Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach for Improving the Recommendation System by Knowledge of Semantic Web in Web Usage Mining

The World Wide, We has influenced a lot to both users as well as the web site owners. Massive growth of World Wide Web increases the complexity for users to browse effectively. For increasing web site uses and to achieve desired goal efficiently, web server activities are hypothetical to be changed as per users’ interests. To achieve this they have to record and analyze user access pattern which are captured in the form of log files. Web usage mining refers to process of analyzing interaction of user with different web application and deriving some important knowledge out of it. Web usage mining process produces result based on maximum usage history, stored in the web server access logs. So we propose the system, which uses semantic knowledge, derived from each page, along with knowledge derived from WUM. The system generates the most efficient and applicable recommendation list, and also provides equal opportunity for the content, which is added recently, to be incorporated in the list, if this best matches with users interest.
Dimension Reduction of Biochemical Network Models: Implementation and Comparison of Dierent Algorithms

In this thesis different dimension reduction algorithms for linear, timeinvariant systems are applied to biochemical network models. A general framework for the linearization of biochemical network models from the literature is shown and a way to export reduced order, linear systems to the format Systems Biology Markup Language is developed. The well known dimension reduction algorithms balanced truncation, singular perturbation approximation and matrix-Padé-via-Lanczos are described and compared for their computational cost and for the behaviour of their output models in the time domain. Finally an open-source program for dimension reduction of biochemical network models in the SBML format is introduced.
Surface electromyographic amplitude normalization methods: a review

The electromyogram is the summation of the motor unit action potentials occurring during contraction measured at a given electrode location. The voltage potential of the surface electromyographic signal detected by electrodes strongly depends on several factors, varying between individuals and also over time within an individual. Thus, the amplitude of the EMG signal itself is not useful in group comparisons, or to follow events over a long period of time. The fact that the recorded electromyographic amplitude is never absolute is mainly because impedance varies between the active muscle fibers and electrodes and its value is unknown. The EMG signal is highly variable and is dependent upon many factors. Thus, the amplitude of the temporally processed electromyography can only be used to assess short-term changes in the activity of a single muscle from the same individual when the electrode setup has not been altered. To allow comparison of activity between different muscles, across time, and between individuals, the EMG signal should be normalized, i.e. expressed in relation to a reference value obtained during standardized and reproducible conditions. Notwithstanding the importance of electromyographic amplitude normalization, studies on functional activities, such as gait, do not seem to show a uniform methodology. Taking this into account, the main purpose of this chapter is to review and discuss different normalization procedures to relate the most appropriate method for specific situations, based on how the normalization method might influence data interpretation. In addition, this review supports the development of proper normalization procedures for biomechanical studies of functional activities like human gait.
Adaptive frequency-domain regularization for sparse-data tomography

A novel reconstruction technique, called Wiener Filtered Reconstruction Technique (WIRT), for sparse-data tomographic imaging is introduced in this article. This six-step method applies a spatially-varying constrained least-squares filter combined with a regularization method based on total variation. The WIRT reconstruction is implemented in the frequency domain, where the information based on measurements and regularization can be treated separately. The algorithm applies regularization selectively in the frequency regions where the frequency component values cannot be defined by the measurements. This leads to computational benefits when compared to conventional iterative reconstruction methods such as algebraic reconstruction technique (ART). Both qualitative and quantitative comparisons against state-of-the-art methods suggest that WIRT is a promising reconstruction algorithm for sparse-data imaging regimes, especially with higher noise levels.
Experimental and Computational Tools for Analysis of Signaling Networks in Primary Cells

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein‐protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high‐quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow‐up in vitro/in vivo validation studies. Curr. Protoc. Immunol. 104:11.11. 1‐11.11. 23. © 2014 by John Wiley & Sons, Inc.
Foodborne Listeria monocytogenes: A Real Challenge in Quality Control

Listeria monocytogenes is a foodborne pathogen, and the detection and differentiation of this bacterium from the nonpathogenic Listeria species are of great importance to the food industry. Differentiation of Listeria species is very difficult, even with the sophisticated MALDI-TOF MS technique because of the close genetic relationship of the species and the usual gene transfer. The present paper emphasizes the difficulties of the differentiation through the standardized detection and confirmation according to ISO 11290-1:1996 and basic available L. monocytogenes detection methods and tests (such as API Listeria test, MALDI-TOF MS analysis, and hly gene PCR). With the increase of reports on the pathogenesis of atypical Listeria strains in humans, the significance of species level determination has become questionable, especially in food quality control, and the detection of pathogenic characteristics seems to be more relevant.
Time to overcome the neglect of effect sizes in teaching psychological research findings.

