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In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to "blow away the fog to get a clearer vision" about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future. • A bird's eye view of the AI application in all spectrum of geosciences is provided. • The mandatory modular steps of typical Earth AI workflows are summarized. • Twelve grand challenges in Earth AI and potential opportunities are introduced. [ABSTRACT FROM AUTHOR]

The traditional literature analysis mainly focuses on the literature metadata such as topics, authors, keywords, references, and rarely pays attention to the main content of papers. However, in many scientific domains such as science, computing, engineering, the methods and datasets involved in the papers published carry important information and are quite useful for domain analysis and recommendation. Method and dataset entities have various forms, which are more difficult to recognize than common entities. In this paper, we propose a novel Method and Dataset Entity Recognition model (MDER), which is able to effectively extract the method and dataset entities from the main textual content of scientific papers. The model is the first to combine rule embedding, a parallel structure of Convolutional Neural Network (CNN) and a two-layer Bi-directional Long Short-Term Memory (BiLSTM) with the self-attention mechanism. We evaluate the proposed model on datasets constructed from the published papers of different research areas in computer science. Our model performs well in multiple areas and features a good capacity for cross-area learning and recognition. Ablation study indicates that building different modules collectively contributes to the good entity recognition performance as a whole. The data augmentation positively contributes to model training, making our model much more robust. We finally apply the proposed model on PAKDD papers published from 2009–2019 to mine insightful results over a long time span. 1 1 PAKDD is the abbreviation of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Our source code and datasets are available at https://github.com/houlinlinvictoria/MDER. [ABSTRACT FROM AUTHOR]

• Fileless malware has no signature because it does not leverage executable files. • We suggest a memory-based approach for detecting and analyzing fileless malware. • This proposed method offers useful insight for the experts working in this field. • The proposed methodʼs applicability was demonstrated using a real case study sample. The rapid advancements in cyber-attack strategies are in parallel with the measures for detection, analysis, and prevention. Attackers have recently developed fileless malware that can simply bypass existing security mechanisms. Researchers publish reports to help discover fileless malware and to better understand the threatʼs scope to counteract it. However, with the lack of studies on fileless malware regarding the classification and the scale of the threat, they have not been thoroughly analyzed. As a result, in this research, we explored the most recent advancements in fileless malware prevention and detection and highlighted future research challenges. We also propose an analytical approach based on the attack strategies and attributes of the selected sample. Our method simplifies feature extraction and reduces processing load. Furthermore, compared to the static analysis we do not need for decompression and unpacking for the analysis. We applied the proposed method on a real case example. It has been seen that information about fileless malware detection, working mechanism, attack method and attacker named "Kovter" can be accessed. Our approach is advantageous and can be applied as a new technique for fileless malware detection to protect systems from cyber threats. This paper also presents an insight to the fileless malware threat and provides a basic review of the methods and techniques used in the detection and analysis of fileless malware attacks. [ABSTRACT FROM AUTHOR]

[Display omitted] • AI and automation can help fungi-based bioprocesses to increase production yields. • Machine learning can discover non-linear interactions during fungi cultivation. • Soft sensors enable the understanding of the complex bioprocess dynamics. • Robots minimize direct labor costs while improving the quality of fungal products. • Further improvements are needed for automating fungi-based industrial bioprocesses. By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi. [ABSTRACT FROM AUTHOR]

This paper comments on the published work dealing with "DSKmeans: A new kmeans-type approach to discriminative subspace clustering" [Knowledge-Based Systems, Vol. 70, pp. 293–300, 2014] proposed by X. Huang et al. Their clustering approach is based on a new mathematical model with two groups of variables: cluster centers and clusters memberships. They proposed an iterative algorithm to obtain the solution of this model. In each iteration, they fixed clusters memberships and claimed that the optimal cluster centers can be obtained by setting the derivative of the objective function of the model to zero. In this paper, we show that their proposed method cannot obtain the optimal solution of cluster centers for any given clusters memberships and some values of the model parameter. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a special class of recurrent neural network, termed Zhang neural network (ZNN), is investigated for the online solution of the time-varying matrix pseudoinverse. Meanwhile, a novel activation function, named Li activation function, is employed. Then, based on two basic Zhang functions (ZFs) and the intrinsically nonlinear method of ZNN design, two finite-time convergent ZNN models (termed ZNN-1 model and ZNN-2 model) are first proposed and investigated for time-varying matrix pseudoinversion. Such two ZNN models can be accelerated to finite-time convergence to the time-varying theoretical pseudoinverse. The upper bound of the convergence time is also derived analytically via Lyapunov theory. By exploiting the other three simplified ZFs and the extended nonlinearization method, three simplified finite-time convergent ZNN models (termed ZNN-3 model, ZNN-4 model and ZNN-5 model) are sequentially proposed. In addition, the link between the ZNN models and the Getz–Marsden (G–M) dynamic system is discovered and presented in this paper. Computer-simulation results further substantiate the theoretical analysis and demonstrate the effectiveness of ZNN models based on different ZFs for the time-varying matrix pseudoinverse. [Copyright &y& Elsevier]

