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An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [ABSTRACT FROM AUTHOR]

In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top-k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top-k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms. [ABSTRACT FROM AUTHOR]

Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice. [ABSTRACT FROM AUTHOR]

In this paper analysis of different methods to remove stopwords in Punjabi language have been done. For organizing unstructured text in order to implement stopwords removal techniques, text preprocessing has to be applied. Text processing describes a variety of processing that is performed on raw data to prepare it for one more processing procedure which will be more helpful for performing some further, more purposeful analytic tasks. The words are called stopwords that occur most frequently in a document and contain very little information which is not essential in a document such as ਦ ????????? etc. A list of such words is known as 'stopwords list' or 'stopwords corpus'. These words are removed in the preprocessing phase of the text classification process. The process of removing stopwords help to save time and reduces the size of the document. It also helps to increase performance of information retrieval (IR) tasks. Most of the researchers worked on languages such as English, Arabic, Sanskrit etc. in the IR field. Therefore a lot of work and efforts need to be done in languages other than the languages in which the research has been already done to a great extent. The main goal of this paper is to remove the stopwords in Punjabi language by using different techniques. Punjabi language is 11th most-spoken language of India which is written in Gurmukhi script. Punjabi language is also used in mass media such as news, advertisements, movies, music etc. There is no standard stopword list created for Punjabi language as most of the stopwords lists are created for English and other languages. In this paper, four different methods and its algorithms i.e Classic method using a pre-compiled stoplist, method based on frequency, method based on removing singletons, method based on Punjabi words corpus have been proposed, implemented and the results are analyzed to remove Punjabi stopwords. Thus the size of the document is reduced by 20-30% by eliminating the set of such stopwords [ABSTRACT FROM AUTHOR]

In this paper, we propose two sequence allocation schemes to overcome the drawbacks that protocol sequence allocation in vehicular ad hoc networks heavily relies on the help of roadside units and that lots of sequence resource is wasted owing to the one-to-one scheme. Specifically, the rectangle-cell (R-C) scheme is proposed for neighboring nodes to occupy sequences without overlapping on straight roads. Furthermore, the hexagon-cell (H-C) scheme is proposed to handle the sequence allocation problem on city roads. The sum of generalized prime sequences which builds the foundations for the proposed schemes is fully studied. Besides, the algorithms for vehicles to generate cell marks and occupy sequences without any inter-vehicle interference are given in detail. Simulation results show that both the R-C scheme and the H-C scheme can take full advantage of the sequence resource and exhibit their superiority in sequence utilization and throughput compared to the one-to-one scheme. [ABSTRACT FROM AUTHOR]

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

The problem of counting triangles in graphs has been well studied in the literature. However, all existing algorithms, exact or approximate, spend at least linear time in the size of the graph (except a recent theoretical result), which can be prohibitive on today's large graphs. Nevertheless, we observe that the ideas in many existing triangle counting algorithms can be coupled with random sampling to yield potentially sublinear-time algorithms that return an approximation of the triangle count without looking at the whole graph. This paper makes these random sampling algorithms more explicit, and presents an experimental and analytical comparison of different approaches, identifying the best performers among a number of candidates. [ABSTRACT FROM AUTHOR]

Quantum information science is an interdisciplinary subject spanning physics, mathematics, and computer science. It involves finding new ways to apply natural quantum-mechanical effects, particularly superposition and entanglement, to information processing in an attempt to exceed the limits of traditional computing. In addition to promoting its mathematical and physical foundations, scientists and engineers have increasingly begun studying cross-disciplinary fields in quantum information processing, such as quantum machine learning, quantum neural networks, and quantum image processing (QIMP). Herein, we present an overview of QIMP consisting of a succinct review of state-of-the-art techniques along with a critical analysis of several key issues important for advancing the field. These issues include improving current models of quantum image representations, designing quantum algorithms for solving sophisticated operations, and developing physical equipment and software architecture for capturing and manipulating quantum images. The future directions identified in this work will be of interest to researchers working toward the greater realization of QIMP-based technologies. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The commencement of decryption process of Advanced Encryption Standard (AES) algorithm is dependent on availability of the last round key. In this paper, we propose a look-ahead technique for increasing the speed of implementation of AES key schedule using which the last round key can be made available fast. The other round keys can also be computed in a parallel path using the proposed technique. Applications such as key search engines need to be agile to key changes for decrypting given encrypted messages using all the keys in the available key space so that fast decryption is possible. The FPGA implementation results using Xilinx XC5VLX85 are also provided. [ABSTRACT FROM AUTHOR]

