Abstract In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.

Malware is widely regarded as one of the most severe security threats to modern technologies. Detecting malware in the Internet of Things (IoT) infrastructures is a critical and complicated task. The complexity of this task increases with the recent growth of malware variants targeting different IoT CPU architectures since the new malware variants often use anti-forensic techniques to avoid detection and investigation. There-fore, we cannot utilize the traditional machine learning (ML) techniques that require domain knowledge and sophisticated feature engineering in detecting the unseen mal ware variants. Re-cent deep learning approaches have performed well on mal ware analysis and detection while using minimum feature engineering requirements. In this paper, we propose BERTDeep- Ware, a real-time cross-architecture malware detection solution tailored for IoT systems. BERTDeep- Ware analyzes the executable file's operation codes (OpCodes) sequence representations using Bidi-rectional Encoder Representations from Transformers (BERT) Embedding, the state-of-the-art natural language processing (NLP) approach. The extracted sentence embedding from BERT is fed into a customized hybrid multi-head CNN-BiLSTM-LocAtt model. This deep learning (DL) model combines the convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and the local attention mechanisms (locAtt) to capture contextual features and long-term dependencies between OpCode sequences. We train and evaluate BERTDeep- Ware using the datasets created for three different CPU architectures. The performance evaluation results confirm that the proposed multi-head CNN-BiLSTM-LocAtt model produces more accurate classification results with higher detection rates and lower false positives than a number of baseline ML and DL models.

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed Graph Convolutional Networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high quality graph's node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. For evaluation, we design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.

In the area of cloud infrastructure environment, the management tool to monitor and control the cloud resources is the important factor that can drive the cost benefit of the cloud vendors. But most these tools are bundled within the high cost commercial platforms and are optimized to run on desktop computers. With the vision that Mobile Cloud Computing will be the future technology paradigm that dominates the IT industry, we want to create a cloud management tool that is open source, fast, lightweight and mobile friendly. We take the initial steps by implementing our framework using several popular technologies such as RESTful, Java Message Service, JSON, and we call it “High performance and Lightweight Mobile Cloud Infrastructure Monitor and Benchmark Service” or HiLiCloud. The initial testings show competitive evaluation results.

We take the first step to study the price competition in a heterogeneous market cloud computing formed by public provider and cloud broker, all of which are also known as cloud service providers. We formulate a price competition between cloud broker and public provider as a two-stage non-cooperative game. In stage one, where cloud service providers set their service prices to maximize their revenue, we use the Nash equilibrium concept to study the equilibria for the price setting game. Cloud users can select the services (from the cloud broker or public provider) that provide them the best payoff in terms of performance (i.e., delay) and price. To that end, cloud users can adapt their service selection behavior by observing the variations in price and quality of service offered by the different cloud service providers. For the service selection game of cloud users in stage two, we use the evolutionary game model to study the evolution and the dynamic behavior of cloud users. Furthermore, the Wardrop equilibrium and replicator dynamics is applied to determine the equilibrium and its convergence properties of the service selection game. Numerical results illustrate that our game model captures the main factors behind the heterogeneous market cloud pricing and service selection, thus represents a promising framework for the design and understanding of the heterogeneous market cloud computing.

In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single multimedia service provider (MSP) scenario and multiple MSPs scenario. In each scenario, we formulate and solve the MSPs' revenue maximization problem under eviction probability constraint of users. Numerical results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a maximum revenue.

In-memory Computing (IMC) is the new trend for enabling high-performance computation and fast data processing. It is currently being used for large enterprises, e-commerce shops who need real-time interactions, low latency responses and instant results. Given the enhancement of the IMC, we apply this new paradigm to the Virtual Desktop Environment (VDE), and look into the performance differences in comparison with traditional VDE. The end results shows positive feedback, but there are trade-offs we need to concern for the In-memory Virtual Desktop Environment.

In dynamic spectrum access (DSA) control, the prevalent approach to provide economics incentives for operators is pricing, whereas load balancing gives congestion-avoidance incentives to secondary users (SUs). Despite complexities of i) the couplings between pricing, load balancing and SUs' spectrum access decision, and ii) the heterogeneity of primary users' traffic and SUs types, we propose to solve the joint load balancing and pricing problem to maximize operator' revenue in a monopoly market. In this market, we first show there exists a unique SUs' equilibrium arrival rate to the monopolist's channels, and then we show that the joint problem can be solved efficiently by exploiting its convex structure. We next propose a low-complexity algorithm that enable the operator to maximize its revenue.