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Hierarchical Attention Based Recurrent Neural Network Framework for Mobile MOBA Game Recommender systems

Hierarchical Attention Based Recurrent Neural Network Framew...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Qiongjie Yao Xiaofei Liao Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728111421;9781728111414

The mobile multiplayer online battle arena (MOBA) game is a genre of real-time strategy video games on mobile devices, such as King of Glory. The main business model is to drive players to purchase items like heroes or skins. Recommending items based on player interest is the core task of recommender systems. In the MOBA game, player interest changes over the game experience, which is implied in player behavior based on historical game matches. Match sequences, that consist of every match in the timeline, indicate how players interact with the game and the change process of player interest. Recurrent neural networks (RNNs) are employed by many recommendation scenes to model sequence data to profile user preference for better recommendation accuracy. However, their RNNs based frameworks ignore the interpretability of recommendation results, which is an important requirement for mobile MOBA games. To solve this challenge, we propose an interpretable RNN framework based on hierarchical attention in this work, which is inspired by the attention mechanism applied in machine translation. The main component long short-term memory (LSTM), that is the RNN variant, models player interest from historical match sequences, and the hierarchical attention is used to measure the effect factors of matches and behavior events happened in a match. To verify effectiveness, we train several models on real mobile MOBA game King of Glory datasets. Compared to non-sequence models, our model achieves 2% higher accuracy; with hierarchical attention, the proposed model can interpret the recommendation results effectively compared to naive RNN based models.

关键词： Games Recurrent neural networks Logic gates Predictive models Computer architecture Recommender systems Land mobile radio

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Is Your Graph Algorithm Eligible for Nondeterministic Execution?

Is Your Graph Algorithm Eligible for Nondeterministic Execut...

引用

International Conference on Parallel Processing (ICPP)

作者： Zhiyuan Shao Lin Hou Yan Ai Yu Zhang Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

Graph algorithms are used to implement data mining tasks on graph data-sets. Besides conducting the algorithms by the default deterministic manner, some graph processing frameworks, especially those supporting asynchronous execution model, provide interfaces for the algorithms to be executed in nondeterministic manner, which can improve the scalability and performance of the algorithm's executions. However, is the graph algorithm eligible for nondeterministic execution, and will the execution produce expected results? The literature gives few answers to these questions. In this paper, we study the nondeterministic execution of graph algorithms by considering the scenario where data dependences happen in the edges in graph processing frameworks that employ asynchronous execution model. Our study reveals that only by guaranteeing the atomicity of individual reads and writes, some algorithms (e.g., Graph traversal algorithms) can converge by recovering from corrupted intermediate results with nondeterministic execution, and thus tolerate even write-write conflicts, while some other algorithms (e.g., Fixed point iteration algorithms) can converge but tolerate only read-write conflicts. By conducting graph algorithms on real-world graphs in Graph Chi, and comparing their performances and results with deterministic executions, we find that their performance gains are generally scalable to the available processors with nondeterministic executions, and the results at convergence of fixed point iteration algorithms from nondeterministic executions exhibit larger variances from one run to another than their deterministic executions.

关键词： Scheduling Parallel processing Convergence Instruction sets Data models Computational modeling

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ONHelp: Components in Building Secure Cloud Based on OpenNebula

ONHelp: Components in Building Secure Cloud Based on OpenNeb...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Kao Zhao Hai Jin Deqing Zou Weiqi Dai Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781479965144

Due to the rapid development of cloud computing, several cloud computing platforms are developed to build cloud for individuals and companies. Open Nebula is known as one of the most popular open-source cloud computing software platforms. However, Open Nebula does not perform effectively in security. Virtual machines face risks of being attacked, which leads to services halt and data loss. We analyze the latest version of Open Nebula as well as some other similar products, and find out some functions are missing in these software platforms which turn out to be essential in secure cloud environment. We present ON Help, security components assisting Open Nebula to build a securer cloud platform, including trustworthiness attestation of computing nodes and VMs, deep monitoring of VMs, service-level fault tolerant service, cloud anti-virus in VMs and secure cloud storage. Our experimental results show that ON Help can be easily deployed with Open Nebula when constructing a cloud computing platform, and it can enhance the stability of cloud service and the security of cloud computing environment.

