Miracle-dz/UniFAR-FTUs huggingface dataset:
Video Affective Content Recognition Based on Film Grammars and Fuzzy Evaluation

Affective content analysis is an unsolved technical problem in sophisticated video retrieval and high-level applications. In order to recognize emotion types of video scenes, a new algorithm, which composes of two sub-models, is proposed. Firstly, the sub-models of two low-level features extraction are built up based on the film grammars. Secondly, a classification sub-model of scene emotion is presented. This sub-model includes two functions: fuzzy relation matrix computed by fuzzy membership functions which are built from a large number of fuzzy experiments, and affective type decision function based on the maximizing decision making of fuzzy evaluation. Experimental results show the proposed algorithm is feasible and achieves a high recognition accuracy which exceeds 80 percent.
The Application of Ultrasonic Guided Wave in Grounding Grid Corrosion Diagnosis

Abstract—Grounding grid corrosion diagnosis has always been a difficulty in the field of power system, which has aroused the concern of many scholars. In order to solve this problem, two-point method and partition method, which are based on ultrasonic guided wave theory and grounding grid structure features, are proposed in this paper. Two-point method excites guided waves at two points firstly. Secondly, to establish two equations based on the experiments. Then the location of the corrosion coordinates can be determined by the equations. The partition method is suitable for large and medium-sized grounding grid. In this method, the grid needs to be split into several blocks. Then Two-point method can be used in each block.
Categories Create Mind-Sets: The Effect of Exposure to Broad versus Narrow Categorizations on Subsequent, Unrelated Decisions

The authors find that exposure to different types of categories or assortments in a task creates a mind-set that changes how consumers process information in subsequent tasks. That is, these mind-sets have a spillover effect that alters consumers' decision making in a variety of subsequent and unrelated tasks, from basic cognitive behaviors (e.g., grouping) and consumer decisions (e.g., new product adoptions) to more general decision-making strategies (e.g., susceptibility to heuristics). Consumers previously exposed to broad assortments or categorizations base their decisions on fewer pieces of information, typically those made salient by the environment. In contrast, consumers previously exposed to narrow assortments or categorizations employ multiple pieces of information, both salient and nonsalient, without exerting any extra effort. Consequently, prior exposure to broad versus narrow categorizations leads to greater susceptibility to some common context effects and to heuristic decision making.
Evolution game: which came first, the receptor or the ligand?

On the basis of a bioinformatic approach, we suggest that in invertebrates many ligands interact with a single, ancestral and generalized receptor driving ligand evolution. In vertebrates, on the other hand, the occurrence of gene/genome duplications induced the shift to a ligand-directed evolution of receptors.
Learned Attention

Unlike many approaches to machine learning, human learning involves selective attention. When confronted by new things to learn, people can rapidly shift attention, thereby increasing speed of acquisition and decreasing interference with previous knowledge. The shift of attention is itself learned, so that attention is allocated to particular cues in particular contexts. While selective attention benefits acquisition, it can also lead to distortions of knowledge that are evident when the knowledge is transferred to novel situations. Several mathematical models have been designed to implement selective attention in learning; the models quantitatively fit human performance in many experiments. This presentation reviews various research projects of the author.
Advanced fuzzy logic controllers and self-tuning strategy

Advanced fuzzy logic controllers and self-tuning strategy " (1994). Retrospective Theses and Dissertations. Paper 10481. This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete unauthorized copyright material had to be removed, a note wiH indicate the deletion. sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Signature was redacted for privacy.
THE CD-INDEX OF THE BOOLEAN LATTICE

We study some properties of the cd-index of the Boolean lattice. They are extremely similar to the properties of the ab-index, or equivalently, the flag h-vector of the Boolean lattice and hence may be viewed as their cd- analogues. We define a different algebra structure on the polynomial algebra khc, di and give a derivation on this algebra. It is of significance for the Boolean lattice and forms our main tool. Using similar methods, we also prove some results for the cd-index of the cubical lattice. We show that the Dehn- Sommerville relations for the flag f-vector of an Eulerian poset are equivalent to certain simple identities that exist in our algebra.
Assessing the use of multiseason QuickBird imagery for mapping invasive species in a Lake Erie coastal Marsh