Abstract: The 3D elastic problem has not been widely studied because of the computational burden. Over the past few years, 3D elastic full waveform inversion (FWI) techniques in the time and frequency domains have been proposed by some researchers based on developments in computer science. However, these techniques still have the non-uniqueness and high nonlinearity problems. In this paper, we propose a 3D elastic FWI algorithm in the Laplace domain that can mitigate these problems. To efficiently solve the impedance matrix, we adopt a first-order absorbing boundary condition that results in a symmetric system. A conjugate gradient (CG) solver can be used because the Laplace-domain wave equation is naturally positive definite. We apply the Jacobi preconditioner to increase the convergence speed. We identify the permissible range of Laplace damping constants through dispersion analysis and accuracy tests. We perform the Laplace-domain FWI based on a logarithmic objective function, and the inversion examples are designed for a land setting, which means that the source is vertically excited and multi-component data are considered. The inversion results indicate that the inversion that uses only the vertical component performs slightly better than the multi-component inversion. This unexpected result is obtained partly because we use a vertically polarized source. We analyze the residuals and Frechet derivatives for each component to examine the characteristics of the Laplace-domain multi-component FWI. The results indicate that the residuals and Frechet derivatives for the horizontal component have a singularity problem. The numerical examples demonstrate that the singularity problem is related to the directivity of the displacement and to taking the logarithm of Laplace-domain wave fields. To avoid this singularity problem, we use a simple method that excludes the data near the singular region. Although we can use either simultaneous or sequential strategies to invert the Laplace-domain data, we apply a simultaneous inversion strategy in this paper. Nonetheless, the numerical examples demonstrate that our inversion algorithm yields reasonable long-wavelength velocity structures. [Copyright &y& Elsevier]

Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology. [ABSTRACT FROM AUTHOR]

A new school subject, Leaving Certificate Computer Science, was introduced in September 2018 into Irish schools in a period of curriculum reform and change. Drawing on concepts of teacher agency and learner agency, and viewed through a figurational sociology lens, this paper explored the realities of teaching LCCS and the role teacher agency and learner agency played in such enactment. Through a case study design, four teachers were interviewed in a focus group semi-structured manner. To delve deeper into the pedagogical approaches used in LCCS, a further one teacher and their nine students were interviewed. The findings highlight an influential relationship (positive and negative, encouraging and discouraging) between teachers' agentic decisions in their process of achieving teacher agency and the students process of achieving learner agency. The paper offers a figurational viewpoint of the relationship between teacher agency and learner agency through a constructed Figure. We explicitly argue that the responsibility should not be placed on the teacher and student in achieving agency (as a product) but it is a combination of the teacher and the students and the other interdependent relationships within their figurations, alongside contextual factors, and educational social processes, which determine the fluctuation of the process of achieving teacher and learner agency. The paper provides considerations for computer science teacher education. • Explores the relationship between teacher agency and learner agency in Computer Science subject. • Uses figurational sociology to further understand the process of achieving agency. • Qualitative research study. • Considerations provided for Computer Science teacher education. [ABSTRACT FROM AUTHOR]

In this paper, the problem of H ∞ filtering for descriptor systems with strict linear matrix inequalities (LMIs) is investigated. The necessary and sufficient conditions for the solvability and the expression of the solution are obtained for both continuous-time and discrete-time descriptor systems. The proposed continuous-time filters extend the results presented by Xu and Lam (2006) by removing semi definite conditions and introducing new formulation for the filter parameters which does not require determination of intermediate variables. For discrete-time descriptors, differing from existing methods that provide sufficient conditions only, two necessary and sufficient conditions are introduced. New proposed design methods propose less conservative results comparing with existing works. Two numerical examples are presented to demonstrate the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

Many real-world networks have a topological structure characterized by cohesive groups of vertices. To perform the task of identifying such subsets of vertices, community detection in networks has aroused the interest of researchers and practitioners alike. In spite of the existence of various efficient community detection algorithms in the literature, most of them uses global information about the network, not applicable to distributed networks. This paper proposes a genetic-based algorithm to detect communities in directed networks based on local information to generate the offspring. The major difference between the proposed strategy and those found in the literature is the way of exploiting target regions of interest in the solution space. This step is directly influenced by the crossover operator that depends largely on the individual representation. In the introduced strategy, GA-LP, the individual is locally stored in the vertices as labels, what brings more flexibility in the system to be adapted to address applications that involve, for example, dynamic networks. In computational experiments, the proposed strategy showed an outstanding performance, being fast, achieving the best results on average in the networks tested. [ABSTRACT FROM AUTHOR]

Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data. [ABSTRACT FROM AUTHOR]

The n -dimensional torus T ( k 1 , k 2 , … , k n ) (including the k -ary n -cube Q n k ) is one of the most popular interconnection networks. A paired k -disjoint path cover (paired k -DPC for short) of a graph is a set of k disjoint paths joining k distinct source-sink pairs that cover all vertices of the graph. In this paper, we consider the paired 2-DPC problem of n -dimensional torus. Assuming k i ≥ 3 for i = 1 , 2 , … , n , with at most one k i being even, then T ( k 1 , k 2 , … , k n ) with at most 2 n − 3 faulty edges always has a paired 2-DPC. And the upper bound 2 n − 3 of edge faults tolerated is optimal. The result is a supplement of the results of Chen [3] and [4] . [ABSTRACT FROM AUTHOR]