关键词： Monitoring Cloud computing Security Fault tolerant systems Fault tolerance Semantics

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Automatic Security Bug Classification: A Compile-Time Approach

Automatic Security Bug Classification: A Compile-Time Approa...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Tie Du Long Zheng Shaopeng Chen Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509053827

Program security bugs pose a great threat to users' privacy and security. A great deal of effort, e.g., runtime defense, dynamic detection, and static detection, has been conducted to attempt to be aware of the existence of security bugs. Most of the prior work focuses on detecting the security bugs. They report a mixed set of security bugs, regardless of whether the elements in the set are useful to the developers for the debugging. In this paper, we are instead devoted to automatically classifying the security bugs for the purpose of the productivity to the developers. Our insight is that the existing common security bugs can be featured by a simple rule that can be further simplified into a mathematical assertion problem. Based on this insight, we propose a Compile Time Error Segregator (CTES), which can automatically classify the security bugs into three categories, including deterministic bugs, internal indeterministic bugs, and external indeterministic bugs. The core idea of achieving the above includes three steps: 1) building a rule library according to the feature of each type of security bugs (e.g., buffer overflow, null-pointer dereference, and divide-by-zero), 2) obtaining the requisite information appearing in the rule, 3) verifying if the rule is established. If so, a deterministic bug is found, otherwise, a novel inverse taint analysis is further performed to distinguish the remaining two categories. We implement CTES on top of LLVM (3.5.0), running in parallel with normal compile procedure. Our experimental results on micro-benchmark and 14 real programs demonstrate the efficiency of CTES, and also show that CTES is able to precisely make the reported security bugs well-classified into three-categories.

关键词： Computer bugs Security Libraries Runtime Semantics Productivity Syntactics

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Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance system

Vehicle Tracking with Non-overlapping Views for Multi-camera...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Wenbin Jiang Chang Xiao Hai Jin Shuo Zhu Zhiwei Lu Services Computing Technology and System Lab Cluster and Grid Computing Huazhong University of Science and Technology Wuhan China

With the rapid development of intelligent video surveillance system for transportation, traditional single-camera-based video analysis has become insufficient. Many researches have focused on the non-overlapping multi-camera target tracking. However, the tracking precision and the computing overhead are still big obstacles. This paper proposes a novel vehicle tracking approach for non-overlapping multi-camera targets with data fusion by using minimum cost and maximum flow method. Structured information of moving targets is extracted and associated with other information such as motion time, the topology of camera network to solve targeted vehicle tracks. Besides, to improve the performance of the process, a parallelization algorithm for camera network topology partitioning is presented, which makes it possible for each sub-graph to track target independently in parallel. The experiment results show that the presented approach is able to perform target vehicle tracking analysis with high efficiency and accuracy.

关键词： Cameras Target tracking Vehicles Feature extraction Network topology Topology

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News Feeds Optimization via Users' Interaction Graph over Mobile Social Networks

News Feeds Optimization via Users' Interaction Graph over Mo...

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International Conference on Cloud computing and Big Data (CloudCom-Asia)

作者： Jinzhou Huang Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

Online social networks are increasingly playing roles of propagating news. We are surrounded with diverse data every day in today's information exploding era and suffer from the littery contents. Furthermore, due to most users of online social networks have turned their platforms from PCs to smart phones, it is a challenge to select contents that users interested in with resource constraints in mobile social networks. Considering the contradiction between large volume of data and memory constraints in mobile social networks, we present an efficient news filtering mechanism via users' interaction to avoid unnecessary display of data that barely read. By leveraging the Bloom filter technology, we can record users' interaction dynamically with a slight false positive, and filter the little possible readable updates of friends from their News Feed sites. We conduct a simulation to evaluate our design. Results show significant performance for storage saving and high filtering rate.