QuickBird multispectral satellite images taken in September 2002 (peak biomass) and April 2003 (pre-growing season) were used to map emergent wetland vegetation communities, particularly invasive Phragmites australis and Typha spp. , within a diked wetland at the western end of Lake Erie. An unsupervised classification was performed on a nine-layer image stack consisting of all four spectral bands from both dates plus a September Normalized Difference Vegetation Index image. The resulting eight cover classes distinguished three monodominant genera (Phragmites australis, Typha spp., Nelumbo lutea), three multigenera plant communities (wet meadow, other non persistent emergents, woody vegetation), and two unvegetated cover types (water, bare soil). Field validation at 196 data points yielded an overall classification accuracy of 62%, with producer’s accuracy for the eight individual classes ranging from 41 to 91% and user’s accuracy from 17 to 90%. Three-fourths of areas designated as Phragmites were correctly mapped, but 14% were found to be cattail (Typha) during field validation. Lotus (Nelumbo lutea) beds were accurately mapped on multiseason imagery (producer’s accuracy = 91%); these beds had not yet emerged above water in April, but were fully developed in September. Other types of non persistent vegetation were confused with managed areas in which vegetation had been cut and burned to control invasive Phragmites. Multiseason QuickBird imagery is promising for distinguishing certain wetland plant species, but should be used with caution in highly managed areas where vegetation changes may reflect human alterations rather than phenological change.
Modeling and Characterization of On-Chip Transformers for Silicon RFIC

A broadband and scalable lumped-element model for silicon on-chip transformers is presented. Model elements are driven from layout and process technology specifications. We provide simple and accurate expressions for evaluating the self inductance and the mutual coupling coefficient. The effects of various layout parameters, including transformer area, number of turns, and turns ratio, on transformer electrical response have been investigated. Model accuracy is demonstrated by comparing simulated and measured S-parameters, minimum insertion loss, quality factor, coils inductance, and magnetic coupling of several transformers with a wide range of configurations
Evaluating the SWAN ( Scaling Walls A Note at a time ) Program : How Do Their Music Lessons Impact the Children of Incarcerated Parents ?

This paper evaluates the SWAN program, an organization that offers free music lessons to children with a history of parental incarceration. The goal of this research project was to assess to what extent SWAN provides children with cultural capital, or the non-financial resources that enable individuals to overcome difficulties they face and achieve higher status in society. A total of seven children participated in this study. Seven additional children matched by sex and age comprised the comparison group and all were found from networking through local organizations. Surveys were distributed that collected information on children’s musical knowledge, academic grades, commitment to SWAN, self-evaluations, and pertinent demographics. Results showed that children in SWAN received significantly more cultural capital, as measured by their amount of musical knowledge, than children not in the program. The data also indicates that children enrolled in SWAN have better overall academic grades than children not enrolled in SWAN although results were not statistically significant. The majority of children had academic GPA increases after taking SWAN music lessons for a year. Finally, we found that children in SWAN reported doing less homework over time but have high music lesson attendance rates. Limitations to this study are examined and recommendations for the program are discussed.
Trust enhanced secure multi-path routing protocol for detecting and mitigating misbehaving nodes

Security in infrastructure-less networks like MANETs (Mobile Ad Hoc Networks) has proven to be a challenging task. Inherently secure routing protocols is a must for operational continuity of such networks. A number of secure routing protocols based on trust have recently been proposed. These protocols use the traditional route discovery model where along with normal nodes misbehaving nodes also participate in the route discovery process to incapacitate the network. To avoid the participation of misbehaving nodes, a secure and efficient route to destination can be calculated using weighted average of the number of nodes in the route and their trust values. However, the challenge is to minimize overhead of the above weighted average based routing technique while ensuring good throughput. In this paper we propose efficient trust based multi-path dynamic source routing protocol (TMDSR). Unlike previous approaches which are based on broadcast and hence ignore the path from one hop neighbor of destination, the protocol proposed in this paper consider such path as it uses unicasting of route discovery packet from one hop neighbor of destination. Results show that our method increases the throughput of the network while minimizing the routing overhead.
Induction of Multiple Decision Trees Using Multiobjective Particle Swarm Optimization

