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Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases. [ABSTRACT FROM AUTHOR]

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning. [ABSTRACT FROM AUTHOR]

Deoxyribonucleic Acid (DNA) coding technology is a new research field developed by the combination of computer science and molecular biology, that has been gradually applied in the field of image encryption in recent years. Furthermore, sensitivity to initial conditions, pseudo-random properties, and state ergodicity of coupled chaotic maps can help produce good pseudo-random number generators and meet the requirements of an image encryption system well. In this paper, an image encryption algorithm based on high-dimensional coupled chaotic maps and DNA coding is proposed. A pseudo-random sequence is generated by a long short-term memory (LSTM) architecture using the proposed maps and evaluated through a set of statistical tests to show the high performance of the proposed generator. All intensity values of an input image are converted to a binary sequence, which is scrambled globally by the high-dimensional coupled chaotic maps. The DNA operations are performed on the scrambled binary sequences instead of binary operations to increase the algorithm efficiency. Simulation results and performance analyses demonstrate that the proposed encryption scheme is extremely sensitive to small changes in secret keys, provides high security and can resist differential attack. [ABSTRACT FROM AUTHOR]

Low-rank matrix completion, which aims to recover a matrix with many missing values, has attracted much attention in many fields of computer science. A low-rank matrix fitting (LMaFit) method has been proposed for fast matrix completion recently. However, this method cannot converge accurately on matrices of real-world images. For improving the accuracy of LMaFit method, an improved low-rank matrix fitting (ILMF) method based on the weighted L 1 , p norm minimization is proposed in this paper, where the L 1 , p norm is the summation of the p -power (0 < p < 2) of L 1 norms of rows in a matrix. In the proposed method, i.e. the ILMF method, the incomplete matrix that may be corrupted by noises is decomposed into the summation of a low-rank matrix and a noise matrix at first. Then, a weighted L 1 , p norm minimization problem is solved by using an alternating direction method for improving the accuracy of matrix completion. Experimental results on real-world images show that the ILMF method has much better performances in terms of both the convergence accuracy and convergence speed than the compared methods. [ABSTRACT FROM AUTHOR]

How can non-classical logic contribute to the analysis of complexity in computer science? In this paper, we give a step towards this question, taking a logical model-theoretic approach to the analysis of complexity in fuzzy constraint satisfaction. We study fuzzy positive-primitive sentences, and we present an algebraic characterization of classes axiomatized by this kind of sentences in terms of homomorphisms and direct products. The ultimate goal is to study the expressiveness and reasoning mechanisms of non-classical languages, with respect to constraint satisfaction problems and, in general, in modelling decision scenarios. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Data stream learning in non-stationary environments and skewed class distributions has been receiving more attention in machine learning communities. This paper proposes a novel ensemble classification method (ECSDS) for classifying data streams with skewed class distributions. In the proposed ensemble method, back-propagation neural network is selected as the base classifier. In order to demonstrate the effectiveness of our proposed method, we choose three baseline methods based on ECSDS and evaluate their overall performance on ten datasets from UCI machine learning repository. Moreover, the performance of incremental learning is also evaluated by these datasets. The experimental results show our proposed method can effectively deal with classification problems on non-stationary data streams with class imbalance. [ABSTRACT FROM AUTHOR]

In this paper, an efficient image encryption technique using beta chaotic map, nonsubsampled contourlet transform, and genetic algorithm is proposed. Initially, the nonsubsampled contourlet transform is utilized to decompose the input image into subbands. The beta chaotic map is used to develop pseudo-random key that encrypts the coefficients of subbands. However, it requires certain parameters to encrypt these coefficients. A multiobjective fitness function for genetic algorithm is designed to find the optimal parameter of beta chaotic map. The inverse of nonsubsampled contourlet transform is performed to obtain a ciphered image. The performance of the proposed technique is compared with recently developed well-known meta-heuristic based image encryption techniques. Experimental results reveal that the proposed technique provides better computational speed and high encryption intensity. The comparative analyses show effectiveness of the proposed image encryption technique. [ABSTRACT FROM AUTHOR]

Presents abstracts of articles pertaining to computer science, featured in the June 2001 issue of the 'International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.' 'Symbolic-Interval Cooperation in Constraint Programming,' by L. Granvilliers, E. Monfroy and F. Benhamou; 'Type 2 Fuzzy Sets: An Appraisal of Theory and Applications,' by R. John.

