Authors: Azadeh Farzan Zachary Kincaid, Andreas Podelski
Abstract: The correctness of a sequential program can be shown by the annotation of its control flow graph with inductive assertions. We pro-pose inductive data flow graphs, data flow graphs with incorporated inductive assertions, as the basis of an approach to verifying concurrent programs. An inductive data flow graph accounts for a set of dependencies between program actions in interleaved thread executions, and therefore stands as a representation for the set of concurrent program traces which give rise to these dependencies. The approach first constructs an inductive data flow graph and then checks whether all program traces are represented. The size of the inductive data flow graph is polynomial in the number of data dependencies (in a sense that can be made formal); it does not grow exponentially in the number of threads unless the data dependencies do. The approach shifts the burden of the exponential explosion towards the check whether all program traces are represented, i.e., to a combinatorial problem (over finite graphs).
Title: Minimizing Transmit Power for Cooperative Multicell System with Massive MIMO
Authors: Jinkyu Kang and Joonhyuk Kang
Abstract: We consider the problem of designing transmit beam former and power for downlink cooperative base-station(BS) system with a large antenna arrays. Since the design of the beam forming vector at the transmitter requires high computational complexity, in a large antenna arrays, we utilize the zero-forcing transmit beamformer, which is the simplest form and the optimal performance in a large antenna arrays. Therefore, this paper focuses on the design of power allocation with n¼üxed transmit beam former for minimizing the transmit power while meeting target signal-to-interference-and-noise-ratio(SINR) of each user and power constraints. We consider two scenarios according to the power constraints of cooperative BSs.One scenario is the sum power constraint on the cooperative base-stations. In this case, the cooperative BSs share the total available transmit power. However, each BS exists a maximum availabletransmit power in practical implementations. Thus, we consider amore realistic per BS power constraints. We proposed the solution strategies for both scenarios: For the sum power constraint case,a simple intuitive solution, where the power is allocated withoutregard to the power constraint until the SINR constraints issatisn¼üed, is presented. For the per BS power constraints case, weuse the properties of a large antenna arrays to n¼ünd the solution ofclosed form. We also demonstrate, via numerical simulation, the performance of proposed strategy is convergent to the optimal performance which is achieved by using the iterative algorithm.
Authors: Julia Borghoff, Lars R Knudsen, Gregor Leander, and Sren S Thomsen
Abstract: This paper considers PRESENT-like ciphers with key-dependent S-boxes. We focus on the setting where the same selection of S-boxes is used in every round. One particular variant with 16 rounds, proposed in 2009, is broken in practice in a chosen plaintext/chosen cipher text scenario. Extrapolating these results suggests that up to 28 rounds of such ciphers can be broken. Furthermore, we outline how our attack strategy can be applied to an extreme case where the S-boxes are chosen uniformly at random for each round, and where the bit permutation is key-dependent as well.
Publish Year: 2013
Published in: Journal of Cryptography - Journal of Springer
Title: Visualizing Uncertainty in Multi-resolution Volumetric Data Using Marching Cubes
Authors: J Ma,D Murphy,M Hayes,G Provan
Abstract: Data sets acquired from complex scientific simulation, high precision engineering experiment and high-speed computer network have been exponentially increased, and visualization and analysis of such large-scale of data sets have been identified as a significant challenge to the visualization com-munity. Over the past years many scientists have made at-tempt to address this problem by proposing various data reduction techniques. Consequently the size of data can be reduced and issues associated to the visualization can be improved (e.g. real-time interaction and visual overload).However, during the process of data reduction, the information of original data sets was approximated and potential errors were introduced. It leads to a new problem with regard to the integrity of the data and might mislead users for incorrect decision making. Therefore in this paper we aim to solve the problem by introducing three novel uncertainty visualization methods, which depict both the multi-resolution(MR) approximations of the original data set and the errors associated with each of its low resolution representations. As a result we faithfully represent the MR data sets and allow users to make suitable decisions from the visual output. We applied our techniques on a data set from medical domain to demonstrate their effectiveness and usability.
