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For efficiency and economic reasons, a cloud system would be the most attractive choice for high computation tasks. But, computation on cloud is mostly done on clear text. As a result, the risk of data leak would be very high. The singular value decomposition (SVD) is widely used in several scientific computation areas including computer science, engineering, bioinformatics and physics, and the computation of the SVD consumes high computing power especially for large matrices. Hence, it would be efficient to outsource such computation to a cloud. In addition, many matrices are sparse containing lots of zeroes, and may have no meaning, whereas some applications contain sensitive bitmap images which the positions of zeroes are very significant. In other words, knowing the positions of zeroes would clearly expose the whole image. This paper proposes a novel secure SVD computation on cloud, and the main idea is to locally encrypt a source matrix before sending it to a cloud. The cloud then computes the SVD in an encrypted matrix without requiring any special algorithm, and the outputs will be locally decrypted to obtain the final results. For the encryption, our approach adds a random matrix to the source matrix to ensure that no element including zeroes is exposed in a clear format on the cloud. Moreover, the encryption will preserve the equivalent SVD computation on cloud. The security analysis demonstrates that our proposed scheme gives secure and correct computation while all zeroes are kept hidden. In addition, our experimental results show that the entropy of our encrypted matrix is high; consequently, it would give high resistance to attacks. Furthermore, the performance analysis shows that the complexity of the local workload is O (n 2) while the complexity of the cloud workload is O (n 3). [ABSTRACT FROM AUTHOR]

This paper mainly focuses on how to construct concept lattice effectively and efficiently based on improved variable precision rough set. On the basis of preprocessing formal concept, one algorithm that can determine the value range of variable precision parameter β according to the approximate classification quality is proposed. An improved β -upper and lower distribution attribute reduction algorithm is also proposed based on the improved variable precision rough set, the algorithm can be used for attribute reduction on the original data of the concept lattice, and to eliminate the redundant knowledge or noises of the formal context. For the reduced formal context, the paper combines the concept construction algorithm with an improved rule acquisition algorithm seamlessly, and proposes a novel approach of concept lattice construction based on improved variable precision rough set. Finally, a concept lattice generation prototype system is developed, this paper also performs comprehensive experiments, and the effectiveness of the improved algorithm is proved through the experimental results. [ABSTRACT FROM AUTHOR]

The paper reports on the 40 years of development of Computer Graphics and, more recently, Visual computing (VC) at the Technische Universität Darmstadt in Germany, from its beginning in 1975 to the leading “Computer Graphics and Visual Computing Hub” in Europe as of 2014. This development is described along three axes. First, the institutional development and its rational to establish Computer Graphics as a discipline of Computer Science and as an enabling technology for developing our Knowledge Society, are described. Second, the scientific and technological impact based on the teaching activities and the large number of theses submitted in Darmstadt for the area during these 40 years are addressed. Finally, the research road maps of the Computer Graphics and Visual Computing Hub in Darmstadt are presented relatively to the different stages of CG and VC research, relatively to a scientific view to the large number of projects implemented over these 40 years and, finally, also relatively to the project results as seen from the media. In order to manage the quantity as well as the complexity of the information available, the description of these road maps is divided in four time periods: 1975–1984, 1985–1994, 1995–2004 and 2004–2015. The paper also gives the view of the authors on how they see the future of Computer Graphics and Visual Computing. At the end, the paper includes an extensive list of references for the reported content. [ABSTRACT FROM AUTHOR]

Icon plays a critical role in computer interface design. Studies on icon taxonomy explain the way in which various types of icon represent the objects and provide designers creation rules by which icons are more in line with users’ cognitive psychology. However, along with larger and larger use of icons, the previous classification criterion causes the boundary between categories blur. What’s more, Single classification standard is not able to well illustrate the icons applied in today’s computer applications. The purpose of this paper is to present an objective-oriented icon taxonomy which proposes to categorize icons into action icon and knowledge icon. To assess this proposition, we analyzed a sample of icons that applied in computer interface and suggest precise application domains to both action icon and knowledge icon categories. The results of this practice manifested that action icon and knowledge icon implied a high relation with applied environment and explicated the development trace of computer icons. This work is one of the first to point out the notion of knowledge icon and to highlight the importance of objective of icon application. Findings in this paper could enrich icon use in computer interface design, especially provides possible way to improve online knowledge sharing by visual tool like icon. [ABSTRACT FROM AUTHOR]

In a recent paper (M. Barash, A. Okhotin, “An extension of context-free grammars with one-sided context specifications”, Inform. and Comput. , 2014), the authors introduced an extension of the context-free grammars equipped with an operator for referring to the left context of the substring being defined. This paper proposes a more general model, in which context specifications may be two-sided, that is, both the left and the right contexts can be specified by the corresponding operators. The paper gives the definitions, presents several examples of grammars and establishes a basic normal form theorem. This normal form, in particular, leads to a simple parsing algorithm working in time O ( n 4 ) , where n is the length of the input string. [ABSTRACT FROM AUTHOR]

