy this state of affairs, we propose a new legal construct, which we call copyright trust. In designing this new tool we draw on insights from property and corporate theory — two areas of research that have long dealt with the challenges of collaborative enterprises and co-ownerships. The doctrine of copyright trust is predicated on the insight of decoupling ownership from control. Essentially, it would empower courts to appoint one contributor as an owner-trustee with full managerial rights and the exclusive power to control the use of the work, while recognizing all other contributors as owner-beneficiaries, who would be entitled to receive a certain percentage of the proceeds from the work. Copyright trusts would enable courts to retain the benefits of having a single owner without sacrificing the rightful claims of other contributors who would be entitled to receive a just reward for their efforts. The proposed doctrine of copyright trust would supplement, not replace, current doctrine. It is designed to enrich the menu of options available to courts in deciding authorship issues. The addition of our solution to the judicial toolbox would not only make it richer, but would also infuse current law with much needed flexibility that is sorely missing from other authorship doctrines