Many companies now use crowdsourcing to leverage external as well as internal crowds to perform specialized work, and so methods of improving efficiency are critical. Tasks in crowdsourcing systems with specialized work have multiple steps and each step requires multiple skills. Steps may have different flexibilities in terms of obtaining service from one or multiple agents due to varying levels of dependency among parts of steps. Steps of a task may have precedence constraints among them. Moreover, there are variations in loads of different types of tasks requiring different skill sets and availabilities of agents with different skill sets. Considering these constraints together necessitate the design of novel schemes to allocate steps to agents. In addition, large crowdsourcing systems require allocation schemes that are simple, fast, decentralized, and offer customers (task requesters) the freedom to choose agents. In this paper, we study the performance limits of such crowdsourcing systems and propose efficient allocation schemes that provably meet the performance limits under these additional requirements. We demonstrate our algorithms on data from a crowdsourcing platform run by a nonprofit company and show significant improvements over current practice. [ABSTRACT FROM AUTHOR]

Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. Computational provenance is often the key to enable such reuse. In this paper, we show how reusable research objects can utilize provenance to correctly repeat a previous reference execution, to construct a subset of a research object for partial reuse, and to reuse existing contents of a research object for modified reuse. We describe two methods to summarize provenance that aid in understanding the contents and past executions of a research object. The first method obtains a process-view by collapsing low-level system information, and the second method obtains a summary graph by grouping related nodes and edges with the goal to obtain a graph view similar to application workflow. Through detailed experiments, we show the efficacy and efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

Referring to the issue that the hop value of the range-free DV-hop positioning algorithm could not reflect the distance between two nodes within the communication range, this paper proposes an algorithm named as HCRDV-Hop. According to the proposed algorithm, the distance measured by hops between two nodes is re-treated as the specific value with dividing the received signal strength (denoted as RSSI) between nodes with the maximum average signal strength of nodes (RSSImax). Based on that, we also introduce the mathematical reasoning as well as provide the implementation procedures of the proposed algorithm. Moreover, the simulation verification is then elaborated in order to evaluate the algorithm performance. According to the simulation result, we discover that the proposed HCRDV-Hop algorithm reveals better performance in the distribution of node positioning deviations and the relative positioning deviations. [ABSTRACT FROM AUTHOR]

As the core of rational utilization of computing resources, the scheduling algorithm has gained the interest of many researchers. Aimed at meta-job scheduling in heterogeneous environments, this paper puts forward three algorithms: the meta-job scheduling algorithm based on deviation (MaxD-min heuristic), the meta-job scheduling algorithm based on relative deviation (MaxRD-min heuristic), and the Cordwood algorithm (CA). The former two Algorithm are improvements of the classic Min-min algorithm. The latter CA algorithm is a new method that tries to place each building block on the tray with minimal amount of Sufferage. The result of experimenting shows that the three algorithms, in comparison to Min-Min and Max-Min algorithm, can effectively reduce the total span of scheduling (makespan). [ABSTRACT FROM AUTHOR]

Copyright of Revista Virtual Universidad Católica del Norte is the property of Revista Virtual Universidad Catolica del Norte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Semantic Relatedness computation has been a fundamental as well as an essential step for domains like Information Retrieval, Natural Language Processing, Semantic Web, etc. Many techniques for Semantic Relatedness calculation in a single domain have been proposed. However, these techniques give inappropriate results for the massive multidomain dataset because they provide a relation between concepts across different domains, which are not related to each other. Their similarities should be minimized. In this paper, a novel method, "modified Balanced Mutual Information(MBMI)," to calculate the semantic relatedness of multidomain data has been proposed. In this proposed method, to get semantic relatedness, concepts are extracted, followed by a fuzzy vector from a given corpus. A comparison of the proposed method with other existing methods has been performed. We used medical and computer science articles as our dataset. The proposed method shows better results for multidomain data. [ABSTRACT FROM AUTHOR]

The paper presents CSVisFrame, a framework for making visualizations, which solves the understanding difficulty on learning abstract concepts in computer science including data structures and algorithms. With the framework, instructors and developers can develop all varieties of interactive visualizations, with which students can learn and understand abstract concepts in computer science more easily. CSVisFrame has been applied to both offline and online computer courses. Students from Sichuan Normal University have enjoyed visualizations based on CSVisFrame in their Algorithm Design and Analysis course, and thousands of students of Jisuanke have benefitted from online CSVisFrame-based visualized computer science courses. The effectiveness of CSVisFramebased visualizations has been demonstrated by our sample survey, which shows that visualizations are widely accepted, and almost all students can achieve a better learning. CSVisFrame is opensourced1, and demonstrations based on CSVisFrame are free2. [ABSTRACT FROM AUTHOR]

Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e. the estimator converges in probability or in some stronger sense to the Bayes risk or to the Bayes decision function. Kernel methods based on minimizing the regularized risk over a reproducing kernel Hilbert space (RKHS) belong to these statistical machine learning methods. It is in general unknown which kernel yields optimal results for a particular data set or for the unknown probability measure. Hence various kernel learning methods were proposed to choose the kernel and therefore also its RKHS in a data adaptive manner. Nevertheless, many practitioners often use the classical Gaussian RBF kernel or certain Sobolev kernels with good success. The goal of this paper is to offer one possible theoretical explanation for this empirical fact. [ABSTRACT FROM AUTHOR]

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances. [ABSTRACT FROM AUTHOR]

Many commercial products and academic research activities are embracing behavior analysis as a technique for improving detection of attacks of many sorts—from retweet boosting, hashtag hijacking to link advertising. Traditional approaches focus on detecting dense blocks in the adjacency matrix of graph data, and recently, the tensors of multimodal data. No method gives a principled way to score the suspiciousness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. In this paper, we first give a list of axioms that any metric of suspiciousness should satisfy; we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute; moreover, we propose CROSSSPOT, an algorithm to spot dense blocks that are worth inspecting, typically indicating fraud or some other noteworthy deviation from the usual, and sort them in the order of importance (“suspiciousness”). Finally, we apply CROSSSPOT to the real data, where it improves the F1 score over previous techniques by 68 percent and finds suspicious behavioral patterns in social datasets spanning 0.3 billion posts. [ABSTRACT FROM AUTHOR]

Max-flow has been adopted for semi-supervised data modelling, yet existing algorithms were derived only for the learning from static data. This paper proposes an online max-flow algorithm for the semi-supervised learning from data streams. Consider a graph learned from labelled and unlabelled data, and the graph being updated dynamically for accommodating online data adding and retiring. In learning from the resulting non stationary graph, we augment and de-augment paths to update max-flow with a theoretical guarantee that the updated max-flow equals to that from batch retraining. For classification, we compute min-cut over current max-flow, so that minimized number of similar sample pairs are classified into distinct classes. Empirical evaluation on real-world data reveals that our algorithm outperforms state-of-the-art stream classification algorithms. [ABSTRACT FROM AUTHOR]

Due to the fact that existing database systems are increasingly more difficult to use, improving the quality and the usability of database systems has gained tremendous momentum over the last few years. In particular, the feature of explaining why some expected tuples are missing in the result of a query has received more attention. In this paper, we study the problem of explaining missing answers to top-k queries in the context of SQL (i.e., with selection, projection, join, and aggregation). To approach this problem, we use the query-refinement method. That is, given as inputs the original top-k SQL query and a set of missing tuples, our algorithms return to the user a refined query that includes both the missing tuples and the original query results. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently. [ABSTRACT FROM AUTHOR]

Flash storage devices are widely used for mobile consumer electronics due to small size, low power consumption, and high performance. Generally, the flash storage device consists of NAND flash memories. Compared to traditional magnetic disks, NAND flash memory requires an additional erase operation and its blocks have limited erase cycles. For extending its endurance, various wearleveling algorithms have been proposed. However, they invoke many read/write/erase operations and use many memory resources for managing their block states because they do not consider the property of the flash translation layer. To solve these problems, a new wear-leveling algorithm for the log-based flash translation layer is proposed in this paper. In the log-based flash translation layer, since log blocks are frequently updated and erased, the cold block rarely removed is reserved for a next log block so that all the blocks are evenly erased. In addition, the proposed algorithm reduces the usage of memory resources by exploiting k-bitwise erase table that only needs small k-bit erase flags for managing its block erase state. Through various experiments with related wear-leveling algorithms, this paper shows the superiority of the proposed wear-leveling algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a layered approach to model Jackson Pollock's dripping style of paintings. Having analyzed fractal-based algorithms and observed the details of Pollock's paintings, we designed a layered modeling approach that divides Pollock's artwork into four layers: from bottom up are background layer, irregular shape layer, line layer and water drop layer. The layers are drawn sequentially and independent, forming the desired Pollock style. We have developed a program using Processing to generate artworks of the dripping style. The parameters of our program can be randomly generated or tuned by the user, supporting high flexibility and effectiveness. Experimental results show that our layered modeling approach can systematically generate images resembling Pollock's dripping style. [ABSTRACT FROM AUTHOR]