The seven papers included in this special issue represent a selection of extended contributions presented at the 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020, held in Burgos, Spain, September 2020, and organized by University of Burgos and BISITE and GICAP Research Groups. SOCO 2020 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solving real-world problems on the mentioned fields. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for identifying data samples that are likely to be mislabeled in a c -class classification problem (dataset). The methodology relies on an assumption that the generalization error of a model learned from the data decreases if a label of some mislabeled sample is changed to its correct class. A general classification model used in the paper is OP-ELM; it also provides a fast way to estimate the generalization error by PRESS Leave-One-Out. It is tested on two toy datasets, as well as on real life datasets for one of which expert knowledge about the identified potential mislabels has been sought. [ABSTRACT FROM AUTHOR]

Induction plays a key role in reasoning in many areas of mathematics and computer science. A central problem in the automation of proof by induction is the non-analytic nature of induction invariants. In this paper we present an algorithm for proving universal statements by induction that separates this problem into two phases. The first phase consists of a structural analysis of witness terms of instances of the universal statement. The result of such an analysis is a tree grammar which induces a quantifier-free unification problem which is solved in the second phase. Each solution to this problem is an induction invariant. The arguments and techniques used in this paper heavily exploit a correspondence between tree grammars and proofs already applied successfully to the generation of non-analytic cuts in the setting of pure first-order logic. [ABSTRACT FROM AUTHOR]

The International Planning Competition is organized in the context of the International Conference on Automated Planning and Scheduling (ICAPS) and it is considered a reference source for the planning and scheduling community. The competition is typically organized every two years and deals with relevant issues for the community such as the definition of evaluation standards, the publication of benchmarks and the collection and dissemination of data about state-of-the-art planners. This paper focuses on the deterministic part, the longest-running part of the International Planning Competition. The paper describes its format, the participants, the selection of benchmarks and the generated results accompanied with analysis from different perspectives. The paper also examines the results of a brand new track created to explore the potential of planners that exploit the power of multi-core processors. Overall, the results of the competition indicate significant progress with respect to previous competitions, but they also reveal that some issues remain open and need further research, such as the coverage of temporal planners when concurrency is required and the performance in the multi-core track. As a novelty, all the data and the software generated for running the competition have been made publicly available allowing researchers to reproduce the competition and to carry out different analysis of the results. [ABSTRACT FROM AUTHOR]

This paper presents a model for semantic network representation. According to the model, the main purpose of a semantic network is to help to correctly and quickly understand the features and structure of the solutions, and then make reliable predictions about their outcomes and rewards. These objectives are achieved under constrains like limited resources (e.g., memory size and access), time limitations, and variations of solution characteristics. The paper summarizes the elements of the model and presents a set of properties that describe the effectiveness of semantic networks in creative problem solving, including design. Six examples (based on three case studies from the related literature and three experimental studies conducted by the authors) discuss the model elements, characteristics, and properties. [ABSTRACT FROM AUTHOR]

Many problems in Computer Science can be abstracted to the following question: given a set of objects and rules respectively, which new objects can be produced? In the paper, we consider a succinct version of the question: given a set of binary strings and several operations like conjunction and disjunction , which new binary strings can be generated? Although it is a fundamental problem, to the best of our knowledge, the problem hasn't been studied yet. In this paper, an O ( m 2 n ) algorithm is presented to determine whether a string s is representable by a set W , where n is the number of strings in W and each string has the same length m . However, looking for the minimum subset to represent a given string is shown to be NP -hard. Also, finding the smallest subset to represent each string in the original set is NP -hard. We establish tight inapproximability results and approximation algorithms for these NP -hard problems. In addition, we prove that counting the number of representable strings is # P -complete. We then explore how the problems change when the operator negation is available. For example, if the operator negation can be used, the counting problem is as simple as some power of 2. This difference may help us to better understand the problem. [ABSTRACT FROM AUTHOR]

Finding heavy-elements (heavy-hitters) in streaming data is one of the central, and well-understood tasks. Despite the importance of this problem, when considering the sliding windows model of streaming (where elements eventually expire) the problem of finding L 2 -heavy elements has remained completely open despite multiple papers and considerable success in finding L 1 -heavy elements. Since the L 2 -heavy element problem doesn't satisfy certain conditions, existing methods for sliding windows algorithms, such as smooth histograms or exponential histograms are not directly applicable to it. In this paper, we develop the first polylogarithmic-memory algorithm for finding L 2 -heavy elements in the sliding window model. Our technique allows us not only to find L 2 -heavy elements, but also heavy elements with respect to any L p with 0 < p ≤ 2 on sliding windows. By this we completely “close the gap” and resolve the question of finding L p -heavy elements in the sliding window model with polylogarithmic memory, since it is well known that for p > 2 this task is impossible. We demonstrate a broader applicability of our method on two additional examples: we show how to obtain a sliding window approximation of the similarity of two streams, and of the fraction of elements that appear exactly a specified number of times within the window (the α -rarity problem). In these two illustrative examples of our method, we replace the current expected memory bounds with worst case bounds. [ABSTRACT FROM AUTHOR]

