Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary learning and search-based content generation in computer games

Modern computer games achieved an impressive level of realism featuring advanced 3D graphics and sophisticated physics engine able to simulate complex game environments. However, the increasing complexity of the game environment poses many challenges for developing the Artificial Intelligence which controls the non-player characters. In many games, the non-player characters show very simple and predictable behaviors which are not able to evolve over time or to adapt to the user’s gameplay. Another problem, caused from the complexity of the game environment, is the generation of a large quantity of game content, e.g., levels, maps, racing tracks, etc. In most complex computers games, the content generation can require the work of several designers and can last even for months. Thus, there is the necessity of techniques that can automate or support some aspects of the game development process. In this context, Computational Intelligence can be a valid approach to deal with some of the issues in computer games. Computational Intelligence is a field of the Artificial Intelligence which includes techniques and mathematical models which are inspired by nature, such as Evolutionary Algorithms and Neural Networks. In the recent years, the number of scientific works involving Computational Intelligence and Game is quickly growing and suggests that this is a promising research direction. In this thesis, we focus on the application of Computational Intelligence, and in particular Evolutionary Algorithms, to computer games. In the first part, we investigate different learning paradigms which can support the development of non-player characters. In particular, we investigate on-line learning, i.e., a learning process applied during the game to generate sophisticated non-player characters that can evolve over time. In the second part, we focus on game content generation. We investigate several representations and different approaches for evaluating the game content. In our experimental analysis we take into account different computer games: racing games, shooters and platform games. The results show that all the proposed methods are promising and Computational Intelligence represents an effective approach to support the game development process.
Evolution simulation system for graphs isomorphism discernment

The possibility of construction of intelligent systems of decision making support, the simulation of principles of nature evolution development, the adaptation and cooperation with environment, the construction of order of chaos; the homeostatic support in artificial systems, and the hierarchic self-organization for genetic search to decide combinatorial logical problems on graphs are considered in this paper. The main attention was paid to the development of algorithms of isomorphism identification of the homogeneous graphs with automorphic groups. The genetic algorithm based on logical and greedy heuristics is described to decide this problem. The triune approach based on models of micro-, macro- and meta evolutions to find substitution of graphs isomorphism is proposed. The enlarged scheme of genetic search to decide combinatorial and logical problems on graphs based on informative feedback and concepts of models of integrated evolution with synergetic and homeostatic principles of control is given.
Gratia: New Challenges in Grid Accounting

Gratia originated as an accounting system for batch systems and Linux process accounting. In production since 2006 at FNAL, it was adopted by the Open Science Grid as a distributed, grid-wide accounting system in 2007. Since adoption Gratia's next challenge has been to adapt to an explosive increase in data volume and to handle several new categories of accounting data. Gratia now accounts for regular grid jobs, file transfers, glide-in jobs, and the state of grid services. We show that Gratia gives access to a thorough picture of the OSG and discuss the complexity caused by newer grid techniques such as pilot jobs, job forwarding, and backfill.
BUILDING SUSTAINIBILITY ECO TOURISM THROUGH ENVIRONMENT BASED MARKETING

This study sought to establish whether there is indeed a relationship between Environment based marketing practices and performance of ecotourism in Indonesia. The population of the study was the 15 tour and travel firms that are eco-certified. A sample of 15 ecotourism destination providers was targeted. Primary data was collected by use of a semi structured questionnaire that was administered to the marketing managers in these firms. All the respondents agreed that their firms practiced environment based marketing. The major factors influencing this decision were to comply with the existing environmental policies and for environmental conservation. The study revealed that in supporting the local communities, the tourist accommodation service providers are involved in various activities from buying products to providing various services such as education facilities and environmental conservation as well offering them employment. The study also revealed that tourist accommodation service providers engage in different practices on their products/services to enhance level of environment based marketing practices. The major practice in this regard was making sure that the product is engaging and simple. Nearly all of the respondents mentioned that there is a relationship between environment based marketing and ecotourism sector in Indonesia. From the findings, environment based marketing has many benefits to the ecotourism sector. The most notable is customer satisfaction as mentioned by majority of the respondents. The study recommends that ecotourism destination service providers in the country should continue to practice environment based marketing since it has benefits that can enhance the performance of their firms. The government of Indonesia should develop a new strategy focusing on sustainable community based ecotourism. This will ensure that the community reaps the socio and economic benefits of development and conservation. It will further boost the respect and understanding between the community, local authorities and the ecotourism destinations.
The Gamification of Meditation: A Randomized-Controlled Study of a Prescribed Mobile Mindfulness Meditation Application in Reducing College Students’ Depression

