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New algorithm for Modeling S-box in MILP Based Differential and Division Trail Search 1

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10th International Conference on Security for Information Technology and Communications (SECITC)

作者： Sasaki, Yu Todo, Yosuke NTT Secure Platform Labs 3-9-11 Midori Cho Musashino Tokyo 1808585 Japan

ISBN: (数字)9783319692845

ISBN: (纸本)9783319692845;9783319692838

This paper studies an automated differential-trail search against block ciphers in which the problem of finding the optimal trail is converted to one of finding the optimal solution in a mixed-integer-linear programming (MILP). The most difficult part is representing differential properties of an S-box, known as differential distribution table (DDT), with a system of inequalities. Previous work builds the system by using a general-purpose mathematical tool, SAGE Math. However, the generated system for general-purpose contains a lot of redundant inequalities for the purpose of differential-trail search, thus inefficient. Hence, an auxiliary algorithm was introduced to minimize the number of inequalities by hoping that it minimizes the runtime to solve the MILP. This paper proposes a new algorithm to improve this auxiliary algorithm. The main advantage is that while the previous algorithm does not ensure the minimum number of inequalities, the proposed algorithm does ensure it. Moreover it enables the users to choose the number of inequalities in the system. In addition, this paper experimentally shows that the above folklore "minimizing the number of inequalities minimizes the runtime" is not always correct. The proposed algorithm can also be used in the MILP-based division-trail search, which evaluates the bit-based division property for integral attacks.

关键词： Differential trail Division trail Automated search tool S-box Mixed integer linear programming greedy algorithm

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Prolonging Lifetime in Wireless Sensor Networks with Interference Constraints 1

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12th International Conference on Green, Pervasive, and Cloud Computing (GPC)

作者： Carrabs, Francesco Cerulli, Raffaele D'Ambrosio, Ciriaco Raiconi, Andrea Univ Salerno Dept Math Via Giovanni Paolo 2 132 I-84084 Fisciano SA Italy

ISBN: (数字)9783319571867

ISBN: (纸本)9783319571867;9783319571850

In this work, we consider a scenario in which we have to monitor some locations of interest in a geographical area by means of a wireless sensor network. Our aim is to keep the network operational for as long as possible, while preventing certain sensors from being active simultaneously, since they would interfere with one another causing data loss, need for retransmissions and overall affecting the throughput and efficiency of the network. We propose an exact approach based on column generation, as well as a heuristic algorithm to solve its separation problem. Computational tests prove our approach to be effective, and that the introduction of our heuristic in the Column Generation framework allows significant gains in terms of required computational effort.

关键词： Wireless sensor network Column generation Maximum lifetime Interference constraints greedy algorithm

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Improvement of cross section model in COCAGNE code of the calculation chain of EDF

Improvement of cross section model in COCAGNE code of the ca...

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作者： THI HIEU LUU Universite Pierre et Marie Curie - Paris VI

学位级别：博士

In order to optimize the operation of its nuclear power plants, the EDF's R&D department is currently developing a new calculation chain to simulate the nuclear reactors core with state of the art tools. These calculations require a large amount of physical data, especially the cross-sections. In the full core simulation, the number of cross-section values is of the order of several billions. These cross-sections can be represented as multivariate functions depending on several physical parameters. The determination of cross-sections is a long and complex calculation, we can therefore pre-compute them in some values of parameters (offline calculations), then evaluate them at all desired points by an interpolation (online calculations). This process requires a model of cross- section reconstruction between the two steps. In order to perform a more faithful core simulation in the new EDF's chain, the cross-sections need to be better represented by taking into account new parameters. Moreover, the new chain must be able to calculate the reactor in more extensive situations than the current one. The multilinear interpolation is currently used to reconstruct cross-sections and to meet these goals. However, with this model, the number of points in its discretization increases exponentially as a function of the number of parameters, or significantly when adding points to one of the axes. Consequently, the number and time of offline calculations as well as the storage size for this data become problematic. The goal of this thesis is therefore to find a new model in order to respond to the following requirements: (i)-(offline) reduce the number of pre-calculations, (ii)-(offline) reduce stored data size for the reconstruction and (iii)-(online) maintain (or improve) the accuracy obtained by multilinear interpolation. From a mathematical point of view, this problem involves approaching multivariate functions from their pre-calculated values. We based our research on the Tucke

