Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems

We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.
Magnetic properties of amorphous Si films doped with rare-earth elements

Amorphous silicon films doped with Y, La, Gd, Er, and Lu rare-earth elements (a-Si:RE) have been prepared by cosputtering and studied by means of electron-spin resonance (ESR) and dc magnetization. For comparison purposesthe magnetic properties of laser-crystallized and hydrogenated a-Si:RE films were also studied. It was found that the rare-earth species are incorporated in the a-Si:RE films in the RE 3 + form and that the RE doping depletes the neutral dangling bond (D 0 ) density. The reduction of D 0 density is significantly larger for the magnetic RE's (Gd 3 + and Er 3 + ) than for the nonmagnetic ones (Y 3 + , La 3 + , Lu 3 + ). These results are interpreted in terms of an exchangelike interaction H i n t ∼-J R E - D B S R E S D B between the spin of the magnetic RE's and that of the n 0 . All our Gd-doped Si films showed basically the same broad ESR Cd 3 + resonance (ΔH p p 850 Oe) at g2.01, suggesting the formation of a rather stable RE-Si complex in these films.
5-HT2A Serotonin Receptor Density in Adult Male Rats’ Hippocampus after Morphine-based Conditioned Place Preference

Introduction: A close interaction exists between the brain opioid and serotonin (5-HT) neurotransmitter systems. Brain neurotransmitter 5-HT plays an important role in the regulation of reward-related processing. However, a few studies have investigated the potential role of 5-HT2A receptors in this behavior. Therefore, the aim of the present study was to assess the influence of morphine and Conditioned Place Preference (CPP) on the density of 5-HT2A receptor in neurons of rat hippocampal formation. Methods: Morphine (10 mg/kg, IP) was injected in male Wistar rats for 7 consecutive days (intervention group), but control rats received just normal saline (1 mL/kg, IP). We used a hotplate test of analgesia to assess induction of tolerance to analgesic effects of morphine on days 1 and 8 of injections. Later, two groups of rats were sacrificed one day after 7 days of injections, their whole brains removed, and the striatum and PFC immediately dissected. Then, the NR1 gene expression was examined with a semi-quantitative RT-PCR method. Results: Our data showed that the maximum response was obtained with 2.5 mg/kg of morphine. The density of 5-HT2A receptor in different areas of the hippocampus increased significantly at sham-morphine and CPP groups (P<0.05). On the other hand, the CPP groups had more 5-HT2A receptors than sham-morphine groups and also the sham-morphine groups had more 5-HT2A receptors than the control groups. Conclusion: We concluded that the phenomenon of conditioned place preference induced by morphine can cause a significant increase in the number of serotonin 5-HT2A receptors in neurons of all areas of hippocampus.
Making RAMs Oblivious Requires Superlogarithmic Overhead

We prove a time-space tradeoff lower bound of T = Ω ( n log( n S ) log log( n S ) ) for randomized oblivious branching programs to compute 1GAP , also known as the pointer jumping problem, a problem for which there is a simple deterministic time n and space O(log n) RAM (random access machine) algorithm. Ajtai [3, 4] and Damgard, Meldgaard, and Nielsen [11] recently derived simulations of general RAMs by randomized oblivious RAMs with only a polylogarithmic factor increase in time and space. Our lower bound implies that a superlogarithmic factor increase is indeed necessary in any such simulation.
Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform

How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.
Consequences of plant invasions on compartmentalization and species’ roles in plant–pollinator networks