The problem of automated theorem finding is one of the 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. To solve the problem, an approach of forward deduction based on the strong relevant logics was proposed. Following the approach, this paper presents a systematic methodology for automated theorem finding. To show the effectiveness of our methodology, the paper presents two case studies, one is automated theorem finding in NBG set theory and the other is automated theorem finding in Peano's arithmetic. Some known theorems have been found in our case studies. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. [Copyright &y& Elsevier]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Depth information has been discovered to improve the performance of computer vision tasks, such as semantic segmentation and object recognition. However, careful acquisition of depth data needs highly developed depth sensors which are expensive. As a classic computer vision task, depth estimation from a single image has obtained promising results based on supervised learning methods. In this paper, we investigate the extension of color images with corresponding deep-regressed depth images in boosting the performance of semantic segmentation. Furthermore, the usage of combining color channels with the estimated depth or the ground truth depth channel is compared. Specifically, there are two stages in our work. Firstly, we adopt the framework of convolutional neural networks (CNN) for the depth estimation by combing the global depth network and the depth gradient network. After refining based on these two networks, the depth image map can be estimated in a deep-regressed manner. Secondly, after augmenting the color images with the predicted depth images, fully convolutional networks (FCN) are further used to implement the pixel-level semantic labeling. In the experiments, we employ two popular RGBD datasets, i.e., SUNRGBD and NYUDv2, for 37 and 40-class semantic segmentation, respectively. By comparing with the ground truth depth images, experimental results demonstrate that the networks trained on the estimated depth images can achieve comparable performance on facilitating the accuracy of semantic segmentation task. [ABSTRACT FROM AUTHOR]

The fractal and self-similarity are important properties in complex networks. Information dimension is a useful dimension for complex networks to reveal these properties. In this paper, an information dimension is proposed for weighted complex networks. Based on the box-covering algorithm for weighted complex networks (BCANw), the proposed method can deal with the weighted complex networks which appear frequently in the real-world, and it can get the influence of the number of nodes in each box on the information dimension. To show the wide scope of information dimension, some applications are illustrated, indicating that the proposed method is effective and feasible. [ABSTRACT FROM AUTHOR]

Today’s world depends largely on information and communication technologies. These technologies are in use in different areas of human life and work. Each day, more examples of possible application of information and communications technology are being discovered. In most cases, computer science is used to solve complex problems which have mathematical background. The most important and challenging job in agriculture is plant protection. This is due to its complexity and the lack of specialized tools that could predict when the conditions for specific infections are fulfilled. In this paper authors use different mathematics-based techniques for data processing and prediction of possible fruit disease infection. Six significant weather variables and one variable representing the month in the year are selected as predictor variables. Implemented techniques are compared with each other in order to select the best. Prediction includes two most important diseases of cherry fruit: monilinia laxa and coccomyces hiemalis . Data sets used in this research include data of eight year time period, collected over the region of Toplica in Serbia. The best achieved prediction accuracy is 95.8%. Additionally, the same implemented methods can be applied on other fruit species and other diseases for which data are known. [ABSTRACT FROM AUTHOR]

In the Closest String problem we are given an alphabet Σ, a set of strings S = { s 1 , s 2 , … , s k } over Σ such that | s i | = n and an integer d . The objective is to check whether there exists a string s over Σ such that d H ( s , s i ) ≤ d , i ∈ { 1 , … , k } , where d H ( x , y ) denotes the number of places strings x and y differ at. Closest String is a prototype string problem. This problem together with several of its variants such as Distinguishing String Selection and Closest Substring have been extensively studied from parameterized complexity perspective. These problems have been studied with respect to parameters that are combinations of k , d , | Σ | and n . However, surprisingly the kernelization question for these problems (for the versions when they admit fixed-parameter tractable algorithms) is not studied at all. In this paper we fill this gap in the literature and do a comprehensive study of these problems from kernelization complexity perspective. We settle almost all the problems by either obtaining a polynomial kernel or showing that the problem does not admit a polynomial kernel under a standard assumption in complexity theory. [ABSTRACT FROM AUTHOR]

