Miracle-dz/UniFAR-FTUs huggingface dataset:
PERFORMANCE EVALUATION OF ASSOCIATION RULE MINING ON PARTITIONED DATA

Association rule mining is one of the major domain in data mining that is one of the excellent researched paradigms for the discovery of interesting and desired relations between variables from large databases. In many applications, it is required to identify strong and efficient rules in the databases using assorted measures of interestingness. This approach or paradigm is used widely in number of domains including banking, finance, market basket analysis, agriculture and many others. The kind of information fetched as association rules can be used as the base for decisions about marketing activities such as promotional pricing or items placements. Moreover, from market basket analysis, the association rules can also be employed in intrusion detection, Bio-Statistics, Continuous production and bioinformatics. In contrast to the sequence mining, association rule learning typically do not considers the sequence of products within a transaction or across multiple transactions. In this manuscript, the proposed research implemented the Apriori Algorithm as base algorithm for investigation on multidimensional database relations and finally the results are optimized using empirical algorithmic approach. Additionally, the formation and the prediction of the Association Rules of the Items Patterns shall be implemented by proposing the improvements in the using fuzzy logic. The simulation and implementation has been performed and the results extractions are interpreted for the efficiency analysis of the proposed research work. The proposed version of the Apriori algorithm alongwith the optimization module processing is proved to be efficient in terms of the execution time and obviously the complexity and phase reduction that will lead the proposed technique efficient as compared to the classical approach. This work mainly focuses on the performance of multidimensional database and its association with the rule
The Innovative Enterprise and Corporate Governance Submitted to the Commission : March 1998

Abstract The centrality of corporate enterprises for allocating resources in the economy has sparked the recent debate among economists about the manner in which corporations should be governed to enhance economic performance. This article demonstrates the implications of innovation for corporate governance. To deal with the economics of innovation, a theory of corporate governance must come to terms with the developmental, organisational, and strategic dimensions of innovative resource allocation. The leading theories of corporate governance -the shareholder and stakeholder theories -do not, however, incorporate a systematic analysis of innovation in their analytical frameworks. I show that both of these perspectives, in relying on concepts of resource allocation as individual and optimal, contradict what we know about the innovation process.
A Direct Radiative Transfer Equation Solver for Path Loss Calculation of Underwater Optical Wireless Channels

In this letter, we propose a fast numerical solution for the steady state radiative transfer equation in order to calculate the path loss due to light absorption and scattering in various type of underwater channels. In the proposed scheme, we apply a direct non-uniform method to discretize the angular space and an upwind type finite difference method to discretize the spatial space. A Gauss-Seidel iterative method is then applied to solve the fully discretized system of linear equations. The accuracy and efficiency of the proposed scheme is validated by Monte Carlo simulations. Index Terms Radiative transfer equation, underwater optical path loss, finite difference method, Gauss-Seidel iteration.
Caffeinated and Decaffeinated Coffee Consumption and Risk of Type 2 Diabetes: A Systematic Review and a Dose-Response Meta-analysis

OBJECTIVE Previous meta-analyses identified an inverse association of coffee consumption with the risk of type 2 diabetes. However, an updated meta-analysis is needed because new studies comparing the trends of association for caffeinated and decaffeinated coffee have since been published. RESEARCH DESIGN AND METHODS PubMed and Embase were searched for cohort or nested case-control studies that assessed the relationship of coffee consumption and risk of type 2 diabetes from 1966 to February 2013. A restricted cubic spline random-effects model was used. RESULTS Twenty-eight prospective studies were included in the analysis, with 1,109,272 study participants and 45,335 cases of type 2 diabetes. The follow-up duration ranged from 10 months to 20 years. Compared with no or rare coffee consumption, the relative risk (RR; 95% CI) for diabetes was 0.92 (0.90–0.94), 0.85 (0.82–0.88), 0.79 (0.75–0.83), 0.75 (0.71–0.80), 0.71 (0.65–0.76), and 0.67 (0.61–0.74) for 1–6 cups/day, respectively. The RR of diabetes for a 1 cup/day increase was 0.91 (0.89–0.94) for caffeinated coffee consumption and 0.94 (0.91–0.98) for decaffeinated coffee consumption (P for difference = 0.17). CONCLUSIONS Coffee consumption was inversely associated with the risk of type 2 diabetes in a dose-response manner. Both caffeinated and decaffeinated coffee was associated with reduced diabetes risk.
Decadal Changes in the Abundance and Length of Snapper (Chrysophrys auratus) in Subtropical Marine Sanctuaries

