Miracle-dz/UniFAR-FTUs huggingface dataset:
Optronics sensor development using an imaging simulation system

Extensive use of modelling and simulation in the development of electro-optical sensor systems results in reduced risk, lower cost and shorter development time scales. In order to develop, evaluate and optimise system performance under diverse environmental conditions a radiometrically calibrated imaging scene simulator is required. A brief overview of such a simulation system is presented, focussing on physics-based scene radiometry and weapon system sensor modelling. Application of the simulator in the development and evaluation of infrared missile seeker sensor systems is presented.
Emotional Intelligence and At-Risk Students

This study examined the relationships between emotional intelligence (EI) and the delinquent behavior (DB) of the students. The level of DB reported by the students is categorized under the headings of crime, drugs, vandalism, pornography and sexual behavior, other misbehavior, and dishonesty. Meanwhile, EI is investigated by looking at the level of EI domains, such as self-awareness, self-regulation, self-motivation, empathy, social skills, maturity, and spiritual awareness. Data were gathered from a sample of 300 secondary school students aged 15 to 18 years in Selangor. The schools they attended were selected from the so-called “hardcore schools,” which were identified by Schools Division in the State of Selangor. Two instruments, namely, surveys on the “Behavior of Students” and “Malaysian Emotional Quotient Inventory (R)–Adolescence (MEQI),” were utilized to collect the research data and were analyzed using SPSS 19.0. The data showed that the highest delinquency among the adolescents was misbehavior in school, followed by crime, vandalism, pornography, dishonesty, and drugs. Results also revealed a negative linear relationship between EI (r = −.208, n = 300, p = .001) and DB, implying that adolescents with better EI had lower levels of delinquency. Multiple regression analysis revealed that EI is a significant predictor of DB and self-awareness is the main factor of DB. This study contributes to the knowledge of the importance of EI in understanding DB. EI can be used to identify and discriminate emotional skills among those adolescents who exhibit DB. Addressing the role of EI as a predictor would probably prove to be effective in reducing DB.
A Q-learning based approach to design of intelligent stock trading agents

The portfolio management for trading in stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an automated portfolio management system that can recommend financial decisions for daily stock trades. However, most previous attempts to predict future returns of stocks have been largely unsuccessful due to the high complexity of integrating price prediction results with trading strategies. Motivated by this, this paper presents a new stock trading system, named Q-trader, that can overcome the limitations of the existing approaches through the joint optimization of the prediction results and the corresponding trading strategies. Specifically, the proposed framework employs four cooperative Q-learning agents that adaptively interact with their environments by use of feedforward neural networks. In order to achieve computational efficiency for global trend prediction, a novel data structure is proposed for compact representation of long-term dependencies of stock price changes. Experimental results on KOSPI 200 show that the proposed approach outperforms the trading systems trained by supervised learning both in profit and risk management.
Compressive sampling for microwave tomography

This communication deals with the solution of microwave imaging problems exploiting a Compressive Sampling (CS) based method, an emerging technique for data acquisition and signal recovery based on its property of requiring lower dimensional data. In particular, the inversion procedure was tested on the Contrast Source-Extended Born model. We also considered the classic Born inversion in order to remark the reconstruction ability of the proposed method.
Autonomy support and motivational responses across training and competition in individual and team sports

This study examined: (a) whether athletes’ (N = 348) perceived autonomy support (i.e., showing interest in athletes’ input and praising autonomous behavior) differs across contexts (training vs. competition) and sport types (individual vs. team sports), and (b) whether the relationships between autonomy support and effort, enjoyment, and anxiety are affected by context and sport type. Perceived autonomy support did not vary across contexts, but interest in athletes’ input was higher in individual than in team sports. Praise for autonomous behavior was associated positively with effort only when interest in input was high, and this effect was stronger in training than in competition. Finally, praise for autonomous behavior was also positively related to enjoyment in training, while interest in input was positively associated with anxiety in individual sports.
Measuring Source Code Similarity Using Reference Vectors

This paper discusses on a method of measuring similarities between program source codes. Unlike many of existing similarity measuring method we do not compare a pair of source codes directly but compare them indirectly with using reference source codes. Using reference vectors calculated from each source codes and reference source codes reduced considerable amount of computation time for similarity measurement. To examine our method, we built a system implemented this algorithm and made computational experiments on Java program source codes submitted as assignments for a programming class. From results, we confirmed there are evident similarities between program source codes have close reference vectors each other
First statistical analysis of a one-year global dataset of re-processed ERS-2 imagettes for oceanic applications