Objective. Roughly 35% of college students report depression as a significant concern, which unaddressed, can lead to an increased likelihood to quit school and develop comorbid conditions. Traditional depression interventions are useful; however, they are often expensive, stigmatizing, tedious, and time-intensive. We examined a prescribed mindfulness meditation regimen, which incorporates gamification principles, in reducing symptoms of depression compared to a control group. Materials and methods. We recruited and randomly assigned 72 college students to an experimental group (n = 33) or a control group (n = 39). The prescription for experimfiental group participants was ten 10-minute mobile mindfulness meditation sessions over a 14-day period; control group participants continued with business as usual. The Patient Health Questionnaire (PHQ-9) was used to test our hypothesis. Results. Repeated-measures ANOVA demonstrated a significant interaction of group by time for depression scores, supporting the hypothesis that experimental group participants would significantly decrease their depression symptom severity compared to control group participants post-intervention. Within-subjects contrasts and between-group analyses showed a significant decrease in depression symptom severity scores. Conclusion. Prescribed use of a gamified mindfulness meditation application significantly decreased depression symptom severity as measured by the PHQ-9. College students and mental health providers should consider these fun, inexpensive, and non-stigmatizing applications as a feasible intervention for college students to improve symptoms associated with depression.
Behaviour Based Worm Detection and Signature Automation

Problem statement: A worm is a malicious piece of code that self-prop agates, often via network connections, to exploit security flaws in c omputers connected through the network. In general, worms do not need any human intervention to propagate and are considered a real threat to network assets and the properties of organizations. An Intr usion Detection Systems (IDSs) are employed to detect the presence of the worms in the network. Approach: This study proposed a new behaviour- based worm detection and signature automation approach that consists of scanning characteristics to find vulnerable hosts and indicate the correlation between an infected host and potential destination hosts. Results: This approach can be distinguish between network s canning (random and sequential TCP and UDP worm scanning) triggered by infected and non-infected hosts. In addition, the ability to detect the worms based on its behaviours. Conclusion: Identifying network worms at an early stage can increase the protection of network services and vulnerable hosts.
Producing ground deformation maps automatically: the DIAPASON concept

The use of radar interferometry has now been amply demonstrated. The limits of the technique are well understood, if not always predictable. In addition, the basic signal processing procedure seems to have reached a plateau. Further improvements can only be achieved if the number of processed scenes and the variety of experiences in the people using the technique are greatly increased. This can be done only if the access to the interferometric technique is given to a large group of users who are not necessarily radar specialists and who use standard computers or workstations. The architecture used at CNES for interferometric processing, called DIAPASON, has been designed to be user friendly and to require minimal work through the use of standard interfaces. Initially designed for optimal signal processing and throughput, DIAPASON proved it could run automatically in the vast majority of cases. A first course session organized in 1996 for twelve geophysicists indicated that, in one week, they could learn to use the software and to perform trouble shooting when necessary. The software has been already distributed in several laboratories and countries for industrial as well as scientific use. The author describes the main features of the software and its conditions of use.
AlGaN/GaN heterojunction FETs for high‐power applications

GaN and its heterojunction structures are attracting considerable attention as materials for high-voltage, high-frequency power device applications. This paper gives an overview of the basic device structure, process technology, and recent device performance achieved with AlGaN/GaN heterojunction FET structures. The emphasis is on the performance of power amplifiers developed for use in cellular base station transmitters and pseudo-millimeter-wave wireless access systems. Future research topics and prospects of nitride-based transistors are also discussed. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(12): 52–60, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10161
DEVELOPMENT OF SOFTWARE FOR ANALYZING STEP LENGTH COURSES IN SPRINT DASH WITH THE USE OF THE LASER VELOCITY MEASURING DEVICE AND DIGITAL VIDEO CAMERA

We have developed software (sprint-performance analyzer: SPA) for estimating step length courses in sprint dash with the use of the laser velocity measuring device and digital video camera. It gives a quick feedback to coaches and athletes and helps to evaluate performance of athletes. In this research, we compared time of analyzing by the SPA with that not utilizing the software. In addition, step length courses estimated in this measuring method were reexamined. A statistical analysis was done between estimated step length (ESL) by SPA and actual step length (ASL) by using Two-dimensional motion analysis method. Time of analyzing by SPA was 10 minutes/trial, which was shorter than 50 minutes/trial of that not utilizing the software. There were very small differences (Δd) between the filtered ESL and the filtered ASL at respective trials (Δd=0.00-0.04m).
Selling Family Planning: Using Social Marketing Principles to Reduce Unplanned Pregnancy