The decision tree is a popular and widely-used classification model. The two main objectives in decision tree induction are accurate predictions for unseen instances and human comprehensibility. In this paper, we use multiobjective optimization for the evolution of decision tree classifiers that are efficient both with respect to classification accuracy and classifier complexity. Simpler decision trees are generally more comprehensible to humans at the expense of accuracy. We employ the Multiobjective Particle Swarm Optimization using Crowding Distance (MOPSO-CD) algorithm to evolve a population of decision trees that are optimal on two objectives: classification accuracy and classifier complexity based on the Minimum Description Length Principle. The validity and performance of this approach is evaluated on several commonly-used benchmark datasets. The results show that our approach is indeed effective in inducing multiple decision trees that are accurate and simple.
Designlab , Making Space for Doing

The University of Twente has developed the DesignLab initiative to feed the “high tech, human touch” mission of the organisation. The combination of high tech and human touch was made one of the spearheads of the future, in order to bank on the universities unique position as a ‘dual-core university’ with both a strong science and engineering faculty on the one hand and a social sciences faculty at the other. This DesignLab has the mission to integrate the expertise of both academic kernels in “science2design4society”, which means that design knowledge is used to make new technologies available for users and that, together with the latest insights in humanities and business, these are used to tackle problems in contemporary society. To make this happen, the DesignLab needed a dedicated space for doing design in multidisciplinary teams. Because a lot of the prospective users of the lab would not be trained as a designer, it was decided that the actual space of the lab should support the design process. The space that was developed and implemented was therefore not only furnished for the desired design activities, but also structured according the desired design process. It is not yet evidenced that the space actually contributes to a more designerly way of working, the users are however very positive and the design process metaphor proved to be very helpful to build a shared vision within all the stakeholders involved in the DesignLab project.
Cooperation among Competitors. Its Social Mechanisms through Network Analyses

This paper argues that cooperation among competitors is facilitated by social processes (among others: learning, bounded solidarity, social control, regulation) that can be modelled using network analyses. Entrepreneurs get involved in social exchanges and these exchanges require relational investments, protection of these investments, social niche seeking and status competition – which trigger and drive these social processes. To illustrate this theory, I draw on sociological research using the analysis of social and organizational networks in business. These analyses model and substantiate the complex social discipline that helps interdependent, but competing entrepreneurs cooperate. Finally, I speculate about the implications of this knowledge of complex interdependencies and coordination, social discipline and social processes among entrepreneurs for public authorities involved in social control of markets.
Database compression

Despite the fact that computer memory costs have decreased dramatically over the past few years, data storage still remains, and will probably always remain, an important cost factor for many large scale database applications. Compressing data in a database system is attractive for two reasons: data storage reduction and performance improvement. Storage reduction is a direct and obvious benefit, while performance improves because smaller amounts of physical data need to be moved for any particular operation on the database. We address several aspects of reversible data compression and compression techniques:general concepts of data compression; a number of compression techniques; a comparison of the effects of compression on common data types; advantages and disadvantages of compressing data; and future research needs.
A hybrid fusion scheme for color face recognition

In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses the hybrid fusion scheme to combine these three kinds of matching scores for classification of the test sample. The hybrid fusion scheme exploits low- and high-order components of three kinds of matching scores based on the sum and product rule. Scores from each color channel generated from TPTSR includes both little correlated and very correlated scores, to extract low- and high-order components of these scores will allow them to be well integrated and used for classification. For evaluating the proposed method, we not only make a comparison of our method with some global and local methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel PCA (KPCA), kernel LDA (KLDA), locality preserving projection (LPP) and TPTSR. We also make a comparison of our method with some recently proposed local feature based methods, such as color local Gabor wavelets (CLGW), color local binary pattern (CLBP) and tensor discriminant color space (TDCS).
Paths, Trees, and Flowers

A graph G for purposes here is a finite set of elements called vertices and a finite set of elements called edges such that each edge meets exactly two vertices, called the end-points of the edge. An edge is said to join its end-points. A matching in G is a subset of its edges such that no two meet the same vertex. We describe an efficient algorithm for finding in a given graph a matching of maximum cardinality. This problem was posed and partly solved by C. Berge; see Sections 3.7 and 3.8.
Scalable and deterministic key agreement for large scale networks