关键词： cross-sections Tucker decomposition low-rank tensor approximation higher-order singular value decomposition greedy algorithm neutronics reduced model sparse grids

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Efficient Selection of Influential Nodes for Viral Marketing in Social Networks

Efficient Selection of Influential Nodes for Viral Marketing...

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IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)

作者： Menta, Venkata Pushpak Teja Singh, Parikshit Kishor Aurigo Software Technol Bengaluru India Indian Inst Informat Technol Kota Dept Elect & Commun Engn MNIT Campus Jaipur Rajasthan India

ISBN: (纸本)9781509049974

There has been an exponential growth in social networking and online shopping with the internet revolution in the recent past. Viral marketing, exploiting social networks to promote various products, has proved successful in influencing the public compared to other media. One well known task in this area is to choose the best nodes that maximize the overall influence propagated in the social network. t-Influence Maximization problem has been addressed in this paper which can be defined as maximizing the overall influence in a social network by selecting seeds for the given t products with their seed requirements. Constraints are set on number of products to be recommended for any single user (to avoid spamming) and total number of seeds to be selected for a particular product (budget constraint). The technique we have used, a greedy algorithm to the aforementioned t-influence maximization problem. It not only allocates seeds with maximum total influence, but also ensures that any one product doesn't dictate the overall influence and a fair selection is done. An efficient algorithm is designed for calculating the approximate influence of the selected nodes which is important in practical situations. Effectiveness and scalability of the algorithm is analyzed and verified using simulations on real-life facebook data.

关键词： viral marketing influence maximization social networks independent cascade model greedy algorithm Oracle algorithm spam limit prediction

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Accelerating Community-Search Problem through Faster Graph Dedensification

Accelerating Community-Search Problem through Faster Graph D...

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IEEE International Conference on Big Data and Smart Computing (BigComp)

作者： Khan, Kifayat Ullah Anh, Tu Nguyen Akhond, Mostafijur Rahman Nawaz, Waqas Lee, Young-Koo Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea Islamic Univ Almadinah Medina Saudi Arabia

ISBN: (纸本)9781509030156

Community-search is the problem of finding a densely connected subgraph from a large graph, for given set of query nodes. It is useful, for example, when some high profiled researchers want to arrange a conference or some celebrities wish to arrange a party, and are searching for other researchers and similar personalties respectively. Technically, the problem involves maximizing the minimum degree to find a highly connected subgraph. Well known greedy algorithm for this purpose, iteratively deletes nodes with minimum degree to meet certain objective function. We observe that greedy operates in an in-efficient manner due to densely connected regions in a graph, referred as Hot Spots. In this paper, we provide a new concept of performing community-search on a dedensified graph. Our aim is to sparsify the hot spots to accelerate global searching method of greedy to make it applicable on a large graph. Recently a graph dedensification approach has been proposed that adds Compressor Nodes in a graph for dedensification. However, this method is in-efficient since it has to traverse entire graph to compress the hot spots. To solve this problem, we propose a faster graph dedensification algorithm by using Locality Sensitive Hashing (LSH). We improve time complexity of existing graph dedensification method from O (vertical bar E vertical bar) to O (vertical bar N vertical bar+vertical bar eHDN vertical bar+k), where N, E, e(HDN), and k are nodes, edges, edges of High Degree Nodes (HDN), and hash functions respectively, and vertical bar N vertical bar>> vertical bar HDN vertical bar. Once the graph is dedensified, we use it to accelerate community-search operation. We perform experiments on two real world graphs, and observe significant improvement in execution time of greedy algorithm, add lesser compressor nodes, and perform reduced traversals for graph dedensification.

