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Sub‐seasonal forecasts are becoming more widely used in the energy sector to inform high‐impact, weather‐dependent decisions. Using pattern‐based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern‐based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly‐mean anomalies of electricity demand and demand‐net‐wind across 28 European countries. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill than grid‐point forecasts. All methods have relatively low skill at weekly‐mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision‐making, as it combines the dynamical robustness in the large‐scale flow conditions from the pattern‐based methods with local information present in the grid‐point forecasts. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods (e.g., weather regimes or targeted circulation types) across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill. All methods have relatively low skill beyond day 12. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction: when >50% of the pattern forecasts are in agreement skill increases significantly. AI ha

The Border Gateway Protocol (BGP) is a path vector protocol whose fundamental aim is to exchange the information across the Internet, which directs data between autonomous systems. The significant drawback of the BGP is that it does not address security; path hijacking is one of the top-rated cyber hijacks. Existing methods such as sBGP, soBGP and PGBGP have focused more on detecting path hijacking rather than preventing. Hence, we propose an intelligent model to detect abnormal behavior of a network and to predict and prevent BGP path hijacking (DPPBGP) in software-defined networks. The main objective of our proposed model is to reduce detection time and the controller workload with SFlow-integrated OpenFlow. Three modules of our model are as follows: (1) Based on the abnormal behavior of the network, we evaluated the statistics. We use the statistic features in the cumulative sum abnormal detection algorithm to detect abnormal behavior and flows proficiently and perfectly with less detection time. (2) An intelligent machine learning approach knows as a Pattern Sequence Forecasting algorithm is used to forecast the behavior of the network. (3) After the detection or the forecast of abnormality, path hijack is prevented by killing the appropriate PID based on SFlow analyzer. Simulation results show how large the network of this model can perform accurately and effectively.

Artificial-intelligence researchers are trying to fix the flaws of neural networks. Artificial-intelligence researchers are trying to fix the flaws of neural networks.

Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. In this context, we study the use of different types of attention, computed at joint, body part, and full pose levels. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW validate the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at https://github.com/wei-mao-2019/HisRepItself.

Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign. The authors review the advantages and future prospects of neuromorphic computing, a multidisciplinary engineering concept for energy-efficient artificial intelligence with brain-inspired functionality.

Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both unsupervised learning and non-linear feature extraction. By highlighting the contributions and challenges of recent research papers, this work aims to review state-of-the-art auto-encoder algorithms. Firstly, we introduce the basic auto-encoder as well as its basic concept and structure. Secondly, we present a comprehensive summarization of different variants of the auto-encoder. Thirdly, we analyze and study auto-encoders from three different perspectives. We also discuss the relationships between auto-encoders, shallow models and other deep learning models. The auto-encoder and its variants have successfully been applied in a wide range of fields, such as pattern recognition, computer vision, data generation, recommender systems, etc. Then, we focus on the available toolkits for auto-encoders. Finally, this paper summarizes the future trends and challenges in designing and training auto-encoders. We hope that this survey will provide a good reference when using and designing AE models.

Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing. Inanimate matter is beginning to show some signs of basic intelligence—the ability to sense, actuate and use memory, as controlled by an internal communication network in functional materials.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before it is sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

This study develops a skill evaluation metric for an individual forecast by applying a Taylor expansion to the commonly-used temporal correlation skill. In contrast to other individual forecast evaluation metrics, which depend on the amplitude of forecasted and observed anomalies, the so-called “association strength (AS) skill” is less affected by the anomaly amplitude and mainly depends on the degree of similarity between the forecasted and the observed values. Based on this newly developed index, the forecast skill is evaluated for an individual case, then, a group is categorized with respect to the AS skill. The cases with the highest AS skill exhibit the highest correlation skill than any group randomly selected, indicating that the AS skill is a powerful metric to evaluate the non-dimensionalized forecast skill. This strategy is adopted for the subseasonal East Asian summer precipitation forecasts produced by the UK Met Office’s ensemble Global Seasonal forecast system version 5 (GloSea5). In the group with the highest AS skill of the East Asian summer precipitation index (i.e., highest AS cases), the geopotential height anomalies showed quasi-stationary Rossby waves from the North Atlantic to East Asia. The spatial distribution of the dominant subseasonal anomalies for cases with the highest AS is distinct from the cases or groups with the lowest AS skill. Furthermore, the dominant pattern with the highest AS is not solely explained by any well-known typical subseasonal climate patterns, such as the Madden–Julian Oscillation, circumglobal teleconnection pattern, Pacific-Japan pattern, or the Summer North Atlantic Oscillation. This implies that the excitation of well-known climate patterns only partly contributes to increasing the mid-latitude climate predictability in the GloSea5.

Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and hand-crafted techniques. We also explain a generic architecture to recognize human actions in the real world and its current prominent research topic. At long last, we are able to offer some study analysis concepts and proposals for academics. In-depth researchers of human action recognition will find this review an effective tool.