Compartmentalization—the organization of ecological interaction networks into subsets of species that do not interact with other subsets (true compartments) or interact more frequently among themselves than with other species (modules)—has been identified as a key property for the functioning, stability and evolution of ecological communities. Invasions by entomophilous invasive plants may profoundly alter the way interaction networks are compartmentalized. We analysed a comprehensive dataset of 40 paired plant–pollinator networks (invaded versus uninvaded) to test this hypothesis. We show that invasive plants have higher generalization levels with respect to their pollinators than natives. The consequences for network topology are that—rather than displacing native species from the network—plant invaders attracting pollinators into invaded modules tend to play new important topological roles (i.e. network hubs, module hubs and connectors) and cause role shifts in native species, creating larger modules that are more connected among each other. While the number of true compartments was lower in invaded compared with uninvaded networks, the effect of invasion on modularity was contingent on the study system. Interestingly, the generalization level of the invasive plants partially explains this pattern, with more generalized invaders contributing to a lower modularity. Our findings indicate that the altered interaction structure of invaded networks makes them more robust against simulated random secondary species extinctions, but more vulnerable when the typically highly connected invasive plants go extinct first. The consequences and pathways by which biological invasions alter the interaction structure of plant–pollinator communities highlighted in this study may have important dynamical and functional implications, for example, by influencing multi-species reciprocal selection regimes and coevolutionary processes.
Evaluation of Android Anti-malware Techniques against Dalvik Bytecode Obfuscation

Popularity and growth of Android mobile devices has paved the way for exploiting popular apps using various Dalvik byte code transformation methods. Testing the antimalware techniques against obfuscation identifies the need of proposing effective detection methods. In this paper, we explore the resilience of anti-malware techniques against transformations for Android. The Proposed approach employs variable compression, native code wrapping and register renaming, in addition to already implemented transformations on Dalvik byte code. Evaluation results indicate low resilience of the antimalware detection engines against code obfuscation. Furthermore, we evaluate resilience of Androguard's code similarity and AndroSimilar's robust statistical feature signature against code obfuscated malware.
Joint Forest Management : Experience and Modeling

The experience with Joint Forest Management (JFM) in different countries has varied considerably, succeeding in limiting deterioration of the forest in some cases but not in others. Inequality within the forest community has also had a tendency to increase. The purposes of this chapter are (1) to review relevant literature on JFM, (2) to develop a multi-purpose model that could be used to identify conditions that can influence the likelihood of success of JFM in improving the welfare of those living and working in forest communities as well as making forest use more sustainable, and (3) to highlight the role of forest externalities and institutional conditions in analyzing the effects of JFM, and (4) to suggest applications and extensions that could provide valid policy implications tailored to specific circumstances. Although highly simplified, the model is designed so as to be * An earlier version of this chapter was presented to the Conference on the Economics of Social Forest Management at the University of Toronto May 21-23, 2004. The authors express their appreciation for comments on the paper at the conference, on a related paper presented at the 78 Annual Meeting of the Western Economic Association International, Denver, Colorado, July 11-14, 2003 and especially the written comments of Albert Berry and Shashi Kant. They also express thanks to Rahul Nilakantan for his excellent research assistance.
Monitoring and Modelling of Social Networks

In this thesis we contribute to the understanding of online social networks, temporal networks, and non-equilibrium dynamics. As the title of this work suggests, this thesis is split into two parts, \emph{monitoring} and \emph{modelling} social networks. In the first half we look at current methods for understanding the behaviour and influence of individual users within a social network, and assess their robustness and effectiveness. In particular, we look at the role that the temporal dimension plays on these methods and the various representations that temporal networks can take. We introduce a new temporal network representation which describes a temporal network in terms of node behaviour which we use to characterise individuals and collectives. The new representation is illustrated with examples from the online social network Twitter. We model two particular aspects of social networks in the second half of this thesis. The first model, a generalisation of the popular Voter model, considers the dynamics of two opposite opinions in a heterogeneous society which differ by the resolve of their opinion. The second model investigates how the presence of `anti-bandwagon' agents can prevent the spread of ideas and innovations on a social network, particularly on networks with restrictive topologies. This contribution offers new ways to analyse temporal networks and online social media, and also provokes new and interesting questions for future research in the field.
Eliciting Information on the Distribution of Future Outcomes

