Spotify: The Addiction

The redesign of the Swedish digital music streaming service called Spotify gives it the potential to play an even larger role in the world by becoming its own social media platform. Even with Spotify\u27s successes, Spotify currently still lacks the best social experience because of the limited ability to share music within the mobile app. A redesign of the Spotify app offers interaction opportunities between users to make it a solid form of social media and a stronger competitor to the rising TikTok app. Combined with user experience criteria and new visualizations, I was able to build a prototype that enables friends and followers to connect with each other in the Spotify app. With the creation of new pages and social features, this project\u27s redesign of Spotify transformed the way users can connect on the app

A LOOK AT THE CLOUD FROM BOTH SIDES NOW: AN ANALYSIS OF CLOUD MUSIC SERVICE USAGE

ABSTRACT Despite the increasing popularity of cloud-based music services, few studies have examined how users select and utilize these services, how they manage and access their music collections in the cloud, and the issues or challenges they are facing within these services. In this paper, we present findings from an online survey with 198 responses collected from users of commercial cloud music services, exploring their selection criteria, use patterns, perceived limitations, and future predictions. We also investigate differences in these aspects by age and gender. Our results elucidate previously under-studied changes in music consumption, music listening behaviors, and music technology adoption. The findings also provide insights into how to improve the future design of cloud-based music services, and have broader implications for any cloudbased services designed for managing and accessing personal media collections

Background Music Dependent Reduction of Aversive Perception and Its Relation to P3 Amplitude Reduction and Increased Heart Rate.

Music is commonly used to modify mood and has attracted attention as a potential therapeutic intervention. Despite the well-recognized effects of music on mood, changes in affective perception due to music remain majorly unknown. Here, we examined if the perception of aversive stimuli could be altered by mood-changing background music. Using subjective scoring data from 17 healthy volunteers, we assessed the effect of relaxing background music (RelaxBGM), busy background music (BusyBGM), or no background music (NoBGM) conditions on response to aversive white noise stimulation. Interestingly, affective response to the white noise was selectively alleviated, and white noise-related P3 component amplitude was reduced in BusyBGM. However, affective responses as well as P3 amplitude to reference pure tone stimuli were similar regardless of background music conditions. Interestingly, heart rate (HR) increased in BusyBGM, whereas no increase in HR was found in similar distress, NoBGM condition. These findings suggest that increase in HR, which happens during BusyBGM exposure, can be a reflecting feature of music that ameliorates the affective response to aversive stimuli, possibly through selective reduction in neurophysiological response

Classifying tor traffic using character analysis

Tor is a privacy-preserving network that enables users to browse the Internet anonymously. Although the prospect of such anonymity is welcomed in many quarters, Tor can also be used for malicious purposes, prompting the need to monitor Tor network connections. Most traffic classification methods depend on flow-based features, due to traffic encryption. However, these features can be less reliable due to issues like asymmetric routing, and processing multiple packets can be time-intensive. In light of Tor’s sophisticated multilayered payload encryption compared with nonTor encryption, our research explored patterns in the encrypted data of both networks, challenging conventional encryption theory which assumes that ciphertexts should not be distinguishable from random strings of equal length. Our novel approach leverages machine learning to differentiate Tor from nonTor traffic using only the encrypted payload. We focused on extracting statistical hex character-based features from their encrypted data. For consistent findings, we drew from two datasets: a public one, which was divided into eight application types for more granular insight and a private one. Both datasets covered Tor and nonTor traffic. We developed a custom Python script called Charcount to extract relevant data and features accurately. To verify our results’ robustness, we utilized both Weka and scikit-learn for classification. In our first line of research, we conducted hex character analysis on the encrypted payloads of both Tor and nonTor traffic using statistical testing. Our investigation revealed a significant differentiation rate between Tor and nonTor traffic of 95.42% for the public dataset and 100% for the private dataset. The second phase of our study aimed to distinguish between Tor and nonTor traffic using machine learning, focusing on encrypted payload features that are independent of length. In our evaluations, the public dataset yielded an average accuracy of 93.56% when classified with the Decision Tree (DT) algorithm in scikit-learn, and 95.65% with the j48 algorithm in Weka. For the private dataset, the accuracies were 95.23% and 97.12%, respectively. Additionally, we found that the combination of WrapperSubsetEval+BestFirst with the J48 classifier both enhanced accuracy and optimized processing efficiency. In conclusion, our study contributes to both demonstrating the distinction between Tor and nonTor traffic and achieving efficient classification of both types of traffic using features derived exclusively from a single encrypted payload packet. This work holds significant implications for cybersecurity and points towards further advancements in the field.Tor is a privacy-preserving network that enables users to browse the Internet anonymously. Although the prospect of such anonymity is welcomed in many quarters, Tor can also be used for malicious purposes, prompting the need to monitor Tor network connections. Most traffic classification methods depend on flow-based features, due to traffic encryption. However, these features can be less reliable due to issues like asymmetric routing, and processing multiple packets can be time-intensive. In light of Tor’s sophisticated multilayered payload encryption compared with nonTor encryption, our research explored patterns in the encrypted data of both networks, challenging conventional encryption theory which assumes that ciphertexts should not be distinguishable from random strings of equal length. Our novel approach leverages machine learning to differentiate Tor from nonTor traffic using only the encrypted payload. We focused on extracting statistical hex character-based features from their encrypted data. For consistent findings, we drew from two datasets: a public one, which was divided into eight application types for more granular insight and a private one. Both datasets covered Tor and nonTor traffic. We developed a custom Python script called Charcount to extract relevant data and features accurately. To verify our results’ robustness, we utilized both Weka and scikit-learn for classification. In our first line of research, we conducted hex character analysis on the encrypted payloads of both Tor and nonTor traffic using statistical testing. Our investigation revealed a significant differentiation rate between Tor and nonTor traffic of 95.42% for the public dataset and 100% for the private dataset. The second phase of our study aimed to distinguish between Tor and nonTor traffic using machine learning, focusing on encrypted payload features that are independent of length. In our evaluations, the public dataset yielded an average accuracy of 93.56% when classified with the Decision Tree (DT) algorithm in scikit-learn, and 95.65% with the j48 algorithm in Weka. For the private dataset, the accuracies were 95.23% and 97.12%, respectively. Additionally, we found that the combination of WrapperSubsetEval+BestFirst with the J48 classifier both enhanced accuracy and optimized processing efficiency. In conclusion, our study contributes to both demonstrating the distinction between Tor and nonTor traffic and achieving efficient classification of both types of traffic using features derived exclusively from a single encrypted payload packet. This work holds significant implications for cybersecurity and points towards further advancements in the field

