Miracle-dz/UniFAR-FTUs huggingface dataset:
AN EXPERIMENTAL STUDY FOR TRANSFORMING AND DIFFERENCING EFFECTS IN MULTIPLICATIVE NEURON MODEL ARTIFICIAL NEURAL NETWORK FOR TIME SERIES FORECASTING

Forecasting problems play an important role in time series. In recent years, to solve these problems, many good alternative methods like artificial neural networks have been proposed in the literature. Although the most used artificial neural networks type is multilayer perceptron artificial neural networks, multiplicative neuron model artificial neural networks (MNM-ANNs) have been used to obtain forecasts for a few years. Many of previous studies were used to original series without any transformations such as differencing operation, BoxCox transformations. Although stationary is an important assumption, previous studies have shown that forecasts obtained from ANNs were employed to non-stationary time series. BoxCox transformations have been often used to time series because of heteroscedasticity. We used particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms to train MNM-ANNs, and investigated differencing effects of original and transformed data which are obtained from Box-Cox transformations. Istanbul stock exchange (IEX) data sets which are made up of five time series for years between 2009 and 2013 are analyzed. The sets have comprised of first five months for these years. The results are interpreted and discussed. It is shown that transformation operations are useful for forecasting IEX as a result of statistical hypothesis tests.
When truth is efficient: analysing concurrency

Concurrent systems are hard to develop and are even harder to analyse. The usual way to analyse concurrent systems is to give them interleaving semantics and exploit automata-based methods to investigate the resultant interleaved model. Such an approach is often hard to scale without additional tools to curb the interleaving-induced state space explosion. In this work we make an alternate case: for directly capturing the behaviour of concurrent systems using true concurrency. We show how to build composable, truly concurrent models for real-world programs written using one of the most widely adopted paradigms for developing massively parallel systems, the Message Passing Interface Standard (MPI). Our method employs general event structures to symbolically capture executions of MPI programs and uses this truly concurrent model, combined with our novel deadlock characterisation, to formulate a precise, scalable decision procedure that finds communication deadlocks in large MPI programs. We show that our analysis scales to systems with hundreds of processes and strongly outperforms state of the art interleaving semantics based approaches.
Direction of arrival estimation based on phase differences using neural fuzzy network

A new high-resolution direction of arrival (DOA) estimation technique using a neural fuzzy network based on phase difference (PD) is proposed. The conventional DOA estimation method such as MUSIC and MLE, are computationally intensive and difficult to implement in real time. To attack these problems, neural networks have become popular for DOA estimation. However, the normal neural networks such as the multilayer perceptron (MLP) and radial basis function network (RBFN) usually produce the extra problems of low convergence speed and/or large network size (i.e., the number of network parameters is large). Also, the may to decide the network structure is heuristic. To overcome these defects and take use of neural learning ability, a powerful self-constructing neural fuzzy inference network (SONFIN) is used to develop a new DOA estimation algorithm. By feeding the PDs of the received radar-array signals, the trained SONFIN can give high-resolution DOA estimation. The proposed scheme is thus called PD-SONFIN. This new algorithm avoids the need of empirically determining the network size and parameters in normal neural networks due to the powerful on-line structure and parameter learning ability of SONFIN. The PD-SONFIN can always find itself an economical network size in the fast learning process. Our simulation results show that the performance of the new algorithm is superior to the RBFN in terms of convergence accuracy, estimation accuracy, sensitivity to noise, and network size.
Probabilistic Modeling of Progressive Filtering

Progressive filtering is a simple way to perform hierarchical classification, inspired by the behavior that most humans put into practice while attempting to categorize an item according to an underlying taxonomy. Each node of the taxonomy being associated with a different category, one may visualize the categorization process by looking at the item going downwards through all the nodes that accept it as belonging to the corresponding category. This paper is aimed at modeling the progressive filtering technique from a probabilistic perspective, in a hierarchical text categorization setting. As a result, the designer of a system based on progressive filtering should be facilitated in the task of devising, training, and testing it.
Attachment in adults with high-functioning autism

This study assessed attachment security in adults with high-functioning autism spectrum disorders, using the Adult Attachment Interview (AAI; George, Kaplan, & Main, 1996). Of 20 participants, three were classified as securely attached, the same proportion as would be expected in a general clinical sample. Participants' AAIs were less coherent and lower in reflective function than those of controls, who were matched for attachment status and mood disorder. A parallel interview suggested that some aspects of participants' responses were influenced by their general discourse style, while other AAI scale scores appeared to reflect their state of mind with respect to attachment more specifically. There was little evidence that attachment security was related to IQ, autistic symptomatology or theory of mind. This study suggests that adults with autism can engage with the AAI and produce scoreable narratives of their attachment experiences, and a minority demonstrate secure attachment.
Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning

Reinforcement learning suffers scalability problems due to the state space explosion and the temporal credit assignment problem. Knowledge-based approaches have received a significant attention in the area. Reward shaping is a particular approach to incorporate domain knowledge into reinforcement learning. Theoretical and empirical analysis of this paper reveals important properties of this principle, especially the influence of the reward type, MDP discount factor, and the way of evaluating the potential function on the performance.
Being Aquifex aeolicus: Untangling a Hyperthermophile’s Checkered Past

Lateral gene transfer (LGT) is an important factor contributing to the evolution of prokaryotic genomes. The Aquificae are a hyperthermophilic bacterial group whose genes show affiliations to many other lineages, including the hyperthermophilic Thermotogae, the Proteobacteria, and the Archaea. Previous phylogenomic analyses focused on Aquifex aeolicus identified Thermotogae and Aquificae either as successive early branches or sisters in a rooted bacterial phylogeny, but many phylogenies and cellular traits have suggested a stronger affiliation with the Epsilonproteobacteria. Different scenarios for the evolution of the Aquificae yield different phylogenetic predictions. Here, we outline these scenarios and consider the fit of the available data, including three sequenced Aquificae genomes, to different sets of predictions. Evidence from phylogenetic profiles and trees suggests that the Epsilonproteobacteria have the strongest affinities with the three Aquificae analyzed. However, this pattern is shown by only a minority of encoded proteins, and the Archaea, many lineages of thermophilic bacteria, and members of genus Clostridium and class Deltaproteobacteria also show strong connections to the Aquificae. The phylogenetic affiliations of different functional subsystems showed strong biases: Most but not all genes implicated in the core translational apparatus tended to group Aquificae with Thermotogae, whereas a wide range of metabolic and cellular processes strongly supported the link between Aquificae and Epsilonproteobacteria. Depending on which sets of genes are privileged, either Thermotogae or Epsilonproteobacteria is the most plausible adjacent lineage to the Aquificae. Both scenarios require massive sharing of genes to explain the history of this enigmatic group, whose history is further complicated by specific affinities of different members of Aquificae to different partner lineages.
Local approximate inference algorithms

We present a new local approximation algorithm for computing Maximum a Posteriori (MAP) and log-partition function for arbitrary exponential family distribution represented by a finite-valued pair-wise Markov random field (MRF), say $G$. Our algorithm is based on decomposition of $G$ into {\em appropriately} chosen small components; then computing estimates locally in each of these components and then producing a {\em good} global solution. We show that if the underlying graph $G$ either excludes some finite-sized graph as its minor (e.g. Planar graph) or has low doubling dimension (e.g. any graph with {\em geometry}), then our algorithm will produce solution for both questions within {\em arbitrary accuracy}. We present a message-passing implementation of our algorithm for MAP computation using self-avoiding walk of graph. In order to evaluate the computational cost of this implementation, we derive novel tight bounds on the size of self-avoiding walk tree for arbitrary graph. As a consequence of our algorithmic result, we show that the normalized log-partition function (also known as free-energy) for a class of {\em regular} MRFs will converge to a limit, that is computable to an arbitrary accuracy.
Speeding Up Similarity Queries over Large Chinese Calligraphic Character Databases Using Data Grid

This paper proposes a novel data-grid-based k nearest neighbor query over large Chinese calligraphic character databases, which can significantly speed up the retrieval efficiency. Three steps are made. Firstly, when a user submits a query request to a query node, a process of character set reduction is performed using iDistance index in different data nodes, followed by sending the candidate characters to the executing nodes through a package-based transfer technique. Secondly, a refinement process of the candidate characters is conducted in the executing nodes in parallel to get the answer set. Finally, the answer set is transferred to the query node. The proposed method incorporates a uniform-start- distance-based character data allocation policy and character reduction algorithm. The analysis and experimental results show that the performance of the algorithm is effective in minimizing the response time by decreasing network transfer cost and increasing the parallelism of I/O and CPU.
Lifting User Generated Comments to SIOC