Title: Visualizing Uncertainty in Multi-resolution Volumetric Data Using Marching Cubes
Authors: J Ma,D Murphy,M Hayes,G Provan
Abstract: Data sets acquired from complex scientific simulation, high precision engineering experiment and high-speed computer network have been exponentially increased, and visualization and analysis of such large-scale of data sets have been identified as a significant challenge to the visualization com-munity. Over the past years many scientists have made at-tempt to address this problem by proposing various data reduction techniques. Consequently the size of data can be reduced and issues associated to the visualization can be improved (e.g. real-time interaction and visual overload).However, during the process of data reduction, the information of original data sets was approximated and potential errors were introduced. It leads to a new problem with regard to the integrity of the data and might mislead users for incorrect decision making. Therefore in this paper we aim to solve the problem by introducing three novel uncertainty visualization methods, which depict both the multi-resolution(MR) approximations of the original data set and the errors associated with each of its low resolution representations. As a result we faithfully represent the MR data sets and allow users to make suitable decisions from the visual output. We applied our techniques on a data set from medical domain to demonstrate their effectiveness and usability.
Title: Thermal phenomena associated with water transport across a fuel cell membrane: Soret and Dufour effects
Authors: K Glavatskiy a,n , JG Pharoah b , S Kjelstrup
Abstract: We present calculations of the coupling effects that take place when heat and water are transported across a membrane relevant to fuel cells, using the theory of non-equilibrium thermodynamics. Numerical results are given for the Nafeon membrane bounded by surfaces of molecular thickness in contact with water vapor of varying relative humidity. Analytical expressions for thermal effects of water transport are given. We show how reversible heat transport (Dufour effects) can be understood in terms of coupling coefficients (heats of transfers). The sign of the enthalpy of adsorption of water in the membrane determines the sign of the coupling coefficient, the Dufour and Soret effect as well as thermal osmosis effects meaning that the effect can be large at interfaces. Weshow how data presented in the literature can be understood in terms of the presented theory. Using common estimates for transport properties in the membrane and its surface, we ?nd that the more detailed equations predict a 10 30% variation in the heat and mass fluxes as the membrane thickness drops below 1 mm. Analysis of experiments on thermal osmosis suggests that more accurate measurements on the water content as a function of activity are required.
Publish Year: 2013
Published in: Journal of Membrane Science - Science Direct
Title: An artificial bee colony algorithm for the maximally diverse grouping problem
Authors: Francisco J Rodriguez , M Lozano a , C GarcaMartnez b , Jonathan D GonzlezBarrera
Abstract: In this paper, an artificial bee colony algorithm is proposed to solve the maximally diverse grouping problem. This complex optimization problem consists of forming maximally diverse groups with restricted sizes from a given set of elements. The artificial bee colony algorithm is a new swarm intelligence technique based on the intelligent foraging behavior of honeybees. The behavior of this algorithm is determined by two search strategies: an initialization scheme employed to construct initial solutions and a method for generating neighboring solutions. More specifically, the proposed approach employs a greedy constructive method to accomplish the initialization task and also employs different neighborhood operators inspired by the iterated greedy algorithm. In addition, it incorporates an improvement procedure to enhance the intensification capability. Through an analysis of the experimental results, the highly effective performance of the proposed algorithm is shown in comparison to the current state-of-the-art algorithms which address the problem.
Publish Year: 2013
Published in: Information Sciences - Science Direct
Title: A Survey of Ant Colony Optimization-Based Approaches to Routing in Computer Networks
Authors: Peter Janacik Dalimir Orfanus Adrian Wilke
Abstract: Nature has provided an elegant solution for the routing problem millions of years ago, when ant colonies started to use swarm intelligence to discover food and route it reliably to their formicaries. The approach utilized by ants has several advantages that are also useful in computer networks: complete distribution, load balancing finding shortest paths with a high probability. Several routing protocols designed for the area of computer networks have made use of this approach, called ant colony optimization. This paper provides first a broad overview of ant colony optimization-based routing protocols, while focusing on four selected approaches in later sections, describing their operation and discussing their properties in detail.