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Verifying and specifying multi-agent systems in an appropriate inconsistency-tolerant logic are of growing importance in Computer Science since computer systems are generally used by or composed of inconsistency-tolerant multi-agents. In this paper, an inconsistency-tolerant logic for representing multi-agents is introduced as a Gentzen-type sequent calculus. This logic (or calculus) has multiple negation connectives that correspond to each agent, and these negation connectives have the property of paraconsistency that guarantees inconsistency-tolerance. The logic proposed is regarded as a modified generalization of trilattice logics, which are known to be useful for expressing fine-grained truth-values in computer networks. The completeness, cut-elimination and decidability theorems for the proposed logic (or sequent calculus) are proved as the main results of this paper. [ABSTRACT FROM AUTHOR]

Since the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems published its first issue in 1993, it has made important contributions to the research field of computer science. In this study, based on the dataset of the publications published in this journal between 1993 and 2016 retrieved from Web of Science, a general overview of this journal is performed using bibliometric methods and visualized networks. First, the productive and influential publications, authors, institutions, countries/territories, and supraregions are analysed based on the total number of citations, publications, and different citation thresholds. Second, network visualization analysis is applied to illustrate the links and connections between terms by using the VOSviewer software. Moreover, the most cited journals and common author keywords of three continents, including North America, Europe, and Asia, are also presented. This paper will hopefully help researchers understand the research patterns of this journal. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

The amount of information available on the Web has grown considerably in recent years, leading to the need to structure it in order to access it in a quick and accurate way. In order to develop techniques to automate the structuring process, the Knowledge Base Population (KBP) track of the Text Analysis Conference (TAC) was created. This forum aims to encourage research in automated systems capable of capturing knowledge in unstructured information. One of the tasks proposed in the context of the KBP track is named entity linking, and its goal is to link named entities mentioned in a document to instances in a reference knowledge base built from Wikipedia. This paper focuses on the entity linking task in the context of KBP 2010, where two different varieties of this task were considered, depending on whether the use of the text from Wikipedia was allowed or not. Specifically, the paper proposes a set of modifications to a system that participated in KBP 2010, named WikiIdRank, in order to improve its performance. The different modifications were evaluated in the official KBP 2010 corpus, showing that the best combination increases the accuracy of the initial system in a 7.04%. Though the resultant system, named WikiIdRank++, is unsupervised and does not take advantage of Wikipedia text, a comparison with other approaches in KBP indicates that the system would rank as 4th (out of 16) in the global comparison, outperforming other approaches that use human supervision and take advantage of Wikipedia textual contents. Furthermore, the system would rank as 1st in the category of systems that do not use Wikipedia text. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of feature subset selection in classification-oriented datasets with a (very) large number of attributes. In such datasets complex classical wrapper approaches become intractable due to the high number of wrapper evaluations to be carried out. One way to alleviate this problem is to use the so-called filter-wrapper approach or Incremental Wrapper-based Subset Selection (IWSS), which consists of the construction of a ranking among the predictive attributes by using a filter measure, and then a wrapper approach is used by following the rank. In this way the number of wrapper evaluations is linear on the number of predictive attributes. In this paper we present two contributions to the IWSS approach. The first one is related with obtaining more compact subsets, and enables not only the addition of new attributes but also their interchange with some of those already included in the selected subset. Our second contribution, termed early stopping, sets an adaptive threshold on the number of attributes in the ranking to be considered. The advantages of these new approaches are analyzed both theoretically and experimentally. The results over a set of 12 high-dimensional datasets corroborate the success of our proposals. [ABSTRACT FROM AUTHOR]

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17 [ABSTRACT FROM AUTHOR]

This paper introduces strict fuzzy orderings in the framework of similarity-based fuzzy orderings, i.e. where a context of similarity/indistinguishability is given by means of a fuzzy equivalence relation. We consider how to construct strict fuzzy orderings from partial fuzzy orderings and vice versa. The appropriateness of the concepts introduced in this paper is underlined by theoretical results and examples. We observe that the strongest results are achieved if the underlying triangular norm induces an involutive residual negation. [ABSTRACT FROM AUTHOR]

Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data. [ABSTRACT FROM AUTHOR]

Scientific practice has been rapidly evolving in the last years under the pressure of developments in computer science and technology. In this paper we present some of the results of our research activity at the boundary between computer science and philosophy of science started in 1997 under Marco Somalvico's impulse and guidance. In particular, we discuss two roles that multiagent systems can play in scientific discovery. From the one hand, they can support scientific practice; from the other hand, they can represent scientific results. The theoretical framework presented in this paper is exemplified in concrete by illustrating specific implemented systems, both taken from the literature and developed by ourselves. [ABSTRACT FROM AUTHOR]

In this paper, we focus on convergence packet routing techniques in an all-optical network, obtained from an Eulerian routing in the digraph modeling the target network. Given an Eulerian circuit C in a digraph C, we deal with the maximal number diamWC of arcs that a packet has to follow on C from its origin to its destination (we talk about the ending guarantee of the routing). We consider the Eulerian diameter of C as defined by E(C) = mm C ∈ Eul(G) diamWc, where E(G) is the set of all the Eulerian circuits in C. After giving a preliminary result about the complexity of finding ((C) for any digraph G, we give some lower and upper bounds of this parameter. The main part of the paper is devoted to the description of a combinatorial design of various network topologies having good Eulerian diameters. [ABSTRACT FROM AUTHOR]

This paper focuses on image filtering using weighted median (WM) filters, a nonlinear filter class taking advantages of the robust order-statistic theory and capability to adapt a filter behavior for a variety of statistics related to the desired signals and the noise distributions. The main contribution of the paper is the analysis of the four WM optimization schemes, namely genetic WM optimization, non-adaptive WM optimization algorithm and adaptive WM filtering utilizing the linear and the sigmoidal approximation of the sign function. The analysis is done by extensive simulations, in which several features such as noise reduction, edge preservation, error estimation and dependence of error criteria on the degree of the impulse noise corruption, are examined. [ABSTRACT FROM AUTHOR]

In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates. [ABSTRACT FROM AUTHOR]

It is well known that the Shannon Sampling Theorem allows us to fully recover a continuous-time band-limited signal from its digital samples, as long as the sampling rate to be chosen is not smaller than the Nyquist frequency. This theory applies to all bandlimited signals, which may or may not occupy the entire frequency band. Hence, it is intuitively convincing that for continuous-time signals, such as those in speech, that do not fully utilize the entire frequency intervals, less digital samples are required for their full recovery. Current techniques in sub-band coding are used for achieving this goal. The objective of this paper is to present a wavelet theory for establishing the mathematical foundation of this sub-band coding approach. A wavelet packet decomposition of the signal provides the optimal sub-band coding bit-rate by using the Shannon wavelet library introduced in this paper. [ABSTRACT FROM AUTHOR]

A new kind of one-time password authentication scheme, directed one-time password authentication scheme, is presented in this paper. The distinguished feature of the directed authentication scheme is that only the specified verifier can validate the verifiee's genuine identity, without the help of this specified verifier, anyone else cannot verify it. One strong directed one-time password authentication scheme based on discrete logarithm is proposed in this paper and the security and performance of which are also analyzed. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

In this paper a platform is presented, that given a stochastic context-free grammar (SCFG), automatically outputs the description of the parser in synthesizable hardware description language (HDL) which can be downloaded in an Field Programmable Gate Arrays (FPGA) board. Initially, according to our methodology the SCFG is augmented with attributes which store the probability values and can be evaluated through corresponding stack actions. The architecture of the produced system is based on a proposed extension of Earley's parallel algorithm, which given an input string, generates the parse trees in the form of an AND-Or parse tree. This AND-or parse tree is then traversed using a proposed tree traversal technique in order to execute the corresponding actions in the correct order, so as to compute the necessary probabilities. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The parser generated by the presented platform has been tested for various SCFGs and compared to software approaches. The performance comparison is one to two orders of magnitude in favor of the presented hardware, compared to previous software approaches, depending on the application, the input string length and the number of produced trees. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