Since 1991, the ERS satellites have collected high resolution SAR data over the oceans on a global and continuous basis. Operating in wave mode the ERS SAR yields a patchy coverage of the oceans with about 1500 images of 10 by 5 km size each day. These data have recently been shown to be able to deliver wind speed and wave spectra as well as information on individual waves. In the scope of the ESA AO WaveAtlas, the ERS-2 raw data acquired in 1999 and 2000, one year i.e. approximately half a million of reprocessed single look complex imagettes were used for a first statistical evaluation providing global ocean maps of mean intensity, standard deviation, modulation, inhomogeneity and their regional change during all seasons of a year for the first time. Furthermore the paper demonstrates the consequences of these results for different oceanographic applications. Special emphasize is put on wind, wave and sea ice measurements. In addition atmospheric features can be analyzed. The data in particular enable the estimation of two-dimensional sea surface elevation fields which are of high practical relevance for ship design or offshore operations. Mean wave parameters like the mean period derived from SAR wave mode data can be used for assimilation of numerical wave models thus helping to improve wave forecast. The reprocessing of wave mode data is planned to be extended to the full lifetime of ERS-1 and ERS-2, which is at least 19912004. This will allow the derivation of wave climatologies on a decadal basis in particular enabling the analysis of climate trends. As wave mode data are also available from the ENVISAT mission a consistent update of these climatologies is possible.
Feature-Fusion Framework for Spam Filtering Based on SVM

In this paper, we propose a novel framework based on support vector machine (SVM): feature-fusion framework. SVM is proved to be a good method to filter spam on large-scale text collections with state-of-the-art performance by many researchers in the world. Feature extraction is the key step to SVM classification. The feature-fusion the framework based on SVM is a feature level method and has advantages to solve some practical problems of spam filtering and lead to high performance, especially reduces the computational cost. The feature-fusion method proposed in this paper can fuse information of words in an email by using TFIDF formula and the words in the subject header field of an email by increasing its weight, and improve the performance of spam filtering. Experimental results show that the feature-fusion framework outperforms pure SVM and other SVM-based methods for large-scale spam filtering in terms of ROC curves.
Biomolecular analysis of a defective nontransforming Epstein-Barr virus (EBV) from a patient with chronic active EBV infection

A virus recovered from the saliva of a child with chronic active Epstein-Barr virus (EBV) infection for 8 years was shown to induce EBV early antigen (EBV-EA) in Raji cells and to be expressed into EBV-EA in fresh EBV-negative peripheral blood leukocytes. However, it did not replicate its DNA. Oropharyngeal epithelial cells scraped from recurrent mouth lesions were similarly positive for EBV-EA. DNA extracted from these cells and digested with BamHI contained a 6-kilobase-pair fragment homologous to BamHI fragment V and B1 EBV DNA probes. Furthermore, Southern blots of the BamHI and EcoRI digests of the DNA extracted from the cell lines of the patient (transformed with EBV strain B95-8) and of her mother (spontaneous) revealed, in addition to the expected BamHI G, H, H2, and B1 fragments used as probes, additional shorter ones of a presumably endogenous defective virus.
Educational Choice and Student Participation

The Supplemental Educational Services (SES) provision of the No Child Left Behind Act offers free tutoring services to students attending perennially underperforming schools. The author assesses the extent to which Chicago Public Schools students most in need of and who could potentially benefit the most from additional academic instruction participate in SES. The author uses multilevel cohort and longitudinal analyses to explore the characteristics of students participating in SES in Chicago Public Schools from 2004–2005 to 2007–2008. The results of this study, among the first empirical evaluations of SES participation, suggest that students with higher observed cognitive achievement are less likely to engage the SES provision, whereas students with better noncognitive performance are more likely to participate in SES.
Epidemiologia do transtorno obsessivo-compulsivo: uma revisão

Epidemiological surveys are important because clinical samples are subject of several selection biases. Sociodemographic factors and clinical aspects of the morbid condition itself influence help seeking behaviors. Due to the egodystonic nature of obsessive-compulsive disorder, sufferers tend to hide their symptoms and avoid or delay treatment seeking. However, most of our current knowledge about obsessive-compulsive disorder is based on clinical samples, which do not represent the totality of cases. A conventional Medline, PsychoInfo and Lilacs review of epidemiological studies on obsessive-compulsive disorder from 1980 to 2004 was conducted, using the following keywords: "epidemiology", "obsessive-compulsive disorder", "populational surveys" and "prevalence". Studies from different countries show an average point-prevalence of 1% and lifetime prevalence of 2-2.5% for obsessive-compulsive disorder. In contrast with clinical samples, most populational samples have a predominance of females and cases with only obsessions. The frequent comorbidity with other mental disorders, particularly depression and other anxiety disorders, has also been found in cases from the community, which have an association with substances misuse as well. Many sufferers are not been treated, particularly the "pure" cases. Indicators of functional incapacitation show a considerable negative impact of obsessive-compulsive disorder. It is necessary to increase the awareness about obsessive-compulsive disorder symptoms in the community and among health professionals, in order to increase help seeking, as well as the proper identification and treatment of this rather serious medical condition.
The impact of corporate social responsibility initiatives on consumers' behavioural intentions in the Egyptian market

