Miracle-dz/UniFAR-FTUs huggingface dataset:
High Dynamic Range Images: Evolution, Applications and Suggested Processes

Three dimensional (3D) and computer graphics (CG) industries nowadays require 'instant' realism and extreme special effects while imposing the balance in cost, time and solution acceptance standard. It has been known that realistic rendering is time consuming, difficult to use, complex, expensive and often produces insufficient digital output to predict particularly in the early visualization process. This paper focuses on one of the emerging technologies, High dynamic range image (HDRI) and how it can be developed in achieving effective solution, while maintaining 'realism' or required result based on a specific application. We firstly introduce the overall HDRI processes before continue to present a framework of captured low density range image (LDRI) source sequence effectively in HDRI environment. This paper concludes with discussions and lists the application of HDRI.
Tit for Others' Tat Repeated Prisoner's Dilemma Experiments with Third-Party Monitoring and Indirect Punishment

Two pairs of two participants each interact repeatedly in two structurally independent but informationally linked Prisoner's Dilemma games. Neither pair receives feedback about past choices by their own partner but is fully informed about the choices by the other pair. Considering this as a four-person infinite horizon game allows for Folk-Theorem-like voluntary cooperation. We ask whether monitoring and indirect punishment with the help of others are comparable to direct monitoring and punishment in establishing and maintaining voluntary cooperation. The treatment effects we find are rather weak. Others' monitoring of own activities is only an insufficient substitute for direct observability.
Evolution and Adaptation in Pseudomonas aeruginosa Biofilms Driven by Mismatch Repair System-Deficient Mutators

Pseudomonas aeruginosa is an important opportunistic pathogen causing chronic airway infections, especially in cystic fibrosis (CF) patients. The majority of the CF patients acquire P. aeruginosa during early childhood, and most of them develop chronic infections resulting in severe lung disease, which are rarely eradicated despite intensive antibiotic therapy. Current knowledge indicates that three major adaptive strategies, biofilm development, phenotypic diversification, and mutator phenotypes [driven by a defective mismatch repair system (MRS)], play important roles in P. aeruginosa chronic infections, but the relationship between these strategies is still poorly understood. We have used the flow-cell biofilm model system to investigate the impact of the mutS associated mutator phenotype on development, dynamics, diversification and adaptation of P. aeruginosa biofilms. Through competition experiments we demonstrate for the first time that P. aeruginosa MRS-deficient mutators had enhanced adaptability over wild-type strains when grown in structured biofilms but not as planktonic cells. This advantage was associated with enhanced micro-colony development and increased rates of phenotypic diversification, evidenced by biofilm architecture features and by a wider range and proportion of morphotypic colony variants, respectively. Additionally, morphotypic variants generated in mutator biofilms showed increased competitiveness, providing further evidence for mutator-driven adaptive evolution in the biofilm mode of growth. This work helps to understand the basis for the specific high proportion and role of mutators in chronic infections, where P. aeruginosa develops in biofilm communities.
A comprehensive study of various biometric identification techniques

Biometric recognition deals with recognition of an individual from distinctive anatomical and behavioural characteristics for effective identification solution. This paper presents a brief introduction on what is biometric, how it is helpful for security purposes and what are the various techniques that are being used in current scenario. A comparison has been performed for various biometric techniques.
An Epidemic Model of Investor Behavior

Abstract I test whether social influence affects individual investors’ trading and stock returns. In each of the 20 most active stocks in Finland over 9 years, the number of owners in a municipality multiplied by the number of investors who do not own a stock, a measure of the rate of transmission of diseases and rumors through social contact, predicts individual investor trading. I control for known determinants of trade, including daily news, and show that competing explanations for the relation are unlikely. Socially motivated trades predict stock returns, and the effects are not reversed, suggesting that individuals share useful information. Individuals’ susceptibility to social influence has declined during the period, but the opportunities for social influence have increased.
Keyphrase Extraction Abstracts Instead of Full Papers