Multiple object tracking is a key problem for many computer vision applications such as video surveillance, advanced driver assistance or animation. Most of existing tracking-by-detection methods are mainly based on object appearances and motions. However, the contextual information around the target has not been fully exploited. In this paper, we pay more attention to the contextual information and propose an Exchanging Object Context (EOC) model, which takes full advantage of the context information. Specifically, we implement an efficient and accurate online multiple object tracking algorithm with a novel affinity measure to associate detections. This measure calculates the similarity between targets and detections with the background smoothness after exchanging the contexts between detections and targets, using a novel color histogram descriptor. We refine the bounding boxes by measuring the context changes. Extensive experimental results on two public benchmarks demonstrate the effectiveness of the proposed tracking method with comparisons to several state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

As a fundamental subject of theoretical computer science, the maximum independent set (MIS) problem not only is of purely theoretical interest, but also has found wide applications in various fields. However, for a general graph determining the size of a MIS is NP-hard, and exact computation of the number of all MISs is even more difficult. It is thus of significant interest to seek special graphs for which the MIS problem can be exactly solved. In this paper, we address the MIS problem in the pseudofractal scale-free web and the Sierpiński gasket, which have the same number of vertices and edges. For both graphs, we determine exactly the independence number and the number of all possible MISs. The independence number of the pseudofractal scale-free web is as twice as the one of the Sierpiński gasket. Moreover, the pseudofractal scale-free web has a unique MIS, while the number of MISs in the Sierpiński gasket grows exponentially with the number of vertices. [ABSTRACT FROM AUTHOR]

In the combinatorial study of words, the Parikh matrix mapping was introduced by Mateescu et al. in 2001 as a natural expansion of the classical Parikh mapping. Solving the general injectivity problem of Parikh matrices remains as one of the most sought after triumph among researchers in this area of study. In this paper, we tackle this problem by extending Şerbǎnuţǎ's work regarding prints and M -unambiguity to the context of strong M -equivalence. Consequently, we obtain results on the finiteness of strongly M -unambiguous prints for any finite alphabet. Finally, a related conjecture by Şerbǎnuţǎ is conclusively addressed. [ABSTRACT FROM AUTHOR]

Portfolio optimization (PO) has been catching more and more attention in the artificial intelligence and the machine learning communities. In this paper, we propose a novel Trend Representation based Log-density Regularization (TRLR) system for portfolio optimization. Its novelty falls into two aspects. First, it introduces a log-density regularization to the increasing factor of portfolio, which is seldom addressed by previous PO systems. It reflects a relationship between the portfolio and the price relative at an equilibrium point. Second, TRLR exploits a novel trend representation by taking the time variable as regressor in a weighted ridge regression, hence TRLR captures price trend patterns effectively. Extensive experiments conducted on 5 benchmark datasets from real-world financial markets demonstrate that TRLR achieves significantly better performance than other state-of-the-art strategies and runs fast, which shows its effectiveness and efficiency for large-scale applications. [ABSTRACT FROM AUTHOR]

In this paper, a couple of new algorithms for independent component analysis (ICA) are proposed. In the proposed methods, the independent sources are assumed to follow a predefined distribution of the form f ( s ) = α exp ( − β | s | p ) and a maximum likelihood estimation is used to separate the sources. In the first method, a gradient ascent method is used for the maximum likelihood estimation, while in the second, a non-iterative algorithm is proposed based on the relaxation of the problem. The maximization of the log-likelihood of the estimated source X T w given the parameter p and the data X is shown to be equivalent to the minimization of l p -norm of the projected data X T w . This formulation of ICA has a very close relationship with the Lp-PCA where the maximization of the same objective function is solved. The proposed algorithm solves an approximation of the l p -norm minimization problem for both super-( p  < 2) and sub-Gaussian ( p  > 2) cases and shows superior performance in separating independent sources than the state of the art algorithms for ICA computation. [ABSTRACT FROM AUTHOR]

In a non-stationary environment, newly received data may have different knowledge patterns from the data used to train learning models. As time passes, a learning model’s performance may become increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years. However, very few existing methods pay attention to small regional drifts, and their accuracy may vary due to differing statistical significance tests. This paper presents a novel concept drift detection method, based on regional-density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first is a k -nearest neighbor-based space-partitioning schema (NNPS), which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation. The second is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall differences. The third component is a tailored statistical significance test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift and has been proven to follow a normal distribution. As a result, the NN-DVI’s accuracy and false-alarm rate are statistically guaranteed. Additionally, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing problems related to concept drift-detection. [ABSTRACT FROM AUTHOR]

