Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarity score for information filtering thresholds

The rapid growth of on-line information has led to the development of many techniques for information filtering. The tremendous growth in the amount of information available and the number of visitors to Web sites in recent years poses some key challenges for information filtering and retrieval. Web visitors not only expect high quality and relevant information, but also wish that the information be presented in as efficient a way as possible. The traditional filtering methods, however, only consider the relevant values of document. These conventional methods fail to consider the efficiency of document retrieval. In this paper, we propose a new algorithm to calculate an index called document similarity score based on elements of the document. Using the index, document profile is derived. Any documents with the similarity score above a given threshold are clustered. Using these pre-clustered documents, information filtering and retrieval can be made more efficient. Experimental results clearly show our proposed method tremendously improves the efficiency of information filtering and retrieval.
Collapsed Cooperative Video Cache for Content Distribution Networks

In this work we describe the Collapsed Cooperative Video Cache (CCVC), a novel scalable technique for delivering video streams over content distribution networks (CDNs). C-CVC is based on the idea of collapsing client buffers into the proxy caches on the CDN edges nearest to the clients while enforcing transparently cooperation among clients that share the same video content. As a result of storing videos close to the clients, C-CVC decreases the average initial video exhibition latency. C-CVC also reserves cache space in the proxy, therefore absorbing more jitter and using less CDN resources. Most importantly, by implementing the collapsed cooperative video cache globally across CDN’s proxies, C-CVC enables VoD systems to become highly scalable.
Housing Affordability Versus Location Affordability: The Rent’s Too Damn High! But the Metrocard Is a Pretty Good Deal

This policy brief focuses on housing and transportation costs in New York City. It looks at the Location Affordability Index as determined by the U.S. Department of Housing and Urban Development (HUD) and compares New York City household costs to those in 21 other large cities. It is found that New York City's low transportation costs help to balance out the higher housing prices, making New York City more affordable than many other cities.
Using Numerical Simulation to Test the Validity of Neo-Darwinian Theory

Evolutionary genetic theory has a series of apparent “fatal flaws” which are well known to population geneticists, but which have not been effectively communicated to other scientists or the public. These fatal flaws have been recognized by leaders in the field for many decades—based upon logic and mathematical formulations. However population geneticists have generally been very reluctant to openly acknowledge these theoretical problems, and a cloud of confusion has come to surround each issue. Numerical simulation provides a definitive tool for empirically testing the reality of these fatal flaws and can resolve the confusion. The program Mendel’s Accountant (Mendel) was developed for this purpose, and it is the first biologically-realistic forward-time population genetics numerical simulation program. This new program is a powerful research and teaching tool. When any reasonable set of biological parameters are used, Mendel provides overwhelming empirical evidence that all of the “fatal flaws” inherent in evolutionary genetic theory are real. This leaves evolutionary genetic theory effectively falsified—with a degree of certainty which should satisfy any reasonable and open-minded person.
Healthy whole-grain choices for children and parents: a multi-component school-based pilot intervention

Abstract Objective The aim of the present study was to pilot-test a school-based intervention designed to increase consumption of whole grains by 4th and 5th grade children. Design This multi-component school-based pilot intervention utilised a quasi-experimental study design (intervention and comparison schools) that consisted of a five-lesson classroom curriculum based on Social Cognitive Theory, school cafeteria menu modifications to increase the availability of whole-grain foods and family-oriented activities. Meal observations of children estimated intake of whole grains at lunch. Children and parents completed questionnaires to assess changes in knowledge, availability, self-efficacy, usual food choice and role modelling. Setting/sample Parent/child pairs from two schools in the Minneapolis metropolitan area; 67 in the intervention and 83 in the comparison school. Results Whole-grain consumption at the lunch meal increased by 1 serving (P < 0·0001) and refined-grain consumption decreased by 1 serving for children in the intervention school compared with the comparison school post-intervention (P < 0·001). Whole-grain foods were more available in the lunches served to children in the intervention school compared with the comparison school post-intervention (P < 0·0001). The ability to identify whole-grain foods by children in both schools increased, with a trend towards a greater increase in the intervention school (P = 0·06). Parenting scores for scales for role modelling (P < 0·001) and enabling behaviours (P < 0·05) were significantly greater for parents in the intervention school compared with the comparison school post-intervention. Conclusions The multi-component school-based programme implemented in the current study successfully increased the intake of whole-grain foods by children.
Unique properties of core shell Ag@Au nanoparticles for the aptasensing of bacterial cells