Haptic technologies allow for adding a new “touching” modality into virtual scenes. 3D reconstruction of a real life scene results, however, often in millions of polygons which cannot be simultaneously visualized and haptically rendered. In this paper, we propose a way of haptic interaction with real life scenes where multiple original images of the real scenes are augmented with reconstructed polygon meshes. We present our solution to the problems of haptic model alignment with the images and interactive haptic rendering of large polygon meshes with reconstruction artifacts. In particular, the presented collision detection algorithm is not restricted by any hypothesis and robust enough to support smooth interaction with millions of polygons. The feasibility and usability of the proposed solution is evaluated in a user study. [ABSTRACT FROM AUTHOR]

In this paper, we study the global stability and attractivity of the endemic equilibrium for a network-based SIS epidemic model with nonmonotone incidence rate. The model was introduced in Li (2015). We prove that the endemic equilibrium is globally asymptotically stable if α (a parameter of this model) is sufficiently large, and is globally attractive if the transmission rate λ satisfies λ λ c ∈ ( 1 , 2 ] , where λ c is the epidemic threshold. Some numerical experiments are also presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new fuzzy time series (FTS) forecasting method based on optimal partitions of intervals in the universe of discourse and optimal weighting vectors of two-factors second-order fuzzy-trend logical relationship groups (TSFTLRGs). The proposed method uses particle swarm optimization (PSO) techniques to obtain the optimal partitions of intervals and the optimal weighting vectors simultaneously. The proposed FTS forecasting method outperforms the existing methods for forecasting the TAIEX and the NTD/USD exchange rates in terms of forecasting accuracy rates. It provides us with a useful way to deal with forecasting problems to get higher forecasting accuracy rates. [ABSTRACT FROM AUTHOR]

SACM (Structured Assurance Case Metamodel) is a standard for assurance case specification and exchange. It consists of an argumentation metamodel and an evidence metamodel for justifying that a system satisfies certain requirements. For assurance of safety-critical systems, SACM can be used to manage safety evidence and to specify safety cases. The standard is a promising initiative towards harmonizing and improving system assurance practices, but its suitability for safety evidence management needs to be further studied. To this end, this paper studies how SACM 1.1 supports this activity according to requirements from industry and from prior work. We have analysed the notion of evidence in SACM, its evidence lifecycle, the classes and associations of the evidence metamodel, and the link of this metamodel with the argumentation one. As a result, we have identified several improvement opportunities and extension possibilities in SACM. The notions of evidence and evidence assertion should be clarified, the overlaps between metamodel elements should be reduced, and a wider support to the lifecycle of the artefacts used as safety evidence could be provided. Addressing these aspects will allow SACM to better fit safety evidence management needs and practices, especially beyond the scope of a safety case. The results and the conclusions drawn are especially valuable for practitioners interested in SACM adoption and vendors interested in developing tool support for SACM-based safety evidence management. [ABSTRACT FROM AUTHOR]

There is an increase use of security driven approaches to support software development activities, such as requirements, design and implementation. The objective of this paper is to identify the existing software security approaches used in the software development lifecycle (SDLC). In order to meet our goal, we conducted a systematic mapping study to identify the primary studies on the use of software security techniques in SDLC. In total, we selected and categorized 118 primary studies. After analyzing the selected studies, we identified 52 security approaches and we categorized them in to five main categories, namely, ‘secure requirements modeling’, ‘vulnerability identification, adaption and mitigation’, ‘software security focused process’, ‘extended UML-based secure modeling profiles’, ‘non UML-based secure modeling notations’. The results show that the most frequently used approaches are static analysis and dynamic analysis that provide security checks in the coding phase. In addition, our results show that many studies in this review considered security checks around the coding stage of software development. This work will assist software development organizations in better understanding the existing software security approaches used in the software development lifecycle. It can also provide researchers with a firm basis on which to develop new software security approaches. [ABSTRACT FROM AUTHOR]

Introduced in the mid-1970s as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemerédi’s regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science. Roughly, it states that every graph can be approximated by the union of a small number of random-like bipartite graphs called regular pairs. In other words, the result provides us a way to obtain a good description of a large graph using a small amount of data, and can be regarded as a manifestation of the all-pervading dichotomy between structure and randomness. In this paper we will provide an overview of the regularity lemma and its algorithmic aspects, and will discuss its relevance in the context of pattern recognition research. [ABSTRACT FROM AUTHOR]

Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired aspects associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned. [ABSTRACT FROM AUTHOR]