Dealing with context dependent knowledge is a well-known area of study that roots in John McCarthy's seminal work. More recently, the Contextualized Knowledge Repository (CKR) framework has been conceived as a logic-based approach in which knowledge bases have a two layered structure, modeled by a global context and a set of local contexts. The global context not only contains the meta-knowledge defining the properties of local contexts, but also holds the global (context independent) object knowledge that is shared by all of the local contexts. In many practical cases, however, it is desirable to leave the possibility to “override” the global object knowledge at the local level: in other words, it is interesting to recognize the pieces of knowledge that can admit exceptional instances in the local contexts that do not need to satisfy the general axiom. To address this need, we present in this paper an extension of CKR in which defeasible axioms can be included in the global context. The latter are verified in the local contexts only for the instances for which no exception to overriding exists, where exceptions require a justification in terms of facts that are provable from the knowledge base. We formally define this semantics and study some semantic and computational properties, where we characterize the complexity of the major reasoning tasks, among them satisfiability testing, instance checking, and conjunctive query answering. Furthermore, we present a translation of extended CKRs with knowledge bases in the Description Logic SROIQ -RL under the novel semantics to datalog programs under the stable model (answer set) semantics. We also present an implementation prototype and examine its scalability with respect to the size of the input CKR and the amount (level) of defeasibility in experiments. Finally, we compare our representation approach with some major formalisms for expressing defeasible knowledge in Description Logics and contextual knowledge representation. Our work adds to the body of results on using deductive database technology such as SQL and datalog in these areas, and provides an expressive formalism (in terms of intrinsic complexity) for exception handling by overriding. [ABSTRACT FROM AUTHOR]

Remediation actions are performed in scenarios in which consequences of a problem should be promptly mitigated when its cause takes too long to be addressed or is unknown. Such scenarios are recurrent in the real world, including in the context of computer science. Existing approaches that address these scenarios are application-specific. Nevertheless, the reasoning about remediation actions as well as cause identification and resolution, in order to address problems permanently, can be abstracted in such a way that they can be incorporated to autonomous software components, often referred to as agents. They can thus autonomously deal with these scenarios, which we refer to as critical cause-effect situations . In this paper, we propose a domain-independent extension to the belief-desire-intention (BDI) architecture that provides such agents with this automated reasoning. Our work provides an extensible solution to this recurrent problem-solving strategy and allows agents to flexibly deal with resource-constrained scenarios. This solution removes the need for manually implementing the coordination of actions performed by agents, using causal models to capture the knowledge required to carry out this task. Therefore, it not only allows the development of systems with remediative behaviour, but also enables the reduction of development effort by means of a reusable infrastructure that can be used in several different domains. Our approach was evaluated based on an existing solution in the network resilience domain, which showed that our extended agent can autonomously address a network challenge, with a reduction in the development effort and no impact in agent performance. [ABSTRACT FROM AUTHOR]

There is a need for further research in the area of ontology evaluation specifically dealing with ontology development exploiting concept maps. The existing literature on ontology evaluation primarily emphasis on ontology formalisation as well as on performing logical inferences, which is usually not directly relevant for concept maps as they are commonly exploited as communication instruments for learning purposes. Commonly used techniques for evaluating concept maps for knowledge assessment may be adopted for a kind of criteria-based evaluation of a domain concept map with respect to a particular aspect. However, this makes its validity limited to a particular aspect or criteria. This paper presents a mathematical ontology evaluation technique to measure the correctness of domain ontologies engineered using concept maps. It is based on the notion of merging two different mathematical measures, namely closeness index and similarity index to come up with a combined index that takes different criteria or aspects into account while performing ontology evaluation. Therefore, the proposed technique makes the evaluation process more reliable and robust. Two case studies were conducted employing the proposed technique for evaluating two different domain ontologies that were engineered using concept maps. Calculations and results from the case studies showed that depending on the correctness of individual ontology, different values of combined Index was calculated manifesting the measure of correctness of each individual ontology in a quantifiable form. Moreover, the results depict that the technique provides in-depth evaluation, it is easy to adopt, requires no special skills, and is conveniently replicable. [ABSTRACT FROM AUTHOR]

We in this paper consider the η -anti-Hermitian solution to some classic matrix equations A X = B , A X B = C , A X A η * = B , E X E η * + F Y F η * = H , respectively. We derive the necessary and sufficient conditions for the above matrix equations to have η -anti-Hermitian solutions and also provide the general expressions of solutions when those equations are solvable. As applications, for instance, we give the solvability conditions and general η -anti-Hermitian solution to equation system A X = B , C Y = D , M X M η * + N Y N η * = G . [ABSTRACT FROM AUTHOR]