In this article, it is shown that the efficiency of an electrochemical aptasensing device is influenced by the use of different nanoparticles (NPs) such as gold nanoparticles (Au), silver nanoparticles (Ag), hollow gold nanospheres (HGN), hollow silver nanospheres (HSN), silver–gold core shell (Ag@Au), gold–silver core shell (Au@Ag), and silver–gold alloy nanoparticles (Ag/Au). Among these nanomaterials, Ag@Au core shell NPs are advantageous for aptasensing applications because the core improves the physical properties and the shell provides chemical stability and biocompatibility for the immobilization of aptamers. Self-assembly of the NPs on a cysteamine film at the surface of a carbon paste electrode is followed by the immobilization of thiolated aptamers at these nanoframes. The nanostructured (Ag@Au) aptadevice for Escherichia coli as a target shows four times better performance in comparison to the response obtained at an aptamer modified planar gold electrode. A comparison with other (core shell) NPs is performed by cyclic voltammetry and differential pulse voltammetry. Also, the selectivity of the aptasensor is investigated using other kinds of bacteria. The synthesized NPs and the morphology of the modified electrode are characterized by UV-Vis absorption spectroscopy, scanning electron microscopy, energy dispersive X-ray analysis, and electrochemical impedance spectroscopy.
Study of Objectionable Sound Recognition Based on Histogram Features and SVM

It is important to protect children from harmful effects of objectionable materials, such as pornography, which are now prevalent on the Internet. In this paper, a new method from the feature porno-sounds recognition point of view is proposed to detect adult video sequences automatically which serves as a complementary approach to the recognition method from image’s point of view. To the special of erotic sound, a new set of features based on histograms and contours are introduced for training and classification. Experimental comparisons with popular features for audio classification are presented and discussed. A recognition method is designed based on the standard SVM classification framework due to its extraordinary generalization performance. These efforts result in superior performance that 89.17% recall rate and 10.78% false positive rate are achieved for experiments on real data from the Internet.
Model-Based Reconstructive Elasticity Imaging Using Ultrasound

Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.
Chain-Type Wireless Sensor Network for Monitoring Long Range Infrastructures: Architecture and Protocols*

We present in this paper an investigation of a special class of wireless sensor networks for monitoring critical infrastructures that may extend for hundreds of miles in distances. Such networks are fundamentally different from traditional sensor networks in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Based on careful analysis of existing sensor network architectures, we first demonstrate the need to develop new architecture and networking protocols to match the unique topology of chain-type sensor networks. We then propose hierarchical network architecture that consists of clusters of sensor nodes to enable the chain-type sensor networks to be scalable to cover typically long range infrastructures with tolerable delay in network-wide data collection. To maintain energy efficient operations and maximize the lifetime for such a chain-type sensor network, we devise a smart strategy for the deployment of cluster heads. Protocols for network initialization and seamless operations of the chain-type sensor networks are also developed to match the proposed hierarchical architecture and cluster head deployment strategy. Simulations have been carried out to verify the performance of the hierarchical architecture, the smart node deployment strategy, and the corresponding network initialization and operation protocols.
An Effective RM-Based Scheduling Algorithm for Fault-Tolerant Real-Time Systems

Dependability is the representative property that predominantly distinguishes a hard real-time system from other computer systems besides timeliness. Primary/alternate version technique is a cost-effective means which trades the quality of computation results for promptness to tolerate the software faults. The kernel algorithm proposed in this paper employs the off-line backwards-RM scheme to pre-allocate time intervals to the alternate version and the on-line RM scheme to dispatch the primary version. The methodology is a dual-purpose strategy, which aims to (1) tolerate potential software faults by ensuring the accomplishment of the alternate version once its primary fails to execute or re-execute and (2) achieve better quality of service by maximizing the success rate of primary version.
Using Stochastic Computing to Reduce the Hardware Requirements for a Restricted Boltzmann Machine Classifier