In wireless ad hoc networks, maintaining network connectivity is very important as high level network functions all depend on it. However, how to measure network connectivity remains a fundamental challenge. For example, a network can have good overall k -connectivity and yet still have a communication bottleneck. In this paper, we address how to locate bottlenecks and relieve them. A new connectivity measure based on the Cheeger's Constant is used for bottleneck discovery, and a partition algorithm that divides the network at the bottleneck is developed. After the network is partitioned, we consider deploying a relay node to increase the conductance of the network across the partition. The relay node deployment problem is formulated as an integer linear program to maximize the number of connections between the two sides of the cut, and then a convex optimization algorithm is used to find the precise location of the relay node, which is within the convex hull defined by the radio transmission ranges of all the nodes that can connect to the relay node. We show that the relay node significantly relieves the bottleneck and improves network connectivity, which is manifested by several network connectivity metrics. The partition and relay node deployment algorithms are then extended to the case where multiple relay nodes are available. Multiway partition and multiple relay node deployment algorithms are presented. Simulation results show this approach effectively enhances network connectivity with a small number of relay nodes. [ABSTRACT FROM AUTHOR]

Link-cut trees have been introduced by D.D. Sleator and R.E. Tarjan [12] with the aim of efficiently maintaining a forest of vertex-disjoint dynamic rooted trees under cut and link operations. These operations respectively disconnect a subtree from a tree, and join two trees by an edge. Additionally, link-cut trees allow to change the root of a tree and to perform a number of updates and queries on cost values defined on the arcs of the trees. All these operations are performed in O ( log ⁡ n ) amortized or worst-case time, depending on the implementation, where n is the total size of the forest. In this paper, we show that a list of elements implemented using link-cut trees (we call it a log -list ) allows us to obtain a common running time of O ( log ⁡ n ) for the classical operations on lists, but also for some other essential operations that usually take linear time on lists. Such operations require to find the minimum/maximum element in a sublist defined by its endpoints, the position of a given element in the list or the element placed at a given position in the list; or they require to add a value a , or to multiply by −1, all the elements in a sublist. Furthermore, we use log-lists to implement several existing algorithms for sorting permutations by transpositions and/or reversals and/or block-interchanges, and obtain O ( n log ⁡ n ) running time for all of them. In this way, the running time of several algorithms is improved, whereas in other cases our algorithms perform as well as the best existing implementations. [ABSTRACT FROM AUTHOR]

The conditional diagnosability of many well-known networks has been explored. In this paper, we analyze the conditional diagnosability of a family of networks, called the round matching composition networks, which are a class of networks composed of r ( r ≥ 4 ) components of the same order linked by r perfect matchings. Applying the result, we determine the conditional diagnosability of the k -ary n -cubes and the recursive circulant graphs under the PMC model and the MM model, respectively. [ABSTRACT FROM AUTHOR]

Security becomes increasingly important in computing systems. Data integrity is of utmost importance. One way to protect data integrity is attaching an identifying tag to individual data. The authenticity of the data can then be checked against its tag. If the data is altered by the adversary, the related tag becomes invalid and the attack will be detected. The work presented in this paper studies an existing tag design (CETD) for authenticating memory data in embedded processor systems, where data that are stored in the memory or transferred over the bus can be tampered. Compared to other designs, this design offers the flexibility of trading-off between the implementation cost and tag size (hence the level of security); the design is cost effective and can counter the data integrity attack with random values (namely the fake values used to replace the valid data in the attack are random). However, we find that the design is vulnerable when the fake data is not randomly selected. For some data, their tags are not distributed over the full tag value space but rather limited to a much reduced set of values. When those values were chosen as the fake value, the data alteration would likely go undetected. In this article, we analytically investigate this problem and propose a low cost enhancement to ensure the full-range distribution of tag values for each data, hence effectively removing the vulnerability of the original design. [ABSTRACT FROM AUTHOR]

The Box–Cox transformation [1,2] (Box and Cox, 1964; Sakia, 1992) has been regarded as a parametric pre-processing technique aimed at making the distribution of a set of points approximately Gaussian. Since normality represents an assumption underlying many statistical data analysis tools, such technique has been widely applied in different fields of Computer Science. In this paper we will provide evidence that this technique can be useful also in the case of Pattern Classification, where Gaussianity of datasets is not so critical. By letting the Box–Cox transform work in operational ranges which do not necessarily correspond to an increase in Gaussianity, we will show that class separability can be improved: this is likely due to the non linear nature of the Box–Cox transformation, which deforms the space in a nonuniform way. We will also provide some suggestions on criteria that can be used to automatically estimate the best parameter of the Box–Cox transformation in the Pattern Classification context. [ABSTRACT FROM AUTHOR]

Visibly Pushdown Automata ( VPA ) are a special case of pushdown machines where the stack operations are driven by the input. In this paper, we consider VPA with multiple stacks , namely n-VPA , with n > 1 . These automata introduce a useful model to effectively describe concurrent pushdown systems using a simple communication mechanism between stacks. We show that n -VPA are strictly more expressive than VPA. Indeed, n -VPA accept some context-sensitive languages that are not context-free and some context-free languages that are not accepted by any VPA. Nevertheless, we show that the class of languages accepted by n -VPA is closed under union and intersection. On the contrary, this class turns out to be neither closed under complementation nor determinizable. Moreover, we show that the emptiness problem for n -VPA is undecidable. By adding an ordering constraint on stacks ( n -OVPA), decidability of emptiness can be recovered, as well as the closure under complementation. Using these properties along with the automata-theoretic approach, one can prove that the model checking problem over n -OVPA models against n -OVPA specifications is decidable. [ABSTRACT FROM AUTHOR]

