Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Intelligence and the Role of Motivation Within the Context of Career Guidance Counselling for Those Experiencing Unemployment

The purpose of this paper is to discuss the impact of emotional intelligence (EI) on motivation, and consequently to address the question of how to help motivate those experiencing unemployment to engage in the job search process by improving their EI. Presently, there is little existing research on the examination of EI and motivation, not to mention a more specific exploration on how EI impacts an individual’s capacity to engage in a sustained job search. In order to examine the motivation component for this paper the BIS/BAS scale developed by Carver and White (1994) was adopted. The BIS (behavioural inhibition system) function causes the individual to have feelings of fear, anxiety, and agitation in the face of possible negative outcomes. Conversely, the BAS (behavioural approach system) function creates feelings of joy, excitement, and elation at the prospect of potential outcomes. Questionnaires were supplied to clients of a government funded organisation (Northside Partnership) which provides support to unemployed clients of the service in north county Dublin, Ireland. The findings demonstrated that the more clients are aware of their own emotions, or the better able they are at managing others emotions, the more likely they are to be motivated by the Behavioural Activation System (BAS). The findings also indicated that the greater awareness clients have of other people’s emotions, the more they are motivated by the Behavioural Inhibition System (BIS).
Utility Based Query Dissemination in Spatial Data Grid

Spatial Information Grid is an ideal infrastructure to handle the data-intensive and computing-intensive geo-spatial processing. In order that each agency could ad hoc connect to this computing environment and make autonomous decision, we build a Geospatial Data Grid in peer-to-peer way. The query processor module in each peer can decompose the user's query into sub-queries that executed in different nodes. One problem in the parallel spatial join query optimization is how to determine an appropriate node group to disseminate the sub-queries. Especially, if there is more than one node sharing the same area of interest, there is a dilemma: on the one hand, the task scheduler tends to decompose this query into sub-queries and disseminate them to as many as possible nodes so that they could process the user's query in parallel; on the other hand, recruiting too many nodes will also bring in overhead in repetitive computing, redundant data transmission, and the result merging. Based on the study of trade-off between increasing parallelism and reducing redundancy using the Utility Theory in economics, we put forward a fast node selection algorithm for the parallel spatial join query dissemination. The test in our system shows this strategy could balance the above two conflict demands and is appropriate for use in Data Grid.
Ceiling Vision Based Autonoumous Navigation of a Mobile Robot uisng Hybrid Visual Features in a Large Indoor Environment

This paper describes the research results on an indoor robot navigation system using visual features extracted from ceiling images of a large building environment. To reduce the cost and complexity of the system, we developed a single camera-based system that utilizes ceiling images acquired from a camera installed to point upwards. To make the operation reliable, we proposed a method using hybrid visual features which include natural features in a natural scene and artificial features observable in an infrared ray domain. Compared with works utilizing only infrared based features (1), our method reduced the required number of artificial features as we exploit both natural and artificial features. In addition, compared with works using only natural scene (2), our method has an advantage in the convergence speed and robustness as an observation of an artificial feature provides a crucial clue for robot pose estimation. In an experiment with challenging situations in a real environment, our method was performed impressively in terms of the robustness and accuracy. To our knowledge, our method is the first ceiling vision based navigation method using features from both visible and infrared rays domains. Our system can be easily utilized with a variety of service robot applications in a large indoor environment.
High Performance Grid Computation of the Scattered Field Formulation for Nth Order Debye Modeling of the General Dispersive Media

With the advent of the current wireless communication revolution and the increase in its applications, the electromagnetic researchers in conjunction with the medical physicians take the initiative of studying the price of this beneficial technical revolution. It is the health hazards due to the electromagnetic radiation on the human body. The most cost efficient mean of studying these effects is the numerical simulation using the popular FDTD numerical technique. The FDTD simulates the existence of the human body tissues using many fitting models. One of the most famous one is the Debye model. In this paper, the Nth-order Debye model for modelling the human tissues is represented using the scattered field formulation for deducing the FDTD update equation. The scattered field formulation is an accurate method of implementing the different excitation mechanisms for the waves that impinging on the human dispersive tissues media. Although the FDTD is an efficient method, it is a heavy computational one. It may take time of several hours or even days to simulate a single run of a specific problem. In this paper, a parallel scheme is utilized to speed up the running time of the 3D FDTD that includes The Nth-order Debye model. The parallel computations are running on VO (Virtual organization) of the EUMED grid platform. The speed up and behaviour over different number of processors is monitored.
Aberrant methylation patterns in colorectal cancer: a meta-analysis

Colorectal cancer is among the leading causes of cancer death worldwide. Despite numerous molecular characterizations of the phenomenon, the exact dynamics of its onset and progression remain elusive. Colorectal cancer onset has been characterized by changes in DNA methylation profiles, that, owing to the stability of their patterns, are promising candidates to shed light on the molecular events laying at the base of this phenomenon. To exploit this stability and reinforce it, we conducted a meta-analysis on publicly available DNA methylation datasets generated on: normal colorectal, adenoma (ADE) and adenocarcinoma (CRC) samples using the Illumina 450k array, in the systems medicine frame, searching for tumor gene episignatures, to produce a carefully selected list of potential drivers, markers and targets of the disease. The analysis proceeds from a differential meta-analysis of the methylation profiles using an analytical pipeline recently developed by our group [1], through network reconstruction, topological and functional analyses, to finally highlight relevant epigenomic features. Our results show that genes already highlighted for their genetic or transcriptional alteration in colorectal cancer are also differentially methylated, reinforcing -regardless of the level of cellular control- their role in the complex of alterations involved in tumorigenesis. These findings were finally validated in an independent cohort from The Cancer Genome Atlas (TCGA).
Activation of Metabotropic Glutamate Receptor 1 Dimers Requires Glutamate Binding in Both Subunits

Group I metabotropic glutamate receptors (mGluRs) form stable, disulfide-linked homodimers. Lack of a verifiably monomeric mGluR1 mutant has led to difficulty in assessing the role of dimerization in the molecular mechanism of mGluR1 activation. The related GABAB receptor exhibits striking intradimer cross talk (ligand binding at one subunit effectively produces G protein activation at the other), but it is unclear whether group I mGluRs exhibit analogous cross talk. Signaling of heterologously expressed mGluR1 was examined in isolated rat sympathetic neurons by measuring glutamate-mediated inhibition of native calcium currents. To examine mGluR1 activity when only one dimer subunit has access to glutamate ligand, wildtype mGluR1 was coexpressed with mGluR1 Y74A, a mutant with impaired glutamate binding, and the activity of the heterodimer (mutant/wild type) was examined. The mGluR1 Y74A mutant alone had a dose-response curve that was shifted by about 2 orders of magnitude. The half-maximal dose of glutamate shifted from 1.3 (wild-type mGluR1) to about 450 (mGluR1 Y74A) μM. However, the maximal effect was similar. Wild-type mGluR1 was expressed with excess Y74A mGluR1 to generate a receptor population consisting largely of mutant homodimers and mutant/wild-type heterodimers but without detectable wild-type homodimers. Under these conditions, no glutamate-mediated calcium current inhibition was observed below ∼300 μM glutamate, although wild-type mGluR1 protein was detectable with immunofluorescence. These data suggest that mutant/wild-type heterodimeric receptors are inactive at ligand concentrations favoring glutamate association with receptor dimers at only one subunit.
Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.
A Secure Message Broadcast System ( SMBS )

There is a general consensus in the computer security comunity that traditional password based authentication mechanisms are insu cient in today's globally connected environment. Mechanisms such as one-timepasswords are a partial solution to the problem. The issue that these protocols don't address is the lack of mutual authentication. The Kerberos family of systems addresses the issue of mutual authentication but relies heavily on the physical security of the server and safekeeping of the password database.
A Fully Automated Derivation of State-Based Eigentriphones for Triphone Modeling with No Tied States Using Regularization

Recently we proposed an alternative method called eigentriphone to solve the data insufﬁciency problem in triphone acoustic modeling without the need of state tying. The idea is to treat the acoustic modeling problem of infrequent triphones (“poor triphones”) as an adaptation problem from the more frequent triphones (“rich tri-phones”): ﬁrstly, an eigenbasis is developed over the rich triphones that have sufﬁcient training data and the eigenvectors are called eigentriphones ; then the poor triphones are adapted in a fashion similar to eigenvoice adaptation. Since, in general, no states are tied in our method, all triphones (states) are distinct so that they can be more discriminative than tied-state triphones. In our previous work, the number of eigentriphones was determined in advance with a set of development data. In this paper, we investigate simply using all of them with the help of regularization to naturally penalize the less important ones. In addition, the model-based eigenbasis is replaced by three state-based eigenbases. Experimental evaluation on the WSJ 5K task shows that triphone models trained using our new eigentriphone approach without state tying perform at least as well as the common tied-state triphone models.
Inferring User Interests on Social Media from Text and Images

Inferring user interests on social media from text and images is addressed as a multi-class classification problem. We proposed approaches to infer user interest on Social media where often multi-modal data (text, image etc.) exists. We use user-generated data from Pinterest.com as a natural expression of users' interests. We consider each pin (image-text pair) as a category label that represents a broad user interest, since users collect images that they like on the social media platform and often assign a category label. This task is useful beyond Pinterest because most user-generated data on the Web is not necessarily readily categorized into interest labels. In addition to predicting users' interests, our main contribution is exploiting a multi-modal space composed of images and text. This is a natural approach since humans express their interests with a combination of modalities. Exploiting multi-modal spaces in this context has received little attention in the literature. We performed eleven experiments using the state-of-the-art image and textual representations, such as convolutional neural networks, word embeddings, and bags of visual and textual words. Our experimental results show that in fact jointly processing image and text increases the overall interest classification accuracy, when compared to uni-modal representations (i.e., using only text or using only images).
Compensatory Pathways Induced by MEK Inhibition Are Effective Drug Targets for Combination Therapy against Castration-Resistant Prostate Cancer

Targeted therapies have often given disappointing results when used as single agents in solid tumors, suggesting the importance of devising rational combinations of targeted drugs. We hypothesized that construction of such combinations could be guided by identification of growth and survival pathways whose activity or expression become upregulated in response to single-agent drug treatment. We mapped alterations in signaling pathways assessed by gene array and protein phosphorylation to identify compensatory signal transduction pathways in prostate cancer xenografts treated with a MAP/ERK kinase (MEK) inhibitor PD325901. In addition to numerous components of the extracellular signal–regulated kinase (ERK) signaling pathway, components of the IKK, hedgehog, and phosphoinositide 3-kinase/Akt/mTOR pathways were upregulated following treatment with PD325901. Combinations of PD325901 with inhibitors of any one of these upregulated pathways provided synergistically greater growth inhibition of in vitro cell growth and survival than the individual drugs alone. Thus, the identification of compensatory signal transduction pathways paves the way for rational combinatorial therapies for the effective treatment of prostate cancer. Mol Cancer Ther; 10(9); 1581–90. ©2011 AACR.
Disruption of the centrifugal visual system inhibits early eye growth in chicks.

PURPOSE Emmetropization, the process by which neonatal refractive errors are reduced toward zero, is partially dependent on brain-retina connectivity. Here, we investigated the role of the centrifugal visual system, a visually driven retinal feedback projection, as one potential influence on this complex mechanism. METHODS Lesions of the isthmo-optic nucleus/tract or sham surgeries were performed in fifty-four 4- to 5-day-old chicks to disrupt centrifugal efferents to the contralateral retina. Prior to surgery, baseline refractive error measurements were made using streak retinoscopy. Postoperative ocular phenotyping, which (in addition to retinoscopy) comprised A-scan ultrasonography and infrared keratometry, was performed 7 days and 21 days postsurgery. A pathway-tracing paradigm was used to determine lesion success, whereby an injection of wheat-germ agglutinin was made into the vitreous chamber contralateral to the lesion. Postmortem, tissue processing, immunohistochemistry, and stereological analysis of intact centrifugal neurons were performed. Subsequently, chicks were divided into quartile groups based on percentage lesion success. RESULTS Seven days postsurgery, chicks in the quartile of highest percentage lesion success exhibited significant axial hyperopia in the "treated eye" (contralateral to the lesion) relative to the "control eye" (ipsilateral to the lesion) eye, when compared with subjects within quartile groups of lower percentage lesion success (P = 0.004). However, by 21 days postsurgery, the induced hyperopia was no longer evident. CONCLUSIONS Unilateral disruption of centrifugal efferents to the retina of the contralateral eye induces an initial axial hyperopia, which is subsequently reversed through increased vitreous elongation in the affected eyes.
Combining the best of two worlds: a conceptual proposal for evidence-informed school improvement

Abstract Background: Data-based decision-making (DBDM) and research-informed teaching practice (RITP) are key to teacher and school improvement. Currently, however, DBDM and RITP represent two distinct approaches to developing evidence-informed practice (EIP) and do not correspond to the all-encompassing notion of EIP envisaged by many academics and commentators. Purpose: DBDM and RITP are usually employed independently of each other. Each is associated with its own theoretical perspectives and research base, and each has its own pitfalls and strengths. Yet the approaches employed appear to be complementary, suggesting that there might be value in combining DBDM and RITP into one overarching process for achieving EIP. This paper presents the conceptual analysis and arguments for this proposal. Sources of evidence: Drawing from literature and previous research in the fields of DBDM, RITP and EIP, we describe both DBDM and RITP, before comparing and contrasting the integral aspects of each. Main argument: Our analysis leads us to suggest that not only is there overlap between these two approaches, but the strengths of each appear to mirror and compensate for the weaknesses of the other. As such, we argue that it is important that decisions in education are based on a combination of personal judgement, research evidence and local school data. This is because such a combination is likely to lead to equitable, effective and efficient decisions that are informed by values and preferences, grounded in context and steeped in practices that have been shown to be effective elsewhere. Conclusions: We suggest that an effective strategy for EIP might be to achieve ‘the best of two worlds’ by integrating DBDM and RITP. In line with evidence-informed practices in medicine and management, this means EIP in education can finally be engaged in as a holistic approach to educational decision-making that critically appraises different forms of evidence before key improvement decisions are made. Our proposed approach, Evidence informed School and Teacher Improvement, is thus designed with the aim of enhancing the quality of educational provision by employing these evidence types as part of a systematic cycle of inquiry, focused on continuously improving the quality of learning in schools.
BOAO PHOTOMETRIC SURVEY OF GALACTIC OPEN CLUSTERS. III. CZERNIK 24 AND CZERNIK 27

We present BV CCD photometry for the open clusters Czernik 24 and Czernik 27. These clusters have never been studied before, and we provide, for the first time, the cluster parameters; reddening, distance, metallicity and age. Czernik 24 is an old open cluster with age , metallicity $[Fe/H]
Sound synthesis and musical timbre : a new user interface

Sound creation and editing in hardware and software synthesizers presents usability problems and a challenge for HCI researchers. Synthesis parameters vary considerably in their degree of usability, and musical timbre itself is a complex and multidimensional attribute of sound. This paper presents a user-driven search-based interaction style where the user engages directly with sound rather than with a mediating interface layer.
Image Compression Using Haar Transform And Modified Fast Haar Wavelet Transform

Wavelet Transform has been proved to be a very useful tool for image processing in recent years. Digital images require large amounts of memory to store and, when retrieved from the internet, can take a considerable amount of time to download. Compression makes it possible for creating file sizes of manageable, storable and transmittable dimensions The Haar wavelet transform provides a method of compressing image data so that it takes up less memory. The most distinctive feature of Haar Transform lies in the fact that it lends itself easily to simple manual calculations. Modified Fast Haar Wavelet Transform is one of the algorithms which can reduce the calculation work in Haar Transform. The present paper attempts to describe the algorithm for image compression using MFHWT.
A New Two-Layer Topology for Data Center Network

In the cloud computing era, the goal of data center network is not only to interconnect a large number of servers, but also to provide low latency and high bandwidth. A new 2-layer architecture, C-tree, is proposed for cloud computing in this paper. It is a flat topology, in which the horizontal traffic's latency is obviously lower than in traditional three-layer architectures. Also, C-tree can provide high bisection bandwidth which is important for bandwidth-intensive applications. We analyze C-tree theoretically and compare it with other architectures in scalability, accommodation capability, network diameter, bisection bandwidth, path diversity and regularity. We have developed a load balancing routing mechanism to balance the traffic on the parallel links. A network simulation platform is set up to verify the performance of C-tree.
Optimal scheduling for constant-rate multi-mode systems

Constant-rate multi-mode systems are hybrid systems that can switch freely among a finite set of modes, and whose dynamics is specified by a finite number of real-valued variables with mode-dependent constant rates. The schedulability problem for such systems is to design a mode-switching policy that maintains the state within a specified safety set. The main result of the paper is that schedulability can be decided in polynomial time. We also generalize our result to optimal schedulability problems with average cost and reachability cost objectives. Polynomial-time scheduling algorithms make this class an appealing formal model for design of energy-optimal policies. The key to tractability is that the only constraints on when a scheduler can switch the mode are specified by global objectives. Adding local constraints by associating either invariants with modes, or guards with mode switches, lead to undecidability, and requiring the scheduler to make decisions only at multiples of a given sampling rate, leads to a PSPACE-complete schedulability problem.