Publish Year: 2013
Published in: ISMS - IEEE
Number of Pages: 6
موضوع: الگوریتم کلونی مورچه‌ها (Ant Colony Algorithm) – مسیریابی هوشمند در شبکه‌های کامپیوتری (Routing in Computer Networks)
Title: Three-stage hybrid-flowshop model for cross-docking
Authors: Adrien Bellanger , Said Hanafl , Christophe Wilbaut
Abstract: This paper deals with the optimization of a cross-docking system. It is modeled as a three-stage hybrid flowshop, in which shipments and orders are represented as batches. The flrst stage corresponds to the receiving docks, the second stage corresponds to the sorting stations, and the third stage corresponds to the shipping docks. The objective of the problem is to flnd a schedule that minimizes the completion time of the latest batch. In order to obtain good quality feasible solutions, we have developed several heuristic schemes depending on the main stage considered, and several rules to order the batches in this stage. Then, we propose a branch-and-bound algorithm that takes into account the decomposition of the problem into three stages. To evaluate the heuristics and to reduce the tree size during the branch-and-bound computation, we also propose lower bounds. Finally, the computational experi- ments are presented to demonstrate the efflciency of our heuristics. The results show that the exact approach can solve instances containing up to 9 10 batches in each stage (i.e., up to 100 jobs). In addition, our heuristics were evaluated over instances with up to 3000 jobs, and they can provide good quality feasible solutions in a few seconds (i.e., less than 2 s per heuristic).
Publish Year: 2013
Published in: Computers & Operations Research - Science Direct
Title: A Secure Authentication Protocol among Mobile Phone and Wireless Sensor Networks
Authors: Ndibanje Bruce, Hoon Jae Lee
Abstract: The interaction between mobile phone and wireless sensor networks has increased these days due to the WSN emergence and its ubiquitous nature. Through cellular network, a user can access gateway of wireless sensor networks and gets the data. This paper proposes a secure authentication protocol where a user is strongly verified before accessing the data. The secure authentication proposed protocol provides many security principles to the users for instance user, mutual authentication, and secure session key establishment. Furthermore, security analysis shows .that the proposed protocol possesses many advantages against popular attacks, and achieves better efficiency at low computation cost.
Publish Year: 2013
Published in: ICACT - IEEE
Number of Pages: 8
موضوع: امنیت شبکه (Network Security) – احراز هویت (Authentication)
Title: Graph Semantic Based Design of XML Data Warehouse: A Conceptual Perspective
Authors: Anirban Sarkar Sankhayan Choudhury Narayan C Debnath
Abstract: This paper has proposed a Graph � semantic based conceptual data model for XML based Data Warehouse (DW) system called GXDW model, to conceptualize the different facets of multidimensional databases which may contain semi- structured data. The model is an extension of object oriented paradigm and defines a set of graph based formal constructs, variety of relationship types with participation constraints. It is accompanied with a rich set of graphical notations those are used to specify the conceptual level design of semi-structured data based DW system. The proposed approach facilitates modeling multidimensional data with the existence of irregular, heterogeneous, partially organized and unordered data set. Moreover, a transformation mechanism also has been proposed for guiding the transformation of GXDW model schema into the related set of XML documents.
Title: Reconstructing a Fragmented Face from a Cryptographic Identification Protocol
Authors: Andy Luong, Michael Gerbush, Brent Waters, Kristen Grauman
Abstract: Secure Computation of Face Identification (SCiFI) [20]is a recently developed secure face recognition system that ensures the list of faces it can identify (e.g., a terrorist watch list) remains private. In this work, we study the consequences of malformed input attacks on the system-from both a security and computer vision standpoint. In particular, we present 1) a cryptographic attack that allows dishonest user to undetectably obtain a coded representation of faces on the list, and 2) a visualization approach that exploits this breach, turning the lossy recovered codes into human-identifiable face sketches. We evaluate our approach on two challenging datasets, with face identification tasks given to a computer and human subjects. Whereas prior work considered security in the setting of honest in-puts and protocol execution, the success of our approach underscores the risk posed by malicious adversaries to to-days automatic face recognition systems.
Title: Online Advertising and its Security and Privacy Concerns
Authors: Angelia, Davar Pishva
Abstract: Commercial advertising has greatly benefitted from Internet services and online advertising can even be considered as the foundation of web economy. However, despite the availability of many books on how to create, use and make profit from online advertisement; there is little in-depth study on its true nature, security and privacy concerns. Through this research, the authors were able to establish an in-depth understanding of online advertising by collecting and analyzing numerous data on online advertising, with the hope that it could serve as a basis for further study on the field. The authors have also examined the security and privacy concerns of online advertising and the various gimmicks used to victimize innocent people. On the positive side, the paper explains the attractiveness of free online advertising services and the mechanism though which website owners profit from hosting online advertisements despite the fact that neither advertisers nor users pay for the services. On the negative side, the paper shows how online advertising hosts could be victimized by some shroud users for their money making purposes by continuous clicking of online advertisements without any intention of purchase. It also cites cases in which some website owners have marketed users_ private information behind the scene. The paper concludes by highlighting pros and cons of online advertising, Le., despite its numerous advantages, such as efficiency and range, it presents many dangers to advertisers, providers, website owners, and users.