In this paper, we first give the formula of possibility degree to rank the Atanassov's intuitionistic fuzzy numbers. Two methods called Atanassov's intuitionistic fuzzy weighted average (IFWA) and Atanassov's intuitionistic fuzzy weighted geometric mean (IFWGM) are developed to solve the multiple attribute decision making problems under Atanassov's intuitionistic fuzzy environment, in which the performance ratings of alternatives and relative importance of attributes are expressed with Atanassov's intuitionistic fuzzy sets. The IFWA and IFWGM methods, respectively, are treated as an auxiliary pair of fractional programming models and two linear programming (LP) solution procedures are proposed simultaneously by using Charnes and Cooper transformation. Furthermore, two algorithms which are based on the IFWA and IFWGM models, respectively, are developed to solve Atanassov's intuitionistic fuzzy decision making problems where attribute values and weights of attributes are all in Atanassov's intuitionistic fuzzy numbers. The order relationship between IFWA and IFWGM are investigated. Finally, a numerical example is illustrated to show the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In this research paper, we propose an automatic segmentation method of multispectral magnetic resonance image (MRI) of the human brain using an information fusion approach through the framework of the possibility theory. The fusion process is summarized into three essential steps. First, a data is extracted from the various images and modeled in a common mathematical framework, in this step the fuzzy C-means (FCM) algorithm is chosen. The combination rule is used to combine this information in the second step. A final segmented image is the result of the last phase. Our experimental results using simulated brain MRI datasets show that the pro-posed approach overcome the impact of the noise and substantially improve the accuracy of image segmentation. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Clustering has been applied in a wide variety of disciplines and has also been utilized in many scientific areas. Usually, clustering algorithms construct either clusters of rows or clusters of columns of the input data matrix. Biclustering is a methodology where biclusters are formed by both a subset of rows and a subset of columns, such that objects represented by the first are the most similar to each other when compared over the latter. In this paper, we introduce a new biclustering technique, based on association rule mining, which can support different well-known biclustering models proposed in the literature. Experimental tests demonstrate the accuracy and efficiency of the proposed technique with respect to well known related ones. [ABSTRACT FROM AUTHOR]

This paper discusses preferential opponent selection in public goods games. It is shown that a preference to play with successful opponents strongly enhances the prevalence of cooperation. The finding is robust on spatial grids and heterogeneous networks. Importantly, I also demonstrate that positive opponent selection biases can evolve and become dominant in initially randomly mixed populations without selection bias. [ABSTRACT FROM AUTHOR]

Document clustering is one of the most effective techniques to organize documents in an unsupervised manner. In this paper, an Incremental method for document Clustering based on Information Bottleneck theory (ICIB) is presented. The ICIB is designed to improve the accuracy and efficiency of document clustering, and resolve the issue that an arbitrary choice of document similarity measure could produce an inaccurate clustering result. In our approach, document similarity is calculated using information bottleneck theory and documents are grouped incrementally. A first document is selected randomly and classified as one cluster, then each remaining document is processed incrementally according to the mutual information loss introduced by the merger of the document and each existing cluster. If the minimum value of mutual information loss is below a certain threshold, the document will be added to its closest cluster; otherwise it will be classified as a new cluster. The incremental clustering process is low-precision and order-dependent, which cannot guarantee accurate clustering results. Therefore, an improved sequential clustering algorithm (SIB) is proposed to adjust the intermediate clustering results. In order to test the effectiveness of ICIB method, ten independent document subsets are constructed based on the 20NewsGroup and Reuters-21578 corpora. Experimental results show that our ICIB method achieves higher accuracy and time performance than K-Means, AIB and SIB algorithms. [ABSTRACT FROM AUTHOR]