Asynchronously communicating systems involve peers or entities that communicate by exchanging messages via buffers. In general, the size of such buffers is not known apriori, i.e., they are considered to be unbounded. As a result, models of asynchronously communicating systems typically exhibit infinite state spaces and it is well-known that reachability and boundedness problems for such models are undecidable. This, in turn, makes automatic verification of asynchronous systems undecidable as well. We discuss a particular class of asynchronous systems over peers for which the interaction behaviors do not change when the peers are made to communicate synchronously. Such systems are referred to as Synchronizable . Automatic verification of synchronizable systems is decidable as the verification of the system can be performed using its synchronous counterpart. Recently, we have proved that checking whether or not a system is synchronizable is decidable. In this paper, we consider different types of asynchronous communication, where the type is described in terms of the nature of buffering and the number of buffers, and discuss how/if synchronizability is decidable for each type. The new results subsume the existing ones and present a comprehensive synchronizability study of asynchronous systems. [ABSTRACT FROM AUTHOR]

The reed-type valve is a key component of reciprocating compressors affecting the efficiency and reliability of compressors. The convention design of such valve components focused on the study of the individual elements of the system, which loses the opportunity of reaching the global optima. This paper proposes a scientific guide for the system design, based on which the reed valve system was optimized, in which the reed, the plate, the orifice and valve stop were analyzed systematically, design conflict of valve lift and valve life were indentified in the early design stage. In order to decouple the conflict, the valve lift was formulated into standard parameters. Finally, the design process was simplified. All the design parameters gear to each other. An industrial case is introduced to demonstrate the feasibility of the proposed design approach. [ABSTRACT FROM AUTHOR]

Boolean satisfiability (SAT) solving is one of the most competitive research areas of theoretical computer science. The performance of state-of-the-art SAT solvers has been continuously improving in the last decades and has reached a level where SAT solvers can be employed to solve real world problems in fields such as hardware and software verification, automated planning and many others. One of the important driving forces of this progress are the yearly organized (since 2002) SAT competitions. In this paper we describe the 2015 SAT Race that featured the traditional sequential and parallel tracks (with 64 core computers) and introduced the Incremental Library Track, which is particularly interesting for developers of SAT based applications. We describe the 2015 SAT Race and provide a detailed analysis of its results. [ABSTRACT FROM AUTHOR]

In this paper we propose, motivate and solve multiple bichromatic mutual nearest neighbor queries in the plane considering multiplicative weighted Euclidean distances. Given two sets of facilities of different types, a multiple bichromatic mutual ( k , k ′ ) -nearest neighbor query finds pairs of points, one of each set, such that the point of the first set is a k -nearest neighbor of the point of the second set and, at the same time, the point of the second set is a k ′ -nearest neighbor of the point of the first set. These queries find applications in collaborative marketing and prospective data analysis, where facilities of one type cooperate with facilities of the other type to obtain reciprocal benefits. We present a sequential and a parallel algorithm, to be run on the CPU and on a Graphics Processing Unit, respectively, for solving multiple bichromatic mutual nearest neighbor queries. We also present the time and space complexity analysis of both algorithms, together with their theoretical comparison. Finally, we provide and discuss experimental results obtained with the implementation of the proposed sequential and a parallel algorithm. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is a highly interdisciplinary field that requires knowledge from different domains to be gathered and interpreted together. Although there are relatively few major data sources for LCA, the data themselves are presented with highly heterogeneous formats, interfaces, and distribution mechanisms. The lack of agreement among data providers for descriptions of processes and flows creates substantial barriers for information sharing and reuse of practitioners’ models. Nevertheless, the many data resources share a common logic. The use of Semantic Web technologies and text mining techniques can facilitate the interpretation of data from diverse sources. Numerous existing efforts have been made to articulate a knowledge model for LCA. In March of 2015 a joint workshop was held that brought together leading international domain experts with ontology engineers to develop a set of simple models called ontology design patterns (ODPs) for LCA information. In this paper we build on the outcomes of the workshop, as well as prior published works, to derive a minimal “consensus model” for LCA. We use the consensus model to derive a description of an LCA “catalog” that can be used to express the semantic content of a data resource. We generate catalogs of several prominent databases, and make those catalogs available to the public for independent use. Finally, we “link” those catalogs to existing knowledge models using JSON-LD, a linked data format that can expose the catalog contents to Semantic Web tools. We then show by example how the catalogs may be used to answer questions about the scope, coverage, and comparability of data, both within and across data sources, that are difficult to answer when the contents of the catalogs are provided independently and inconsistently. We discuss how the use of semantic catalogs can help address challenges that initiatives such as the “Global Network of Interoperable LCA Databases – Global LCA Data Access” are facing today. [ABSTRACT FROM AUTHOR]

