Miracle-dz/UniFAR-FTUs huggingface dataset:
HISTOR Y, POWER, A ND IDENTITY : AMAZONIAN PERSPECTIVES

The concept of ethnogenesis offers a theoretical approach to hybridity and syncretism that finesses the tensions between “New Amazonian Ethnography” and “New Amazonian History” by simultaneously encompassing the study of indigenous ontologies and alternative constructions of history (i.e., “mytho-historical narratives”) as well as the reconstruction of history from all available sources. Ethnogenesis can be defined as a process of authentically re-making new social identities through creatively rediscovering and refashioning components of ‘tradition,’ such as oral narratives, written texts, and material artefacts. Understood in these terms, ethnogenesis allows us to explore the cultural creativity of indigenous and non-indigenous peoples alike in the making of new interpretive and political spaces that allow people to construct enduring social identities while moving forward in the globalizing nation-states of Latin America. ANOTACIJA Etnogenezės sampratai paaiskinti būtina teoriskai pagrįsti jos daugialypumą bei sinkretiskumą, kas atskleidžiama koncepcijose „Naujoji Amazonės regiono etnografija“ ir „Naujoji Amazonės regiono istorija“. Tai apima ir tenykscių gyventojų ontologijos, ir alternatyviųjų istorijos nuostatų (mitologizuotų istorinių atpasakojimų) tyrimus bei istorijos rekonstrukcijos dalykus, remiantis visais įmanomais saltiniais. Etnogenezė gali būti suvokiama ir kaip naujų socialinių tapatybių autentisko perkūrimo procesas, kūrybiskai is naujo atskleidžiant ir suformuluojant „tradicijos“ sampratos komponentus – žodinius pasakojimus, rasytinius tekstus, materialius dirbinius. Tokių nuostatų kontekste etnogenezė sudaro sąlygas suvokti regiono vietinių ir nevietinių gyventojų kultūrinės veiklos specifiką. Kartu tampa įmanoma atsirasti ir naujoms interpretacijoms bei politinėms erdvėms, kas leidžia žmonėms apsibrėžti ilgalaikes savo socialinio identiteto nuostatas, orientuotas į vis globalesnėmis tampancias Lotynų Amerikos tautų bei valstybių raidos perspektyvas. PAGRINDINIAI ŽODŽIAI: istorija, galia, tapatybė, Amazonės regionas, etnogenezė, kultūra, tradicijos.
Perceptions about the health condition of people with obesity

Objetivo : compreender as percepcoes de pessoas com obesidade sobre esta condicao de saude. Metodos: pesquisa qualitativa realizada em ambulatorio para acompanhamento de pessoas com obesidade. Foram entrevistadas 15 pessoas com diagnostico de obesidade. Dados coletados por entrevistas e submetidos a Analise de Conteudo Tematica. Resultados: emergiram tres categorias: Significando a obesidade, Afetando a vida cotidiana e a saude e Enfrentando a obesidade. Na primeira categoria, a maioria dos entrevistados significou negativamente a propria obesidade; na segunda, perceberam-na como condicao capaz de provocar dano a saude e a capacidade laboral; e, na terceira, como condicao que precisa ser enfrentada e tratada. Conclusao: as percepcoes da obesidade refletiram, sobretudo, sentimentos negativos, acompanhados de algum grau de sofrimento fisico, psiquico e social.
Use of GPU and Feature Reduction for Fast Query-by-Example Spoken Term Detection

For query-by-example spoken term detection (QbE-STD) on low resource languages, variants of dynamic time warping techniques (DTW) are used. However, DTW-based techniques are slow and thus a limitation to search in large spoken audio databases. In order to enable fast search in large databases, we exploit the use of intensive parallel computations of the graphical processing units (GPUs). In this paper, we use a GPU to improve the search speed of a DTW variant by parallelizing the distance computation between the Gaussian posteriorgrams of spoken query and spoken audio. We also use a faster method of searching by averaging the successive Gaussian posteriorgrams to reduce the length of the spoken audio and the spoken query. The results indicate an improvement of about 100x with a marginal drop in search performance.
Designing and Verifying Communication Protocols Using Model Driven Architecture and Spin Model Checker