HTML boilerplate code is acting on webpages as presentation directives for a browser to display data to a human end user. For the machine, our community made tremenduous e orts to provide querying endpoints using consensual schemas, protocols, and principles since the avent of the Linked Data paradigm. These data lifting e orts have been the primary materials for bootstraping the Web of data. Data lifting usually involves an original data structure from which the semantic architect has to produce a mapper to RDF vocabularies. Less e orts are made in order to lift data produced by a Web mining process, due to the di culty to provide an e cient and scalable solution. Nonetheless, the Web of documents is mainly composed of natural language twisted in HTML boilerplate code, and few data schemas can be mapped into RDF. In this paper, we present CommentsLifter, a system that is able to lift SIOC data from user-generated comments in the Web 2.0.
Ginsenoside Rg1 Ameliorates Cigarette Smoke-Induced Airway Fibrosis by Suppressing the TGF-β1/Smad Pathway In Vivo and In Vitro

Small airway fibrosis is a key pathological process accompanying chronic obstructive pulmonary disease (COPD) and includes fibroblast/myofibroblast transdifferentiation and excessive extracellular matrix deposition. Ginsenoside Rg1, one of the main active ingredients of Panax ginseng, has been shown to exert an antifibrotic effect in many tissues. However, little is known about the underlying mechanism and whether ginsenoside Rg1 can exert an effect on small airway fibrosis. We investigated the anti-small airway fibrosis effects of ginsenoside Rg1 in human embryonic lung fibroblasts and in COPD rats. We found that ginsenoside Rg1 effectively reduced the degree of pulmonary fibrosis, decreased the expression of α-smooth muscle actin, collagen I, and matrix metalloproteinase 9, and maintained the ratio of matrix metalloproteinase 9 to tissue inhibitor of metalloproteinase 1. Importantly, ginsenoside Rg1 significantly attenuated cigarette smoke extract-induced upregulation of transforming growth factor β1, TGF-β receptor I, phospho-Smad2, and phospho-Smad3. In addition, ginsenoside Rg1 mimicked the effect of SB525334, a TGF-β receptor I-Smad2/3 inhibitor. Collectively, these results suggest that ginsenoside Rg1 may suppress cigarette smoke-induced airway fibrosis in pulmonary fibroblasts and COPD rats by inhibiting the TGF-β1/Smad signaling pathway.
Network-Adaptive Multipath Video Delivery over Wireless Multimedia Sensor Networks Based on Packet and Path Priority Scheduling

in this paper we investigate multi-path transmission of video over Wireless Multimedia Sensor Networks (WMSNs). Our objective is to provide a transmission mechanism to improve the video quality perceived at the receiver while considering energy, bandwidth and delay constraints of the network. Our approach is based on packet and path priority scheduling in multipath video transmission over WMSN. Video packets are classified according to their types and more important packets are transmitted through paths with better conditions. Path conditions are evaluated by a score mechanism using variant parameters such as free buffer size, energy, hop count and packet loss. Since path conditions are changed over time, our proposed scheme updates path conditions and adapting packet scheduling accordingly. Simulation analysis of the proposed video transmission scheme shows its efficiency in increasing perceived video quality at the receiver.
Reconstruction error based pedestrian detection in infrared videos

Pedestrian detection in infrared videos is a task full of potential, and has gotten more and more attention. To robustly detect the pedestrians in infrared image, a PCA-based detecting framework is designed in this paper. The proposed pedestrian detection system can be divided into two parts: training and classification. When the training stage is running, PCA is performed on two different datasets, pedestrian (positive) samples and non-pedestrian (negative) samples, separately. In the classification stage, the system determine whether the input candidate images belong to the positive samples or not by calculating the reconstruction errors for each of them based on the eigenvectors of positive sample and negative sample space. To improve the detecting performance, both the grayscale and edge descriptors are used in the training step. Experimental results indicated that PCA with the combination of grayscale and edge images could achieve the best performance for pedestrian detection.
Targeting MicroRNA Function in Respiratory Diseases: Mini-Review

MicroRNAs (miRNAs) are small non-coding RNA molecules that modulate expression of the majority of genes by inhibiting protein translation. Growing literature has identified functional roles for miRNAs across a broad range of biological processes. As such, miRNAs are recognized as potential disease biomarkers and novel targets for therapies. While several miRNA-targeted therapies are currently in clinical trials (e.g., for the treatment of hepatitis C virus infection and cancer), no therapies have targeted miRNAs in respiratory diseases in the clinic. In this mini-review, we review the current knowledge on miRNA expression and function in respiratory diseases, intervention strategies to target miRNA function, and considerations specific to respiratory diseases. Altered miRNA expression profiles have been reported in a number of respiratory diseases, including asthma, chronic obstructive pulmonary disease, cystic fibrosis, and idiopathic pulmonary fibrosis. These include alterations in isolated lung tissue, as well as sputum, bronchoalveolar lavage fluids and peripheral blood or serum. The observed alterations in easily accessible body fluids (e.g., serum) have been proposed as new biomarkers that may inform disease diagnosis and patient management. In a subset of studies, miRNA-targeted interventions also improved disease outcomes, indicating functional roles for altered miRNA expression in disease pathogenesis. In fact, direct administration of miRNA-targeting molecules to the lung has yielded promising results in a number of animal models. The ability to directly administer compounds to the lung holds considerable promise and may limit potential off-target effects and side effects caused by the systemic administration required to treat other diseases.
Transition to School: Current Issues and Pacific Islands Early Childhood Contexts