Locally linear embedding is often invalid for sparse data sets because locally linear embedding simply takes the reconstruction weights obtained from the data space as the weights of the embedding space. This paper proposes an improved method for sparse data sets, a united locally linear embedding, to make the reconstruction more robust to sparse data sets. In the proposed method, the neighborhood correlation matrix presenting the position information of the points constructed from the embedding space is added to the correlation matrix in the original space, thus the reconstruction weights can be adjusted. As the reconstruction weights adjusted gradually, the position information of sparse points can also be changed continually and the local geometry of the data manifolds in the embedding space can be well preserved. Experimental results on both synthetic and real-world data show that the proposed approach is very robust against sparse data sets. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive digital image watermarking technique using fuzzy gradient on DCT domain is presented. In our approach, the image is divided into separate blocks and the DCT is applied on each block individually. Then, the watermark is inserted in the transform domain and the inverse transform is carried out. We increase the robustness of the watermark by increasing the watermark strength. However, this reduces the fidelity of the watermarking scheme. This is because the fidelity and robustness of watermarking are generally in conflict with each other. To improve the fidelity, a new fuzzy-based method is introduced. In this method, a fuzzy gradient-based mask is generated from the host image. Then, as a post-processing stage, the generated mask is combined with the watermarked image. Experimental results show that the proposed technique has high fidelity as well as high robustness against a variety of attacks. [ABSTRACT FROM AUTHOR]

The increasing power consumption levels of integrated circuits (ICs) have become a major concern of the semiconductor industry. Excessive power dissipation causes overheating, which can lead to soft errors or permanent damage. It also limits battery life in portable equipment. High power consumption can be reduced by properly increasing area. However, arbitrarily large area, namely high number of functional units (FU) in high-level view, dramatically increases IC cost. This paper describes a new dynamic-power aware High Level Synthesis data path approach that considers dynamic FU allocation while attempting to minimize area, power, or make a trade-off between them. The experimental results have shown that when the area is nearly the same, our approach delivers a 5.99% reduction in power consumption. And when the power consumption is nearly the same, a 11.81% reduction in total FU area occurs. And we can obtain different optimal power-area trade-off values by adjusting power and area ratios. [ABSTRACT FROM AUTHOR]

The evaluation of subjective aspects of HCI, such as human-likeness, likeability or users' emotions towards computers is still quite a neglected issue, especially in the field of non-task oriented conversational systems (chatterbots). In this paper we try to bridge this gap by proposing a new methodology of evaluation. The methods presented were tested in our research on humor-equipped chatterbots. We describe them in details, discuss their drawbacks and usability. In one of the presented methods we used an emotiveness analysis system, which itself can be considered an AI tool, as it was used to detect users' emotions towards conversational systems, and to perform their automatic evaluation. We also propose some methods that we have not used yet, which, however, seem applicable in this field, such as brain scanning techniques. Finally, we give some ideas that should be addressed in the future. [ABSTRACT FROM AUTHOR]

This paper presents an original dynamic subsumption technique for Boolean CNF formulae. It exploits simple and sufficient conditions to detect, during conflict analysis, clauses from the formula that can be reduced by subsumption. During the learnt clause derivation, and at each step of the associated resolution process, checks for backward subsumption between the current resolvent and clauses from the original formula are efficiently performed. The resulting method allows the dynamic removal of literals from the original clauses. Experimental results show that the integration of our dynamic subsumption technique within the state-of-the-art SAT solvers Minisat and Rsat particularly benefits to crafted problems. [ABSTRACT FROM AUTHOR]

AUC-SVM directly maximizes the area under the ROC curve (AUC) through minimizing its hinge loss relaxation, and the decision function is determined by those support vector sample pairs playing the same roles as the support vector samples in SVM. Such a learning paradigm generally emphasizes more on the local discriminative information just associated with these support vectors whereas hardly takes the overall view of data into account, thereby it may incur loss of the global distribution information in data favorable for classification. Moreover, due to the high computational complexity of AUC-SVM induced by the large number of training sample pairs quadratic in the number of samples, sampling is usually adopted, incurring a further loss of the distribution information in data. In order to compensate the distribution information loss and simultaneously boost the AUC-SVM performance, in this paper, we develop a novel structure-embedded AUC-SVM (SAUC-SVM for short) through embedding the global structure information in the whole data into AUC-SVM. With such an embedding, the proposed SAUC-SVM incorporates the local discriminative information and global structure information in data into a uniform formulation and consequently guarantees better generalization performance. Comparative experiments on both synthetic and real datasets confirm its effectiveness. [ABSTRACT FROM AUTHOR]