This paper studies the problem of inducing a presumably knowledgeable expert to reveal true information regarding the probability distribution of a future random outcome. I consider general information that includes, in particular, the statistics of probability distributions (such as mean, median, variance), and all categorical information (such as the most correlated pair of variables). I examine two types of incentive schemes: Those that reward the expert for being truthful, and, for the case of numerical and ordinal information, those that reward the expert increasingly with the accuracy of the prediction. For both cases, I establish simple criteria to determine the information that can be elicited, and offer a complete characterization of the associated schedule fee.
Towards a general infrastructure for location-based smart mobility services

Penetration of smart phones and localization technologies are enabling a variety of smart location-based social mobility services that can notably improve the quality life of citizens on the go. Most existing solutions are shaped from a specific set of data sources processed through predefined computational flows to provide a specific class of services, and rarely strive for generality and urban-scale goals. The contribution of this paper is to presents an architectural model for a general-purpose distributed coordination infrastructure to support the dynamic composition of a variety of smart social mobility services, by focusing on the coordination of the agents involved to address both individual mobility needs and urban-scale mobility issues. Following, it analyzes the key requirements that the implementation of such infrastructure should satisfy. Finally, to exemplify, we show how the framework can be applied to a urban ride-sharing service.
Numerical Analysis and Prototyping Details of a Planar Parallel Redundant Manipulator

Important drawbacks of parallel kinematic machines can be partially overcome by the use of redundancy. Despite of some trade off issue, redundancy is a promising solution to achieve improved dynamic performance and energy efficiency. This manuscript describes preliminary numerical results on the rational use of three levels of kinematic redundancy of a 3 (P)R RR parallel planar manipulator. Moreover, this manuscript describes some practicalities on the design and prototyping of such manipulator. Based on the numerical results, one can conclude that if the redundancy is considered accordingly, the manipulator may present improved dynamic performance and may be more energy efficient. The insights on the design and prototyping of the manipulator have shown that this is a versatile setup. Diverse research topics may be studied using this experimental setup: the use of redundancy, the control of induced vibrations, the role of uncertainties on the manipulator's accuracy, among others.
Visual analytics for maritime domain awareness

Maintaining situation awareness in the maritime domain is a challenging mandate. Task analysis activities were conducted to identify where visual analytics science and technology could improve maritime domain awareness and reduce information overload. Three promising opportunities were identified: the visualization of normal maritime behaviour, anomaly detection, and the collaborative analysis of a vessel of interest. In this paper, we describe the result of our user studies along with potential visual analytics solutions and features considered for a maritime analytics prototype.
Simulation-based optimization using simulated annealing with confidence interval

This paper develops a variant of simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulations. In the proposed variant of SA, the Metropolis criterion depends on whether the objective function values indicate statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, our method uses a constant temperature. We show that the configuration that has been visited most often in the first m iterations converges almost surely to a global optimizer.
Semi-automatic Construction of a General Purpose Ontology

Ontologies are hierarchically organized networks of conceptual information. Used to systematize and model domain knowledge, they play an important role in artificial intelligence, natural language processing, information integration, electronic commerce, and related fields. In this paper, we briefly describe the characteristics of ontologies, and then discuss the Omega ontology, a large general-purpose ontology created semi-automatically from WordNet, Mikrokosmos, and a newly created upper semantic model, and note the relationship between Omega and its predecessor Sensus. We detail the procedures used to create the Omega base ontology and to merge in new ontological information, such as custom ontologies derived from glossaries and instances obtained by data mining. We survey some of the applications in which we have applied Omega. We highlight the tools used and characteristics of the Lisp environment which facilitate the development and maintenance of the ontological structure. We conclude by discussing current and future work.
3D Object Super Resolution using Metric Tensor and Christoffel Symbols

In this paper we address the problem of 3D super resolution. 3D super resolution is a process of generating high resolution point cloud, given a low resolution point cloud. We model 3D object as a set of Riemannian manifolds in continuous and discretized space. We propose to use Riemannian metric tensor and Christoffel symbols as a set of features to capture the inherent geometry of the 3D object. We propose a learning framework to decompose 3D object using metric tensor and Christoffel symbols into a set of basis functions to selectively super resolve the 3D object. We demonstrate the proposed algorithm on 3D objects and achieve better results than reported in literature.
Discovery and Annotation of Functional Chromatin Signatures in the Human Genome