The need of communication protocols in todaypsilas environment increases as much as the network explores. Many new kinds of protocols, e.g. for information sharing, security, etc., are being developed day-to-day which often leads to rapid, premature developments. Many protocols have not scaled to satisfy important properties like deadlock and livelock freedom, since MDA focuses on the rapid development rather than on the quality of the developed models. In order to fix the above, we introduce a 2-Phase strategy based on the UML state machine and sequence diagram to satisfy the properties of communication protocols. We convert these models into PROMELA code for execution on the SPIN model checker. The results are compared with the developed UML models.
PAPR Reduction Using BPSO/PTS and STBC in MIMO OFDM System

Problem statement: The Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system has been receiving a great attention, as one of solutions for achieving high speed, efficient and high-quality service for the wireless communications. However, the transmitted signal still has high PAPR because of OFDM characteristics. Many methods have been proposed to solve this problem, but the most of them decrease high Peak-to-Average Power Ratio (PAPR) as well as the data rate. Approach: This proposal described a new suboptimal technique for reduction of the PAPR by combining two suitable methods for MIMO OFDM systems. The first method was based on Boolean Particle Swarm intelligence Optimization (BPSO) applied to Partial transmit Sequence (PTS) and the second was the Space Time Bloc Coding (STBC). Result: Apply only the PTS technique and independently on each transmitted antenna, was effective to reduce PAPR, but it requires high computation complexity. Therefore, the BPSO/PTS technique provided better performance and it was been promoted as an uncomplicated way for PAPR reduction. Thanks of the BPSO/PTS algorithm; the transmitted sequence was selected with minimizing the maximum PAPR over all transmission antennas. The simulations and the BER performance demonstrated that more inertia weight and phase weighting factor obtained better PAPR reduction performance without bringing much higher complexity. Conclusion: Results show that the added BPSO/PTS method to orthogonal space time bloc coding minimizes computational complexity cost as well as the PAPR and gives best optimal PTS performance in comparison with the conventional methods.
A DC-coupled CMOS transceiver chip with adaptive DC tuning for human activity detection

In this paper, a 5.8 GHz DC-coupled CMOS radar sensor transceiver chip with a bandwidth of 1 GHz for human activity detection was designed and fabricated in IBM 180-nm process. This transceiver chip adopts the direct-conversion quadrature architecture. Variable gain amplifiers are used to provide different gains for the baseband signal in order to carry out different applications. A novel adaptive DC tuning mechanism is implemented to eliminate frequency-dependent distortion and large DC offset at the receiver output. The size of the chip is 1.5 mm by 1.5 mm. Post-layout simulation results showed this transceiver chip has promising performance that is beneficial for human activity related detection.
Intrusion-Tolerance Using Fine-Grain Fragmentation-Scattering

The consequences of faults and intrusions in computer networks are of ever-increasing importance; new efficient techniques are needed to deal with them. The SATURNE [DES 86] project aims at solutions for architectures based on the concept of fault- and intrusion-tolerance rather than avoidance techniques. The subject of the paper concerns the security of files. Files are partitioned and then fragmented before being stored in different, distributed archive sites in order to tolerate intrusions into some of these sites. The choice among different fragmentation cryptographic methods is discussed, related to different kinds of possible attacks. A simple and fast solution is proposed that nevertheless ensures a high level of file security and authenticity.
Graph database partitioning: A study