Abstract: E-commerce web sites typically have large fluctuations in their IT resource usage, while rapid elasticity is an essential characteristic of cloud computing. These characteristics make the cloud a good fit for hosting e-commerce web sites. Cloud providers deploy their cloud in their data centers. However, cloud providers usually have a limited number of data center locations around the world. Thus, e-commerce web sites in the cloud may be far away from their customers. Long client-perceived response latency may cause e-commerce web sites to lose business. To solve this problem, we propose a virtual proxy solution to reduce the response latency of the e-commerce site in the cloud. In our approach, a virtual proxy platform is designed to cache applications and data of e-commerce sites. A k-means based table partitioning algorithm is designed to select frequently used data from the database in the cloud. We have used an industrial e-commerce benchmark TPC-W to evaluate the performance of our approach. The experimental results show that our approach can significantly reduce the client-perceived response time.
Authors: Fatemeh Mirrashed, Vlad I Morariu, Behjat Siddiquie, Rogerio S Feris, Larry S Davis
Abstract: We study the use of domain adaptation and transfer learning techniques as part of a framework for adaptive object detection. Unlike recent applications of domain adaptation work in computer vision, which generally focus on image classification, we explore the problem of extreme class imbalance present when performing domain adaptation for object detection. The main difficulty caused by this imbalance is that test images contain millions or billions of negative image sub windows but just a few image sub windows containing positive instances, which makes it difficult to adapt to changes in the positive classes present new do-mains by simple techniques such as random sampling. We propose an initial approach to addressing this problem and apply our technique to vehicle detection in a challenging urban surveillance dataset, demonstrating the performance of our approach with various amounts of supervision, including the fully unsupervised case.
Title: Thermal energy storage using thermo-chemical heat pump
Authors: MA Hamdan a, , SD Rossides b , R Haj Khalil
Abstract: A theoretical study was performed to investigate the potential of storing thermal energy using a heat pump which is a thermo-chemical storage system consisting of water as sorbet, and sodium chloride as the sorbent. The effect of different parameters namely; the amount of vaporized water from the evaporator, the system initial temperature and the type of salt on the increase in temperature of the salt was investigated and hence on the performance of the thermo chemical heat pump. It was found that the performance of the heat pump improves with the initial system temperature, with the amount of water vaporized and with the water remaining in the system. Finally it was also found that lithium chloride salt has higher effect on the performance of the heat pump that of sodium chloride.
Publish Year: 2013
Published in: Energy Conversion and Management - Science Direct
Title: Clustering local frequency items in multiple databases
Authors: Animesh Adhikari
Abstract: Frequent items could be considered as a basic type of patterns in a database. In the context of multiple data sources, most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be taken effectively using such patterns. Thus, it is important to cluster local frequency items in multiple databases. An overview of the existing measures of association is presented here. For the purpose of selecting the suitable technique of mining multiple databases, we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. The notion of high frequency item sets is introduced here, and an algorithm for synthesizing supports of such item sets is designed. The existing clustering technique might cluster local frequency items at a low level, since it estimates association among items in an item set with a low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A 2, association among items in a high frequency item set is synthesized based on it. The soundness of the clustering technique has been shown. Numerous experiments are conducted using ve datasets, and the results on different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: داده کاوی (Data Mining) - خوشه بندی (Clustering)
Abstract: The purpose of this paper is to describe a new emerging science: Internet science. This paper argues that cur-rent Internet is a direct by-product of Shannon’s Information theory and its master thesis. After that I attempt a general and generic definition of a network and discuss the different alternatives. This discussion results in defining a network as asset of distributed actors cooperating for exchanging information. Based on this definition, I describe how cooperation in networks happens at two conceptual levels: on the structure and the growth of the network and on the information exchange (or forwarding). After describing a potential approach to understand the growth of a network as resulting from an iterative prisoner’s dilemma game, I explained how forwarding cooperation can be divided into three classes: full cooperation, selfishness and non-rational cases. This discussion opens a discussion on the need to develop a new economical theory for understanding networks and information exchange.