The study presented in this paper highlights an important issue that was subject for discussions and research about a decade ago and now have gained new interest with the current advances of grid computing and desktop grids. New techniques are being invented on how to utilize desktop computers for computational tasks but no other study, to our knowledge, has explored the availability of the said resources. The general assumption has been that there are resources and that they are available. The study is based on a survey on the availability of resources in an ordinary office environment. The aim of the study was to determine if there are truly usable under-utilized networked desktop computers available for non-desktop tasks during the off-hours. We found that in more than 96% of the cases the computers in the current investigation was available for the formation of part-time (night and weekend) computer clusters. Finally we compare the performance of a full time and a metamorphosic cluster, based on one hypothetical linear scalable application and a real world welding simulation. [ABSTRACT FROM AUTHOR]

Database integration is an important discipline for constructing heterogeneous multidatabases on one hand. On the other hand, fuzzy information has been introduced into databases and studied extensively. One of the main problems in integration of multidatabases developed in different sites is resolution of conflicts. This problem becomes very complex when databases contain fuzzy information. Little has been reported on integrating fuzzy multidatabases so far. In this paper, we focus on integrating possibility-based fuzzy relational databases. The new types of conflicts arising in schema level and instance level in the fuzzy multidatabase integration are identified. The resolutions of the new types of conflicts are proposed. In particular, a method for identifying the entities in the fuzzy multidatabase integration with incompatible keys is developed. [ABSTRACT FROM AUTHOR]

We introduce in this paper a new type of extended attribute reduction called M-reducts for an inconsistent decision table, which is defined to preserve the membership degree to a maximum decision class for each object of the table. It is shown that a M-reduct can actually preserve more decision information than it does by definition, including the maximum decision class itself and all deterministic decision information. Compared with other types of extended attribute reductions, the proposed type of attribute reduction is a better trade-off between the knowledge preserving capability and reduction efficiency. Illustrative examples are given and an effective algorithm for computing a M-reduct based on two summation functions of attribute sets is proposed together with its complexity analysis. [ABSTRACT FROM AUTHOR]

The human vision system is a very sophisticated image processing and objects recognition mechanism. However, it is a challenge to simulate the human or animal vision system to automate visual function in machines, because it is difficult to account for the view-invariant perception of universals such as environmental objects or processes and the explicit perception of featural parts and wholes in visual scenes. In this paper, we first present an introduction to the importance of biologically inspired computer vision and review general and key vision functions from neuroscience perspective. And most significantly, we give an important summarization to and discussion on the specific applications of biologically inspired modeling, including biologically inspired image pre-processing, image perception, and objects recognition. In the end, we give some important and challenging topics of computer vision for future work. [ABSTRACT FROM AUTHOR]

The comparison of sets of objects is a research topic with applications in diverse fields such as computer science, biology and psychology. Since the introduction of the Jaccard index, many techniques have been proposed. This paper aims at extending an existing framework of comparison indices for sets. Firstly, the novel indices account for similarities between elements, rather than identity of elements as is the case for existing techniques. As a result, a richer framework of comparison indices is obtained. The use of fuzzy quantifiers in this framework is shown. Secondly, the machinery for sets is extended to the case of multisets, which results in two classes of comparison indices. The first class considers each element instance as a separate element, while the second class considers groups of elements instances as an atomic entity. The number of instances is then a property of this group, that is taken into account when calculating similarity between element groups. [ABSTRACT FROM AUTHOR]

In this paper, the Selected Objects Extraction (SOE) CNN is generalized to Directional Extraction (DE) CNN which enhances the capabilities of the SOE CNN. Using analytical approaches, a theorem to design robust templates for DE CNN is established. The theorem provides parameter inequalities to determine parameter intervals in which the templates can implement the corresponding functions. Based on the theorem, an optimal model is set up to design optimally robust templates for DE CNN. Two examples are provided to illustrate the effectiveness of the theorem. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method of identifying better representatives of faulty cells in a memory map to help judge unrepairability and provide economical repair solutions. These representatives, called leading elements (LE), are classified into four primary types based on their characteristics. The proposed method primarily assigns the faulty cells without row or column complements as LE, making them more useful in judging unrepairability and providing economical repair solutions. Some initially identified LE are further replaced with suitable faulty cells for being LE. Experiments on many example maps show the distribution of LE in various sizes of memories with distinct numbers of faulty cells. Compared to the previous work, the improved procedure can identify 7.3% more LE, with only 1.5% additional run time. [ABSTRACT FROM AUTHOR]