The scarcity of research on the public's responses towards corporate social responsibility (CSR) in developing countries has inspired this research. The research is aimed at studying the consumers' awareness of, their attitude and behavioural intentions towards socially responsible companies in the Egyptian market. The relevant CSR literature was recapitulated into a conceptual framework, and an empirical study was conducted through a mixed research design. Exploratory qualitative interviews were conducted with corporate managers of a sample of five companies in the Egyptian market. Then, a descriptive quantitative research was conducted through a survey on a non-probability sample of consumers. The results revealed that consumers in Egypt are actually aware of the CSR concept and even tend to develop positive attitude towards socially responsible companies. Yet, when it comes to the evaluative purchasing criteria that consumers value the most, economic criteria are apparently still given a priority over the social criteria.
Influence of Platelet Lysate on the Recovery and Metabolic Performance of Cryopreserved Human Hepatocytes Upon Thawing

Background. Storage of human hepatocytes is essential for their use in research and liver cell transplantation. However, cryopreservation and thawing (C/T) procedures have detrimental effects on the viability and functionality compared with fresh cells. The aim of this study was to upgrade the standard C/T methodology to obtain better quality hepatocytes for cell transplantation to improve the overall clinical outcome. Methods. Human hepatocytes isolated from donor livers were cryopreserved in University of Wisconsin solution with 10% dimethyl sulfoxide (standard medium), which was supplemented with 10% or 20% of platelet lysate. Thawing media supplemented with up to 30 mM glucose was also investigated. The effects on cell viability, adhesion proteins (e-cadherin, &bgr;-catenin, and &bgr;1-integrin) expression, attachment efficiency, apoptotic indicators, Akt signaling, ATP levels, and cytochrome P450 activities have been evaluated. Results. The results indicate that the hepatocytes cryopreserved in a medium supplemented with platelet lysate show better recovery than those preserved in the standard medium: higher expression of adhesion molecules, higher attachment efficiency and cell survival; decreased number of apoptotic nuclei and caspase-3 activation; maintenance of ATP levels; and drug biotransformation capability close to those in fresh hepatocytes. Supplementation of thawing media with glucose led to a significant decrease in caspase-3 activation and to increased adhesion molecules preservation and Akt signal transduction after C/T. Minor nonsignificant changes in cell viability and attachment efficiency were observed. Conclusions. These promising results could lead to a new cryopreservation procedure to improve human hepatocyte cryopreservation outcome.
Valuation of Go Stimuli or Devaluation of No-Go Stimuli? Evidence of an Increased Preference for Attended Go Stimuli Following a Go/No-Go Task

Attentional inhibition that occurs during discrimination tasks leads to the negative evaluation of distractor stimuli. This phenomenon, known as the distractor devaluation effect also occurs when go/no-go tasks require response inhibition. However, it remains unclear whether there are interactions between attention and response controls when the distractor devaluation effect occurs. The aims of this study were to investigate whether attention to stimuli in the go/no-go task plays a facilitative role in distractor devaluation through response inhibition, and to clarify whether this effect reflects a decreased preference for no-go stimuli. Participants evaluated the preference for pictures before and after a go/no-go task. In Experiments 1 and 2, they made a go or no-go response depending on the category of pictures displayed (gummy candies or rice crackers), whereas in Experiment 3 they did on the basis digit category, even or odd numbers, superimposed on such pictures. Experiments 1 and 2 demonstrated that the pictures presented as no-go stimuli in the preceding go/no-go task were evaluated as less positive than the pictures presented as go stimuli. This devaluation effect reflected an increased preference for the go stimuli but not a decreased preference for the no-go stimuli. Experiment 3 indicated that response inhibition did not affect the preference for the pictures that had not received attention in a preceding go/no-go task. These results suggest that although attention plays an important role in differential ratings for go and no-go stimuli, such differences, in fact, reflect the valuation of go stimuli.
Optimization of Digital Breast Tomosynthesis (DBT) for Breast Cancer Diagnosis