Artificial neural networks are powerful computational systems with interconnected neurons. Generally, these networks have a very large number of computation nodes which forces the designer to use software-based implementations. However, the software based implementations are offline and not suitable for portable or real-time applications. Experiments show that compared with the software based implementations, FPGA-based systems can greatly speed up the computation time, making them suitable for real-time situations and portable applications. However, the FPGA implementation of neural networks with a large number of nodes is still a challenging task. In this paper, we exploit stochastic bit streams in the Restricted Boltzmann Machine (RBM) to implement the classification of the RBM handwritten digit recognition application completely on an FPGA. We use finite state machine-based (FSM) stochastic circuits to implement the required sigmoid function and use the novel stochastic computing approach to perform all large matrix multiplications. Experimental results show that the proposed stochastic architecture has much more potential for tolerating faults while requiring much less hardware compared to the currently un-implementable deterministic binary approach when the RBM consists of a large number of neurons. Exploiting the features of stochastic circuits, our implementation achieves much better performance than a software-based approach.
Avaliação do repertório de habilidades sociais de usuários de substâncias psicoativas em tratamento/Evaluation of the repertory of social skills of users of psychoactive substances under treatment

Introducao: O transtorno mental e de comportamento devido ao uso de substância e um grave problema de saude publica. O deficit nas habilidades sociais pode ser considerado fator de risco ao inicio ou continuidade do uso. Objetivo: Avaliar o repertorio de habilidades sociais de usuarios de multiplas e variadas substâncias psicoativas diagnosticados e em tratamento no Centro de Atencao Psicossocial em Alcool e outras Drogas II. Metodo: Amostra composta de individuos diagnosticados com transtorno decorrente do uso de substância em tratamento no Centro de Atencao Psicossocial para Usuarios de Alcool e Drogas de uma cidade do interior paulista. Utilizaram-se dois instrumentos investigativos: questionario de caracterizacao sociodemografica e uso e Inventario de Habilidades Sociais (IHS). Para a analise dos dados sociodemograficos, utilizaram-se calculos de estatistica descritiva e analise do IHS realizada por psicologo colaborador pelo sistema de correcao informatizada do IHS. Resultados: Participaram 35 usuarios, todos do genero masculino, 42,8% com idade entre 31 e 35 anos e 51,4% solteiros. A media geral classificou-os com bom repertorio, porem uma analise individual apontou 42,8% com repertorio abaixo da media. Dentre os deficits, os fatores mais comprometidos foram fatores 1 e 3 relacionados a habilidades de enfrentamento e autoafirmacao com risco e dificuldades em conversacao e desenvoltura social. E expressivo o numero de usuarios com deficit nas HS, principalmente em habilidades de enfrentamento e autoafirmacao com risco, fato que pode estar diretamente relacionado a recaidas e abandono de tratamento. Conclusao: E importante a avaliacao do repertorio das HS no inicio do tratamento, a fim de contribuir na elaboracao e implantacao de intervencoes especificas de treinamento.
Evolution of a Double Amino Acid Substitution in the 5-Enolpyruvylshikimate-3-Phosphate Synthase in Eleusine indica Conferring High-Level Glyphosate Resistance1

Naturally evolved double mutants in 5-enolpyruvylshikimate-3-phosphate synthase mimic engineered glyphosate-tolerant 5-enolpyruvylshikimate-3-phosphate synthase in crops and confer high-level glyphosate resistance. Glyphosate is the most important and widely used herbicide in world agriculture. Intensive glyphosate selection has resulted in the widespread evolution of glyphosate-resistant weed populations, threatening the sustainability of this valuable once-in-a-century agrochemical. Field-evolved glyphosate resistance due to known resistance mechanisms is generally low to modest. Here, working with a highly glyphosate-resistant Eleusine indica population, we identified a double amino acid substitution (T102I + P106S [TIPS]) in the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene in glyphosate-resistant individuals. This TIPS mutation recreates the biotechnology-engineered commercial first generation glyphosate-tolerant EPSPS in corn (Zea mays) and now in other crops. In E. indica, the naturally evolved TIPS mutants are highly (more than 180-fold) resistant to glyphosate compared with the wild type and more resistant (more than 32-fold) than the previously known P106S mutants. The E. indica TIPS EPSPS showed very high-level (2,647-fold) in vitro resistance to glyphosate relative to the wild type and is more resistant (600-fold) than the P106S variant. The evolution of the TIPS mutation in crop fields under glyphosate selection is likely a sequential event, with the P106S mutation being selected first and fixed, followed by the T102I mutation to create the highly resistant TIPS EPSPS. The sequential evolution of the TIPS mutation endowing high-level glyphosate resistance is an important mechanism by which plants adapt to intense herbicide selection and a dramatic example of evolution in action.
ABSOLUTE FREQUENCY EFFECTS IN SECOND LANGUAGE LEXICAL ACQUISITION