Neighborhood models ( NBM s) are the methods widely used for collaborative filtering in recommender systems. Given a target user and a target item, NBM s find k most similar users or items (i.e., k -nearest neighbors) and make a prediction of a target user on an item based on the rating patterns of those neighbors on the item. In NBM s, however, we have a difficulty in satisfying both the performance and accuracy together. In order to pursue an accurate recommendation, NBM s may find the k -nearest neighbors at every recommendation request to exploit the latest ratings, which requires a huge amount of computation time. Alternatively, NBM s may search for the k -nearest neighbors offline, which consequently results in inaccurate recommendation as time goes by, or even may not able to deal with new users or new items, because they cannot exploit the latest ratings generated after the k -nearest neighbors are determined. In this paper, we propose a novel approach that finds the k -nearest neighbors efficiently by identifying only those users and items necessary in computing the similarity. The proposed approach enables NBM s not to require any offline similarity computations but to exploit the latest ratings, thereby resolving speed-accuracy tradeoff successfully. We demonstrate the effectiveness of the proposed approach through extensive experiments. [ABSTRACT FROM AUTHOR]

Learning rich representations efficiently plays an important role in the multi-modal recognition task, which is crucial to achieving high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-LRF-ELM) structure, while maintaining ELM’s advantages of training efficiency. In this structure, LRF-ELM is first conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance. [ABSTRACT FROM AUTHOR]

Error correction codes (ECCs) are commonly used to deal with soft errors in memory applications. Typically, Single Error Correction-Double Error Detection (SEC-DED) codes are widely used due to their simplicity. However, the phenomenon of more than one error in the memory cells has become more serious in advanced technologies. Single Error Correction-Double Adjacent Error Correction (SEC-DAEC) codes are a good choice to protect memories against double adjacent errors that are a major multiple error pattern. An important consideration is that the ECC encoder and decoder circuits can also be affected by soft errors, which will corrupt the memory data. In this paper, a method to design fault tolerant encoders for SEC-DAEC codes is proposed. It is based on the fact that soft errors in the encoder have a similar effect to soft errors in a memory word and achieved by using logic sharing blocks for every two adjacent parity bits. In the proposed scheme, one soft error in the encoder can cause at most two errors on adjacent parity bits, thus the correctness of memory data can be ensured because those errors are correctable by the SEC-DAEC code. The proposed scheme has been implemented and the results show that it requires less circuit area and power than the encoders protected by the existing methods. [ABSTRACT FROM AUTHOR]

The past half-century has witnessed fast development in the field of fuzzy theory (FT), however, few researches have focused on mapping the development of this area in China. Based on the samples of 12,936 publications authored by Chinese scholars on FT researches during the past 30 years, this paper intends to explore the patterns and dynamics by analyzing the geographic distribution of publications, international collaboration, research hot spot, subject categories and journals, and publication contributors. The results indicate that the scientific publications are highly unbalanced at regional levels in China, and the USA is China's most important partner in FT cooperative researches. Collaborations are not indispensable for high-quality research outputs in FT area. The existing researches in the field of FT from Chinese scholars focus primarily on Computer Science and Engineering. The emerging trends of FT researches from Chinese scholars have shifted away from basic FT researches to the applications, such as the areas of decision making, optimization, modeling and design. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient and effective active online sequential learning approach, named as Less Annotated Active Learning Extreme Learning Machine (LAAL-ELM). It leverages the predictions’ confidence of the new arriving data to actively select both query-annotated samples and confidence-weighted predict-annotated ones to update the classifier, which contributes to less actively query annotation, and applies WOS-ELM, a discriminant model, to significantly reduce the computation complexity for doing online updating in one step. The proposed approach firstly gives a principle to evaluate confidence of the prediction in WOS-ELM; then determines what and how to update the model with new arriving data in the online phase: the uncertain instances are annotated by query their classes, almost-certain ones are weighted on its prediction’s confidence and the certain ones are discarded directly for reducing over-fitting; at last, the weighted and query-annotated samples are used to update the classifier. The proposed approach is evaluated on five real-world benchmark classification issues. And the experimental results demonstrate that the proposed LAAL-ELM can effectively reduce the number of queried samples while maintaining high level of classification performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel unsupervised multi-source domain adaptation approach, named as coupled local–global adaptation (CLGA). At the global level, in order to maximize the adaptation ability, CLGA regards multiple domains as a unity, and jointly mitigates the gaps of both marginal and conditional distributions between source and target dataset. At the local level, with the intention of maximizing the discriminative ability, CLGA investigates the relationship among distinctive domains, and exploits both class and domain manifold structures embedded in data samples. We formulate both local and global adaptation in a concise optimization problem, and further derive an analytic solution for the objective function. Extensive evaluations verify that CLGA performs better than several existing methods not only in multi-source adaptation tasks but also in single source scenarios. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) has been one of the great successes of applied mathematics in the last decade. This paper proposes a new method, combining the advantage of the Compressive Sampling Matching Pursuit (CoSaMP) algorithm and the Quasi–Newton Iteration Projection (QNIP) algorithm, for the recovery of sparse signal from underdetermined linear systems. To get the new algorithm, Quasi–Newton Projection Pursuit (QNPP), the least-squares technique in CoSaMP is used to accelerate convergence speed and QNIP is modified slightly. The convergence rate of QNPP is studied, under a certain condition on the restricted isometry constant of the measurement matrix, which is smaller than that of QNIP. The fast version of QNPP is also proposed which uses the Richardson’s iteration to reduce computation time. The numerical results show that the proposed algorithms have higher recovery rate and faster convergence speed than existing techniques. [ABSTRACT FROM AUTHOR]