Impacto das obras musicais lusófonas nas redes sociais

Trabalho de projecto de mestrado, Engenharia Informática (Sistemas de Informação), Universidade de Lisboa, Faculdade de Ciências, 2016Existem dois problemas dai indústria musical na Web: a quantidade de informação nas re- des sociais sobre artistas musicais lusófonos (quenão permite saber quais os artistas mais populares) e a qualidade da mesma(não existem informações suficientes sobre alguns artistas). Nesta tese é apresentado um projeto chamado Lusica que pretende resolver estes problemas. Este projeto teve a colaboração do SAPO Labs e por esta razão, o principal objetivo foi tornar o Lusica um produto SAPO Labs. O processo de desenvolvimento do Lusica foi dividido em duas Fases. Na Primeira Fase foram recolhidas as informações sobre artistas lusófonos e os respetivos tweets. Desta informação foi feito um mapeamento entre as músicas dos artistas e os seus tweets de forma a construir um historial de popularidade. A esta Fase foram realizados testes intermédios de usabilidade e de segurança por uma equipa especializada do SAPO Labs com o objetivo de lançar uma versão intermédia. A Segunda Fase tira proveito da contribuição dos utilizadores para assim melhorara qualidade da informação apresentada pelo Lusica. Para tal, foram adicionadas funcionalidades que estão disponíveis para o utilizador através de um sistema de autenticação. Os utilizadores autenticados podem então editar informação e expressar as suas preferências musicais. Através desta informação é construído um perfil de utilizador onde são listados os seus gostos, amigos e pontos resultantes da sua contribuição. Após conclusão desta Fase, foram realizados testes de usabilidade presenciais e, uma vez mais, os testes de usabilidade e de segurança da equipa especializada do SAPO Labs. A correção dos resultados destes testes deu origem ao protótipo final, que foi posteriormente lançado como produto SAPO Labs. Foram também realizados testes `a ferramenta Social Impact que faz o mapeamento entre os tweets e as músicas. Na primeira avaliação verificou-se que a precisão era elevada (100%) mas que a abrangência (53%)não o era. Como tal, foram realizadas algumas alterações que resultou num pequeno melhoramento(60%).There are two problems with the music industry on the Web: the quantity of the infor- mation on the social networks about lusophone artists (it’s impossible to know the popu- larity of the artists)and the quality of this information(there isn’t sufficient information about some artists). This thesis presents a project called Lusica that aims to solve these problems. Lusica had SAPO Labs’collaboration and for this reason the main objective was to make Lusica as one of SAPO Labs’product. The Lusica’s development process was divided in two phases. On the First Phase the information about the lusophone artists and the respective tweets about them was collected. This information was used to make a correspondence between the artists’songs and their tweets to build a popularity history. On this Phase were executed usability and security intermediate tests by a SAPO Labs’ specialized team in order to launch an intermediate version. The Second Phase takes advantages of the users’ contribution to improve the quality of the information presented by Lusica. So, there were added features which are available to the user through an authentication system. Authenticated users can edit information and express their musical preferences. With these functionalities a user profile can be created through the user’s preferences and allows them to edit information as well. After this Phase conclusion presential usability tests and once more usability and security intermediate tests by the SAPO Labs’ specialized team were executed. The correction of tests’ results led to the final prototype which was subsequently released as one of SAPO Labs’ product. There were also executed tests to the Social Impact tool that provides a correspondence between tweets and musics. In the first evaluation it was verified that the precision was high(100%)but the recall wasn’t(53%).As so, some alterations were made which resulted in a slight improvement(60%)

