Apple has updated its Store app for iOS and iPadOS with a new tab that shows all the devices linked to your Apple ID along with shopping suggestions based on that list. As 9to5Mac notes, tapping on the tab shows an overview of the iPhones, iPads and Macs you have under the "Your devices" list. The new section also shows accessories you can buy that are compatible with your devices. And if you have an iPhone, tapping on it shows its warranty information. In case it doesn't have one anymore, the app will display a trade-in value instead, as well as a quick link to start the Apple Trade In process.

The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer installation, to which the computational resource of the virtual infrastructure is dynamically mapped. One of the components of this problem is the estimation of the MPI programs execution time on a certain set of computer installations. This is necessary to determine a proper choice of order and place for program execution. The article proposes two new approaches to the program execution time prediction problem. The first one is based on computer installations grouping based on the Pearson correlation coefficient. The second one is based on vector representations of computer installations and MPI programs, so-called embeddings. The embedding technique is actively used in recommendation systems, such as for goods (Amazon), for articles (Arxiv.org), for videos (YouTube, Netflix). The article shows how the embeddings technique helps to predict the execution time of a MPI program on a certain set of computer installations.

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol.

A context-aware recommender system (CARS) applies sensing and analysis of user context to provide personalized services. The contextual information can be driven from sensors in order to improve the accuracy of the recommendations. Yet, generating accurate recommendations is not enough to constitute a useful system from the users' perspective, since certain contextual information may cause different issues, such as draining the user's battery, privacy issues, and more. Adding high-dimensional contextual information may increase both the dimensionality and sparsity of the model. Previous studies suggest reducing the amount of contextual information by selecting the most suitable contextual information using a domain knowledge. Another solution is compressing it into a denser latent space, thus disrupting the ability to explain the recommendation item to the user, and damaging users' trust. In this paper we present an approach for selecting low-dimensional subsets of the contextual information and incorporating them explicitly within CARS. Specifically, we present a novel feature-selection algorithm, based on genetic algorithms (GA), that outperforms SOTA dimensional-reduction CARS algorithms, improves the accuracy and the explainability of the recommendations, and allows for controlling user aspects, such as privacy and battery consumption. Furthermore, we exploit the top subsets that are generated along the evolutionary process, by learning multiple deep context-aware models and applying a stacking technique on them, thus improving the accuracy while remaining at the explicit space. We evaluated our approach on two high-dimensional context-aware datasets driven from smartphones. An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.

In the same way a smart video doorbell keeps a watchful eye over your home, a smart pool water monitor can help keep tabs on the quality of your precious pool water. It may seem like a trivial gadget to add to your home, but if you've spent any time schlepping water from your pool to the pool store every week, you know how time-consuming it can be. And, during a pandemic-riddled summer when social distancing is still in effect, taking matters into your own hands may just be the way to go. I've tried all sorts of smart gadgets before but nothing like the pHin smart pool water monitor. The pHin smart pool water monitor comes neatly packaged and includes everything you need, including a bridge, to set it up.

With Smart Home technology taking off and smart home assistants (like Amazon Alexa, Google Home and Apple Siri) becoming more prevalent in homes the start of a new home technology revolution is underway. Homeowners can monitor and control the house heating and cooling systems, security systems, door locks, garage doors and more all from where ever they have access to internet on their smartphone. Artificial intelligence (AI) will add to that ability by allowing decisions about the home to be made without the need of direct input from the homeowner. For instance a trusted dog walker walks up to the front door during their scheduled time to take Fido out for a walk. The dog walker's face is seen via camera which an artificial intelligence assistant recognizes and knows they are there during the correct time and allows the door to be unlocked so Fido can enjoy some outdoor time while the homeowner is away.

Samsung's phones may be the among the most popular Android handsets, but they're also the furthest from Google's vision, with their own app store, UI, and digital assistant. But with the launch of the Galaxy Note 20 just a week away, a new report from Bloomberg suggests that Google is looking to rein in some of Samsung's freedom. According to correspondence between the two companies, Google is looking to take back search on Samsung's handsets, the foundation for everything Android does. The two companies are discussing a deal that would "promote Google's digital assistant and Play Store for apps" on Galaxy devices. That would be a major change over the current system.

Machine learning is simply a computer learning from data instead of following a recipe. It's meant to mimic how people (and perhaps other animals) learn while still being grounded in mathematics. This post is meant to get you started with a basic machine learning model. Now, we're not re-creating Alexa, Siri, Cortana, or Google Assistant but we are going to create a brand new machine learning program from scratch. This course is meant to be easy assuming you know a bit of Python Programming.

The data contained in dating apps is both very personal and valuable to hackers, who can use it to make highly convincing cyberattacks. So it's always disturbing to learn about dating app security flaws. In a report released today, security research firm CheckPoint Research announced that it found several security vulnerabilities in OKCupid's website and mobile apps. The flaws could have allowed hackers to access users' full profile details, private messages, personal addresses and more. Hackers could even send messages from their victims' profiles.

Executives from NVIDIA, Deepgram, and Sharpen gathered via Zoom on Wednesday to discuss the current state of the voice tech industry, as well as where it's going. Growth in artificial intelligence (AI) technology and machine learning have had a huge hand in lifting the market, but it's only the beginning. Voice tech has seen rapid growth in recent years and isn't predicted to stop: The market is estimated to be worth nearly $32 billion by 2025, a Grand View Research report found. With smart speakers and home assistants like Amazon Alexa, Apple's Siri, and Google Assistant making voice tech mainstream, most consumers are familiar with the concept. However, the technology is more complex than people may think and it has come a long way.