In this paper we consider the properties of the most popular algorithms for genomic similarity search. All these methods use some type of heuristics to improve the speed of search that has negative impact upon the accuracy. Some of these methods split the content into small pieces of data, some of them use hashing, while some of them utilize the computational benefits of suffix tree. In many cases the output depends of the userproved parameters that define the sensitivity and the range of search. [ABSTRACT FROM AUTHOR]

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems. [ABSTRACT FROM AUTHOR]

In this paper, an Arcak-type generalized $$H_2$$ filter is designed for a class of static neural networks with time-varying delay. By employing some inequalities and constructing a suitable Lyapunov functional, a delay-dependent condition is derived by means of linear matrix inequalities such that the filtering error system is globally asymptotically stable and a prescribed generalized $$H_2$$ performance is achieved. It is shown that the design of such a desired filter for a delayed static neural network is successfully transformed into solving a convex optimization problem subject to some linear matrix inequalities. It is thus facilitated readily by some standard algorithms. A numerical example is finally provided to show the effectiveness of the developed approach. A comparison on the generalized $$H_2$$ performance for different gain parameters of the activation function is also given. [ABSTRACT FROM AUTHOR]

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases. [ABSTRACT FROM AUTHOR]

In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression. [ABSTRACT FROM AUTHOR]

Crowdsourcing has been shown to be effective in a wide range of applications, and is seeing increasing use. A large-scale crowdsourcing task often consists of thousands or millions of atomic tasks, each of which is usually a simple task such as binary choice or simple voting. To distribute a large-scale crowdsourcing task to limited crowd workers, a common practice is to pack a set of atomic tasks into a task bin and send to a crowd worker in a batch. It is challenging to decompose a large-scale crowdsourcing task and execute batches of atomic tasks, which ensures reliable answers at a minimal total cost. Large batches lead to unreliable answers of atomic tasks, while small batches incur unnecessary cost. In this paper, we investigate a general crowdsourcing task decomposition problem, called the Smart Large-scAle task DE composer (SLADE) problem, which aims to decompose a large-scale crowdsourcing task to achieve the desired reliability at a minimal cost. We prove the NP-hardness of the SLADE problem and propose solutions in both homogeneous and heterogeneous scenarios. For the homogeneous SLADE problem, where all the atomic tasks share the same reliability requirement, we propose a greedy heuristic algorithm and an efficient and effective approximation framework using an optimal priority queue (OPQ) structure with provable approximation ratio. For the heterogeneous SLADE problem, where the atomic tasks can have different reliability requirements, we extend the OPQ-based framework leveraging a partition strategy, and also prove its approximation guarantee. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments on representative crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

A hypergraph is one of the most developing area for modeling various practical problems in different fields, including computer science, biological sciences, social networks and psychology. Our main discussion in this research paper is to apply the notion of intuitionistic fuzzy sets to extend the theory of hypergraphs. We introduce the concept of isomorphism, dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and 2-section of an intuitionistic fuzzy hypergraph. We present some applications of intuitionistic fuzzy hypergraphs in planet surface networks, selection of authors of of intersecting communities in a social network and grouping of incompatible chemical substances. We design certain algorithms to construct dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and the selection of objects in decision-making problems. [ABSTRACT FROM AUTHOR]

Ternary content addressable memory (TCAM) plays an important role in modern routers due to its capability of performing fast IP address lookup. However, it is expensive, space limited, and a major source of power consumption in a router. In addition, because TCAM only reports the first matching entry, updating TCAM entries would be slow due to necessary entry reordering. In this paper, we present a trie-based algorithm, longest suffix split, to reduce the number of TCAM entries for IP address lookup. The algorithm divides route prefixes into two portions, subprefix and suffix. The route prefixes with the same subprefix and similar suffix portions can then be represented by one TCAM entry and one SRAM entry. Each SRAM entry stores one of two succinct data structures, depending on the threshold number of similar suffixes. The experimental results show that our scheme can reduce 50% to 95% TCAM entries for the original routing tables. Our scheme also supports incremental updates. Because the drawbacks of TCAM are related to the number of required entries, our scheme significantly improves the feasibility of TCAM-based IP address lookup. While network virtualization may store multiple forwarding information bases in a router, the number of supported virtual routers can be increased by our scheme. [ABSTRACT FROM AUTHOR]