关键词： Feeds Facebook Information filters Mobile communication

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PNPFI: An Efficient Parallel Frequent Itemsets Mining Algorithm

PNPFI: An Efficient Parallel Frequent Itemsets Mining Algori...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fang Zhang Yu Zhang Xiaofei Liao Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538614839

Frequent itemsets mining (FIM) plays an important role in many data mining areas. With the explosion of data scale, a number of parallel FIM algorithms have been proposed. Although existing solutions have outstanding scalability, they suffer from high consumption of CPU and memory for recursively mining frequent itemsets based on a tree-structure. In this paper, we propose a novel parallel algorithm, named PNPFI. It employs three novel key optimizations. In detail, the itemsets are stored by the N-list structure, which is more compact than existing tree-based structure. It uses a new structure, called P-Subsume, to generate some frequent itemsets without the process of N-list intersection. In addition, PNPFI proposes a new load balancing strategy, which intelligently divides a large-scale FIM problem into a set of tasks based on the profiled load of each item. Compared with the state-of-the-art algorithms, experimental results show that PNPFI gets a performance improvement of 39% on average (max to 79%), and reduces the memory usage by 58% on average (max to 90%).

关键词： Itemsets Data mining Load management Parallel algorithms Memory management Task analysis Complexity theory

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Memory-Based Data Management for Large-Scale Distributed Rendering

Memory-Based Data Management for Large-Scale Distributed Ren...

引用

IEEE International Conference on e-Business Engineering (ICEBE)

作者： Ran Zheng Jinli Jia Hai Jin Xinqiao Lv Shuai Yang Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509061204

With the increasing requirements of large-scale animation rendering, I/O congestion has become one of the main bottlenecks to constrain the whole performance seriously. What's worse, frequent rendering data access in remote storages brings a heavy burden of storage and delay of data access. Distributed Memory caching system for Rendering (RenDMS) is proposed to alleviate I/O congestion and improve data access performance. Rendering data is accessed from memories but not remote storages to reduce the overhead of data access and transmission. The concept of rendering unit is put forward to cluster rendering nodes, and MPI-based RPC, two-level data management, and dynamic adaptive placement are proposed to make rendering performance much better. Experiments have demonstrated the effectiveness of RenDMS. Compared with direct remote disk access, parallel rendering with RenDMS can not only shorten data access time more than 40%, but also speedup the execution of rendering applications efficiently.

关键词： Rendering (computer graphics) Distributed databases Lenses Data communication Memory management Animation Engines

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A novel parallelized motion estimation algorithm for GPU based video encoding

A novel parallelized motion estimation algorithm for GPU bas...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Wenbin Jiang Pengcheng Wang Min Long Hai Jin Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509021864

Cloud-based video encoding has become more and more popular in Internet, especially for mobile clients, considering their limited resources. Recently, GPUs (Graphics Processor Units) make the cloud-based video encoding more economic and efficient. However, the motion estimation in inter prediction, which usually occupies about 70% encoding time in H.264/AVC, is still a big headache because of its complexity. In this paper, a novel motion estimation algorithm is proposed, which is customized for GPU-based cloud encoding, considering motion tendency. A mean subsampling template is presented for a pre-search approach to get motion tendency, which can reduce computation cost obviously with less quality loss. To improve the efficiency of the CUDA (Compute Unified Device Architecture) thread organization for motion estimation, a section-division method is presented. Experimental results show that the proposed algorithm can reduce nearly 22% computation time with less video quality loss, compared with the state-of-the-art work.

关键词： Motion estimation Graphics processing units Instruction sets Encoding Prediction algorithms Algorithm design and analysis Mobile communication

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Privacy Protection for Perceptual Applications on Smartphones

Privacy Protection for Perceptual Applications on Smartphone...

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IEEE International Conference on Mobile services (MS)

作者： Kao Zhao Deqing Zou Hai Jin Zhangbiaoge Tian Weizhong Qiang Weiqi Dai Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

Today's smartphones are equipped with various embedded motion sensors, such as accelerometer, gyroscope, and orientation sensors. Perceptual applications perceive the environment of mobile users via sensors. However, malicious applications may use these sensors to steal user's privacy, and attackers can use sensors as side channel data to infer user's inputs. Existing solutions suffer from limited sensors and overhead problems. In this paper we present Perceptual Assistant (PA), a practical privacy protection system for all the sensors and untrusted perceptual applications. PA allows users to customize the sensor policy of third-party applications, and prevent malicious application from accessing sensors at runtime. We evaluate PA with several typical perceptual applications that perform diverse tasks. PA system shows both practical and lightweight: it can protect user's privacy efficiently while maintaining reasonable overhead.

关键词： Privacy Smart phones Microphones Cameras Mobile communication Data privacy

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