Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive Modeling of Strategies in Dynamic Tasks

Computer simulations, or microworlds, have been used for studying various topics including problem solving. This work investigates strategies for complex, dynamic problem solving in a fire-fighting microworld. Using data from a study by Canas, Antol, Fajardo & Salmern (2005), an ACT-R cognitive model is developed with the aim of providing insight into the development and selection of strategies participants use. One particular behavior observed in participants when trained repetitively on the same scenario, the creation of a fire-break barrier to prevent the fire spreading, is discussed. It was found that selection of a particular strategy depends on the fine-tuning of ACT-R production rule utilities as a consequence of environmental rewards, highlighting the role of reward size and timing. The model is able to capture various aspects of the data by promoting a free competition of small blocks of behavior based on rational analysis. A key finding is that good performance is linked to effective combination of strategic control with attention to changing task demands reflecting time and care taken in informing and effecting action.
An Approach to Interference Detection for Ultra Wideband Radio Systems

This paper presents an interference detection technique based on analog spectral decomposition for a multi-band ultra wideband (UWB) radio system. The architecture utilizes a cascade of image-rejecting single-sideband mixers with fixed LO frequencies. The proposed technique can rapidly detect portions of the spectrum with the largest interference levels and provide information to avoid transmission and reception in those bands. This information can be used to relax the dynamic range of the receiver and reduce the potential for interference-related signal degradation
Topology Control, Routing and the Better Transmission Path in Wireless Ad Hoc Networks

In wireless ad hoc networks, different applications may have different requirements on data transfer which are not limited to being energy-efficient. Even in a single application, there may be different requirements. However, the existing topology control and routing algorithms usually considered only one factor, which could not satisfy the various requirements. In this paper, we present algorithms to integrate multiple factors into topology control and routing. We also illustrate that topology control and routing should be considered together when multiple factors are taken into account. Simulation results show that topologies derived under our factor-integrated topology control algorithms can preserve more desirable transmission paths which can be found by the factor-integrated routing algorithms.
SRV-TaGS: An Automatic TAGging and Search System for Sensor-Rich Outdoor Videos

Tagging facilitates video search in many social media and web applications. While manual tagging is time consuming, subjective and sometimes inaccurate, auto-tagging facilitated by content-based techniques is compute-intensive and challenging to apply across domains. We have developed a complementary system, named SRV-TAGS, to automatically generate tags for outdoor videos based on their geographic properties, to index the videos based on their generated tags and to provide textual search services. The system works with our geo-referenced video management web portal, enabling users to manage, search and watch videos.
Intricacies of an Automatic Text Summarizer

The creation of an abstract over a text document prepared by a computer program is defined as an Automatic Text Summarizer. This abstract of the text document must however contain all the salient features of the original document. This paper tries to cover the necessary functional modules that complete an automatic text summarizer. It also highlights the trends and challenges in text summarization. Surveys of certain text summarization techniques are also mentioned.
Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

ABSTRACT Music neurotechnology is a new research area emerging at the crossroads of neurobiology, engineering sciences and music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. The authors introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). They have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces the spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.
A Functional Approach towards Understanding the Role of the Mitochondrial Respiratory Chain in an Endomycorrhizal Symbiosis