The problem of unplanned pregnancies is one which has challenged commnnities the world over for decades. The social, economic and public health implications ofthe problem convey upon it immense importance and a position of prominence on the social and health agendas of the United States, its island neighbor Bermuda, and throughout the developing world. No single approach or solution has been identified, but in the past two decades, several programs have demonstrated long-term effectiveness in reducing unplanned pregnancies and impacting the behavior of those at risk for unplanned pregnancies. From these success stories, evidence is mounting which identifies the programmatic elements most useful and effective in preventing unplanned pregnancies. Applying social marketing principles to the complex problem of unplanned pregnancy prevention is a logical endeavor given the publicized success of its techniques. The following discussion will describe the extent of the problem of unplanned pregnancy in the United States and in Bermuda. The principles of social marketing will be summarized and will be considered in the context of their application to the problem of unplanned pregnancy prevention. The rationale and value of using social marketing to address the challenge of unplanned pregnancy prevention in a variety of communities will be demonstrated. Selling Family Planning 2 Selling Family Planning: Using Social Marketing Principles to Reduce Unplanned Pregnancies The societal impact of unplanned pregnancies is profound, complex and multidimensional. Even the term "unplanned pregnancy" requires reflection and defining, to distinguish it from the related term "unintended pregnancy", before its causes and impact are analyzed. Given the degree of complexity of the problem of unplanned pregnancy there is little wonder that its solution is equally complex and elusive. Yet despite its multifaceted nature, there is widespread consensus that unplanned pregnancies are a problem worth addressing for many reasons. (Donovan & Wulf, 2002, p. 1) The staggering number of individuals affected, the health implications and the social and economic impact of the problem of unplanned pregnancies worldwide convey upon it great significance. The close association between unplanned pregnancy and the epidemic of sexually transmitted diseases, including HIV/ AIDS, compounds its public health relevance; its correlation to school drop out, child abuse and long-term poverty demonstrate its social and economic importance. These are just a few of the ramifications of unplanned pregnancy which explain why it is a problem of considerable focus for social scientists and health professionals across the globe. (World Health Organization,
Is a biological temporal learning rule compatible with learning synfire chains

The author investigates how a biologically realistic temporal learning rule and the neuronal firing threshold jointly determine the recall speed of a synfire chain trained by sequential activation of its nodes. Numerical analysis of an idealised system of discrete spike response model neurons yields the relationship between threshold and speed of recall, in particular showing that recall is not possible at all speeds and that recall may not be possible at the speed at which the chain was trained. A continuous approximation to the discrete system is analytically more tractable but does not reflect the stability of the system accurately.
Applications of Structure-Preserving Cryptography and Pairing-Based NIZK Proofs

This habilitation thesis deals with cryptographic primitives that preserve the algebraic structure of underlying objects (messages, keys, etc) and their applications to the design of non-interactive zero-knowledge proofs and privacy-enhancing cryptographic primitives. In 2008, Groth and Sahai showed how to make these proof systems relatively efficient in abelian groups endowed with a bilinear map. These techniques, however, require to work with lower-level primitives where handled objects all live in a cyclic abelian group. Among other things, we need to sign messages without destroying their algebraic structure (in particular, without hashing them first) so as to be able to efficiently prove properties about hidden signed messages. The first part of this thesis describes a structure-preserving signature scheme which was the first efficient realization under previously studied algorithmic assumptions. These tools are also utilized in the design of a novel revocation mechanism for group signatures, which allow users to anonymously sign messages on behalf of a population they belong to. The second part of this thesis considers structure-preserving signatures endowed with homomorphic properties. We show how to use them in the design of non-malleable cryptographic primitives. Using linearly homomorphic structurepreserving signatures, we notably obtain non-malleable commitments to group elements and non-interactive zero-knowledge proofs, as well as public-key encryption schemes that resist chosen-ciphertext attacks.
Reducing Collision In Multipacket Transfer And Receiver Using Token Passing Techniques In Wireless Ad Hoc Network

Transmitting and Receiving the multiple packet on a single channel through a route node is very critical task. There may be a collision between more than one routes. One route node can send and receive the data to the two routes. It may generate a collision and may be the data exchange between more than one routes. That’s why, we used here a token passing technique and time interval for successful transmission. The theoretical analysis show the total scenario and reduce the collision between routes.
PREDICTION OF INFORMATIVE REGIONS IN MEDICAL TEXT USING MACHINE LEARNING TECHNIQUES