This survey paper attempts to study the existence, importance and impact of the notion of context in modern humanistic computing. Given its inherent diversity, the term is nowadays widely acknowledged among computer science tasks and has become a major topic of interest in several of its sub-fields, ranging from contextual semantics to social networks, social media and recently emerged innovative applications, such as travel routing. We start by presenting a brief review of contextual semantics as nowadays they are considered suitable for most common content analysis problems. Focus is also given within the next survey sections on the impact of context within the social networks and media field that came into sight over the last years. A short, closing discussion on the identified challenges and potential future research directions concludes this survey. [ABSTRACT FROM AUTHOR]

This paper investigates the design of functional observers for linear time-invariant descriptor systems. A new method for designing these observers is given by using an LMI (Linear matrix inequality) formulation. The obtained result unifies the design, it considers the continuous-time and discrete-time cases and concerns the observers of various orders. [ABSTRACT FROM AUTHOR]

In light of the recent developments on the international publishing scene, increasingly dominated by L2 writers of English, the question of what is considered to be “good” and “acceptable” English calls for further research. This paper examines in what ways researchers describe the English used for research writing in their field. Interview data were collected from historians and computer scientists working in Finland and Sweden. Our analysis points towards some differences in the way researchers perceive “good” writing in English in their field, and what they themselves report to practice as (co-)authors, readers/reviewers, and proofreaders. The discrepancy between the ideals and realities of research writing in English was clear in the case of the historians. Our findings suggest that in research writing for publication, there is a pull towards some form of standard norm. This standard can be jointly negotiated during the writing, reviewing, and proofreading process. It may also develop in different directions in different disciplines, but it is likely to be based on the principles of understandability and clarity. [ABSTRACT FROM AUTHOR]

The recently proposed Krĕin space Support Vector Machine (KSVM) is an efficient classifier for indefinite learning problems, but with quadratic to cubic complexity and a non-sparse decision function. In this paper a Krĕin space Core Vector Machine (iCVM) solver is derived. A sparse model with linear runtime complexity can be obtained under a low rank assumption. The obtained iCVM models can be applied to indefinite kernels without additional preprocessing. Using iCVM one can solve CVM with usually troublesome kernels having large negative eigenvalues or large numbers of negative eigenvalues. Experiments show that our algorithm is similar efficient as the Krĕin space Support Vector Machine but with substantially lower costs, such that also large scale problems can be processed. [ABSTRACT FROM AUTHOR]

This paper describes an efficient generation of large-scale dataset of human depth images with body part labels. The size of image datasets has recently been increasingly important as it is shown to be strongly related to the performance of learning-based classifiers. In human pose recognition, many datasets for ordinary poses like standing, walking, and doing gestures have already been developed and effectively utilized. However, those for unusual ones like lying fainted and crouching do not exist. Pose recognition for such cases may have a large potential applicability to various assistive scenarios. Moreover, locating each body part could also be important for an accurate care and diagnosis or anomaly detection. We therefore develop a method of generating body part-annotated depth images in various body shapes and poses, which are handled by a flexible human body model and a motion capture system, respectively. We constructed a dataset of 10,076 images with eight body types for various sitting poses. The effectiveness of generated dataset is verified by part labeling tasks with a fully convolutional network (FCN) for synthetic and real test data. [ABSTRACT FROM AUTHOR]

In recent years, monotonic ordinal classification has increased the focus of attention for machine learning community. Real life problems frequently have monotonicity constraints. Many of the monotonic classifiers require that the input data sets satisfy the monotonicity relationships between its samples. To address this, a conventional strategy consists of relabeling the input data to achieve complete monotonicity. As an alternative, we explore the use of preprocessing algorithms without modifying the class label of the input data. In this paper we propose the use of training set selection to choose the most effective instances which lead the monotonic classifiers to obtain more accurate and efficient models, fulfilling the monotonic constraints. To show the benefits of our proposed training set selection algorithm, called MonTSS, we carry out an experimentation over 30 data sets related to ordinal classification problems. [ABSTRACT FROM AUTHOR]