Arbuscular mycorrhizal fungi (AMF) are crucial components of fertile soils, able to provide several ecosystem services for crop production. Current economic, social and legislative contexts should drive the so-called “second green revolution” by better exploiting these beneficial microorganisms. Many challenges still need to be overcome to better understand the mycorrhizal symbiosis, among which (i) the biotrophic nature of AMF, constraining their production, while (ii) phosphate acts as a limiting factor for the optimal mycorrhizal inoculum application and effectiveness. Organism fitness and adaptation to the changing environment can be driven by the modulation of mitochondrial respiratory chain, strongly connected to the phosphorus processing. Nevertheless, the role of the respiratory function in mycorrhiza remains largely unexplored. We hypothesized that the two mitochondrial respiratory chain components, alternative oxidase (AOX) and cytochrome oxidase (COX), are involved in specific mycorrhizal behavior. For this, a complex approach was developed. At the pre-symbiotic phase (axenic conditions), we studied phenotypic responses of Rhizoglomus irregulare spores with two AOX and COX inhibitors [respectively, salicylhydroxamic acid (SHAM) and potassium cyanide (KCN)] and two growth regulators (abscisic acid – ABA and gibberellic acid – Ga3). At the symbiotic phase, we analyzed phenotypic and transcriptomic (genes involved in respiration, transport, and fermentation) responses in Solanum tuberosum/Rhizoglomus irregulare biosystem (glasshouse conditions): we monitored the effects driven by ABA, and explored the modulations induced by SHAM and KCN under five phosphorus concentrations. KCN and SHAM inhibited in vitro spore germination while ABA and Ga3 induced differential spore germination and hyphal patterns. ABA promoted mycorrhizal colonization, strong arbuscule intensity and positive mycorrhizal growth dependency (MGD). In ABA treated plants, R. irregulare induced down-regulation of StAOX gene isoforms and up-regulation of genes involved in plant COX pathway. In all phosphorus (P) concentrations, blocking AOX or COX induced opposite mycorrhizal patterns in planta: KCN induced higher Arum-type arbuscule density, positive MGD but lower root colonization compared to SHAM, which favored Paris-type formation and negative MGD. Following our results and current state-of-the-art knowledge, we discuss metabolic functions linked to respiration that may occur within mycorrhizal behavior. We highlight potential connections between AOX pathways and fermentation, and we propose new research and mycorrhizal application perspectives.
Markov random field based super-resolution mapping for identification of urban trees in VHR images

Extraction of individual tree crown objects from very high resolution imagery is a challenging task given the limited spectral and spatial resolution of space-borne systems and the complexity of the urban space. Besides, traditional pixel based image classification techniques do not fully exploit the spatial and spectral characteristics of tree crowns imaged in remote sensing datasets. In this work, we propose a contextual and probabilistic detection of tree crowns in very high resolution imagery by using super resolution mapping (SRM) based on Markov random fields (MRF). Our method models and objective energy function which considers the conditional probabilities of panchromatic and multispectral values of a Quickbird image and models the prior information as the spatial smoothness of pixels labeled as tree crown. We apply this method for extraction of tree crown objects in a residential area in the Netherlands. We found that the proposed method leads to improvement in tree crown identification compared with a maximum likelihood classification of a pan-sharpened product.
Conceptual and algorithmic development of Pseudo 3D Graphics and Video Content Visualization

The article presents a general concept of the organization of pseudo three dimension visualization of graphics and video content for three dimension visualization systems. The steps of algorithms for solving the problem of synthesis of three dimension stereo images based on two dimension images are introduced. The features of synthesis organization of standard format of three dimension stereo frame are presented. Moreover, the performed experimental simulation for generating complete stereoframes and the results of its time complexity are shown. Keywords:Three dimension visualization, pseudo three dimension stereo, a stereo pair, three dimension stereo format, algorithm, modeling, time complexity.
Utility of Social Cognition and Insight in the Prediction of Inpatient Violence Among Individuals With a Severe Mental Illness

The purpose of this study was to assess the utility of social cognition and insight in the prediction of violence in a psychiatric inpatient sample. Violence history, demographic information, symptomatology, neuropsychological functioning, social cognition (i.e., attributional style), and insight were assessed in 29 inpatients with severe mental illness. Greater posttest violence was associated with greater pretest violence, less education, greater psychiatric distress, neuropsychological impairment, and hostile attributional and personalizing biases. Hierarchical multiple regression analyses showed that history of violence contributed the most variance to posttest violence. Hostile attributional and personalizing biases were also uniquely associated with posttest violence. Overall, this study supported the modest utility of attributional style measures in the prediction of inpatient violence. The predictive value of insight in this context appears limited.
Reshaping educational experience by volunteering in the community: Language learners in the real world

This paper views tertiary language learners’ gaining a sense of cultural belonging and an awareness of intercultural communication through three prisms: (i) the metaphor of "investment", (ii) the notion of "community of practice" and (iii) the concept of "imagined community". Applied to environments of real world learning, specifically the volunteer sector, the notion of "community" holds a key to reshaping the cultural education experiences for participants in language learning programs, including learners of English as an Additional Language (EAL). This paper examines facets of "community" to contextualise the experiences of learners in a project where advanced EAL learners undertook volunteer work as part of a cultural learning curriculum within a Bachelor of Arts in EAL at a tertiary institute in New Zealand. This project gave learners access to "communities of practice" (Lave & Wenger 1992; Wenger 1998), providing contexts where instructors can reshape learners’ higher education experiences by identifying cultural learning opportunities within the volunteer sector of the community. In educational research, the properties of community include insider support, common goals, shared discourse and membership (Rovai 2002a). The occurrence of such features in a student’s volunteer placement depends on the degree of "investment" (Norton 2000; Pittaway 2004) participants have in individual and community goals. In the project, the journalised reflections of the participating migrants, refugees and international students reveal the cultural and ontological value of community work. This paper uses the concepts of real and imagined communities to theorise the participants’ investments in their learning, presents qualitative findings from the project, and describes a range of benefits for EAL learners’ learning in community. The paper concludes that investing in community can prepare learners for their future and imagined communities while reshaping significant aspects of cultural learning.
A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios

This article develops a new bivariate Markov regime switching BEKK-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (RS-BEKK-GARCH) model. The model is a state-dependent bivariate BEKK-GARCH model and an extension of Gray's univariate generalized regime-switching (GRS) model to the bivariate case. To solve the path-dependency problem inherent in the bivariate regime switching BEKK-GARCH model, we propose a recombining method for the covariance term in the conditional variance-covariance matrix. The model is applied to estimate time-varying minimum variance hedge ratios for corn and nickel spot and futures prices. Out-of-sample point estimates of hedging portfolio variance show that compared to the state-independent BEKK-GARCH model, the RS-BEKK-GARCH model improves out-of-sample hedging effectiveness for both corn and nickel data. We perform White's (2000) data-snooping reality check to test for predictive superiority of RS-BEKK-GARCH over the benchmark model and find that the difference in variance reduction between BEKK-GARCH and RS-BEKK-GARCH is not statistically significant for either data set at conventional confidence levels.
Interplays between Soil-Borne Plant Viruses and RNA Silencing-Mediated Antiviral Defense in Roots

Although the majority of plant viruses are transmitted by arthropod vectors and invade the host plants through the aerial parts, there is a considerable number of plant viruses that infect roots via soil-inhabiting vectors such as plasmodiophorids, chytrids, and nematodes. These soil-borne viruses belong to diverse families, and many of them cause serious diseases in major crop plants. Thus, roots are important organs for the life cycle of many viruses. Compared to shoots, roots have a distinct metabolism and particular physiological characteristics due to the differences in development, cell composition, gene expression patterns, and surrounding environmental conditions. RNA silencing is an important innate defense mechanism to combat virus infection in plants, but the specific information on the activities and molecular mechanism of RNA silencing-mediated viral defense in root tissue is still limited. In this review, we summarize and discuss the current knowledge regarding RNA silencing aspects of the interactions between soil-borne viruses and host plants. Overall, research evidence suggests that soil-borne viruses have evolved to adapt to the distinct mechanism of antiviral RNA silencing in roots.
Ontological Support for Bayesian Evidence Management

This paper describes our work on an integrated system that can assist analysts in exploring hypotheses using Bayesian analysis of evidence from a variety of sources. The hypothesis exploration is aided by an ontology that represents domain knowledge, events, and causality for Bayesian reasoning, as well as models of information sources for evidential reasoning. We are validating the approach via a tool, Magellan, that uses Bayesian models for an analyst’s prior and tacit knowledge about how evidence can be used to evaluate hypotheses.
Contribution of microRNA to pathological fibrosis in cardio-renal syndrome: impact of uremic toxins

Progressive reduction in kidney function in patients following myocardial infarction (MI) is associated with an increase in circulating uremic toxins levels leading to increased extracellular matrix deposition. We have recently reported that treatment with uremic toxin adsorbent AST‐120 in rats with MI inhibits serum levels of uremic toxin indoxyl sulfate (IS) and downregulates expression of cardiac profibrotic cytokine transforming growth factor beta (TGF‐β1). In this study, we examined the effect of uremic toxins post‐MI on cardiac microRNA‐21 and microRNA‐29b expression, and also the regulation of target genes and matrix remodeling proteins involved in TGFβ1 and angiotensin II signaling pathways. Sixteen weeks after MI, cardiac tissues were assessed for pathological and molecular changes. The percentage area of cardiac fibrosis was 4.67 ± 0.17 in vehicle‐treated MI, 2.9 ± 0.26 in sham, and 3.32 ± 0.38 in AST‐120‐treated MI, group of rats. Compared to sham group, we found a twofold increase in the cardiac expression of microRNA‐21 and 0.5‐fold decrease in microRNA‐29b in heart tissue from vehicle‐treated MI. Treatment with AST‐120 lowered serum IS levels and attenuated both, cardiac fibrosis and changes in expression of these microRNAs observed after MI. We also found increased mRNA expression of angiotensin‐converting enzyme (ACE) and angiotensin receptor 1a (Agtr1a) in cardiac tissue collected from MI rats. Treatment with AST‐120 attenuated both, expression of ACE and Agtr1a mRNA. Exposure of rat cardiac fibroblasts to IS upregulated angiotensin II signaling and altered the expression of both microRNA‐21 and microRNA‐29b. These results collectively suggest a clear role of IS in altering microRNA‐21 and microRNA‐29b in MI heart, via a mechanism involving angiotensin signaling pathway, which leads to cardiac fibrosis.
Sperm competition games: optimal sperm allocation in response to the size of competing ejaculates