Due to reliance on null-hypothesis significance testing, researchers had widely neglected effect size (ES) in their reporting of psychological research results. The American Psychological Association and others identified this as a problem and initiated reform, and ES reporting is now common in the scholarly literature. To what extent problematic ES neglect occurs in the teaching of psychological research findings remains unknown. In order to address this question, we analysed the content of 10 bestselling psychology textbooks from five popular sub-fields (Study 1). Overall, 76% (95% CI [69%, 79%]) of portrayals of empirical findings were devoid of useful ES information and merely mentioned the direction of the effect discussed. Such a direction-only description of any research finding would appear particularly uninformative if laypeople’s intuitions about the direction of this effect were already accurate. Study 2 therefore tested how well 286 non-psychology undergraduate students could guess the direction (or absence) of a representative sample of effects. Irrespective of psychological sub-field of the effects, average accuracy was only 47% [42%, 51%], demonstrating participants’ poor intuitions regarding these effects. This suggests that the predominant direction-only description of research findings in textbooks is not without merits. However, we argue that greater consideration of ES in the teaching of psychological research findings is desirable. We suggest three simple and efficient strategies to this end and weight their relative merits.
Point-Based Value Iteration Algorithms for Multi-Core Machines

Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods which quickly provide approximate solutions for medium-sized problems. New multi-core machines offer an opportunity to scale up to much larger domains. These machines support parallel execution and can speed up existing algorithms considerably. In this paper we suggest several ways in which point-based algorithms can be adapted to parallel computing. We overview the challenges and opportunities and present experimental evidence to the usability of our suggestions. Our results show that the opportunity lies mainly in parallelizing at the algorithmic level, not at the point-based backup level.
ARE WOMEN MORE RISK-AVERSE THAN MEN?

This paper measures the gender difference in risk aversion using a sample that controls for biases in the level of education and finance knowledge. We survey 1,382 Finance and English professors from universities across the United States and compare their actual portfolio allocations to that of respondents in the Federal Reserve’s Survey of Consumer Finances (SCF). Our findings suggest that when individuals have the same level of education irrespective of their knowledge of finance, women are no more risk averse than men. We also find that the genderrisk aversion relation is a function of age, income, wealth, marital status, race/ethnicity and the number of children under 18 in the household.
Determining the quality of life of temporary migrants using ordered probit model: a review

Small developing economies are facing immense problems in pursuing the dual objective of economic growth and addressing rising social problems. Pacific Island countries are no exception. Issues pertaining to labour market, in particular employment, is a major cause of concern amongst policy makers. Governments actively seek large scale investments as a mean to create employment. How have these projects fared in terms of income generation and poverty alleviation? This study examines the case of migrant workers at a hotel construction project in Fiji. Results from the ordered probit model reveal that the quality of life of the workers have improved since working at the resort and the key factor affecting the improvement in quality of life is higher income. Long distance commuting also affects quality of life, but negatively.
Culture, food, and racism: the effects on African American health

Cultures can be influenced by a number of different factors, one of the most notable being food. Cultural food staples have often traveled from their country of origin of to be integrated into the cuisine of another. African slaves for example, used their knowledge of their West African heritage to forage soul food, a well know cuisine in the American South. The adaptations made to food practices during slavery decreased the nutritional value of the previously healthy West African dishes. After creating a relatively unhealthy food culture through slavery, African Americans continued to face persecution at the hands of the government and society at large. Through the institutionalized racism brought about during the Jim Crow era, African Americans were forced into conditions that juristically reduced their access to resources, like adequate housing and education. The lack of housing and education in the African American community inherently breeds poverty, ultimately affecting access to healthy food resources. Although their food access cannot be immediately influenced, the Oldways African Heritage Diet Pyramid will allow African Americans to capitalize on their food culture while utilizing their poor food access. By understanding the food practices of Africans during the 16th century, the researcher will trace the lineage of African food tendencies to the patterns of food choices for African Americans in the US today. The purpose of this research is to shed light on the history of African American food patterns in the United States and how through institutionalized racism African Americans have come to face a higher prevalence of chronic disease. CULTURE, FOOD, AND RACISM 3
Co-integration and error correction: representation, estimation and testing