Today, graph databases (GDB) represent a requirement for many applications that manage graph-like data, such as social networks. They are able to manage highly interconnected data such as analyzing whole-graph and answering user queries, which are more interested in the relationships between data rather than on the nodes of the graph. Partitioning data over several systems is one of the most techniques used to optimize queries. It allows distributing data over several servers when it is infeasible to query and store them on a single site. The aim is to improve query response time and to minimize the data storage cost. Although, it has been extensively used in traditional databases, data partitioning has many specificities when it is applied in GDB, which are characterized by a dynamic structure and highly interconnected data. In this paper, we study the recent approaches of GDB partitioning. We summarize and then discuss what partitioning options these approaches have used, what partitioning criteria they have optimized and what are the main steps for partitioning GDB.
Singer Identification in Polyphonic Music Using Vocal Separation and Pattern Recognition Methods

This paper evaluates methods for singer identification in polyphonic music, based on pattern classification together with an algorithm for vocal separation. Classification strategies include the discriminant functions, Gaussian mixture model (GMM)-based maximum likelihood classifier and nearest neighbour classifiers using Kullback-Leibler divergence between the GMMs. A novel method of estimating the symmetric Kullback-Leibler distance between two GMMs is proposed. Two different approaches to singer identification were studied: one where the acoustic features were extracted directly from the polyphonic signal and one where the vocal line was first separated from the mixture using a predominant melody transcription system. The methods are evaluated using a database of songs where the level difference between the singing and the accompaniment varies. It was found that vocal line separation enables robust singer identification down to 0dB and -5dB singer-to-accompaniment ratios.
Beginning to See the Big Picture: BSW Student Reflections on Community Macro Practice

This article describes an undergraduate social work macro practice course that included a service-learning project in which BSW students canvassed for a grassroots, community-based agency to increase voter participation in a low-income, disfranchised community. An analysis of students' reflections prior to and during the course and project illustrates how this experience cultivated students' perspectives about macro practice. Embedding a service-learning project into a macro practice course demystified this level of practice for students, increased their understanding of the relationship between the community and their social work practice, and motivated their interest in macro practice. Recommendations for social work education and further research are provided.
Performance and Weight Estimates for an Advanced Open Rotor Engine

NASA’s Environmentally Responsible Aviation Project and Subsonic Fixed Wing Project are focused on developing concepts and technologies which may enable dramatic reductions to the environmental impact of future generation subsonic aircraft. The open rotor concept (also historically referred to an unducted fan or advanced turboprop) may allow for the achievement of this objective by reducing engine fuel consumption. To evaluate the potential impact of open rotor engines, cycle modeling and engine weight estimation capabilities have been developed. The initial development of the cycle modeling capabilities in the Numerical Propulsion System Simulation (NPSS) tool was presented in a previous paper. Following that initial development, further advancements have been made to the cycle modeling and weight estimation capabilities for open rotor engines and are presented in this paper. The developed modeling capabilities are used to predict the performance of an advanced open rotor concept using modern counter-rotating propeller designs. Finally, performance and weight estimates for this engine are presented and compared to results from a previous NASA study of advanced geared and direct-drive turbofans.
Cooperative Software Development in GENESIS: Requirements, Conceptual Model and Architecture

GENESIS is an Open Source distributed platform supporting cooperative software engineering. All requirements concerning cooperation, collaboration, and coordination have been derived from actual user needs, collected from some of the largest software development companies in the European scenario; moreover, an integrated approach, merging both process-centered and product-centered ones, has been adopted. In this paper we present such two basic facets of GENESIS, which constitute some of the main achievements of the project, and therefore provide an innovative platform to be experienced both by practitioners and researchers.
Automatic Classification and Filtering of Electronic Information: Knowledge-Based Filtering Approach

In this paper we propose an artificial intelligent approach focusing information filtering problem. First, we give an overview of the information filtering process and a survey of different models of textual information filtering. Second, we present our E-mail filtering tool. It consists of an expert system in charge of driving the filtering process in cooperation with a knowledge-based model. Neural networks are used to model all system knowledge. The system is based on machine learning techniques to continuously learn and improve its knowledge all along its life cycle. This email filtering tool assists the user in managing, selecting, classify and discarding non-desirable messages in a professional or non -professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as web browsing. The performance of the system is discussed.
Sexual Orientation in the Labor Market