Abundance and length of the highly-targeted snapper Chysophrys auratus were compared between sites in 'no take' areas (Sanctuary Zones: SZ), partial protected areas which are fished (Habitat Protection Zones: HPZ), and areas outside (Outside) the Solitary Islands Marine Park (SIMP), Australia. Baited Remote Underwater Video (BRUV) sampling on shallow rocky reef (15 - 25 m) was conducted annually from 2002 until 2014 in the Austral-winter, covering the decade after these marine park zones were established (2002). Additional deeper sites (25 - 40 m) were sampled in 2010-2011 to assess if findings were more-broadly applicable. Lengths were measured using stereo-BRUVs from 2011-2014. Snapper were significantly more abundant in SZ overall and in most years compared with the other two management types, which did not significantly differ. Snapper rapidly increased after 2 - 3 years protection in all management types, especially SZ. Snapper were present on more SZ deployments than HPZ and Outside after the same period. The positive SZ response in snapper abundance on shallower reef was also found at a broader spatial scale on deeper sites. Again the two fished management types did not show significant differences among each other. There was considerable variation in snapper abundance between years, with strong peaks in 2005, 2009 and 2014 especially in SZ. Abundances remained higher in SZ in the year or two following a strong peak, but decreased to similar abundances to fished areas before the next peak. Snapper length frequency distribution significantly differed between SZ and both fished management types, with more larger snapper within SZ including a higher proportion (58%) that were legal-sized (>25.7 cm FL). HPZ and Outside did not significantly differ from each other, and were dominated by individuals below legal size. Overall, SZ's have positively influenced abundance and length of snapper on these subtropical rocky reefs.
Learning Inductive Riemannian Manifold in Abstract Form by Modeling Embedded Dynamical System

Manifold learning algorithms do not extract the structure of datasets in an abstract form or they do not have high performance for complex data.In this paper, a method for Learning an Inductive Riemannian Manifold in Abstract form (LIRMA) is presented in which the structure of patterns is determined by solving the embedded dynamical system of the patterns. In order to model corresponding system, the true sequence of patterns is estimated using a topology preserving method. LIRMA has the advantage of being an inductive method with low complexity. Additionally, it is a topology preserving method with respect to quantitative measures.
DARK FLUID: TOWARDS A UNIFICATION OF EMPIRICAL THEORIES OF GALAXY ROTATION, INFLATION AND DARK ENERGY

Empirical theories of Dark Matter like MOND gravity and of Dark Energy like f(R) gravity were motivated by astronomical data. But could these theories be branches rooted from a more general hence natural framework? Here we propose the natural Lagrangian of such a framework based on simple dimensional analysis and co-variant symmetry requirements, and explore various outcomes in a top-down fashion. Our framework preserves the co-variant formulation of GR, but allows the expanding physical metric be bent by a single new species of Dark Fluid flowing in space-time. Its non-uniform stress tensor and current vector are simply functions of a vector field of variable norm, resembling the 4-vector electromagnetic potential description for the photon fluid, but is dark (e.g., by very early decoupling from the baryon-radiation fluid). The Dark Fluid framework naturally branches into a continuous spectrum of theories with Dark Energy and Dark Matter effects, including the f(R) gravity, TeVeS-like theories, Einstein-Aether and �� theories as limiting cases. When the vector field degenerates into a pure Higgs-like scalar field, we obtain the physics for inflaton and quintessence. In this broad setting we emphasize the non-constant dynamical field behind the cosmological constant effect, and highlight plausible corrections beyond the classical MOND predictions. Choices of parameters can be made to pass BBN, PPN, and causality constraints. The Dark Fluid is inspired to unify/simplify the astronomically successful ingredients of previous constructions: the desired effects of inflaton plus quintessence plus Cold DM particle fields or MOND-like scalar field(s) are shown largely achievable by one vector field only. Subject headings: Gravitation; Cosmology: theories; Dark Matter; Galaxies: kinematics and dynamics
Corporate Knowledge in Cyberworlds

The aim of this paper is to propose a modeling of corporate knowledge in cyberworlds. An enterprise is considered in the framework of multiagent methodology as a distributed computational system. The Agent-Oriented Abstraction paradigm was proposed earlier to describe in a fully generic way agents and societies of agents. In this paper, we are investigating the application of this paradigm to the abstract modeling of corporate knowledge, extending the scope of traditional knowledge management approaches. We show that such an abstraction mechanism leads to very practical applications for cyberworlds whether on the web or on any other medium. Our approach covers the broader possible scope of corporate knowledge, emphasizing the distributivity and autonomy of agents within cyber systems. This approach can be further used to better simulate and support knowledge management processes.
Performance analysis of a distributed resource allocation scheme for D2D communications

