Self-adaptive software systems continuously adapt in response to internal and external changes in their execution environment, captured as contexts. The COP paradigm posits a technique for the development of self-adaptive systems, capturing their main characteristics with specialized programming language constructs. COP adaptations are specified as independent modules composed in and out of the base system as contexts are activated and deactivated in response to sensed circumstances from the surrounding environment. However, the definition of adaptations, their contexts and associated specialized behavior, need to be specified at design time. In complex CPS this is intractable due to new unpredicted operating conditions. We propose Auto-COP, a new technique to enable generation of adaptations at run time. Auto-COP uses RL options to build action sequences, based on the previous instances of the system execution. Options are explored in interaction with the environment, and the most suitable options for each context are used to generate adaptations exploiting COP. To validate Auto-COP, we present two case studies exhibiting different system characteristics and application domains: a driving assistant and a robot delivery system. We present examples of Auto-COP code generated at run time, to illustrate the types of circumstances (contexts) requiring adaptation, and the corresponding generated adaptations for each context. We confirm that the generated adaptations exhibit correct system behavior measured by domain-specific performance metrics, while reducing the number of required execution/actuation steps by a factor of two showing that the adaptations are regularly selected by the running system as adaptive behavior is more appropriate than the execution of primitive actions.

We present Affect2MM, a learning method for time-series emotion prediction for multimedia content. Our goal is to automatically capture the varying emotions depicted by characters in real-life human-centric situations and behaviors. We use the ideas from emotion causation theories to computationally model and determine the emotional state evoked in clips of movies. Affect2MM explicitly models the temporal causality using attention-based methods and Granger causality. We use a variety of components like facial features of actors involved, scene understanding, visual aesthetics, action/situation description, and movie script to obtain an affective-rich representation to understand and perceive the scene. We use an LSTM-based learning model for emotion perception. To evaluate our method, we analyze and compare our performance on three datasets, SENDv1, MovieGraphs, and the LIRIS-ACCEDE dataset, and observe an average of 10-15% increase in the performance over SOTA methods for all three datasets.

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bah\'ia Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

Open AI, the research company founded by Elon Musk, has just discovered that their artificial neural network CLIP shows behavior strikingly similar to a human brain. This find has scientists hopeful for the future of AI networks' ability to identify images in a symbolic, conceptual and literal capacity. While the human brain processes visual imagery by correlating a series of abstract concepts to an overarching theme, the first biological neuron recorded to operate in a similar fashion was the "Halle Berry" neuron. This neuron proved capable of recognizing photographs and sketches of the actress and connecting those images with the name "Halle Berry." Now, OpenAI's multimodal vision system continues to outperform existing systems, namely with traits such as the "Spider-Man" neuron, an artificial neuron which can identify not only the image of the text "spider" but also the comic book character in both illustrated and live action form.

Technology is reshaping every aspect of our lives. Once a week in The Future Of, we examine innovations in important fields, from farming to transportation, and what they will mean in the years and decades to come. The case was complicated: Shoulder arthroplasty, to deal with an advanced case of arthritis affecting the patient's glenoid -- the ball part of the ball-and-socket joint in the shoulder. To handle the case most effectively, the surgeon wanted assistance from the best. But the best was physically half a world away.

The pictures were shared through the'RAW images' feed on the NASA Perseverance website, which showcases every grab from every camera on the SUV-sized rover NASA's mission will search for signs of ancient life on on the Red Planet. Named Perseverance, the main car-sized rover will explore an ancient river delta within the Jezero Crater. This was once filled with a 1,600ft deep lake and may have been flooded multiple times when Mars was warm. It is believed the region hosted microbial life some 3.5 to 3.9 billion years ago and the rover will examine soil samples to hunt for proof. Perseverance landed inside the crater on February 18 and will also collect samples of the soil to return to Earth.

But the big data industry has significant inertia moving into 2021. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. The "analytic divide" is going to get worse. Like the much-publicized "digital divide" we're also seeing the emergence of an "analytic divide." Many companies were driven to invest in analytics due to the pandemic, while others have been forced to cut anything they didn't view as critical to keep the lights on – and a proper investment in analytics was, for these organizations, analytics was on the chopping block. This means that the analytic divide will further widen in 2021, and this trend will continue for ...

The text message from Billy arrived on students' phones the week of final exams. "It took a lot of hard work, perseverance, and strength to get here, but you've finally made it to the other side -- the end of the semester! I wanted to take a minute and say that I am so proud of you ..." Three emoji hearts concluded the message. "Love you Billy thank you." Heart heart heart. "Thanks Billy, we did it together."

Simplicial complexes form an important class of topological spaces that are frequently used to in many applications areas such as computer-aided design, computer graphics, and simulation. The representation learning on graphs, which are just 1-d simplicial complexes, has witnessed a great attention and success in the past few years. Due to the additional complexity higher dimensional simplicial hold, there has not been enough effort to extend representation learning to these objects especially when it comes to learn entire-simplicial complex representation. In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved. Our method utilizes a simplex-level embedding induced by a pre-trained simplicial autoencoder to learn an entire simplicial complex representation. To the best of our knowledge, this work presents the first method for learning simplicial complex-level representation.

A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment. As this task is challenging and time-consuming, we present the Sketch2aia approach that automates this process. Sketch2aia employs deep learning to detect the most frequent user interface components and their position on a hand-drawn sketch creating an intermediate representation of the user interface and then automatically generates the App Inventor code of the wireframe. The approach achieves an average user interface component classification accuracy of 87,72% and results of a preliminary user evaluation indicate that it generates wireframes that closely mirror the sketches in terms of visual similarity. The approach has been implemented as a web tool and can be used to support the end-user development of mobile applications effectively and efficiently as well as the teaching of user interface design in K-12.