This paper provides an overview of some current complexities related to transition to school from early childhood services in New Zealand. These include theoretical and policy matters concerning: continuity–discontinuity, diversity–standardisation, and ease of transition. We then highlight some key issues arising from a study, reported during the year under review, on transition to school from Pacific Islands early childhood services. Drawing on the main emergent themes, we comment on methodological implications and culturally preferred practices, and identify some concerns and challenges.
Accuracy of Range-Based Localization in Random Sensor Networks

Accurate location information is essential for many wireless sensor network (WSN) applications. A location-aware WSN generally includes two types of nodes: sensors whose locations to be determined and anchors whose locations are known a priori. For range-based localization, sensors' locations are deduced from anchor-to-sensor and sensor-to-sensor range measurements. Localization accuracy depends on the network parameters such as network connectivity and size. This paper provides a generalized theory that quantitatively characterizes such relation between network parameters and localization accuracy. We use the average degree as a connectivity metric and use geometric dilution of precision (DOP), equivalent to the Cramer-Rao bound, to quantify localization accuracy. We prove a novel lower bound on expectation of average geometric DOP (LB-E-AGDOP) and derives a closed-form formula that relates LB-E-AGDOP to only three parameters: average anchor degree, average sensor degree, and number of sensor nodes. The formula shows that localization accuracy is approximately inversely proportional to the average degree, and a higher ratio of average anchor degree to average sensor degree yields better localization accuracy. Furthermore, the paper demonstrates a strong connection between LB-E-AGDOP and the best achievable accuracy. Finally, we validate the theory via numerical simulations with three different random graph models.
Mobility Status as Dynamic Context for Behaviour Optimisation in Self-Organised Networks

A novel technique by which wireless devices such as sensor nodes can deduce their own mobility status,based on analysis of patterns in their local neighbourhood, is described.For many systems in which a neighbour table is maintained through regular status messages or other interaction, the technique incurs no additional communication overhead. The technique does not require that nodes have additional information such as absolute or relative locations, or neighbourhood node density.The work considers systems with heterogeneous time-variant mobility models, in which a subset of nodes follows a random walk mobility model, another subset follows a random waypoint mobility model (i.e.have intermittent movement), some nodes have group mobility and there is a static subset.We simulate these heterogeneous mobility systems and evaluate the performance of the Self-Detection of Mobility Status algorithm (SDMS) against a number of metrics and in a wide variety of system configurations.
Projective-AR system for customizing the appearance and taste of food

It is well known that taste is affected by visual information, such as the color of food. In this study, we propose a projective-AR system which can modify the appearance of food and/or plate with a projector-camera system in real time. In addition, we conducted psychophysical experiments in order to investigate whether the evaluation of taste can be modified by food color of a sponge cake and chips using the present system. The results showed that the sweetness of the sponge cake increases with increasing the chroma, and the flavor of chips can be changed by the hue, suggesting that the present system is useful to investigate such crossmodal effects on food quantitatively.
Bee routing protocol for intermittently connected mobile networks

The Wireless and the Mobile Networks appear to provide a wide range of applications. The achievement of the real world applications are attained through effective routing. The Intermittently Connected Mobile Network (ICMN) is a sparse network where a full connectivity is never possible. ICMN is a disconnected mobile network and is also a Delay Tolerant Network (DTN) that sustains for higher delays. The routing in a disseminated network is a difficult task. A new routing scheme called Bee Routing Protocol (BRP) is been proposed with a motto of achieving optimal result in delivering the data packet towards the destined node. BRP is proposed with the basis of Bee Colony Optimization technique (BCO). The routing in ICMN is done by means of Bee routing protocol. The efficiency of BRP is enchanted in contrast to existing Epidemic routing and Spray and Wait routing protocols. This paper captivates a novel routing methodology for data transmission in ICMN.