Device-to-device (D2D) communications underlaying a cellular infrastructure has recently been proposed as a means of increasing the cellular capacity, improving the user throughput and extending the battery lifetime of user equipments by facilitating the reuse of spectrum resources between D2D and cellular links. In network assisted D2D communications, when two devices are in the proximity of each other, the network can not only help the devices to set the appropriate transmit power and schedule time and frequency resources but also to determine whether communication should take place via the direct D2D link (D2D mode) or via the cellular base station (cellular mode). In this paper we formulate the joint mode selection, scheduling and power control task as an optimization problem that we first solve assuming the availability of a central entity. We also propose a distributed suboptimal joint mode selection and resource allocation scheme that we benchmark with respect to the centralized optimal solution. We find that the distributed scheme performs close to the optimal scheme both in terms of resource efficiency and user fairness.
Molecular and Cellular Pathobiology Wnt Inhibitor Dickkopf-1 as a Target for Passive Cancer Immunotherapy

Dickkopf-1 (DKK1) is an inhibitor of Wnt/β-catenin signaling that is overexpressed in most lung and esoph-ageal cancers. Here, we show its utility as a serum biomarker for a wide range of human cancers, and we offer evidence favoring the potential application of anti-DKK1 antibodies for cancer treatment. Using an original ELISA system, high levels of DKK1 protein were found in serologic samples from 906 patients with cancers of the pancreas, stomach, liver, bile duct, breast, and cervix, which also showed elevated expression levels of DKK1. Additionally, anti-DKK1 antibody inhibited the invasive activity and the growth of cancer cells in vitro and suppressed the growth of engrafted tumors in vivo. Tumor tissues treated with anti-DKK1 displayed significant fibrotic changes and a decrease in viable cancer cells without apparent toxicity in mice. Our findings suggest DKK1 as a serum biomarker for screening against a variety of cancers, and anti-DKK1 antibodies as potential theranostic tools for diagnosis and treatment of cancer.
Tamper Resistance Verification Method for Consumer Security Products

Consumer security products handling confidential information are secured by encrypting data. The encryption standard, which has been widely diffused in recent years, is certified that its decryption is computationally impossible. However, it was recently reported that even if an encryption algorithm is theoretically secured, when the algorithm is incorporated into hardware, confidential information about the algorithm could be improperly revealed using power consumption data when the hardware is operated. Such improper specifications are generally called side-channel attacks. Therefore, when an encryption algorithm is incorporated into hardware, evaluation of the resistance against power analysis attacks (tamper resistance) becomes important in the design stage of the circuit. The present study proposes a new method for efficient verification of tamper resistance against power analysis attacks when designing a cryptography circuit. The validity of the proposed method is confirmed using design data of an experimentally produced chip of the advanced encryption standard (AES).
Expressive pen-based interfaces for math education

Mathematics students almost exclusively use pencil and paper--that is, they learn without computational support. In this research, 16 high school students varying in ability from low to high participated in a comparative assessment of geometry problem solving using: (1) pencil and paper, (2) an Anoto-based digital stylus and paper interface, (3) a pen tablet interface, and (4) a graphical tablet interface. Cognitive Load Theory correctly predicted that as interfaces departed more from familiar work practice, students experienced greater cognitive load and corresponding reductions in their expressive fluency and planning. The results of this study indicate that students' communication patterns and meta-cognitive control can be enhanced by pen-based interfaces during math problem solving activities. In addition, low-performing students do not automatically reap the same advantage as high performers when new interface tools are introduced, which means intervention may be required to avoid expanding the achievement gap between groups unless intervention is undertaken.
Theory for variational area-based segmentation using non-quadratic penalty functions

In this paper a theory is developed for variational segmentation of images using area-based segmentation functionals with non-quadratic penalty functions in the fidelity term. Two small theorems, which we believe are new to the vision community, allow us to compute the Gateaux derivative of the considered functional, and to construct the corresponding gradient descent flow. The functional is minimized by evolving an initial curve using this gradient descent flow. If the penalty function is sub-quadratic, i.e. behaves like the p'th power of the error for p<2, the obtained segmentation model is more robust with respect to noise and outliers than the classical Chan-Vese model and the curve evolution has better convergence properties.
Face Verification Schemes for Mobile Personal devices