关键词： greedy algorithms Acceleration Search problems Time complexity Proposals Computer science Electronic mail greedy algorithm tabu search complexity classes acceleration Line graph electronic mail computer science Hot spots Ganglia greedy method execution time hashing Objective function

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Online and Random-order Load Balancing Simultaneously 17

Online and Random-order Load Balancing Simultaneously

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28th Annual ACM-SIAM Symposium on Discrete algorithms (SODA)

作者： Molinaro, Marco Pontificia Univ Catolica Rio de Janeiro Comp Sci Dept Rio de Janeiro Brazil

ISBN: (纸本)9781510836358

We consider the problem of online load balancing under l(p)-norms: sequential jobs need to be assigned to one of the machines and the goal is to minimize the l(p)-norm of the machine loads. This generalizes the classical problem of scheduling for makespan minimization (case l(infinity)) and has been thoroughly studied. However, despite the recent push for beyond worst-case analyses, no such results are known for this problem. In this paper we provide algorithms with simultaneous guarantees for the worst-case model as well as for the random-order(i.e. secretary) model, where an arbitrary set of jobs comes in random order. First, we show that the greedy algorithm (with restart), known to have optimal O(p) worst-case guarantee, also has a (typically) improved random-order guarantee. However, the behavior of this algorithm in the random-order model degrades with p. We then propose algorithm SIMULTANEOUSLB that has simultaneously optimal guarantees (within constants) in both worst-case and random order models. In particular, the random-order guarantee of SIMULTANEOUSLB improves as p increases. One of the main components is a new algorithm with improved regret for Online Linear Optimization (OLO) over the non-negative vectors in the l(p) ball. Interestingly, this OLO algorithm is also used to prove a purely probabilistic inequality that controls the correlations arising in the random-order model, a common source of difficulty for the analysis. Another important component used in both SIMULTANEOUSLB and our OLO algorithm is a smoothing of the l(p)-norm that may be of independent interest. This smoothness property allows us to see algorithm SIMULTANEOUSLB as essentially a greedy one in the worst-case model and as a primal-dual one in the random-order model, which is instrumental for its simultaneous guarantees.

关键词： algorithms Load balancing greedy algorithm machine jobs Restarting Reduced order model low-order model smoothing

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Propagation based Prototype Prediction 14

Propagation based Prototype Prediction

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14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Su, Tonghua Dai, Hongliang Liu, Ce Yu, Lijun Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China Hainan Canc Hosp Imaging Ctr Haikou Hainan Peoples R China

ISBN: (纸本)9781538635865

The prediction phase is used to interact with end users, so its response speed is critical for a good user experience to large category recognition tasks. This paper presents a novel and fast algorithm for prototype prediction which may solve the current computing challenges in character input applications on smart terminals. We construct a social network for prototypes and their pair-wise connections. Such "prototype network" falls into "scale-free network", emerging "small-world effect". Under reasonable conditions, there exists a small geodesic path between each node pair. This feature guarantees us to search "better" nodes following the directed edges. Unfortunately, the naive search strategy results in a computing complexity of exponential order. To convert the problem to a manageable scale, we enhance the generic breadth-first search with greedy selectivity. As a result, our method just consider a small candidate set and further propagate from those seeds. Thorough analysis both on network structure and algorithmic propagation patterns is conducted and advantages in efficiency and practicality are revealed. Finally, we evaluated the proposed algorithm on a large-scale, large-category handwritten Chinese character recognition task. Experimental results show that the proposed algorithm can be tuned with either faster prediction speed or higher prediction accuracy.

关键词： prototype learning handwritten Chinese character recognition breadth-first search scale-free network analysis greedy algorithm

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Minimizing Data Access Latencies via Virtual Machine Placement Method in Datacenter 14

Minimizing Data Access Latencies via Virtual Machine Placeme...

