Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the ommits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 14,678,222 commits they created.

As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color palettes, density of the piece, thickness or curvatures of any lines in the piece); predicting them could lead to a smarter interactive tool. Or they could be preferences in other walks of life (e.g., music, fashion, food, interior design, paintings) or attributes of the person (e.g., personality type, gender, artistic inclinations); predicting them could lead to improved personalized recommendations for products or experiences. To study this research question, we collect preferences from 311 subjects, both in a specific generative art form and in other walks of life. We analyze the preferences and train machine learning models to predict a subset of preferences from the remaining. We find that preferences in the generative art form we studied cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.

Everything starts with a good business case that aligns with urban strategy. Business cases should start with the needs of the citizen or the business.

Data from Strategy Analytics show that smart speaker shipments in the Nordic countries of Denmark, Norway, and Sweden reached 900,000 in 2019. That figure is up sharply from shipments of only about 200,000 in 2018. These figures indicate impressive growth and smart speaker interest in countries that collectively claim only about 20 million in population. Strategy Analytics estimates the household installed base for smart speakers these Nordic countries is about 6%. Finland and Iceland were not included in the analysis since none of the leading smart speaker makers have offerings with language localization for these countries. Google Home (a.k.a Google Nest) dominates smart speaker market share in the Nordic countries accounting for over 80% of all devices shipped according to David Watkins, director of connected home devices at Strategy Analytics.

Machine learning methods allow us to make recommendations to users in applications across fields including entertainment, dating, and commerce, by exploiting similarities in users' interaction patterns. However, in domains that demand protection of personally sensitive data, such as medicine or banking, how can we learn such a model without accessing the sensitive data, and without inadvertently leaking private information? We propose a new federated approach to learning global and local private models for recommendation without collecting raw data, user statistics, or information about personal preferences. Our method produces a set of prototypes that allows us to infer global behavioral patterns, while providing differential privacy guarantees for users in any database of the system. By requiring only two rounds of communication, we both reduce the communication costs and avoid the excessive privacy loss associated with iterative procedures. We test our framework on synthetic data as well as real federated medical data and Movielens ratings data. We show local adaptation of the global model allows our method to outperform centralized matrix-factorization-based recommender system models, both in terms of accuracy of matrix reconstruction and in terms of relevance of the recommendations, while maintaining provable privacy guarantees. We also show that our method is more robust and is characterized by smaller variance than individual models learned by independent entities.

The major factors propelling the growth of the Global Artificial Intelligence (AI) market include the rise in the adoption of cloud-based applications and services and the growth of big data across the globe. The advanced AI technologies is continuously enhancing the performance of economies, businesses and different industries. The growing demand for intelligent virtual assistants across different verticals in several industries will have a positive impact on the Global Artificial Intelligence (AI) market during the forecast period. The increased integration of image recognition technology with optical character recognition, pattern matching and face recognition in various end-use applications such as drones, self-driving cars and robotics will propel the growth of the artificial intelligence market during the forecast period. Various multinational giants are largely focusing on mergers and acquisitions with emerging start-ups in order to capture the highest market share and gain a competitive advantage over the other market players.

Artificial intelligence can be found in many of the devices and software systems we use for our personal lives and professional responsibilities. Whether it's the digital assistant you keep in your kitchen to set voice timers while cooking or the customer service representative you're sending messages to about return policies online, this technology is encountered more and more each day as its capabilities expand. Many organizations are integrating digital software programs that come with AI technology built into them in order to modernize, digitize, and increase efficiency for purchasing and production. Chatbots, one of the most popular types of tech benefiting businesses, are digital assistants often incorporated into digital software programs internally to save the time and energy of an employee. The chatbot serves the purpose of answering site visitor questions and handling customer service requests, which can free up time on employees' busy schedules and allow them to dedicate more time to other projects.

Siri means a secret in Swahili and in Norse, a beautiful woman who leads you to victory. To those of us in Appleland using the voice-controlled personal assistant, it stands for "Speech Interpretation and Recognition Intelligence." And when you put Siri to work, she can move productivity mountains. I use Siri to give me directions while driving with Apple CarPlay. I ask her to remind me to stop by the grocery store on my Apple Watch.

Artificial intelligence (AI) is one of the hottest topics in today's headlines. It powers natural language recognition for voice-powered assistants like Siri and Alexa, beats world-class Google Go players, and enables hyper-targeted e-commerce and content recommendations across the web on industry-giant websites that include Target and Netflix. But recently, AI has begun actively expanding its footprint in the enterprise. Executives are trying to more fully comprehend what AI is and how they can use it to capitalize on business opportunities by gaining insight into the data they collect and engaging with customers more productively to hone their competitive edge. AI is the frontier of enterprise technology, but there remain many misperceptions about what it is and how it works.

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length $T$ is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.