In the present paper we consider keyphrase extraction problem from scientific articles. Finding an appropriate solution is important for the organization of fast navigation in databases, indexing, clustering and classification of academic papers. The base collection includes keyphrases selected by the experts for each text (SemEval2010). It is shown that the use of abstracts instead of full texts allows to improve the results obtained by processing full texts or abstracts with introduction and conclusion section. Our approach uses the extraction of keyphrases with linguistic patterns (part of speech-based), patterns are built on the basis of an auxiliary dataset. The use of abstracts in this approach allows to reduce the number of words sequences extracted with patterns, as compared to the use of full texts. It allows to simplify or totally omit the ranking stage. Ranking is usually needed, because out of many keyphrases candidates we have to choose only 10-15. This stage is the most difficult and its effectiveness depends on the number of the selected candidates to keyphrases. The use of abstracts makes it possible to considerably reduce the number of candidate phrases and at the same time yields high recall.
Optimal Planning with a Goal-directed Utility Model

Classical AI planning adopts a very narrow notion of plan quality, namely that a plan is good just in case it achieves a specified goal. Despite the fact that planning is intractable in the worst case, goal-satisfying planning algorithms can effectively solve classes of problems by using the goal to focus the search for a solution (by using backward-chaining techniques), and by exploiting domain-specific heuristic knowledge to control search. Our work extends the definition of plan quality to take into account partial satisfaction of the goal and the cost of resources used by the plan, while at the same time building an effective planning algorithm by exploiting classical planning techniques like backward chaining and knowledge-based search control rules. This paper presents PYRRHUS, an extension to the UCPOP planning system (Barrett et al. 1993) that finds optimal plans for a class of goal-directed utility models suggested by Haddawy and Hanks (Haddawy & Hanks 1993). Our empirical results suggest that optimal plans can be generated effectively by a planner using domain-specific heuristic knowledge, and furthermore that the planner can use the same knowledge as a goal-satisfying planner to solve corresponding optimization problems.
A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images

Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following some preprocessing steps, i.e., color normalization and contrast enhancement. The entire segmented images establish a dataset of regions. To classify these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic-based algorithm is used to rank the features and identify the subset that gives the best classification results. The selected feature vectors are then classified using a multilayer neural network classifier. The algorithm was implemented using a large image dataset consisting of 300 manually labeled retinal images, and could identify affected retinal images with 96.0% sensitivity while it recognized 94.6% of the normal images, i.e., the specificity. Moreover, the proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level.
Gradient orientation based feature detection: an application for extracting retinal blood vessels

A computer-assisted system for diagnosis of eye diseases has been in great demand. This paper presents a robust and efficient method for the detection of anatomical features in retinal images. The method is based on the analysis of gradient orientation and is not directly affected by image intensity. For this, the method performs very well despite the inherent problems of the retinal images, such as low contrast and non-uniform illumination. The method is particularly suitable for detecting features with circular and linear structures. A multi-scale approach is employed to detect various sizes of features, especially blood vessels with varying diameters. The blood vessel network is finally extracted from the detected features by global thresholding with some morphological operations.
Human Adaptive Mechatronics

This article explains the background of human adaptive mechatronics (HAM) and the skill level of individuals to operate machines. It is natural to consider both human and environmental factors in the field of robotics and mechatronics. The progress of machines cannot be achieved without paying attention to these factors; a culture that accepts that this evolved through the contributions of many researchers and developers. Prof. Fumio Harashima contributed to the development of HAM significantly. He has done cooperative research with many companies (Hitachi, Toshiba, Mitsubishi Electric, Fuji Electric, Toyo Electric, Dengensha Co., Toyota Corp., Tohoku Electric Power Co., Sumitomo Heavy Machine Corp., Yazaki Corp., and many others) and promoted several large research projects concerned with this culture, such as “Electromagnetic Interference” in the Japan Society for the Promotion of Science project (1998-2003), “Interaction and Intelligence” in the Sakigake Project (2001-2006), and “Human Adaptive Mechatronics” in the HAM project by coordinating with Prof. Furuta during 2004-2008. The HAM project was initiated by Prof. Harashima and Prof. Furuta, my supervisors when I was a student at the Tokyo Institute of Technology, and this project gave me an opportunity to meet Prof. Harashima.
Age-Independent Synaptogenesis by Phosphoinositide 3 Kinase