Abstract A number of longitudinal studies of L2 production have reported frequency effects wherein learners' produce more frequent words as a function of time. The current study investigated the spoken output of English L2 learners over a four-month period of time using both native and non-native English speaker frequency norms for both word types and word tokens. The study also controlled for individual differences such as first language distance, English proficiency, gender, and age. Results demonstrated that lower level L2 learners produced more infrequent tokens at the beginning of the study and that high intermediate learners, when compared to advanced learners, produced more infrequent tokens at the beginning of the study and more frequent tokens toward the end of the study. Main effects were also reported for proficiency level, age, and language distance. These results provide further evidence that L2 production may not follow expected frequency trends (i.e., that more infrequent tokens are produced as a function of time).
Salience-driven Contextual Priming of Speech Recognition for Human-Robot Interaction

The paper presents an implemented model for priming speech recognition, using contextual information about salient entities. The underlying hypothesis is that, in human-robot interaction, speech recognition performance can be improved by exploiting knowledge about the immediate physical situation and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (objects, events already mentioned in the dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment changes. It is used to establish expectations about which words are most likely to be heard in the given context. The update is realised by continuously adapting the word-class probabilities specified in a statistical language model. The paper discusses the motivations behind the approach, and presents the implementation as part of a cognitive architecture for mobile robots. Evaluation results on a test suite show a statistically significant improvement of salience-driven priming speech recognition (WER) over a commercial baseline system.
M-Flash: Fast Billion-scale Graph Computation Using Block Partition Model

Recent graph computation approaches such as GraphChi, X-Stream, TurboGraph and MMap demonstrated that a single PC can perform ecient computation on billion scale graphs. While they use dierent techniques to achieve scalability through opti- mizing I/O operations, such optimization often does not fully exploit the capabilities of modern hard drives. We contribute: (1) a novel and scalable graph computation framework called M-Flash that uses a block partition model to boost computation speed and reduce disk ac- cesses, by logically dividing a graph and its node data into blocks that can fully t in RAM for reuse; (2) a exible and deliberatively simple programming model, as part of M-Flash, that enables us to implement pop- ular and essential graph algorithms, including the rst single-machine billion-scale eigensolver; and (3) exten- sive experiments on real graphs with up to 6.6 billion edges, demonstrating M-Flash's consistent and signi- cant speed-up against state-of-the-art approaches.
"Learning to Be More Human": Perspectives of Respect by Young Irish People in Prison.

Respect is a fundamental aspect of how human beings relate to each other and, arguably, is a significant factor in the relationship between student and teacher. For incarcerated adults, the relationships they foster with their teachers (and by extension the respect or disrespect cultivated within it) often have a considerable impact on their educative development. This research explores how respect, and disrespect, is perceived to be communicated within prison education by 13 students and 13 teachers in an Irish Young Offenders institution. The values at the core of prison educators’ practice and their capacity to promote self-respect within their students emerged as central to this cultivation of respect. The place of socialisation within these educative relationships also emerged as a prominent factor and is discussed in relation to MacMurray’s (2012) assertion that the primary function of education is ‘learning to be human’.
Radial velocities and membership of stars in the old, distant open cluster Berkeley 29

Multi slit spectroscopy at the Telescopio Nazionale Galileo was employed to measure radial velocities for 20 stars in the direction of the old open cluster Berkeley 29, the farthest known in our Galaxy. Membership information was derived for stars all along the red giant branch, in particular near its tip, and on the red clump. The sample of bona fide cluster members was used to revise the cluster distance to ∼15 kpc, on the basis of an empirical comparison with the red clump in open clusters with known distances. A metallicity (Fe/H) = −0.74 ± 0.18 was also estimated using the colours of spectroscopically confirmed red giant stars.