Optimizations performed by compilers, usually expressed as rewrites on program graphs, are a core part of modern compilers. However, even production compilers have bugs, and these bugs are difficult to detect and resolve. In this paper we present Morpheus, a domain-specific language for formal specification of program transformations, and describe its executable semantics. The fundamental approach of Morpheus is to describe program transformations as rewrites on control flow graphs with temporal logic side conditions. The syntax of Morpheus allows cleaner, more comprehensible specifications of program optimizations; its executable semantics allows these specifications to act as prototypes for the optimizations themselves, so that candidate optimizations can be tested and refined before going on to include them in a compiler. We demonstrate the use of Morpheus to state, test, and refine the specification of a variety of transformations, including a simple loop peeling transformation for single-threaded code and a redundant store elimination optimization on parallel programs. [ABSTRACT FROM AUTHOR]

Recently multi-task feature learning has become a widely applied approach for visual tracking, since it is benefited from the shared features across tasks. However, selecting features appropriately from multiple tasks is still a challenging problem due to the complex variation of the appearance of moving objects, which influences not only the features of single task but also the relationships between the features of multiple tasks. To address this problem, this paper presents a novel sparse learning model for selecting multi-task features adaptively. Compared to the existing multi-task models, the proposed model is capable of both calibrating the loss function according to the noise level of a task to keep its specific features, and identifying the relevant and irrelevant (outlier) tasks simultaneously by decomposing the regularized matrix into two specified structures. The proposed model allows to preserve specific features of individual tasks via calibration and to exploit sparse pattern over the relevant task via identification. Empirical evaluations demonstrate that the proposed method has better performance than a number of the state-of-the-art trackers on available public image sequences. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectral graph wavelet approach for 3D shape retrieval using the bag-of-features paradigm. In an effort to capture both local and global characteristics of a 3D shape, we present a three-step feature description framework. Local descriptors are first extracted via the spectral graph wavelet transform having the Mexican hat wavelet as a generating kernel. Then, mid-level features are obtained by embedding local descriptors into the visual vocabulary space using the soft-assignment coding step of the bag-of-features model. A global descriptor is subsequently constructed by aggregating mid-level features weighted by a geodesic exponential kernel, resulting in a matrix representation that describes the frequency of appearance of nearby codewords in the vocabulary. Then, we compare the global descriptor of a query to all global descriptors of the shapes in the dataset using a dissimilarity measure and find the closest shape. Experimental results on two standard 3D shape benchmarks demonstrate the effectiveness of the proposed shape retrieval approach in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hierarchies, such as the tree of shapes, are popular representations for image simplification and segmentation thanks to their multiscale structures. Selecting meaningful level lines (boundaries of shapes) yields to simplify image while preserving intact salient structures. Many image simplification and segmentation methods are driven by the optimization of an energy functional, for instance the celebrated Mumford–Shah functional. In this paper, we propose an efficient approach to hierarchical image simplification and segmentation based on the minimization of the piecewise-constant Mumford–Shah functional. This method conforms to the current trend that consists in producing hierarchical results rather than a unique partition. Contrary to classical approaches which compute optimal hierarchical segmentations from an input hierarchy of segmentations, we rely on the tree of shapes, a unique and well-defined representation equivalent to the image. Simply put, we compute for each level line of the image an attribute function that characterizes its persistence under the energy minimization. Then we stack the level lines from meaningless ones to salient ones through a saliency map based on extinction values defined on the tree-based shape space. Qualitative illustrations and quantitative evaluation on Weizmann segmentation evaluation database demonstrate the state-of-the-art performance of our method. [ABSTRACT FROM AUTHOR]

Shape clustering is a difficult visual task due to large intra-class variations and small inter-class variations induced by shape articulation, rotation, occlusion, etc . To tackle this problem, we attempt to leverage the complementary nature among features of different statistics ( e.g. , skeleton-based descriptors and contour-based descriptors) for robust clustering. In this paper, a similarity fusion framework based on spectral analysis is proposed. The proposed method, which we call co-spectral, is a spectral clustering algorithm. It has two inborn merits for shape clustering: (1) it can automatically make use of the complementarity of various shape similarities based on a co-training framework; (2) it does not require shape representations to be vectors. Co-spectral is evaluated on several popular shape benchmarks. The experimental results demonstrate that co-spectral outperforms the state-of-the-art algorithms by a large margin. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for classification of human actions using pose based features. We demonstrate that statistical information of key movements of actions can be utilized in designing an efficient input representation, using fuzzy membership functions. The ability of stacked auto encoder to learn the underlying features of input data is exploited to recognize human actions. The efficacy of the proposed approach is demonstrated on CMU MOCAP and Berkeley MHAD datasets. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSNs) have been used as the means for a variety of applications. For example, social networking platform has been used in employment system, e-Commerce and CRM system to improve the quality of recommendations with the assistance of social networks. In these applications, social influence acts as a significant role, affecting people's decision-making. However, the existing social influence evaluation methods do not fully consider the social contexts, i.e., the social relationships and the social trust between participants, and the preferences of participants, which have significant impact on social influence evaluation in OSNs. Thus, these existing methods cannot deliver accurate social influence evaluation results. In our paper, we propose a Trust-Oriented Social Influence evaluation method, called TOSI , with taking the social contexts into account. We conduct experiments onto two real social network datasets, i.e., Epinions and DBLP . The experimental results illustrate that our TOSI method greatly outperforms the state-of-the-art method SoCap in terms of effectiveness, efficiency and robustness. [ABSTRACT FROM AUTHOR]