The relationship between cointegration and error correction models, first suggested by Granger, is here extended and used to develop estimation procedures, tests, and empirical examples. A vector of time series is said to be cointegrated with cointegrating vector a if each element is stationary only after differencing while linear combinations a8xt are themselves stationary. A representation theorem connects the moving average , autoregressive, and error correction representations for cointegrated systems. A simple but asymptotically efficient two-step estimator is proposed and applied. Tests for cointegration are suggested and examined by Monte Carlo simulation. A series of examples are presented. Copyright 1987 by The Econometric Society.
Bringing basic research on early experience and stress neurobiology to bear on preventive interventions for neglected and maltreated children

A major focus in developmental psychopathology is on understanding developmental mechanisms and, armed with this information, intervening to improve children's outcomes. Translational research attempts to bridge the distance between understanding and intervention. In the collaborations that have formed the core of our research network on early experience, stress, and prevention science, we have focused on translating basic research on early experiences and stress neurobiology into preventive interventions for neglected and abused children. Our experiences in attempting to move from bench to bedside have led us to recognize the many challenges that face translational researchers. This review provides a brief synopsis of the animal model literature on early experience and stress neurobiology from which we glean several key bridging issues. We then review what is currently known about the impact of childhood neglect and abuse on stress neurobiology in human adults and children. Next, we describe how this work has informed the evaluation of our preventive interventions with maltreated children. Finally, we discuss several considerations that should facilitate a more complete integration of basic research on early experience and stress neurobiology into preventive intervention strategies.This paper reflects the work of the Early Experience, Stress and Prevention Science Network (R21 MH65046), whose members are Mary Dozier, Philip Fisher, Nathan Fox, Megan Gunnar, Seymour Levine, Charles Neal, Seth Pollak, Paul Plotsky, Mar Sanchez, and Delia Vazquez. Preparation of this manuscript was supported by a Senior Scientist Award (K05 MH66208) to Megan Gunnar, and by MH59780 and MH65046, NIMH, U.S. PHS; MH46690, NIMH and ORMH, U.S. PHS; and DA17592, NIDA, NIH, U.S. PHS to Philip Fisher.
Competence Based Business Development - Organizational Competencies as Basis for Successful Companies

The paper at hand presents a new competence focused management approach which is called competence based business development (CbBD). CbBD should support organizations of all kinds in the effective identification, measurement, combination and integration of organizational competences, which can be seen as a bundle of human competencies, tools and material. The creation of new business fields, products and services or the competence based organizational development can be named as fields of application. The theoretical background and the general framework for this methodology are presented in this paper as a first contribution to the scientific community, which manages the matching of human competence with organizational resources to build organizational competencies.
Real-Time Data and Fiscal Analysis: A Survey of the Literature

This paper surveys the empirical research on fiscal policy analysis based on real-time data. This literature can be broadly divided in three groups that focus on: (1) the statistical properties of revisions in fiscal data; (2) the political and institutional determinants of projection errors by governments and (3) the reaction of fiscal policies to the business cycle. It emerges that, first, fiscal data revisions are large and initial releases are biased estimates of final values. Second, the presence of strong fiscal rules and institutions leads to relatively more accurate releases of fiscal data and small deviations of fiscal outcomes from government plans. Third, the cyclical stance of fiscal policies is estimated to be more ‘counter-cyclical’ when real-time data are used instead of ex-post data. Finally, more work is needed for the development of real-time datasets for fiscal policy analysis. In particular, a comprehensive real-time dataset including fiscal variables for industrialized (and possibly developing) countries, published and maintained by central banks or other institutions, is still missing.
Demonstration Abstract: Software-Only System-Level Record and Replay in Wireless Sensor Networks