Most analyses of sexual orientation and earnings find that gay men face a wage gap, whereas lesbian women earn higher wages than similar heterosexual women. However, analyses rarely consider bisexual men and women as a unique group separate from other sexual minorities. I argue that such binary views of sexual orientation—treating sexual minorities as a homogenous non-heterosexual group—have obscured understandings of the impact of sexual orientation on labor market outcomes. Specifically, I predict that unequal outcomes for gay men and lesbian women are partly due to the influence of family arrangements and their effects on earnings. In contrast, I argue that bisexual men and women should be the most disadvantaged in the labor market, due to particularly disadvantaging stereotypes, perceptions of choice to their sexual orientation, and prejudicial treatment. Using data from the General Social Survey (N = 13,554) and the National Longitudinal Study of Adolescent to Adult Health (N = 14,714), I show that family arrangements explain some of the observed earnings differentials for gay men and lesbian women. Bisexual men and women, in contrast, face wage penalties that are not explained by human capital differences or occupational characteristics. Perceptions of prejudicial treatment partially explain the observed wage gaps.
A Quantitative Comparison of Sticky-Price and Sticky-Information Models of Price Setting

I estimate sticky-price and sticky-information models of price setting for the United States via maximum-likelihood techniques, reaching several conclusions. First, the sticky-price model fits best, and captures inflation dynamics as well as reduced-form equations once hybrid-behavior is allowed. Second, the importance of hybrid behavior in sticky-price models is potentially consistent with a role for some information imperfections, such as sticky information, as a complement to nominal price rigidities. Finally, the favorable results herein for the hybrid sticky-price model when evaluated by statistics that summarize the relative fit of different models is consistent with the existing literature that is both supportive and dismissive of such models, as this literature has largely ignored fit in evaluating such models. Many previous studies have focused on ancillary issues, such as the standard errors associated with certain parameters or Granger-causality tests that may not provide much information about sticky-price models.
The use of respondent and interviewer debriefing studies as a way to study response error in survey data

This paper describes the use of specially designed debriefing studies as a way to explore the respondent and interviewer components of response error in survey data. Such studies are useful in developing new questionnaires, as well as identifying sources of error in existing questionnaires. This discussion is illustrated through recent work conducted at the US Bureau of the Census, in conjunction with the US Bureau of Labor Statistics, to redesign the Current Population Survey questionnaire.
Semantic based 3D model retrieval

Semantic based 3D model retrieval (SB3DMR) has attracted more and more research interests, and is a challenge research problem in the field of content based 3D model retrieval (CB3DMR). Current studies concentrate on the relevance feedback or supervised learning to reduce the semantic gap between 3D model low-level features and high-level semantic. In this paper, a new method in extracting semantic feature for 3D model is proposed. It can get high-level semantic information automatically from low-level. First, invariant descriptors are extracted from 3D models to efficient semantic annotation. An unsupervised learning method to describe the semantics of the 3D models is proposed. Second, and based on the resulting semantic annotation, 3D models are semantically classified. Finally, spatial relationships are used to derive other high-level semantic features to augment our knowledge base and to improve the retrieval accuracy. An ontology based 3D model retrieval framework is used to combine the semantic concepts and visual features and introduce the query by semantic example.
Multiple DNA-binding sites in Tetrahymena telomerase

Telomerase is a ribonucleoprotein enzyme that maintains chromosome ends through de novo addition of telomeric DNA. The ability of telomerase to interact with its DNA substrate at sites outside its catalytic centre (‘anchor sites’) is important for its unique ability to undergo repeat addition processivity. We have developed a direct and quantitative equilibrium primer-binding assay to measure DNA-binding affinities of regions of the catalytic protein subunit of recombinant Tetrahymena telomerase (TERT). There are specific telomeric DNA-binding sites in at least four regions of TERT (the TEN, RBD, RT and C-terminal domains). Together, these sites contribute to specific and high-affinity DNA binding, with a Kd of ∼8 nM. Both the Km and Kd increased in a stepwise manner as the primer length was reduced; thus recombinant Tetrahymena telomerase, like the endogenous enzyme, contains multiple anchor sites. The N-terminal TEN domain, which has previously been implicated in DNA binding, shows only low affinity binding. However, there appears to be cooperativity between the TEN and RNA-binding domains. Our data suggest that different DNA-binding sites are used by the enzyme during different stages of the addition cycle.
Detecting and Localizing the Attackers in Multiple Networks