Objective video quality assessment (VQA) plays an important role in controlling video quality. Most of the existing VQA methods measure motion-related temporal distortion based on optical-flow methods, which are not consistently reliable in modeling general visual dynamics. This paper presents a full-reference temporal distortion measure based on spacetime texture , a uniform and distributive descriptor of a broad set of spacetime structures. Our method measures the distortion of spacetime texture in video with a motion-tuning strategy, which effectively captures temporal distortion along the motion trajectories. Then it estimates self-information based visual saliency for spatial pooling by reusing the motion descriptors. We evaluated our method on two public VQA databases with a wide variety of distortion types, in which the videos were viewed on a large screen or mobile devices. The results show that our method correlates highly with the subjective quality and has high computational efficiency. [ABSTRACT FROM AUTHOR]

Let P be an orthogonal polygon with n vertices. A sliding camera travels back and forth along an orthogonal line segment s ⊆ P corresponding to its trajectory . The camera sees a point p ∈ P if there is a point q ∈ s such that p q ‾ is a line segment normal to s that is completely contained in P . In the Minimum-Cardinality Sliding Cameras (MCSC) problem , the objective is to find a set S of sliding cameras of minimum cardinality to guard P (i.e., every point in P can be seen by some sliding camera in S ), while in the Minimum-Length Sliding Cameras (MLSC) problem the goal is to find such a set S so as to minimize the total length of trajectories along which the cameras in S travel. In this paper, we answer questions posed by Katz and Morgenstern (2011) by presenting the following results: (i) the MLSC problem is polynomially tractable even for orthogonal polygons with holes, (ii) the MCSC problem is NP -complete when P is allowed to have holes, and (iii) an O ( n 3 log ⁡ n ) -time 2-approximation algorithm for the MCSC problem on [NE]-star-shaped orthogonal polygons with n vertices (similarly, [NW]-, [SE]-, or [SW]-star-shaped orthogonal polygons). [ABSTRACT FROM AUTHOR]

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance. [ABSTRACT FROM AUTHOR]

We propose in this paper the Security Policy Language (SePL), which is a formal language for capturing and integrating distributed security policies. The syntax of SePL includes several operators for the integration of policies and it is endowed with a denotational semantics that is a generic semantics, i.e., which is independent of any evaluation environment. We prove the completeness of SePL with respect to set theory. Furthermore, we provide a formalization of a large subset of the eXtensible Access Control Markup Language (XACML), which is the well-known standard informal specification language of Web security policies. We also provide a semantics for XACML policy combining algorithms. [ABSTRACT FROM AUTHOR]

Empirical evidence suggests the effectiveness of robotics as a learning complementary tool in tertiary education. In this context, some experiences benefited from the link between educational practice and theory. However, a comprehensive survey on initiatives that explores this link in universities and colleges is missing. This work systematically reviews quantitatively assessed robots applications, grounded in learning theories, in tertiary institutions. By applying a protocol review in different bibliographic databases, 15 papers were selected for synthesis. As a result, experiences developing non-robotic concepts and skills in universities and colleges were found. In most of the cases, Computer Science and Engineering undergraduate courses were involved. In addition, empirical results reported by the selected publications suggest that some literature proposals can be useful in practice. Based on the panorama obtained, this work also points out future directions for practitioners and researchers in education. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the testing image categories are not seen in the training samples. Most studies on ZSL utilize side information such as attributes or word vectors to bridge the relations between the seen classes and the unseen classes. However, existing approaches on ZSL typically exploit a shared space for each type of side information independently, which cannot make full use of the complementary knowledge of different types of side information. To this end, this paper presents an MBFA-ZSL approach to embed different types of side information as well as the visual feature into one shared space. Specifically, we first develop an algorithm named Multi-Battery Factor Analysis (MBFA) to build a unified semantic space, and then employ multiple types of side information in it to achieve the ZSL. The close-form solution makes MBFA-ZSL simple to implement and efficient to run on large datasets. Extensive experiments on the popular AwA, CUB, and SUN datasets show its superiority over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper we present the Large Crater Clustering (LCC) tool set, an ArcGIS plugin that supports the quantitative approximation of a primary impact location from user-identified locations of possible secondary impact craters or the long-axes of clustered secondary craters. The identification of primary impact craters directly supports planetary geologic mapping and topical science studies where the chronostratigraphic age of some geologic units may be known, but more distant features have questionable geologic ages. Previous works (e.g., McEwen et al., 2005; Dundas and McEwen, 2007) have shown that the source of secondary impact craters can be estimated from secondary impact craters. This work adapts those methods into a statistically robust tool set. We describe the four individual tools within the LCC tool set to support: (1) processing individually digitized point observations (craters), (2) estimating the directional distribution of a clustered set of craters, back projecting the potential flight paths (crater clusters or linearly approximated catenae or lineaments), (3) intersecting projected paths, and (4) intersecting back-projected trajectories to approximate the local of potential source primary craters. We present two case studies using secondary impact features mapped in two regions of Mars. We demonstrate that the tool is able to quantitatively identify primary impacts and supports the improved qualitative interpretation of potential secondary crater flight trajectories. [ABSTRACT FROM AUTHOR]