Key agreement is a central problem to build up secure infrastructures for networks. Public key technology may not be suitable in many networks of low-end devices, such as ad hoc networks and sensor networks, because of its computation inefficiency and the lack of central authorities in those distributed scenarios. Conventional distributed symmetric key agreement schemes lack scalability due to their large memory cost (O(N), where N is the total number of nodes), and their probabilistic nature cannot ensure key agreement between every pair of nodes. In this paper, we propose a novel symmetric key agreement scheme, which is scalable for large scale networks with very small memory cost per node. A t-degree (k+1)-variate symmetric polynomial is used to achieve key agreement between nodes. The memory cost per node for a network of N nodes is reduced to around k+1radic(k(k+1)! /2) kradicN, where k ges 1. Our scheme is also deterministic in that every pair of nodes can establish a shared key.
On the impact of the video quality assessment in 802.11e ad-hoc networks using adaptive retransmissions

This paper investigates the influence of the video quality assessment at the medium access control (MAC) layer of an 802.11e distributed network in which the purpose is to adapt the retry limit of each video packet for maximizing the quality perceived by the end user. Moving from an existing Markov model of the 802.11e access scheme, the number of retransmissions associated to each video packet is evaluated to satisfy the distortion requirements inferred using two quality assessment measures: the mean squared error (MSE) and the structural similarity (SSIM) index. The aim of the proposed work is to discuss the suitability of these measures for managing the retransmission policy in the presence of contention-based mechanisms. The numerical results are obtained using a network simulator implementing the 802.11e enhanced distributed channel access (EDCA) in which the video sequences are encoded using the H.264 scalable video coding (SVC) standard.
The food system, climate change and CSR: from business to government case [in: Corporate social responsibility in food and agriculture]

Findings: It is argued that interventions to tackle climate change are political rather than economic and depend on power relationships among different actors, such as states and large corporations, involved in their implementation. The main conclusion of the article is that a renovated agenda to tackle climate change ought to be based on the two pillars of soft regulation-voluntary CSR and binding state regulation. In this new scenario corporate and antitrust laws should be used to correct the growing imbalance between corporate rights and corporate responsibility, with binding regulations supporting voluntary CSR. Originality: Application of CSR has been left to corporations who have pursued their own piecemeal agenda; and the predominant creed of neoliberalism has been ineffectual in governance. This article questions its effectiveness and proposes an original and potentially sustainable alternative.
Is Importing ELT Pedagogies Viable Anymore?: The Case of Sri Lanka

English Language Teaching (ELT) pedagogies in a country must adjust to the customs and habits of its people and its traditions. Importing of ELT pedagogies and their uniform application across the board may not be effective if learners prefer strategies contrary to the ones these pedagogies want them to adopt. The study described in this paper investigated language- learning strategies of 886 ESL students who belong to three distinct subcultures as demarcated by their L1, Religion and Ethnicity in Sri Lanka. The findings show differences in strategy use or rather the ways the three groups learn the target language indicating that these strategy choices are closely correlated to their Religion and Ethnicity. Based on the findings of this Sri Lankan study ELT pedagogy cannot be independent of its cultural context. It is therefore strongly suggested that ELT pedagogies should: (1) develop within the socio-cultural contexts of the learners; (2) orientate the learners to the culture of the native speakers of learners' socio-cultural context; (3) incorporate teaching material based on rhetoric indigenous to their culture.
L2TAP+SCIP: An audit-based privacy framework leveraging Linked Data

We describe a framework designed to facilitate privacy auditing while accommodating a variety of privacy scenarios and policies that involve multiple participants. Our proposal is based on two ontologies, L2TAP and SCIP, designed for deployment in a Linked Data environment. L2TAP provides provenance enabled logging of events. SCIP synthesizes contextual integrity concepts and enables query based solutions for two important privacy processes (compliance and obligation derivation). We include an experimental validation of the scalability of our approach.
Measuring Inequality from Contaminated Data

We consider the problem of constructing an interval estimator for inequality indices from survey data afflicted with occasional measurement errors so-called contaminations. Drawing on earlier work on partial identification from contaminated data, we derive a method-ofmoments interval estimator for decomposable inequality indices with sample moment equal to a combination of order statistics. Sufficient conditions guaranteeing its asymptotic normality and the asymptotic validity of confidence intervals are established. Unlike existing estimators, the proposed method-of-moments interval estimator reports ambiguity arising from both sample variability and contamination. The implementation of the estimator is illustrated in the case of constructing confidence intervals for the coefficient of variation using both simulated and real data. Results from simulated data indicate that the proposed method produces reliable interval estimates. The application with real data shows that an apparent decrease in household wealth inequality between 1992 and 2001 in the United States observed in data from the Survey of Consumer Finances may be attributed to contamination.