Co-training is a framework for semi-supervised learning that has attracted much attention due to its good performance and easy adaptation for various learning algorithms. In a recent work, Caldas et al. proposed a co-training-based method using the recently proposed supervised learning method named minimal learning machine (MLM). Although the proposed method, referred to as Co-MLM, presented results that are comparable to other semi-supervised algorithms, using MLM as a base learner resulted in a formulation with heavy computational cost. Aiming to mitigate this problem, in this paper, we propose an improved variant of Co-MLM with reduced computational cost on both training and testing phases. The proposed method is compared to Co-MLM and other Co-training-based semi-supervised methods, presenting comparable performances. [ABSTRACT FROM AUTHOR]

Recent smart devices have adopted heterogeneous multi-core processors which have high-performance big cores and low-power small cores. Unfortunately, the conventional task scheduler for heterogeneous multi-core processors does not provide appropriate amount of CPU resources for multimedia applications (whose QoS is important to users), resulting in energy waste; it often executes multimedia applications and non-multimedia applications on the same core. In this paper, we propose an advanced task scheduler for heterogeneous multi-core processors, which provides appropriate amount of CPU resources for multimedia applications. Our proposed task scheduler isolates multimedia applications from non-multimedia applications at runtime, exploiting the fact that multimedia applications have a specific thread for video/audio playback (to play video/audio, a multimedia application should use a function that generates the specific thread). Since multimedia applications usually require a smaller amount of CPU resources than non-multimedia applications due to dedicated hardware decoders, our proposed task scheduler allocates the former to the small cores and the latter to the big cores. In our experiments on an Android-based development board, our proposed task scheduler saves system-wide (not just CPU) energy consumption by 8.9 percent, on average, compared to the conventional task scheduler, preserving QoS of multimedia applications. In addition, it improves performance of non-multimedia applications by 13.7 percent, on average, compared to the conventional task scheduler. [ABSTRACT FROM PUBLISHER]

Atmospheric discharges offer great risks to the population and activities that involve different systems such as telecommunications, energy distribution and transportation. Lightning prediction can contribute to minimize the risks of this natural phenomenon. Therefore the present paper presents a model for lightning prediction based on satellite atmospheric sounding data, calibrated and validated with lightning data in an Amazon region particular area through an investigation that considered five period cases for validation of lightning prediction: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). The machine learning technique used to predict lightning was the Artificial Neural Network (ANN) trained with Levenberg-Marquardt backpropagation algorithm to classify modeling related to lightning prediction. This classification relied on the possibility of lightning prediction from the vertical profile of air temperature obtained from satellite NOAA-19. Results show that ANN was capable of identifying adequately the class to which a new event belongs to in relation to categories of occurrence and absence of lightning with better performance than traditional methodologies. [ABSTRACT FROM AUTHOR]

Recent links between computer science and synthetic biology allow for construction of many kinds of algorithmic processes within cells, obtained either by a direct engineered design or by an evolutionary search. In the latter case, horizontal gene transfer and especially transfer of plasmids by conjugation is generally respected as a crucial source of genetic diversity in bacteria. While some previous studies focused on mutations as the crucial principle to obtain diversity for engineered evolution, here we consider conjugation itself as a tool to generate diversity from a pre-determined library of biocircuits basic components. The recent development of CRISPR-Cas9 and its programmable DNA cutting ability makes it a powerful selection tool able to remove nonfunctional biocircuits from a cell population. In this paper, we describe a framework for controlled bacterial evolution of biocircuits based on conjugation and on CRISPR-Cas9, resulting in a direct biological implementation of an evolutionary algorithm. In silico experiments provide data to estimate the computational/search capability of plasmid-based engineered evolution. [ABSTRACT FROM AUTHOR]

This paper presents an application for generating a dynamic selection screen in ABAP and the need and advantages of this solution instead of the manual modification of the coding itself for adding one or more parameters in the whole logic of the program. Depending on data found in a customizing table, we would like to generate a program with a selection screen, containing select options based on the criteria specified in the customizing table. The presented solution can be adapted to any dynamically generated ABAP program, according to the desired selection screen elements, and may be used as a template even for other programs. The advantages of the proposed solution are also presented along with the disadvantages, underlining the particularities of the ABAP programming language. [ABSTRACT FROM AUTHOR]

The triangle partition problem is a generalization of the well-known graph matching problem consisting of finding the maximum number of independent edges in a given graph, i.e., edges with no common node. Triangle partition instead aims to find the maximum number of disjoint triangles. The triangle partition problem is known to be NP-complete. Thus, in this paper, the focus is on the local maximization variant, called maximal triangle partition (MTP). Thus, paper presents a new self-stabilizing algorithm for MTP that converges in O( m) moves under the unfair distributed daemon. [ABSTRACT FROM AUTHOR]

Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attack. [ABSTRACT FROM AUTHOR]