– Spoofing attack which easily attack the network and reduce the performance of the network. In this Paper, Spoofing attacks are detected, Finding the number of attackers that masquerade the node identity, Localizing all the attackers node and calculate the speed of a node, by using RSS (received signal strength) for spoofing detection. The number of attacker is determined by cluster-based method. Support Vector Machines (SVM) is to improve the accuracy of finding number of attackers. Evaluate our method in two real office, an 802.11 (WiFi) network and an 802.15. 4 (ZigBee) network. Our method gives over 90 percent of Hit Rate.
A new deniosing method in spread spectrum systems based on wavelet transform

Wavelets are good decorrelators capable of separating signal from noise and have been used in denoising methods recently. Spread spectrum communication systems have been used for both military and commercial applications for many years. These systems have the ability to work in very low SNRs and are robust to intentional jamming. In such systems, noise cancellation can be very effective to improve performance. In this paper we propose a new denoising method in spread spectrum systems based on wavelet transform and compare it with general spread spectrum denoising methods
Feasibility Study and Design for Wireless Sensor Networks in a Space Environment

Wireless sensor networks is a technology that has been used in a vast number of applications and environments with successful results in the past. Therefore increasing nowadays the number of users of this type of devices and their new applications. In this thesis we worked hand-in-hand with the ISIS B.V. Company to provide a proper WSN design to be deployed inside a spacecraft structure and satisfy a group of defined requirements. ISIS or Innovative Solutions in Space is a private company founded in January 2006 that focus on the development of nanosatellites and several space related services.
Proposal for an Interference Experiment to Test the Applicability of Quantum Theory to Event-Based Processes

We analyze a single-particle Mach-Zehnder interferometer experiment in which the path length of one arm may change (randomly or systematically) according to the value of an external two-valued variable x, for each passage of a particle through the interferometer. Quantum theory predicts an interference pattern that is independent of the sequence of the values of x. On the other hand, corpuscular models that reproduce the results of quantum optics experiments carried out to date show a reduced visibility and a shift of the interference pattern depending on the details of the sequence of the values of x. The proposed experiment will show that: (1) it can be described by quantum theory, and thus not by the current corpuscular models, or (2) it cannot be described by quantum theory but can be described by the corpuscular models or variations thereof, or (3) it can neither be described by quantum theory nor by corpuscular models. Therefore, the proposed experiment can be used to determine the extent to which quantum theory provides a description of observed events beyond the usual statistical level.
On the Application of Generic Summarization Algorithms to Music

Several generic summarization algorithms were developed in the past and successfully applied in fields such as text and speech summarization. In this paper, we review and apply these algorithms to music. To evaluate their performance, we adopt an extrinsic approach: we compare a Fado genre classifier's performance using truncated contiguous clips against the summaries extracted with those algorithms on two different datasets. We show that Maximal Marginal Relevance (MMR), LexRank, and Latent Semantic Analysis (LSA) all improve classification performance in both datasets used for testing.
From the solutions of diffusion equation to the solutions of subdiffusive one

Starting with Green's functions found for normal diffusion, we construct exact time-dependent Green's functions for a subdiffusive equation with fractional time derivatives. Green's functions satisfy the boundary conditions involving a linear combination of fluxes and concentrations. The method is particularly useful to calculate the concentration profiles in a multi-part system where different kinds of transport occur in each part of it. As an example, we find the solution of the subdiffusive equation for the system composed of two parts with normal diffusion and subdiffusion.