به دنبالطرح معلمکه تخفیف های ارزنده ای را برای کاربران ارائه کرده است تعدادی از کاربران خواستار دریافت تخفیف های بدون محدودیت زمانی بودند لذا با موافقت مدیریت محترم مجموعه ایران سای و در راستای هر چه بیشتر یاری رساندن به کاربران محترم، تعرفه هایی به شرح زیر ارائه می گردد. امیدواریم با این گام بسیار کوچک کمکی به کاربران ایران سای کرده باشیم.
کلیه تعرفه های زیر بدون محدودیت زمانی می باشند و این اطلاعیه تا پایان اردیبهشت ماه 1392 در قالب طرح معلم2 معتبر خواهد بود.
کاربرانی که 10 امتیاز خریداری می کنند 12 امتیاز دریافت خواهند کرد.
کاربرانی که 20 امتیاز خریداری می کنند 25 امتیاز دریافت خواهند کرد.
کاربرانی که 30 امتیاز خریداری می کنند 38 امتیاز دریافت خواهند کرد.
کاربرانی که 40 امتیاز خریداری می کنند 51 امتیاز دریافت خواهند کرد.
کاربران که 50 امتیاز خریداری می کنند 65 امتیاز دریافت خواهند کرد.
کاربران گرامی پس از خرید امتیاز، لطفا ایمیلی را بهFinancial@IranSci.irبا عنوان تخفیف طرح معلم 2 ارسال نمایند (یا به پشتیبانی آنلاین اطلاع دهند) تا نسبت به ارتقاء امتیازشان اقدام شود.
Abstract: Neurons in the medial entorhinal cortex of rats have been found to respond in a two-dimensional hexagonal grid pattern anchored to the environment. Grid cells with different spatial frequencies are thought to contribute to the creation of unimodal place cell responses useful for spatial navigation. In this paper we present results from an analog VLSI circuit that generates a hexagonal grid of activity using continuous attractor dynamics and transmits this pattern via neuron-like spikes. This circuit is a component of a larger system for modeling the neural circuits underlying mammalian spatial navigation.
Abstract: The concept of the strongest path plays a crucial role in fuzzy graph theory. In classical graph theory, all paths in a graph are strongest, with a strength value of one. In this article, we introduce Menger’s theorem for fuzzy graphs and discuss the concepts of strength- reducing sets and t-connected fuzzy graphs. We also characterize t-connected and t-arc connected fuzzy graphs.
Publish Year: 2013
Published in: Information Sciences - Science Direct
Title: Clinical Information Systems in Private Hospitals
Author: Aini Aman
Abstract: With the tremendous developments in Information Technology (IT), many organizations in private sectors use IT to transform their services and operation. Like any other sectors, the health sector has been benefited from the adoption of IT in transforming their services for society well-being. The applications of IT have greatly improved accessibility, quality and cost-effectiveness of health care. Clinical Information Systems (CIS) are one of the examples of the applications of IT in healthcare. CIS are computer applications that support the operations of clinicians engaged in providing care to patients. However, CIS does not always improve the outcomes and performance of the health professionals, but might bring about negative impacts on their performance and practices. Studies on medical errors have shown that errors in healthcare and medicine are not rare and may bring about severe harm to patients. This paper aims to understand the use of CIS in hospitals and their impact on patient safety. Using interviews with medical practitioners in private hospitals in Malaysia, the findings provide insights for system developers and hospital managers on the usability of CIS to ensure the delivery of better patient care since error reduction is one of the dimensions of health service quality.