Community detection is an important task for uncovering underlying structures and analyzing group behavior in complex networks. A fuzzy community detection method is proposed in this paper to detect fuzzy community structures without any prior knowledge. Compared with previous studies, we introduce a structural similarity to measure fuzzy relation between vertices based on local interactions between neighboring vertices. In our method, we take the fuzzy similarity between vertices and fuzzy transitivity of the similarity in network topology into consideration. Moreover, multiresolution community structures can be detected by varying the fuzzy threshold. Experimental results and comparisons with some state-of-the-art methods are presented on a variety of benchmark graphs. It shows that the method is efficient in detecting communities on both real-world and synthetic networks. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are problem-solving methods which try to find good-enough solutions to very hard optimization problems, at a reasonable computation time, where classical approaches fail, or cannot even been applied. Many existing meta-heuristics approaches are nature-inspired techniques, which work by simulating or modeling different natural processes in a computer. Historically, many of the most successful meta-heuristic approaches have had a biological inspiration, such as evolutionary computation or swarm intelligence paradigms, but in the last few years new approaches based on nonlinear physics processes modeling have been proposed and applied with success. Non-linear physics processes, modeled as optimization algorithms, are able to produce completely new search procedures, with extremely effective exploration capabilities in many cases, which are able to outperform existing optimization approaches. In this paper we review the most important optimization algorithms based on nonlinear physics, how they have been constructed from specific modeling of a real phenomena, and also their novelty in terms of comparison with alternative existing algorithms for optimization. We first review important concepts on optimization problems, search spaces and problems’ difficulty. Then, the usefulness of heuristics and meta-heuristics approaches to face hard optimization problems is introduced, and some of the main existing classical versions of these algorithms are reviewed. The mathematical framework of different nonlinear physics processes is then introduced as a preparatory step to review in detail the most important meta-heuristics based on them. A discussion on the novelty of these approaches, their main computational implementation and design issues, and the evaluation of a novel meta-heuristic based on Strange Attractors mutation will be carried out to complete the review of these techniques. We also describe some of the most important application areas, in broad sense, of meta-heuristics, and describe free-accessible software frameworks which can be used to make easier the implementation of these algorithms. [ABSTRACT FROM AUTHOR]

Parameterized Approximation is a topic of considerable interest in the field of Parameterized Complexity. In the past decade, new color coding-related techniques, including the breakthrough representative sets technique, have been proven extremely powerful in the design of fast parameterized algorithms. Furthermore, packing problems, which are often solved via color coding-related techniques, have enjoyed a race towards obtaining the fastest parameterized algorithms that solve them. Therefore, it is natural to ask whether packing problems admit efficient parameterized approximation algorithms. In this paper, we answer this question affirmatively. We present tradeoffs that improve the running times of algorithms for well-known special cases of the 3 -Set k -Packing problem at the cost of their accuracy. Consider a packing problem for which there is no known algorithm with approximation ratio α , and a parameter k . If the value of an optimal solution is at least k , we seek a solution of value at least αk ; otherwise, we seek an arbitrary solution. Clearly, if the best known parameterized algorithm that finds a solution of value t runs in time O ⁎ ( f ( t ) ) for some function f , we are interested in running times better than O ⁎ ( f ( α k ) ) . Our main contribution lies in the adaptation of notions fundamental to the representative sets technique to the design of approximation algorithms: We introduce the definition of “approximate lopsided universal sets”, combine partial executions of representative sets-based algorithms with approximation algorithms, and adapt the iterative expansion framework (in the context of representative sets) to the design of parameterized approximation algorithms. Our ideas are intuitive, and may be relevant to the design of other parameterized approximation algorithms. [ABSTRACT FROM AUTHOR]

The combinatorics of nonstandard squares in a word depends on how the equivalence of halves of the square is defined. We consider Abelian squares, parameterized squares, and order-preserving squares. The word uv is an Abelian (parameterized, order-preserving) square if u and v are equivalent in the Abelian (parameterized, order-preserving) sense. The maximum number of ordinary squares in a word is known to be asymptotically linear, but the exact bound is still investigated. We present several results on the maximum number of distinct squares for nonstandard factor equivalence relations. Let SQ Abel ( n , σ ) and SQ Abel ′ ( n , σ ) denote the maximum number of Abelian squares in a word of length n over an alphabet of size σ which are distinct as words and which are nonequivalent in the Abelian sense, respectively. For σ ≥ 2 we prove that SQ Abel ( n , σ ) = Θ ( n 2 ) , SQ Abel ′ ( n , σ ) = Ω ( n 3 / 2 ) and SQ Abel ′ ( n , σ ) = O ( n 11 / 6 ) . We also give linear bounds for parameterized and order-preserving squares for alphabets of constant size: SQ param ( n , O ( 1 ) ) = Θ ( n ) , SQ op ( n , O ( 1 ) ) = Θ ( n ) . The upper bounds have quadratic dependence on the alphabet size for order-preserving squares and exponential dependence for parameterized squares. As a side result we construct infinite words over the smallest alphabet which avoid nontrivial order-preserving squares and nontrivial parameterized cubes (nontrivial parameterized squares cannot be avoided in an infinite word). A preliminary version of this paper was published at DLT 2014 [24] . In this full version we improve or extend the bounds on all three kinds of squares. [ABSTRACT FROM AUTHOR]