Wireless sensor networks (WSNs) are plagued by the possibility of bugs manifesting only at deployment. However, debugging deployed WSNs is challenging for several reasons— the remote location of deployed sensor nodes, the non- determinism of execution that can make it di � cult to replicate a buggy run, and the limited hardware resources available on a node. In particular, existing solutions to record and replay debugging in WSNs fail to capture the complete code execution, thus negating the possibility of a faithful replay and causing a large class of bugs to go unnoticed. In short, record and replay logs a trace of predeﬁned events while a deployed application is executing, enabling replaying of events later using debugging tools. Existing recording methods fail due to the many sources of non-determinism and the scarcity of resources on nodes. In this demo, we present Trace And Replay Debugging In Sensornets ( Tardis ), a software-only approach for deterministic record and replay of WSN nodes. Tardis is able to record all sources of non-determinism, based on the observation that such information is compressible using a combination of techniques specialized for respective sources. Despite their domain-speciﬁc nature, the techniques presented are applicable to the broader class of resource-constrained embedded systems.
Feasibility and Optimization of Fast Quadruped Walking with One-Versus Two-ata-Time Swing Leg Motions for RoboSimian

This paper presents two planning methods for generating fast walking gaits for the quadruped robot RoboSimian and analyzes their feasibility and performance as a function of joint velocity limits. One approach uses a two-at-a-time swing leg motion gait, generated using preview control of the zero moment point (ZMP) on a narrow, double-support base of support. The second method uses a one-at-a-time swing leg crawl gait. We employ and compare both value iteration and gradient descent methods to generate the motions of the swing leg through free space for the crawl gait. For this gait, our optimization strategies exploit redundant degrees of freedom in the limb, which proves to increase walking speed significantly. By contrast, the ZMP-based gait requires nearly symmetric motions of the (fixed) stance legs and (free) swing legs with respect to the body, resulting in limited ability to improve the joint trajectories of the limbs through clever planning, and so an inverse kinematics (IK) table solution is used for all limb kinematics here. Our ZMP-based planning is only feasible if joint velocity limits are sufficiently large. Also, both the overall step size and speed of this gait vary nonlinearly as velocity limits increase. Lastly, comparing the two methods, we show that walking speed is faster using two-at-a-time swing leg motions when the joint velocity limit is between 1.62 and 3.62 rad/sec and that the crawl gait is optimal otherwise.
Assisted common information

Secure multi-party computation is a central problem in modern cryptography. An important sub-class of this are problems of the following form: Alice and Bob desire to produce sample(s) of a pair of jointly distributed random variables. Each party must learn nothing more about the other party's output than what its own output reveals. To aid in this, they have available a set up — correlated random variables whose distribution is different from the desired distribution — as well as unlimited noiseless communication. In this paper we present an upperbound on how efficiently a given set up can be used to produce samples from a desired distribution. The key tool we develop is a generalization of the concept of common information of two dependent random variables [Gács-Körner, 1973]. Our generalization — a three-dimensional region — remedies some of the limitations of the original definition which captured only a limited form of dependence. It also includes as a special case Wyner's common information [Wyner, 1975]. To derive the cryptographic bounds, we rely on a monotonicity property of this region: the region of the “views” of Alice and Bob engaged in any protocol can only monotonically expand and not shrink. Thus, by comparing the regions for the target random variables and the given random variables, we obtain our upperbound.
Stabilization of inverted pendulum system using particle swarm optimization

The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal PID controller parameters for industrial process. PSO is a technique used to explore the search space of a given problem to find the settings or parameters required to maximize a particular objective. The focus will be on the application of the PSO into one of the popular problem setups in the engineering application area of control systems, which is called the inverted pendulum. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. The controller is obtained and validated by simulation; it's implemented to control the pendulum-cart system.
Mathematical Programming in Machine Learning and Data Mining

In this talk I discuss the problem of ﬁtting ‘ 1 regularized prediction models in inﬁnite (possibly non-countable) dimensional feature spaces. Our main contributions are: a. Deriving a generalization of ‘ 1 regularization based on measures which can be applied in non-countable feature spaces; b. Proving that the sparsity property of ‘ 1 regularization is maintained in inﬁnite dimensions; c. Devising a path-following algorithm that can generate the set of regularized solutions in ”nice” feature spaces; and d. Presenting an example of penalized spline models where this path following algorithm is computationally feasible