Logitech's Harmony division makes better universal remote controls than just about anybody. Its latest model, the $250 Harmony Express, might be its most practical, even if it's not the company's most powerful. The Harmony Express doesn't have all the bells and whistles of the company's top-of-the-line Harmony Elite ($350 MSRP), which Logitech will continue to sell. The Express doesn't have a touch-sensitive display, it doesn't have as many programmable buttons, and it can't execute complex macros that incorporate both home entertainment gear and smart home devices. The Express can do much of what the Elite can for a lot less money, and--since it has Amazon's Alexa onboard--it can do some things the Elite can't.

Logitech's popular Harmony universal remotes have long been the go-to solution for tech-savvy nerds who want to replace the bounty of ugly rectangles littering their coffee tables with a single, all-powerful option. But universal remotes are still pretty complex on their own, with dozens of buttons and, in some cases, LCD screens. You're basically swapping several remotes for something that looks like it belongs in one of NASA's Mission Control Centers. Now, there's something simpler: the Harmony Express, a compact universal remote that replaces a slew of buttons with Amazon Alexa voice controls. The $250 Express isn't meant to replace the Harmony Elite, which Logitech released back in 2015 and is still one of the best high-end universal remotes around.

In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units to capture the long-term dependency across layers, our methods can successfully identify important information during recursive neighborhood expansion. In our experiments, we show that our model class achieves state-of-the-art results on three benchmarks: the Pubmed, Reddit, and PPI network datasets. Our in-depth analyses also demonstrate that incorporating recurrent units is a simple yet effective method to prevent noisy information in graphs, which enables a deeper graph neural network.

We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful and is able to better capture the underlying clean signal than the noise, the output of the network helps to disentangle the clean audio from the rest of the signal. The method is completely unsupervised and only trains on the specific audio clip that is being denoised. Our experiments demonstrate favorable performance in comparison to the literature methods, and our code and audio samples are available at https: //github.com/mosheman5/DNP. Index Terms: Audio denoising; Unsupervised learning

Spotify, Pandora and YouTube are currently the top games in town for listening to online music for free, via ads. Possibly add Amazon to the list. Billboard reports that Amazon looks to launch a similar service, which would be available only if you ask Alexa. The songs could be accessed via the Echo connected speakers, which are made by Amazon. Amazon currently offers some 2 million songs available free to Echo owners who also pay $119 yearly to subscribe to the company's Prime for expedited shipping and online music, movies and TV.

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: News Recommender Systems, Session-based Recommendation, Artificial Neural Networks, Context-awareness, Hybridization

Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time, they may pick up on aspects that are predominant in the data, yet not actually meaningful or interpretable. In this paper, we therefore propose a systematic way to diagnose the trustworthiness of deep music representations, considering musical semantics. The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space. We generate known related points through semantically meaningful transformations, both considering imperceptible and graver transformations. Then, we examine within- and between-space distance consistencies, both considering audio space and latent embedded space, the latter either being a result of a conventional feature extractor or a deep encoder. We illustrate how our method, as a complement to task-specific performance, provides interpretable insight into what a network may have captured from training data signals.

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labelled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses its own dataset to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve to the scientific community to test newly proposed methods.

At midnight on Friday, pop singer Taylor Swift updated her official website with a giant countdown clock, which will reach zero at 12:00 a.m. The internet has been ablaze with rumors ever since the countdown clock appeared, most of which revolve around the idea that Swift might be releasing new music on the 26th. But students of history and students of the Assassin's Creed video game series recognize that the situation is far grimmer than even the lead-up to the release of Reputation. April 26 is the anniversary of the Pazzi's attack on the Medici outside the Duomo in Florence, making it an apt day for betrayals and treason. And this isn't the first time a monomaniacal multi-millionaire with a love of gadgets, a passion for revenge, and a stockpile of diamonds has started a ticking clock.

Then it analyzed the world's main region Artificial Intelligence (AI) Chips market conditions, including the product price, profit, capacity, production, …