Sperm competition theory predicts that when males are certain of sperm competition, they should decrease sperm investment in matings with an increasing number of competing ejaculates. How males should allocate sperm when competing with differently sized ejaculates, however, has not yet been examined. Here, we report the outcomes of two models assuming variation in males' sperm reserves and males being faced with different amounts of competing sperm. In the first ‘spawning model’, two males compete instantaneously and both are able to assess the sperm competitive ability of each other. In the second ‘sperm storage model’, males are sequentially confronted with situations involving different levels of sperm competition, for instance different amounts of sperm already stored by the female mating partner. In both of the models, we found that optimal sperm allocation will strongly depend on the size of the male's sperm reserve. Males should always invest maximally in competition with other males that are equally strong competitors. That is, for males with small sperm reserves, our model predicts a negative correlation between sperm allocation and sperm competition intensity, whereas for males with large sperm reserves, this correlation is predicted to be positive.
On minimax density estimation on R

The problem of density estimation on R on the basis of an independent sample X1 . , XN with common density f is discussed. The behaviour of the minimax Lp risk, 1 < p < oc, is studied when f belongs to a Hl61der class of regularity s on the real line. The lower bound for the minimax risk is given. We show that the linear estimator is not efficient in this setting and construct a wavelet adaptive estimator which attains (up to a logarithmic factor in N) the lower bounds involved. We show that the minimax risk depends on the parameter p when p < 2 + 1/s.
Local wavelet correlation: applicationto timing analysis of multi-satellite CLUSTER data

Multi-spacecraft space observations, such as those of CLUSTER, can be used to infer information about local plasma structures by exploiting the timing differences between subsequent encounters of these structures by individual satellites. We introduce a novel wavelet-based technique, the Local Wavelet Correlation (LWC), which allows one to match the corresponding signatures of large-scale structures in the data from multiple spacecraft and determine the relative time shifts between the crossings. The LWC is especially suitable for analysis of strongly non-stationary time series, where it enables one to estimate the time lags in a more robust and systematic way than ordinary cross-correlation techniques. The technique, together with its properties and some examples of its application to timing analysis of bow shock and magnetopause crossing observed by CLUSTER, are presented. We also compare the performance and reliability of the technique with classical discontinuity analysis methods. Key words. Radio science (signal processing) – Space plasma physics (discontinuities; instruments and techniques)
Statement Roy Shilkrot January 2015 Assistive Human Augmentation

Wearable devices that o↵er special manual abilities are a mythical intercultural desire reflected in both traditional and modern narratives. Recently, the advent of wearable computers gave rise to new devices that augment practiced manual gestures and assist those in need through technological means. However there are still vast under-explored territories of bodily augmentation that are waiting to be tapped into. My dissertation research explores finger augmentation as a design agenda to leverage on the anatomy, dexterity and behavior of hands and fingers for assistance and empowerment. Finger augmenting devices, such as the ones I create, allow the designers to utilize inborn human capabilities and gestures, such as pointing, as basic interaction cues that work both for people with and without special needs. My research also extends into the domain of smart handheld tools. By augmenting the tool itself rather than the hand I was able to achieve assistive functionality that spans the spectrum of assistive and augmenting technology. This research area is one of the most prominent up-and-coming themes in HCI, Human Augmentation and Computer Graphics domains, as evident by the surge of interest both in academia and industry.
Cochannel Speaker Identification in Anechoic and Reverberant Conditions

Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We first investigate GMM-based approaches and propose a combined system that integrates two cochannel SID methods. Second, we explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate that our proposed systems significantly improve SID performance over recent approaches in both anechoic and reverberant conditions and various target-to-interferer ratios.