Recent papers have reported the usefulness of DBT as the latest diagnostic modality for breast cancer [1]-[6]. In this chapter, the author describes clinical evaluation of the usefulness regarding DBT with reference to other diagnostic modalities such as Full Field Digital Mammography (FFDM), contrast-enhanced (CE) MRI, contrast-enhanced (CE) CT, and ultrasound (US) in accordance with preliminary experiences in breast cancer diagnosis.
Error identification for large vocabulary speech recognition

This paper proposes two methods for identifying recognition error. The first method is a two-level schema - given the recognition hypothesis of an utterance, an utterance classifier (UC) is first applied to decide if the hypothesis is error-free or erroneous; followed by a word classifier (WC) which is applied to each word hypothesis in the erroneous utterance to decide if the word hypothesis is a misrecognition. The second method is a one-level schema in which a word classifier is applied directly to all word hypotheses to detect word recognition errors. We compare the two methods at both word and utterance levels. Experimental results show that the two methods are comparable in terms of word error detection. However, the two-level schema is very effective in filtering out error-free utterance hypotheses, which offers a key advantage to economize on word error detection.
Multimatch packet classification schemes

Traditional network applications only require best or highest matched rule to be reported. The newer network applications like load balancing, packet level accounting and Intrusion detection demands multiple matches of an incoming packet rather than a single match. In high speed routers the packet classification becomes a bottleneck due to the significant growth of the Internet. A high throughput with minimum memory requirement is optimal for any of the proposed scheme so that to work effectively at line rate. This paper contains the survey of schemes for multi match packet classification. Multimatch packet classification is used in intrusion detection systems and denial of service attacks. The methods considered are Hypercuts, Ternary Content Addressable Memory (TCAM) based approaches, B2PC and distributed and pipelined hash tables.
Incremental sheet forming in cyberspace — A process oriented cognitive robotics application

Incremental Sheet Forming is an advanced manufacturing technology in the recent years. In a lot of cases the process requires the synchronized cooperation of two machines. The complex operation was modelled in a virtual environment, where the cyber objects were deeply connected with the real objects. The result of this virtual cooperation resulted some cognitive observation that is also published in this paper.
Ligra: a lightweight graph processing framework for shared memory

There has been significant recent interest in parallel frameworks for processing graphs due to their applicability in studying social networks, the Web graph, networks in biology, and unstructured meshes in scientific simulation. Due to the desire to process large graphs, these systems have emphasized the ability to run on distributed memory machines. Today, however, a single multicore server can support more than a terabyte of memory, which can fit graphs with tens or even hundreds of billions of edges. Furthermore, for graph algorithms, shared-memory multicores are generally significantly more efficient on a per core, per dollar, and per joule basis than distributed memory systems, and shared-memory algorithms tend to be simpler than their distributed counterparts. In this paper, we present a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write. The framework has two very simple routines, one for mapping over edges and one for mapping over vertices. Our routines can be applied to any subset of the vertices, which makes the framework useful for many graph traversal algorithms that operate on subsets of the vertices. Based on recent ideas used in a very fast algorithm for breadth-first search (BFS), our routines automatically adapt to the density of vertex sets. We implement several algorithms in this framework, including BFS, graph radii estimation, graph connectivity, betweenness centrality, PageRank and single-source shortest paths. Our algorithms expressed using this framework are very simple and concise, and perform almost as well as highly optimized code. Furthermore, they get good speedups on a 40-core machine and are significantly more efficient than previously reported results using graph frameworks on machines with many more cores.
Exploring Servant Leadership across Cultures: A Study of Followers in Ghana and the USA

In the shadow of corporate scandals such as Enron and Worldcom, an increase in attention has been directed towards an approach termed `servant leadership'. To date, servant leadership has been discussed and described almost entirely in the North American context (Farling et al., 1999; Spears, 1995). In the following study, we explored the extent to which followers from Ghana and the USA have experienced three servant leadership dimensions in a work situation, and the extent to which these followers relate servant leadership dimensions to judgments about leadership effectiveness in each culture. After testing for measurement equivalence and adjusting the item weights in each culture, we found that Ghanaians reported experiencing servant leadership behaviours significantly less than North Americans. We also found that vision had a significantly stronger relationship with leader effectiveness for Ghanaians in comparison to North Americans, but that both sub-samples relate service and humility with leader effectiveness similarly. We explored possible explanations and implications based on cultural differences.