Synapses are specialized communication points between neurons, and their number is a major determinant of cognitive abilities. These dynamic structures undergo developmental- and activity-dependent changes. During brain aging and certain diseases, synapses are gradually lost, causing mental decline. It is, thus, critical to identify the molecular mechanisms controlling synapse number. We show here that the levels of phosphoinositide 3 kinase (PI3K) regulate synapse number in both Drosophila larval motor neurons and adult brain projection neurons. The supernumerary synapses induced by PI3K overexpression are functional and elicit changes in behavior. Remarkably, PI3K activation induces synaptogenesis in aged adult neurons as well. We demonstrate that persistent PI3K activity is necessary for synapse maintenance. We also report that PI3K controls the expression and localization of synaptic markers in human neuroblastoma cells, suggesting that PI3K synaptogenic activity is conserved in humans. Thus, we propose that PI3K stimulation can be applied to prevent or delay synapse loss in normal aging and in neurological disorders.
Stop Yelling: Interparental Conflict and Adolescents’ Self-Representations as Mediated by Their Perceived Relationships With Parents

Adolescents’ perceptions of their relationship with both parents were examined as mediators linking interparental conflict to their self-representations (SR). Portuguese adolescents (N = 214; 58.4% girls), aged 10-16 years old (M = 13.39), attending public elementary and secondary schools, filled out self-report measures. Multi-mediator path analysis models revealed that interparental conflict predicted less favorable SR in most evaluated domains. This association was mediated by adolescents’ perceptions of (a) support in the mother–adolescent relationship, related to instrumental, social, emotional, physical appearance, and intelligence SR; (b) negative interactions in the mother–adolescent relationship, related to instrumental and physical appearance SR; (c) support in the father–adolescent relationship, related to social and physical appearance SR; and (d) negative interactions in the father–adolescent relationship, related to instrumental SR. This study emphasizes the relevance of interparental conflict and adolescents’ perceptions of their relationship with both parents in their SR construction. Practical implications point to promoting constructive conflict and improving parent–adolescent relationships.
A novel load balancing algorithm for software defined network based datacenters

In this paper a novel algorithm is proposed for the purpose of load balancing for SDN-based datacenters. Mininet emulator was utilized for the purpose of emulating the proposed system, the suggested algorithm was added to the POX controller. To evaluate our algorithm, we simulated a datacenter with a Fat-Tree topology (k=4). The algorithm was proposed to dynamically balance the load by means of re-routing utilizing the information at the SDN controller. The network performance was tested in term of throughput, loss, and received data size with and without applying the proposed algorithm. Results showed that the proposed algorithm outperforms the traditional load balancing scheme as follows; improves the throughput by a minimum of 21.9%, reduce the loss by 88.2%, increase the received data size by 20.8 %. In addition, the proposed algorithm acts as a congestion control algorithm and a new load balancing algorithm.
Impact of VORTEX2 Observations on Analyses and Forecasts of the 5 June 2009 Goshen County, Wyoming, Supercell

AbstractSeveral data assimilation and forecast experiments are undertaken to determine the impact of special observations taken during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) on forecasts of the 5 June 2009 Goshen County, Wyoming, supercell. The data used in these experiments are those from the Mobile Weather Radar, 2005 X-band, Phased Array (MWR-05XP); two mobile mesonets (MM); and several mobile sounding units. Data sources are divided into “routine,” including those from operational Weather Surveillance Radar-1988 Dopplers (WSR-88Ds) and the Automated Surface Observing System (ASOS) network, and “special” observations from the VORTEX2 project.VORTEX2 data sources are denied individually from a total of six ensemble square root filter (EnSRF) data assimilation and forecasting experiments. The EnSRF data assimilation uses 40 ensemble members on a 1-km grid nested inside a 3-km grid. Each experiment assimilates data every 5 min for 1 h, followed by a 1-h foreca.
Observations online: Finding the ethical boundaries of Facebook research

Informed consent may be unobtainable in online contexts. This article examines the difficulties of obtaining informed consent online through a Facebook case study. It is proposed that there are at least two ways informed consent could be waived in research: first, if the data are public, and second, if the data are textual. Accordingly, the publicness of the Facebook News Feed is considered. Taking account of the wide availability of Facebook users’ data, and reflecting on how public those users perceive their information to be, this paper argues that some Facebook data are properly viewed as public to semi-public in nature. A second issue is whether the Facebook News Feed data collection ought to be classified as document-based or human subjects research. Since the Facebook News Feed involves social interaction that may elicit ‘ethically important moments’, this paper proposes that observing it may constitute human subjects research. While informed consent is desirable for human subjects research, it is suggested that Facebook News Feed observations are comparable to observational research in a public space, and thus waiving informed consent in this online setting could be justifiable.
Evaluation of complex security scenarios using defense trees and economic indexes