Transcriptional regulation in human cells is a complex process involving a multitude of regulatory elements encoded by the genome. Recent studies have shown that distinct chromatin signatures mark a variety of functional genomic elements and that subtle variations of these signatures mark elements with different functions. To identify novel chromatin signatures in the human genome, we apply a de novo pattern-finding algorithm to genome-wide maps of histone modifications. We recover previously known chromatin signatures associated with promoters and enhancers. We also observe several chromatin signatures with strong enrichment of H3K36me3 marking exons. Closer examination reveals that H3K36me3 is found on well-positioned nucleosomes at exon 5′ ends, and that this modification is a global mark of exon expression that also correlates with alternative splicing. Additionally, we observe strong enrichment of H2BK5me1 and H4K20me1 at highly expressed exons near the 5′ end, in contrast to the opposite distribution of H3K36me3-marked exons. Finally, we also recover frequently occurring chromatin signatures displaying enrichment of repressive histone modifications. These signatures mark distinct repeat sequences and are associated with distinct modes of gene repression. Together, these results highlight the rich information embedded in the human epigenome and underscore its value in studying gene regulation.
Development of a Probabilistic Network for Clinical Detection of Classical Swine Fever

Presence of a classical swine fever (CSF) infection is usually first revealed by the occurrence of clinical signs. In order to supply veterinary practitioners with an additional tool to identify CSF suspect situations as early as possible, a decision-support system is being developed with the help of experts in the field of CSF. The system builds upon a probabilistic network that is used for computing the probability of the presence of CSF in individual pigs in herds with clinical problems. We performed a preliminary evaluation study of the network using data from experimental infection trials with CSF virus strains of different virulence. The large differences in the clinical signs observed resulted in distinct differences in the computed probabilities, thereby illustrating the potential of our system.
Design and analysis of novel similarity measure for clustering and classification of high dimensional text documents

The main idea of this research is to first design the similarity measure which can be used to of find the similarity between any two text documents and use the same to perform clustering. The similarity measure designed is analyzed to study the behavior in the best case, average case and worst case situations. The drawback of Euclidean, Cosine, Jaccard similarity measures are overcome using the proposed measure. The similarity measure is evaluated considering reuters-21578 dataset. The results show that the proposed measure overcomes other measures.
Detection of Principle Directions in Unknown Environments for Autonomous Navigation

Autonomous navigation in unknown but wellstructured environments (e.g., parking lots) is a common task for human drivers and an important goal for autonomous vehicles. In such environments, the vehicles must obey the standard conventions of driving (e.g., passing oncoming vehicles on the correct side), but often lack a map that can be used to guide path planning in an appropriate way. The robots must therefore rely on features of the environment to drive in a safe and predictable way. In this work, we focus on detecting one of such features, the principal directions of the environment. We propose a Markov-random-field (MRF) model for estimating the maximum-likelihood field of principal directions, given the local linear features extracted from the vehicle’s sensor data, and show that the method leads to robust estimates of principal directions in complex real-life driving environments. We also demonstrate how the computed principal directions can be used to guide a path-planning algorithm, leading to the generation of significantly improved trajectories.
Re-computing Coverage Information to Assist Regression Testing

This paper presents a technique that leverages an existing regression test-selection algorithm to compute accurate, updated coverage data on a version of the software, Pi+1, without rerunning any test cases that do not execute the changes from the previous version of the software, Pi, to Pi+1-Users of our technique can avoid the expense of rerunning the entire test suite on Pi+1 or the inaccuracy produced by previous approaches that estimate coverage data for Pi+1 or reuse outdated coverage data from Pi. This paper also presents a tool, RECOVER, that implements our technique, along with a set of empirical studies. The studies show the inaccuracies that can exist when an application-regression-test selection-uses estimated and outdated coverage data. The studies also show that the overhead incurred by our technique is negligible.