To contain the competitive influence spread in social networks is to maximize the influence of one participant and contain the influence of its opponent. It is desirable to develop effective strategies for influence spread of the participants themselves instead of blocking the influence spread of their opponents. In this paper, we extend the linear threshold model to establish the diffusion-containment model, abbreviated as D-C model, by incorporating the realistic specialties and characteristics of the containment of competitive influence spread. Then, we discuss the influence spread mechanism for the D-C model, and give the algorithm for the propagation of the diffusion influence (D-influence) and containment influence (C-influence). Further, we define the sub-modular set function of the C-influence in the D-C model and consequently give a greedy algorithm for solving the problem of maximizing the competitive influence containment approximately. Experimental results show the feasibility of our method. [ABSTRACT FROM AUTHOR]

Set intersections are important in computer science. Especially, intersection of inverted lists is a fundamental operation in information retrieval for text databases and Web search engines. In this paper, we discuss an efficient and effective way to implement this operation in the context of very big data sets. The main idea behind it is to do a binary search over sorted interval sequences, each of which corresponds to an inverted list and is constructed by establishing a trie over the sequences of set identifiers as well as a kind of tree encoding, by which each node in the trie is assigned an interval. In many cases, an interval sequence is much shorter than its corresponding inverted list. In particular, the lowest common ancestors of intervals in a trie can be utilized to control a binary search to skip over useless interval containment checks, which enables us to reach an optimal off-line algorithm to do the task, and is theoretically better than any traditional on-line methods (at cost of more space). Experiments have been conducted, showing that the trade-off of space for time is worthwhile. [ABSTRACT FROM AUTHOR]

Traditionally, real-time scheduling algorithms prioritize tasks solely based on their timing parameters and cannot effectively handle tasks that have different execution preferences . In this paper, for a set of periodic real-time tasks running on a single processor, where some tasks are preferably executed as soon as possible (ASAP) and others as late as possible (ALAP) , we investigate Preference-Oriented Fixed-Priority (POFP) scheduling techniques. First, based on Audsley’s Optimal Priority Assignment (OPA) , we study a Preference Priority Assignment (PPA) scheme that attempts to assign ALAP (ASAP) tasks lower (higher) priorities, whenever possible. Then, by considering the non-work-conserving strategy, we exploit the promotion times of ALAP tasks and devise an online dual-queue based POFP scheduling algorithm. Basically, with the objective of fulfilling the execution preferences of all tasks, the POFP scheduler retains ALAP tasks in the delay queue until their promotion times while putting ASAP tasks into the ready queue right after their arrivals. In addition, to further expedite (delay) the executions of ASAP (ALAP) tasks using system slack, runtime techniques based on dummy and wrapper tasks are investigated. The proposed schemes are evaluated through extensive simulations. The results show that, compared to the classical fixed-priority Rate Monotonic Scheduling (RMS) algorithm, the proposed priority assignment scheme and POFP scheduler can achieve significant improvement in terms of fulfilling the execution preferences of both ASAP and ALAP tasks, which can be further enhanced at runtime with the wrapper-task based slack management technique. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique to create scenarios that can be used by a user simulator for exhaustively evaluating spoken dialogue systems. The scenarios are automatically created from simple scenario-templates that the systems' developers create manually employing their knowledge about typical goals of the system's users. The scenarios contain goals, which the user simulator will try to achieve through the interaction with the systems. The goals are represented in the form of semantic frames, which are associated with user utterances of sentences and are taken from utterance corpora. In this way, the scenarios enable speech-based interaction between the simulator and the spoken dialogue systems to be evaluated. Experiments have been carried out employing two spoken dialogue systems (Saplen and Viajero), a user simulator and two utterance corpora previously collected for two different application domains: fast-food ordering and bus travel information. Experimental results show that the technique has been useful for exhaustively evaluating the systems and finding out problems in their performance that must addressed to improve them. Some of these problems are caused by acoustic similarity between some uttered words and strong speaker accents. Thus, we think these problems would have been difficult to uncover employing the user simulation techniques typically used nowadays, as they do not employ real speech and just consider semantics of user intentions. [ABSTRACT FROM AUTHOR]

Defining and computing distances between tree structures is a classical area of study in theoretical computer science, with practical applications in the areas of computational biology, information retrieval, text analysis, and many others. In this paper, we focus on rooted, unordered, uniquely-labelled trees such as taxonomies and other hierarchies. For trees as these, we introduce the intuitive concept of a ‘local move’ operation as an atomic edit of a tree. We then introduce SuMoTED, a new edit distance measure between such trees, defined as the minimal number of local moves required to convert one tree into another. We show how SuMoTED can be computed using a scalable algorithm with quadratic time complexity. Finally, we demonstrate its use on a collection of music genre taxonomies. [ABSTRACT FROM AUTHOR]