The fast growing public ownership and use of mobile personal programmable communication devices (e.g. PDA’s) create new and exciting opportunities for convenient and secure commercial and service providing transactions. The newer generations of these devices incorporate digital cameras and signature pads, raising the possibility of enhancing the security of mobile transactions using biometric-based authentication. However, implementing biometrics-based athentication on devices that are constrained in their memory size and computational power is a tough technological task. This paper is primarily concerned with wavelet techniques for face verification as part of a multi-modal biometrics-based identity verifier for PDA’s that would include voice and hand-written signature. We shall present preliminary results of comparison of accuracy rates of three face verification schemes: PCA applied in the spatial domain, PCA applied in the low-low subband of wavelet transformed images down to level 5, and wavelet-only feature vectors. Although the experimental sample is relatively small, the results indicate that at level 4 and 5 of wavelet decomposition, the latter two verification schemes perform significantly better than the spatial PCA. The wavelet-only feature vector scheme has the best accuracy rate, and is the most efficient scheme suitable for constrained devices.
‘Warren Buffet is my cousin’: shaping public understanding of big data biotechnology, direct-to-consumer genomics, and 23andMe on Twitter

ABSTRACT Scholars, educators, regulators, pundits, and other observers are advocating for regulation and oversight of direct-to-consumer (DTC) genomic testing. As a result, the technology has been subject of highly visible public and regulatory controversy. In this article, we explore the nature and the shape of the sentiment of public discourse about the DTC company, 23andMe. We conduct a quantitative content analysis and qualitative framing analysis on Tweets. We find that the discourse surrounding DTC genomics and 23andMe is largely positive. We also identify a number of frames users deploy to debate, discuss, and share their experiences with DTC genomics and 23andMe. We argue that these frames create meaning around this emerging technology for its users.
Grid Impedance Identification Based on Active Power Variations and Grid Voltage Control

This paper presents a novel method for identification of grid impedance which can be applied to grid connected power converters. Active power variations together with grid voltage control are used to obtain two operating points necessary for the algorithm proposed. Grid currents and voltages in the two operating points are then reported to an arbitrary synchronous reference frame, resulting the equations for identification of inductive and resistive parts of grid impedance. Simulation and experimental results are used to validate the proposed algorithm in case of inductive and resistive grids. The accuracy of impedance identification is comparable to that of commercial products designed for grid impedance measurement.
Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast

Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages.
Collaborative Filtering Process in a Whole New Light

Collaborative filtering (CF) systems are gaining widespread acceptance in recommender systems and e-commerce applications. These systems combine information retrieval and data mining techniques to provide recommendations for products, based on suggestions of users with similar preferences. Nearest-neighbor CF process is influenced by several factors, which were not examined carefully in past work. In this paper, we bring to surface these factors in order to identify existing false beliefs. Moreover, by being able to view the "big picture" from the CF process, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% percent increase in precision in comparison to widely-used CF algorithms. We perform an extensive experimental evaluation, with several real data sets, and produce results that invalidate some existing beliefs and illustrate the superiority of the proposed extensions
Notching up neural stem cell homogeneity in homeostasis and disease

Adult neural stem cells (NSCs) are perceived as a homogeneous population of cells that divide infrequently and are capable of multi-lineage differentiation. However, recent data revealed that independent stem cell lineages act in parallel to maintain neurogenesis and provide a cellular source for tissue repair. In addition, even within the same lineage, the stem and progenitor cells are strikingly heterogeneous including NSCs that are dormant or mitotically active. We will discuss these different NSC populations and activity states with relation to their role in neurogenesis and regeneration but also how these different stem cells respond to aging. NSCs depend on Notch signaling for their maintenance. While Notch-dependence is a common feature among NSC populations, we will discuss how differences in Notch signaling might contribute to adult NSC heterogeneity. Understanding the fate of multiple NSC populations with distinct functions has implications for the mechanisms of aging and regeneration.
Learning Automata on Protein Sequences

Pattern discovery is limited to position-specific characterizations like Prosite's patterns or profile-HMMs which are unable to handle, for instance, dependencies between amino acids distant in the sequence of a protein, but close in its three-dimensional structure. To overcome these limitations, we propose to learn automata on proteins. Inspired by grammatical inference and multiple alignment techniques, we introduce a sequence-driven approach based on the idea of merging ordered partial local multiple alignments (PLMA) under preservation or consistency constraints and on an identification of informative positions with respect to physico-chemical properties . The quality of the characterization is asserted experimentally on two difficult sets of proteins by a comparison with (semi)-manually designed patterns of Prosite and with state-of-the-art pattern discovery algorithms. Further leave-one-out experimentations show that learning more precise automata allows to gain in accuracy by increasing the classification margins.