Energy Efficient Resource Allocation for Virtual Network Services with Dynamic Workload in Cloud Data Centers

Title from PDF of title page, viewed on March 21, 2016Dissertation advisor: Baek-Young ChoiVitaIncludes bibliographical references (pages 126-143)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016With the rapid proliferation of cloud computing, more and more network services and applications are deployed on cloud data centers. Their energy consumption and green house gas emissions have significantly increased. Some efforts have been made to control and lower energy consumption of data centers such as, proportional energy consuming hardware, dynamic provisioning, and virtualization machine techniques. However, it is still common that many servers and network resources are often underutilized, and idle servers spend a large portion of their peak power consumption. Network virtualization and resource sharing have been employed to improve energy efficiency of data centers by aggregating workload to a few physical nodes and switch the idle nodes to sleep mode. Especially, with the advent of live migration, a virtual node can be moved from one physical node to another physical node without service disrup tion. It is possible to save more energy by shrinking virtual nodes to a small set of physical nodes and turning the idle nodes to sleep mode when the service workload is low, and expanding virtual nodes to a large set of physical nodes to satisfy QoS requirements when the service workload is high. When the service provider explicates the desired virtual network including a specific topology, and a set of virtual nodes with certain resource demands, the infrastructure provider computes how the given virtual network is embedded to its operated data centers with minimum energy consumption. When the service provider only gives some description about the network service and the desired QoS requirements, the infrastructure provider has more freedom on how to allocate resources for the network service. For the first problem, we consider the evolving workload of the virtual networks or virtual applications and residual resources in data centers, and build a novel model of energy efficient virtual network embedding (EE-VNE) in order to minimize energy usage in the physical network consists of multiple data centers. In this model, both operation cost for executing network services’ task and migration cost for the live migrations of virtual nodes are counted toward the total energy consumption. In addition, rather than random generated physical network topology, we use practical assumption about physical network topology in our model. Due to the NP-hardness of the proposed model, we develop a heuristic algorithm for virtual network scheduling and mapping. In doing so, we specifically take the expected energy consumption at different times, virtual network operation and future migration costs, and a data center architecture into consideration. Our extensive evaluation results showthatouralgorithmcouldreduceenergyconsumptionupto40%andtakeuptoa57% higher number of virtual network requests over other existing virtual mapping schemes. However, through comparison with CPLEX based exact algorithm, we identify that there is still a gap between the heuristic solution and the optimal solution. Therefore, after investigation other solutions, we convert the origin EE-VNE problem to an Ant Colony Optimization (ACO) problem by building the construction model and presenting the transition probability formula. Then, ACO based algorithm has been adapted to solve the ACO-EE-VNE problem. In addition, we reduce the space complexity of ACO-EE VNE by developing a novel way to track and update the pheromone. For the second problem, we design a framework to dynamically allocate resources for a network service by employing container based virtual nodes. In the framework,each network service would have a pallet container and a set of execution containers. The pal let container requests resource based on certain strategy, creates execution containers with assigned resources and manage the life cycle of the containers; while the execution containers execute the assigned job for the network service. Formulations are presented to optimize resource usage efficiency and save energy consumption for network services with dynamic workload, and a heuristic algorithm is proposed to solve the optimization problem. Our numerical results show that container based resource allocation provide more flexible and saves more cost than virtual service deployment with fixed virtual machines and demands. In addition, we study the content distribution problem with joint optimization goal and varied size of contents in cloud storage. Previous research on content distribution mainly focuses on reducing latency experienced by content customers. A few recent studies address the issue of bandwidth usage in CDNs, as the bandwidth consumption is an important issue due to its relevance to the cost of content providers. However, few researches consider both bandwidth consumption and delay performance for the content providers that use cloud storages with limited budgets, which is the focus of this study. We develop an efficient light-weight approximation algorithm toward the joint optimization problem of content placement. We also conduct the analysis of its theoretical complexities. The performance bound of the proposed approximation algorithm exhibits a much better worst case than those in previous studies. We further extend the approximate algorithm into a distributed version that allows it to promptly react to dynamic changes in users’ interests. The extensive results from both simulations and Planetlab experiments exhibit that the performance is near optimal for most of the practical conditions.Introduction -- Related work -- Energy efficient virtual network embedding for green data centers using data center topology and future migration -- Ant colony optimization based energy efficient virtual network embedding -- Energy aware container based resource allocation for virtual services in green data centers -- Achieving optimal content delivery using cloud storage -- Conclusions and future wor