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14th International Symposium on Pervasive Systems, algorithms and Networks (I-SPAN) / 11th International Conference on Frontier of Computer Science and Technology (FCST) / 3rd International Symposium of Creative Computing (ISCC)

作者： Zhang, Xinyan Zhang, Yong Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China Liaoning Normal Univ Sch Comp & Informat Technol Dalian Peoples R China

ISBN: (纸本)9781538608401

The processing framework of large-scale data is becoming a major concern due to an explosive growth of data intensive applications in the cloud environment, such as MapReduce/Hadoop architecture. Many virtual machines (VMs) are used for processing large-scale data of cloud applications. Therefore, the total completion time of a task is an important index to evaluate the cloud performance. The access latency between nodes is one of the key factors affecting the task completion time for computing-intensive applications. Additionally, minimizing total access time can reduce the overall bandwidth cost of running the job. This paper proposes an optimization model focused on optimizing VMs placement so as to minimize the total data access latency where the data sets have been located. According to the proposed model, our VMs optimization problem is linear programming. Therefore, we obtain the optimum solution of our model by the branch-and-bound algorithm that its time complexity is O(2(NM)) (where N is the number of the data nodes and M is the number of VMs). Simultaneously, we also present a greedy algorithm, which has O(NM) of time complexity, to solve our model. Finally, the simulation results show that all of the solutions of our model are superior to existing models and close to the optimal value.

关键词： datacenter greedy algorithm virtual machine branch-and-bound algorithm

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Implementation of Connected Dominating Set in Social Networks Using Mention Anomaly

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International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES)

作者： Nivetha, S. Sharmila, V. Ceronmani Hindustan Inst Technol & Sci Dept Informat Technol Madras Tamil Nadu India

ISBN: (纸本)9789811031748;9789811031731

Social network sites (SNS) are open platform that allows users to create an account in public sites. Social network site contains more active users, who share the posts, messages, or valuable information in real time. Even though social network provides timely updating still it is hard, because the current event mentions are overwhelmed by other mentions, which are totally irrelevant with the current affairs. The proposed methods mainly concentrate on text mentions in social networks and avoid the current affairs of the social aspect. Here two main methodologies are proposed that will improve the web content mining in social network sites. GBMAD (Geographically based mention anomaly detection), is a one of the best methods to manage dynamic links (i.e., Mentions: shares, likes, re-tweets, comments, etc.). GBMAD detects current events over the particular geographical location and it determines the importance of mentions over the crowd. The main advantage is it dynamically works in the real-time data rather than defined data. The next method is connected dominating set is used for identifying the closely related structure by using graph theory techniques. In the proposed work, the anomaly users are identified using Mention anomaly detection. In the future work, the implementation of Sybil attacks in the network will be identified and prevent from unsolicited friend requests in social networks using graph-based theory.

关键词： Social network Connected dominating set Anomaly detection Geographical heuristic greedy algorithm

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Path Optimization of AUV Based on Smooth-RRT algorithm 14

Path Optimization of AUV Based on Smooth-RRT Algorithm

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IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Yu Lijun Wei Zhihong Wang Zhengan Hu Yukun Wang Hui Harbin Engn Univ Coll Automat Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9781509067596

Aiming at the complex working environment and high real-time requirements for Autonomous Underwater Vehicle (AUV), in order to meet the requirements of path planning, the paper proposes a routing optimization method of Smooth-RRT algorithm. The convergence and angle factor are added to improve the growth point and the exploration point of the expansion tree, so as to improve the speed and practicability of the algorithm. The greedy algorithm is used to smooth the planning path to meet the special requirements of the shortest distance and maneuverability of AUV. The simulation results show that the method can quickly complete the path search, improve the search efficiency and shorten the planning distance. The optimized path is more suitable for robot tracking and meets the requirements of AUV planning system.

关键词： Smooth-RRT algorithm Convergence factor Angle factor greedy algorithm Path Optimization

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