相关论文: A Cross-Repository Model for Predicting Popularity…

With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of…

We present a multi-relational temporal Knowledge Graph based on the daily interactions between artifacts in GitHub, one of the largest social coding platforms. Such representation enables posing many user-activity and project management…

Current works on Information Centric Networking assume the spectrum of caching strategies under the Least Recently/ Frequently Used (LRFU) scheme as the de-facto standard, due to the ease of implementation and easier analysis of such…

Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells…

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of…

Faced with over 100M open source projects most empirical investigations select a subset. Most research papers in leading venues investigated filtering projects by some measure of popularity with explicit or implicit arguments that unpopular…

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and…

On social media platforms, the act of predicting reposting is seen as a challenging issue related to Short Message Services (SMS). This study examines the issue of predicting picture reposting in SMS and forecasts users' behavior in sharing…

We describe a method for proactive information retrieval targeted at retrieving relevant information during a writing task. In our method, the current task and the needs of the user are estimated, and the potential next steps are…

Big time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in various environments. Significant insights can be gained by mining temporal patterns from these time series. Temporal pattern mining…

The present study aims to establish the model of the cryptocurrency price trend based on financial theory using the LSTM model with multiple combinations between the window length and the predicting horizons, the random walk model is also…

A unique feature of Recurrent Neural Networks (RNNs) is that it incrementally processes input sequences. In this research, we aim to uncover the inherent generalization properties, i.e., inductive bias, of RNNs with respect to how…

Caching techniques are widely used in the era of cloud computing from applications, such as Web caches to infrastructures, Memcached and memory caches in computer architectures. Prediction of cached data can greatly help improve cache…

Knowing how many people occupy a building, and where they are located, is a key component of smart building services. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy. However,…

We here describe and present results of a simple neural network that predicts individual researchers' future citation counts based on a variety of data from the researchers' past. For publications available on the open access-server…

Temporal gates play a significant role in modern recurrent-based neural encoders, enabling fine-grained control over recursive compositional operations over time. In recurrent models such as the long short-term memory (LSTM), temporal gates…

One of the most enticing research areas is the stock market, and projecting stock prices may help investors profit by making the best decisions at the correct time. Deep learning strategies have emerged as a critical technique in the field…

Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models with significantly increased complexity and a large number of…

Efficient task scheduling is paramount in the Linux kernel, where the Completely Fair Scheduler (CFS) meticulously manages CPU resources to balance high utilization with interactive responsiveness. This research pioneers the use of deep…

Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead…