Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid SVM/HMM model for the arab phonemes recognition

Hidden Markov Models (HMM) are currently widely used in Automatic Speech Recognition (ASR) as being the most effective models. Yet, they sometimes pose some problems of discrimination. The hybridization of Artificial Neural Networks (ANN) in particular Multi Layer Perceptrons (MLP) with HMM is a promising technique to overcome these limitations. In order to ameliorate results of recognition system, we use Support Vector Machines (SVM) witch characterized by a high predictive power and discrimination. The incorporation of SVM with HMM brings into existence of the new system of ASR. So, by using 2800 occurrences of Arabic phonemes, this work arises a comparative study of our acknowledgment system of it as the following: The use of especially the HMM standards lead to a recognition rate of 66.98%. Also, with the hybrid system MLP/HMM we succeed in achieving the value of 73.78%. Moreover, our proposed system SVM/HMM realizes the best performances, whereby, we achieve 75.8% as a recognition frequency.
Nearest Neighbor Queries with Data Sharing in Mobile Environments

Mobile clients feature increasingly sophisticated wireless networking support that enables real-time information exchange with remote databases. Location-dependent queries, such as determining the proximity of stationary objects (e.g., restaurants and gas stations) are an important class of inquiries. We present a novel approach to support nearestneighbor queries from mobile hosts by leveraging the sharing capabilities of wireless ad-hoc networks. We illustrate how previous query results cached in the local storage of neighboring mobile peers can be leveraged to either fully or partially compute and verify spatial queries at a local host. The feasibility and appeal of our technique is illustrated through extensive simulation results that indicate a considerable reduction of the query load on the remote database. Furthermore, the scalability of our approach is excellent because a higher density of mobile hosts increases its effectiveness.
Boomerang kids in contemporary Greece: young people's experience of coming home again

Under the burden of the severe and protracted Greek crisis, it seems that Greek youngsters face an increasingly precarious situation of suffering and transition. This affects them in multiple ways, concerning both the external socio-economic conditions and their internalised sense of self-biographical continuity. The present qualitative study aims to demonstrate and examine the core dimensions of the crisis-ridden selfstructure of young adults, using semi-structured, in-depth interviews. Taking into consideration the growing “boomerang phenomenon” of Greek young people returning to their parental home (after losing their job or finishing their studies), we investigate the disruption caused in their life trajectories and the need for re-orientation, as it is expressed within subjective living-with-parents experiences. The sample consists of 15 Greek young inhabitants, aged between 24 and 32 years old, who are either unemployed or precariats, and live with their parents. The experienced collapse of linear self-imaginaries and the subsequent frustration along with the reaction to the present reality, as well as the **recuperating strategies emerged from the participants’ narratives, are carefully examined according to Interpretative Phenomenological Analysis and through relational and self-oriented pathways. The present study concludes by highlighting the reflexive deficit depicted on our participants’ psychic investment in obsolete or even unrealistic mentalities, despite their pronounced repudiation.
Flexible smart sensor framework for autonomous structural health monitoring

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of full- scale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.
Temporal and Spatial Variations in Presence: Qualitative Analysis of Interviews from an Experiment on Breaks in Presence

This paper presents qualitative findings from an experiment designed to investigate breaks in presence. Participants spent approximately five minutes in an immersive Cave-like system depicting a virtual bar with five virtual characters. On four occasions the projections were made to go white to trigger clearly identifiable anomalies in the audiovisual experience. Participants' physiological responses were measured throughout to investigate possible physiological correlates of these experienced anomalies. Analysis of subsequent interviews with participants suggests that these anomalies were subjectively experienced as breaks in presence. This is significant in the context of our research approach, which considers presence as a multilevel construct ranging from higher-level subjective responses to lower-level behavioral and automatic responses. The fact that these anomalies were also associated with an identifiable physiological signature suggests future avenues for exploring less intrusive ways of studying temporal fluctuations in presence during the course of the mediated experience itself. The findings also reveal that breaks in presence have multiple causes and can range in intensity, resulting in varying recovery times. In addition, presence can vary in intensity within the same space, suggesting that presence in an immersive virtual environment can fluctuate temporally and that spatial behavior is consistent with what would be expected in an equivalent real environment.
ClickOn_A: An Editor for DL-Lite_A Based Ontology Design

In recent years a lot of effort has been put into research dealing with accessing relational data with the help of ontologies. Solutions related to this issue focus on the Ontology-based Data Access (ODBA) architecture. Therefore the family of DL-Lite formalisms and in particular DL-LiteA has been developed to enable ontology design for efficient processing of conjunctive queries targeting a large amount of data stored in relational data bases. However, due to strong limitations and the comparatively high complexity of constructs comprised by DL-LiteA, it can be quite a challenging task to design ontologies which are based on this formalism. This paper presents an editor for DL-LiteA fulfilling ontology design. Thanks to its features, such as automated generation of axioms, on-the-fly inferring and graphical representation of relations between concepts, the editor simplifies the design process and improves the quality of the resulting ontologies significantly. The application and evaluation of the editor in two projects on data and knowledge integration for energy saving and urban planning confirmed these advantages.
CMOS/Magnetic Hybrid Architectures

The general purpose of spin-electronics is to take advantage of the spin of the electrons in addition to their electrical charge to conceive innovative electronic components. These components combine magnetic materials which are used as spin-polarizer or analyzer together with semiconductors or insulators. SPINTEC Laboratory works on the development of these components and their integration in innovative hybrid CMOS/magnetic architectures. We study in particular the use of magnetic tunnel junctions (MTJ) for the design of magnetic random access memories (MRAM), magnetic FPGA (MFPGA) and non-reprogrammable logical devices (transceivers, adders, decoders). The design of these hybrid architectures requires to develop electrical equivalent models of the magnetic elementary components (magnetic tunnel junctions, spin-valves, Hall crosses) compatible with SPICE-like simulators. Complete simulations of the hybrid devices are performed before experimental realization and testing.
Efficient computation of Euclidean shortest paths in the plane

We propose a new algorithm for a classical problem in plane computational geometry: computing a shortest path between two points in the presence of polygonal obstacles. Our algorithm runs in worst-case time O(nlog/sup 2/ n) and requires O(nlog n) space, where n is the total number of vertices in the obstacle polygons. Our algorithm actually computes a planar map that encodes shortest paths from a fixed source point to all other points of the plane; the map can be used to answer single-source shortest path queries in O(log n) time. The time complexity of our algorithm is a significant improvement over all previous results known for the shortest path problem. <<ETX>>
Integrating Weather Exploitation into Airpower and Space Power Doctrine

The theater commander nervously pondered his options. The success of this combined operation would depend heavily upon the weather. He needed accurate predictions to execute several key parts of the operation, such as cloud cover and low-level winds for airborne operations and favorable moon, tide, and sea states for the amphibious portion. Deception played a significant part of this mission as well; his forces needed to conduct diversionary bombing runs over another portion of the littoral region to deceive the enemy into thinking this would be the main
RVision: An Open and High Configurable Tool for Cluster Monitoring

In this paper we present the design and implementation of RVision (Remote Vision), an open architecture, high configurable tool for cluster monitoring. We focus on the description of it's modular architecture, emphasizing the new concepts we are introducing for cluster monitoring, such as monitoring sessions and the support for dynamic linking of monitoring libraries. In addition, we measure intrusion with several benchmarks and applications under different scenarios. RVision distinguishes itself from other available tools for cluster monitoring because of its open architecture, high configurability, and low intrusion. It is being used in production mode, in our research center, and has proven itself as a powerful alternative for cluster monitoring, especially in heterogeneous clusters and cluster of clusters.
The application of a GIS-based decision support system to predict nitrate leaching to groundwater in southern Germany

Abstract A decision support system has been developed in New Zealand which links national databases with a physically-based, basin-scale surface water quality model within a geographical information system (GIS) interface. In this study, the system is applied to a drainage basin in southern Germany, part of which is underlain by an aquifer which provides water for potable supply. The contamination of the groundwater resource by nitrate is of much concern in this area, and to aid in the prediction of groundwater contamination, the system is modified so that it can predict the concentrations of nitrate leached to groundwater. Model predictions over a six year period are compared with measured flow and concentration data from the river and concentration data from numerous boreholes. Model output in terms of nitrate leaching is displayed as a map over the extent of the aquifer so that areas particularly susceptible to nitrate leaching can be identified.
In Vivo Neuronal Subtype-Specific Targets of Atoh1 (Math1) in Dorsal Spinal Cord

Neural basic helix-loop-helix (bHLH) transcription factors are crucial in regulating the differentiation and neuronal subtype specification of neurons. Precisely how these transcription factors direct such processes is largely unknown due to the lack of bona fide targets in vivo. Genetic evidence suggests that bHLH factors have shared targets in their common differentiation role, but unique targets with respect to their distinct roles in neuronal subtype specification. However, whether neuronal subtype-specific targets exist remains an unsolved question. To address this question, we focused on Atoh1 (Math1), a bHLH transcription factor that specifies distinct neuronal subtypes of the proprioceptive pathway in mammals including the dI1 (dorsal interneuron 1) population of the developing spinal cord. We identified transcripts unique to the Atoh1-derived lineage using microarray analyses of specific bHLH-sorted populations from mouse. Chromatin immunoprecipitation-sequencing experiments followed by enhancer reporter analyses identified five direct neuronal subtype-specific targets of Atoh1 in vivo along with their Atoh1-responsive enhancers. These targets, Klf7, Rab15, Rassf4, Selm, and Smad7, have diverse functions that range from transcription factors to regulators of endocytosis and signaling pathways. Only Rab15 and Selm are expressed across several different Atoh1-specified neuronal subtypes including external granule cells (external granule cell layer) in the developing cerebellum, hair cells of the inner ear, and Merkel cells. Our work establishes on a molecular level that neuronal differentiation bHLH transcription factors have distinct lineage-specific targets.
Amyloid beta oligomers elicit mitochondrial transport defects and fragmentation in a time-dependent and pathway-specific manner

Small oligomeric forms of amyloid-β (A β ) are believed to be the culprit for declined brain functions in AD in part through their impairment of neuronal trafficking and synaptic functions. However, the precise cellular actions of A β oligomers and underlying mechanisms in neurons remain to be fully defined. Previous studies have identified mitochondria as a major target of A β toxicity contributing to early cognitive decline and memory loss in neurodegenerative diseases including Alzheimer ’ s disease (AD). In this study, we report that A β oligomers acutely elicit distinct effects on the transport and integrity of mitochondria. We found that acute exposure of hippocampal neurons to A β oligomers from either synthetic peptides or AD brain homogenates selectively impaired fast transport of mitochondria without affecting the movement of late endosomes and lysosomes. Extended exposure of hipoocampal neurons to A β oligomers was found to result in mitochondrial fragmentation. While both mitochondrial effects induced by A β oligomers can be abolished by the inhibition of GSK3 β , they appear to be independent from each other. A β oligomers impaired mitochondrial transport through HDAC6 activation whereas the fragmentation involved the GTPase Drp-1. These results show that A β oligomers can acutely disrupt mitochondrial transport and integrity in a time-dependent and pathway-specific manner. These findings thus provide new insights into A β -induced mitochondrial defects that may contribute to neuronal dysfunction and AD pathogenesis.
Embryonic GABAergic spinal commissural neurons project rostrally to mesencephalic targets

Although spinal commissural neurons serve as a model system for studying the mechanisms that underlie axonal pathfinding during development, little is known about their synaptic targets. Previously we identified a group of ventromedially located commissural neurons in rat spinal cord that are γ‐aminobutyric acid (GABA)‐ergic and express L1 CAM on their axons. In this study, serial sagittal sections of embryos (E12–15) were processed for glutamic acid decarboxylase (GAD)‐65 and L1 immunocytochemistry and showed labeled commissural axons coursing rostrally within the ventral marginal zone. Both GAD65‐ and L1‐positive axons extended rostrally out of the spinal cord into the central part of the medulla and then into the midbrain. GAD65‐positive axons branched and ended abruptly within the lateral midbrain. To determine the targets of these ventral commissural neurons, embryos (E13–15) were injected with DiI into the ventromedial spinal cord. At all three ages, DiI‐labeled axons projected rostrally in the contralateral ventral marginal zone, turned into the central medulla, and then traveled to the midbrain. DiI‐labeled axons appeared to terminate in the lateral midbrain by branching into small, punctate structures. In reciprocal experiments, DiI injected into the lateral midbrain identified an axon pathway that coursed through the brainstem, into the spinal cord ventral marginal zone, and then filled cell bodies in the contralateral ventromedial spinal cord. A spatial and temporal coincidence was apparent between the GAD65/L1‐ and the DiI‐labeled pathways. Together these findings suggest that some GABAergic commissural neurons are early projection neurons to midbrain targets and most likely represent a spinomesencephalic pathway to the midbrain reticular formation. J. Comp. Neurol. 475:327–339, 2004. © 2004 Wiley‐Liss, Inc.
Food recognition by combined bags of color features and texture features

Food is one of the most important issues for human health. In order to manage the healthy dietary life, we are going to develop an auto food-log record system, which is based on auto food recognition technologies. To realize this system, we aim to propose a discriminated food image representation that can perform effective identification of food images in this paper. The conventional image representation mainly includes color and texture distributions (histogram), which are the statistical information based on uniformly quantized color or texture levels. However, these conventional techniques using uniform quantization of the on-hand color and texture in the image lead much information loss for reliably constructing the image. Therefore, this study proposes to characterize the color and texture information by incorporating the strategy of patch-based bag of features model. This technique can adaptively learn the representative color or texture (prototypes) from the food images for food recognition, and it is possible to recover a more reliable image using the learned prototypes. The experiments using our proposed approaches show that the recognition rate can be greatly improved compared with the conventional method.
Microscopic texture components classification for image segmentation

The paper presents the possibility of using a sliding window for image feature extraction in order to identify image regions of interest. The study includes the comparison of feature extraction methods both in the space and frequency domains using the discrete Fourier transform and the discrete wavelet transform to achieve reliable classification results for a given application. The compactness of feature clusters is evaluated exploiting a proposed numerical criterion. In case of real image data, the clusters compactness can often be improved by employing a chosen smoothing method on the raw data. In this paper, the procedure of smoothing, feature extraction and classification is applied to microscopic images of aluminum alloys in order to isolate regions of similar properties and to study their relationship. To achieve this goal the sliding window classification results are combined and isolated misclassified subregions repaired. The proportion of misclassified regions is then used for the evaluation of the efficiency of the proposed method along with the proposed measure of cluster compactness.
An improved DV-Hop localization algorithm

Node localization is one of the key technologies in wireless sensor networks. Taking into account the reasons for the hardware and cost, DV-Hop algorithm is wildly used. The main DV-Hop localization error is the distance between unknown nodes and anchor nodes. In this paper, the estimation of average hop-length and RSSI value has been applied to reduce the error of measurement, which is the distance between unknown nodes and anchor nodes. Simulation results show that this localization algorithm is effective, which improves the positioning accuracy.
An efficient method for DCT-domain separable symmetric 2-D linear filtering

We propose an efficient method for separable symmetric linear filtering in the discrete cosine transform (DCT) domain. First, separable two-dimensional (2-D) linear filtering is decomposed into the cascade of one-dimensional filtering in the DCT domain. We investigate special characteristics of DCT domain filtering matrices when the filter coefficients are symmetric. Then we present the DCT-domain 2-D filtering method using these characteristics. The proposed method requires a smaller number of multiplications including the typical sparseness of DCT coefficients compared to previous DCT-domain linear filtering methods. Also, the proposed method is composed of simple and regular operations, which would be appropriate for efficient very large scale integration implementation.
Evaluation and treatment of low and anxious mood in Chinese-speaking international students studying in Scotland

There is a rich body of research on the impact of common mental health difficulties such as depression and anxiety. These concepts, formalised by Western-developed diagnostic frameworks, may be less relevant in non-Westernised cultures. Although depression and anxiety may be presented, they can be presented in different ways in non-English speaking populations. Cultural adaptation of interventions has therefore been advocated to enhance engagement and recovery. This thesis focuses on the mental health presentation and support of Chinese-speaking international students with low mood or anxiety. It aims to explore how they might present with symptoms of depression, how they would like to work/engage in treatment, modify and then test the feasibility of delivering an online life skills package aimed at such students as well as bringing together the evidence base around such interventions through a systematic review.
Author Manuscript, Published in "european Conference on Computer Vision, Workshop on Information Fusion in Computer Vision for Concept Recognition Italy (2012)" a Benchmarking Campaign for the Multimodal Detection of Violent Scenes in Movies

We present an international benchmark on the detection of violent scenes in movies , implemented as a part of the multimedia bench-marking initiative MediaEval 2011. The task consists in detecting portions of movies where physical violence is present from the automatic analysis of the video , sound and subtitle tracks. A dataset of 15 Holly-wood movies was carefully annotated and divided into a development set and a test set containing 3 movies. Annotation strategies and resolution of borderline cases are discussed at length in the paper. Results from 29 runs submitted by the 6 participating sites are analyzed. The first year ' s results are promising , but considering the use case , there is still a large room for improvement. The detailed analysis of the 2011 benchmark brings valuable insight for the implementation of future evaluation on violent scenes detection in movies .