In this article, we present a mixed qualitative and quantitative approach for evaluation of information technology (IT) security investments. For this purpose, we model security scenarios by using defense trees, an extension of attack trees with countermeasures and we use economic quantitative indexes for computing the defender's return on security investment and the attacker's return on attack. We show how our approach can be used to evaluate economic profitability of countermeasures and their deterrent effect on attackers, thus providing decision makers with a useful tool for performing better evaluation of IT security investments during the risk management process.
The impact of pre-operative education on recovery following coronary artery bypass surgery. A randomized controlled clinical trial.

AIMS Research has demonstrated the beneficial impact that pre-operative education exerts on the postoperative recovery of patients having surgery but little work has focused specifically on cardiac surgery. Therefore a randomized controlled trial was designed to elucidate the consequences of pre-operative education, given before admission, on postoperative pain, anxiety, depression and wellbeing in the 6 months following a first episode of coronary artery surgery. METHOD AND RESULTS Three hundred and fifty-six people were randomized into the study, with 188 in the experimental and 168 in the control arms. Patients in the experimental group received the intervention, a day of education by members of the multidisciplinary team, prior to admission for surgery. Experimental and control subjects had the usual care, which involved education on admission and throughout their stay in hospital. Measurement was conducted on entry to the study, before randomization, and at 3 days, 6 weeks, 3 months and 6 months following operation. A variety of tools were used: the SF-36 Health Status questionnaire, the Hospital Anxiety and Depression scale, the General Well-Being questionnaire and a pain measurement tool. Analysis was done using the intention-totreat principle and non-parametric statistics. There were no significant differences between groups in the primary outcomes namely anxiety (P=0.09) and pain (P=0.48), or in depression (P=0.62) and wellbeing ('worn out' P=0.11; 'tense and uptight' P=0.29) 6 months after operation. This was also the case 3 days after coronary artery surgery. There was a significant difference in length of hospital stay (P=0.01) with the experimental group having the longer stay. These findings contrast with much of the existing evidence. CONCLUSION The findings demonstrate that there is no benefit to be gained from this form of pre-operative education and that there is an associated increase in length of hospital stay. Future research could examine an ongoing programme of education and support, and might use alternative methods such as CD-ROM or the Internet.
a and b waves detection in acceleration photoplethysmogram

An efficient and robust method based on two moving average filters followed by a dynamic event duration threshold has been developed to detect a and b waves in the acceleration photoplethysmogram signals. The detection of a and b waves is affected by the quality of the photoplethysmogram recordings, especially for the heat stressed collection. The developed a method detects a and b waves in Arrhythmia APG Signals that suffer from: 1) non-stationary effects, 2) low signal-to-noise ratio, The performance of the proposed method was tested on 27 records collected in normal and heat-stressed conditions resulting in 99.68 percent sensitivity and 99.57 percent positive predictivity.
WhereNext: a location predictor on trajectory pattern mining

The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.
Cognitive-Based Biometrics System for Static User Authentication

In today's globally expanding business world, protecting the identity and transactions of online consumers is crucial for any company to reach out for new markets. This directs digital information technologies towards the adoption of stronger and more secure authentication schemes. Although biometric-based user authentication systems have proven superiority over the traditional ones, there are several barriers for their wide scale deployment and application for INTERNET security; barriers include high expensive equipment, and low precision sensor technologies. In this paper, we propose a novel biometric system for static user authentication. It introduces two new cognitive factors, namely visual scan & detection, and short-term memory. These two factors are homogeneously combined with mouse dynamics in one biometric system. Experimental evaluation was performed using mass enrollment of 275 participants, and Neural Network for classification. Results showed an Equal Error Rate (EER) of 3.88%. The promising achieved performance, in addition to the fact that standard mouse is the only data input device required, make this system ideal for static authentication on the INTERNET.