Publish Year: 2013
Published in: ICACT – IEEE
موضوع: سیستمهای اطلاعاتی بیمارستانی (Hospital Information Systems)
Title: Leveraging a Crowd Sourcing Methodology to Enhance Supply Chain Integrity
Authors: Han Lin, Moses Schwartz, John Michalski, Mayuri Shakamuri, Philip Campbell
Abstract: Supply chain integrity (SCI) is emerging as one of the top security issues facing critical systems. The government’s reliance on commercial off-the-shelf (COTS) products is apparent, as is the threat of critical systems being designed and manufactured overseas. To date, few tools or capabilities exist to prevent or even detect these classes of attacks. Programs, such as DARPA Trust, exist to identify solutions; however, alternative strategies must be explored. It is extremely challenging to establish the trustworthiness of a supply chain for a product or system in today’s globalized climate, especially given the complexity and variability of the hardware and software, and the diverse geographical areas where they are made. Counterfeit items, from individual chips to entire systems, have been found both in commercial and government sectors. Supply chain attacks can be inserted at any point during the product or system life cycle and can have detrimental effects to mission success. We hypothesize that wisdom of crowds techniques may be applicable to the analysis of supply chain integrity. Current supply chain security efforts are hindered by a lack of detailed information on a product’s entire supply chain. End-users have virtually no access to supply chain information, and even major manufacturers may have difficulty getting access to their suppliers’ Sub suppliers. Component testing and even reverse engineering can be used to mitigate risks, but these approaches are imperfect, time consuming, and expensive. This paper will discuss the development of a semi-automated supply chain integrity risk analysis framework to assist the supply chain security analysts in assessing the level of risk associated with a component of a mission critical system. This capability can provide the system designer a more rigorous and efficient approach to assess the security of the components in the design. By fusing all of these tools into a centralized framework, we hypothesis that we can create a capability that will enable analysts to more effectively interrogate the data and extract trending as well as critical information.
Title: Data mining agent conversations: A qualitative approach to multi-agent systems analysis
Authors: Emilio Serrano, Michael Rovatsos, Juan A Botia
Abstract: This paper presents a novel method for analyzing the behavior of multivalent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols specified at the knowledge level. More low-level communication mechanisms only allow for a quantitative analysis of the occurrence of message types, the frequency of message sequences, and the empirical distributions of parameter values. Quite differently, the semantics of languages and protocols in multi-agent systems can help to extract qualitative properties of observed conversations among agents. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as the context of an observed interaction, and using them as features of learning samples. The contexts mined from such analyses, or context models, can then be used for various tasks, e.g. for predicting others future responses (useful when trying to make strategic communication decisions to achieve a particular outcome), to support ontological alignment (by comparing the properties of logical constraints attached to messages across participating agents), or to assess the trustworthiness of agents (by verifying the logical coherence of their behavior). This paper details a formal approach that describes our notion of context models in multi-agent conversations, an implementation of this approach in a practical tool for mining qualitative context models, and experimental results to illustrate its use and utility.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: داده کاوی (Data Mining) – عاملهای هوشمند (Intelligent Agents)
Title: Handwritten Text Segmentation using Average Longest Path Algorithm
Authors: Dhaval Salvi, Jun Zhou, Jarrell Waggoner, and Song Wang
Abstract: Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different hand-writing styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraint on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using setof lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the characterize and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.
Publish Year: 2013
Published in: WACV – IEEE
موضوع: تشخصی دست خط (Handwritten Text Recognition)
Title: Influence of Sci-Fi Films on Artificial Intelligence and Vice-Versa
Authors: D Lorenk, M Tarhaniov and P Sincak
Abstract: Sci-fi technological movie domain is an important part of human culture. The paper focus on comparison study of selected robotics sci-fi movie domain from a technological point of view. It is necessary to accomplish technological analysis of studied sci-fi movies and able to distinguish about possible current technology and future direction of the artificial intelligence in the domain of Robot intelligence. The review of existing movies which are in fact influencing thinking of humans is essential since it can influence a future research direction in AI. This information is interesting for inspiration of students and research associates in theory and applications. In conclusion, we envision potential problems of social networks and impact of Internet of things facts which is becoming a reality with IPv6 protocol. The goal of the paper is also to underline the importance of such cultural phenomena as sci-fi movies for the future of humanity.
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Title: Tell Me More? The Effects of Mental Model Soundness on Personalizing an Intelligent Agent
Authors: Todd Kulesza, Simone Stumpf, Margaret Burnett, Irwin Kwan
Abstract: What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agents personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system’s reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system’s reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user’s intentions.
Title: A survey of skyline processing in highly distributed environments
Authors: Katja Hose Akrivi Vlachou
Abstract: During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach have to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.
Title: Efficient stochastic algorithms for document clustering
Authors: Rana Forsati, Mehrdad Mahdav, Mehrnoush Shamsfard, Mohammad Reza Meybodi
Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Addition- ally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document data- sets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).
Publish Year: 2013
Published in: Information Sciences - Science Direct
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