Machine Learning is the science of getting computers to act without being explicitly programmed. Machine learning is gradually making its way into medical domain and has become the most reliable and accurate tool. This paper provides an overview of the development of intelligent data analysis in medical data (Medline) using a machine learning perspective. Medical data contains information about the nature of disease and the effectiveness of treatments. The process of identifying and disseminating the disease and treatment related to a sentence from the Medline abstracts is a difficult task. In this paper we aim at investigating the performance of supervised learning algorithms in order to develop an application that can accurately identify and disseminate the healthcare information that can be utilized by both healthcare providers and patients. This is the first step towards achieving reliable/accurate disease and treatment related information which is useful for better understanding.
Factores macroeconómicos que estimulan el emprendimiento. Un análisis para los países desarrollados y no desarrollados

El presente documento profundiza en los factores macroeconomicos que estimulan el desarrollo de procesos emprendedores, en concreto, se analizan los factores institucionales, economicos y sociales. Para ello se emplea la base de datos GEM que proporciona un amplio abanico de indicadores armonizados referentes al emprendedor y las condiciones de su entorno. Haciendo uso de la estadistica descriptiva se realiza un estudio exhaustivo de dichas condiciones, prestando especial atencion a las diferencias existentes entre las economias desarrolladas y no desarrolladas. Asi, se observa que la actividad emprendedora, medida a traves de la TEA, ha experimentado de manera generalizada un incremento en los ultimos anos, tanto de los paises desarrollados como no desarrollados, siendo este fenomeno mas acusado en estos ultimos. Ademas, se evidencia que a pesar de que en los paises desarrollados la actividad emprendedora es menor, los factores institucionales, economicos y sociales son mas favorables que en el caso de las economias menos desarrolladas.
Novel energy efficient compute architectures on the path to Exascale.

This project conducts research on the area of energy-efficient compute architectures which are primordial for the progression to Exascale. An evaluation of the Tibidabo ARM cluster is performed with the usage of synthetic benchmarks and real HPC applications in order to assert the performance of ARM-based CPU’s as building blocks for HPC systems.
Subject-Verb Agreement and Covert Raising to Subject in Finnish

This paper investigates the morphological realization of subjects' syntactic [case] features in Finnish and the way in which verbs' [phi] feature checking requirements affect this interpretation. I argue that during syntactic computation both morphologically nominative or quirky subjects check a single generalized [case] feature, not specified case feature such as [nominative]. Moreover, I show that the [case] feature is checked covertly. This checking process presents a paradox with regards to morphological realization. The existence of a generalized syntactic [case] feature suggests that lexical insertion takes place post-syntactically (cf. Halle & Matantz 1993). Under this view of morphology, however, the checking of the [case] feature at Logical Form should not effect the morphological case form. I also discuss the connection between the checking status of finite predicates' [phi] features and the morphological interpretation of subject case. This interrelatedness between two autonomous features cannot be encoded in the feature checking model of Chomsky (1995).
N-terminal pro-B-type natriuretic peptide for predicting fluid challenge in patients with septic shock.

Background The aim of this study is to examine whether plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentration could predict fluid responsiveness in septic shock patients following fluid challenge (FC). Methods We reviewed prospectively collected data from 79 septic shock patients who received invasive cardiac output (CO) monitoring following a 500 mL FC. Haemodynamics were recorded, and blood sampling for NT-proBNP values was performed. Patients were divided into responders and non-responders according to fluid responsiveness, which was defined as cardiac index (CI) increase ≥10% induced by FC. The NT-proBNP and the CI changes were analysed using Pearson correlation. The area under the curve (AUC) for NT-proBNP was used to test its ability to distinguish responders and non-responders. Subgroup analyses were also explored. Results Among 79 patients, there were 55 responders. High NT-proBNP values were common in the study cohort. Baseline NT-proBNP values were comparable between responders and non-responders. In general, NT-proBNP values were not significantly correlated with CI changes after FC (r=-0.104, P=0.361). Similarly, the NT-proBNP baseline values could not identify responders to FC with an AUC of 0.508 (95% confidence interval, 0.369-0.647). This result was further confirmed in the subgroup analyses. Conclusions Baseline NT-proBNP concentration value may not serve as an indicator of fluid responsiveness in patients with septic shock and should not be an indicator to withhold fluid loading.
On the complexity of intersecting multiple circles for graphical display

Many experiments in the biomedical field generate vast amounts of data. This is especially true for microarray experiments which measure the expression levels of thousands of genes simultaneously. In this context the display of functional information attributed to the individual gene is important to obtain an overview of the major processes involved. This set data can be displayed as Euler/Venn diagrams in which the circle size corresponds to the cardinality of the set. Efficient algorithms for the calculation of intersections of circles and their resulting boundary have not been published so far. We present two algorithms (one optimal) for intersecting these different sized circles to display set relationships.