Folk Theories, Recommender Systems, and Human-Centered Explainable Artificial Intelligence (HCXAI)

This study uses folk theories to enhance human-centered “explainable AI” (HCXAI). The complexity and opacity of machine learning has compelled the need for explainability. Consumer services like Amazon, Facebook, TikTok, and Spotify have resulted in machine learning becoming ubiquitous in the everyday lives of the non-expert, lay public. The following research questions inform this study: What are the folk theories of users that explain how a recommender system works? Is there a relationship between the folk theories of users and the principles of HCXAI that would facilitate the development of more transparent and explainable recommender systems? Using the Spotify music recommendation system as an example, 19 Spotify users were surveyed and interviewed to elicit their folk theories of how personalized recommendations work in a machine learning system. Seven folk theories emerged: complies, dialogues, decides, surveils, withholds and conceals, empathizes, and exploits. These folk theories support, challenge, and augment the principles of HCXAI. Taken collectively, the folk theories encourage HCXAI to take a broader view of XAI. The objective of HCXAI is to move towards a more user-centered, less technically focused XAI. The elicited folk theories indicate that this will require adopting principles that include policy implications, consumer protection issues, and concerns about intention and the possibility of manipulation. As a window into the complex user beliefs that inform their iii interactions with Spotify, the folk theories offer insights into how HCXAI systems can more effectively provide machine learning explainability to the non-expert, lay public

Procesos de uso y consumo de nuevas tecnologías digitales: un análisis específico sobre las prácticas en torno a dispositivos de reproducción móvil digital

El trabajo que se presenta y desarrolla a continuación se centra en los estilos de consumo y las formas de uso de las distintas tecnologías móviles contemporáneas, con especial énfasis en el uso de los reproductores móviles digitales en entornos urbanos. El objetivo principal de este trabajo es comprender los procesos relacionados con el uso y consumo de nuevas tecnologías móviles y, más específicamente, los dispositivos con capacidad de reproducción móvil digital en la Comunidad de Madrid. Para ello nos apoyamos en metodologías cualitativas, entre las que destacan las entrevistas en profundidad realizadas a usuarios de dichos dispositivos, entrevistas a expertos, etnografía urbana en Madrid Capital y Alcalá de Henares, y análisis audiovisual de los anuncios publicitarios de dichos dispositivos móviles. Gracias a esta combinación, o triangulación metodológica, desarrollamos un modelo aproximado de perfiles de usuario según criterios como las necesidades y las actividades cotidianas de los mismos. A lo largo de estos años hemos puesto el foco en el uso de dispositivos con capacidad de escucha musical, matizando el fenómeno en relación a la supuesta individualización de los sujetos que se imbuyen en este tipo de tecnologías (mp3, mp4, iPod, móviles, Smartphone, tabletas, etc.) quienes crean burbujas auditivas (Bull, 2005, 2006), ya que estos sujetos se mueven dentro de las grandes ciudades, comparten con los demás (Goffman, 1981) y son parte de la sociedad de consumo (Alonso, 2004, 2006). La individualidad y la personalización contrastan así con la colectividad y el consumo colectivo. El paradigma de la movilidad urbana, entendiendo la movilidad como un factor sociológico indispensable en el uso de este tipo de tecnologías, está presente en toda la investigación. El ruido y el ritmo de las grandes metrópolis provocan que los usuarios de tecnologías de audición digital busquen una especie de refugios o de ¿burbujas auditivas¿ a través de estos dispositivos. Pero a su vez también están ¿virtualmente presentes¿ desde la ausencia. Los sujetos están conectados de forma móvil e inalámbrica mientras se desconectan de la vida social con sus auriculares. En muchas ocasiones esta situación les permite también gestionar sus rutinas con otros, o realizar distintas prácticas de ocio a través del mismo o de otro dispositivo (juegos, redes sociales, conexión a internet, apps, etc.). Los principales resultados de la tesis apuntan así al hecho de que llevar un reproductor musical o un móvil representa llevar una parte de sí mismo por las calles de la ciudad, ya que el usuario no sólo transporta su música favorita o sus contactos, sino también parte de una identidad. Y que en esta identidad convergen las elecciones privadas, ya que la mayoría de individuos configuran y personalizan los dispositivos a su gusto..

Understanding User Behavior in Spotify

Abstract-Spotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base