Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation

Research in context-aware recommender systems (CARS) usually requires the identification of the influential contextual variables in advance. In collaborative recommendation, there is a substantial trade-off between applying context very strictly and achieving good coverage and accuracy. Our prior work showed that this tradeoff can be managed by applying the contexts differentially in different components of the recommendation algorithm. In this paper, we extend our previous model and show that our differential context relaxation (DCR) model can also be used to identify demographic and item features that are linked to the contexts. We also demonstrate the application of binary particle swarm optimization as a scalable optimization technique for deriving the optimal relaxation.
INVESTIGATING THE ROLE OF JOB SATISFACTION: DOES IT ACT AS A MODERATOR OR MEDIATOR BETWEEN ORGANIZATIONAL IDENTIFICATION AND WORK ENGAGEMENT?

This study extends Social Identity Theory by examining the link between organizational identification (OI) and work engagement (WE) through enhanced job satisfaction (JS) and testing it in a non-US environment, which makes contribution to the literature. The study was conducted on a large sample (527) of Turkish blue and white-collar employees from business units of 15 independent companies from 10 different industry types and data was analyzed with Structural Equation Modelling. The results showed that organizational identification is positively related with work engagement. Job satisfaction did not moderate the relationship between OI and WE, but it mediated 54 % of the effect of OI and thus, confirmed the applicability of Social Identitiy Theory in a Turkish context. Results revealed that a sense of identiﬁcation may be a precondition for work engagement; but it is the mediating effect of job satisfaction, which enhances this relationship.
Computer Anxiety, Computer Self-efficacy and Attitudes towards the Internet of First Year Students at a South African University of Technology

ABSTRACT Notwithstanding the benefits that information and communications technology (ICT) offers to learning processes, the majority of learners in rural and township areas in South Africa either do not have access to computers and the internet, or they lack sufficient skills to exploit the benefits of ICT. This lack of skills and access to computers may lead to computer anxiety and low computer self-efficacy among undergraduates entering higher education. Low computer anxiety and high computer self-efficacy levels are important factors in assisting students to be academically successful in the present technological era. This article reports on a study that assessed the computer anxiety, computer self-efficacy and attitude towards the internet among first year students at a South African university of technology. Data collection was done through a five section survey. The research revealed that students suffer from moderate computer anxiety; they have a moderate compute self-efficacy; and their attitude towards the internet is positive. The results, research contributions, and limitations are discussed, and implications for future studies are suggested.
Revisiting effectiveness of content-aware switching for Web traffic distribution

Over the past few years, there has been significant interest in content-aware routing that use the information found in the payload of packets to provide intelligent content distribution. However, the effectiveness of the idea of content-aware routing is still unclear though these mechanisms have become increasingly important building block for Internet service providers. The aim of this paper is to evaluate the load distribution policies on a production Web cluster with the content-aware routing mechanism. While most of published performance results are measured with simulation or artificial workload in the lab, this is the first performance study using extensive real traffics. We also find a special effect caused by code 304, which may significantly diminish the benefits of the content-aware routing techniques. We then propose a mechanism that can effectively support to shield off the effects.
Simplified Jacobians in 6-DoF Camera Tracking

Aligning point clouds forms the front end of many visual odometry, 3D reconstruction and SLAM systems. Such alignment of point clouds from two or more different views allows to obtain an overall change in the translation and rotation relative to a given reference view. In this report, we focus on such problem of aligning point clouds particularly looking at the Iterative Closest Point (ICP) [2] which is one of the most popular and simplest algorithms for point cloud alignment. The algorithm alternates between finding the best possible correspondences and optimising over the 6-DoF robot pose given these correspondences. Over the years many different variations [4] have emerged that yield better and robust performance. We use the recommended point-plane variant of ICP in the following. Denoting R = {ri}i=1 as the set of 3D points in the reference view and L = {lj}j=1, the set in the new incoming live view. ICP then seeks to obtain the SE(3) transformation Trl (read it as live to ref transformation) that aligns the point in the live view to the reference view. The point-plane variant of ICP measures the perpendicular distance of the point from the plane and allows to slide the planar regions on top of each other. However, unlike point-point ICP which requires only the 3D positions of the points, this variant comes with an additional computational expense requiring 3D point surface normals to compute the point-plane distance. Next, we formulate the cost functions that measure the discrepency error which is optimised until a standard convergence criteria is satisfied that confirms that points are best possibly aligned. We assume in the following that
Estimation of flow distributions tails from sampled traffic

This paper addresses the problem of estimating the tails of flow duration and size distributions under various sampling methods. For each method the asymptotic equivalence between the flow duration distribution tail and the sampled flow quantities is derived, assuming that the flow process follows a simplified probabilistic model. The analysis is also extended to the flow size distribution tail. The adequacy of the results to estimate the distributions tails is checked against a real Internet trace.
Recovery of jump table case statements from binary code

One of the fundamental problems with the analysis of binary (executable) code is that of recognizing, in a machine-independent way, the target addresses of n-conditional branches implemented via a jump table. Without these addresses, the decoding of the machine instructions for a given procedure is incomplete, as well as any analysis on that procedure. We present a technique for recovering jump tables and their target addresses in a machine and compiler independent way. The technique is based on slicing and expression substitution. The assembly code of a procedure that contains an indexed jump is transformed into a normal form which allows us to determine where the jump table is located and what information it contains (e.g. offsets from the table or absolute addresses). The technique has been tested on SPARC and Pentium code generated by C, C++, Fortran and Pascal compilers. Our tests show that up to 90% more of the code in a text segment can be found by using this technique.
Private Investment and Public Equity Returns

Because of external financing costs, private business owners often need to self-finance new investment projects. These self-financing needs create an incentive for business owners to hold financial assets whose payoffs are positively correlated with self-financing needs. If this effect is aggregated, expected returns on financial assets should be negatively correlated with aggregate private investment self-financing needs. To test the cross-sectional asset pricing implications of this conjecture, we use realized noncorporate investment growth and future forecasted noncorporate investment growth as proxies for self-financing needs. We find that our private investment model can explain a good share of the cross-sectional returns of size-, value- and distress-sorted equity portfolios, almost as well as the Fama–French factors. In contrast to the Fama–French model, however, we find the signs on our estimated coefficients to be consistent with our theoretical predictions.
A Bayesian Matrix Model for Relational Data

Relational learning can be used to augment one data source with other correlated sources of information, to improve predictive accuracy. We frame a large class of relational learning problems as matrix factorization problems, and propose a hierarchical Bayesian model. Training our Bayesian model using random-walk Metropolis-Hastings is impractically slow, and so we develop a block Metropolis-Hastings sampler which uses the gradient and Hessian of the likelihood to dynamically tune the proposal. We demonstrate that a predictive model of brain response to stimuli can be improved by augmenting it with side information about the stimuli.
Needle-Match: Reliable Patch Matching under High Uncertainty

Reliable patch-matching forms the basis for many algorithms (super-resolution, denoising, inpainting, etc.) However, when the image quality deteriorates (by noise, blur or geometric distortions), the reliability of patch-matching deteriorates as well. Matched patches in the degraded image, do not necessarily imply similarity of the underlying patches in the (unknown) high-quality image. This restricts the applicability of patch-based methods. In this paper we present a patch representation called "Needle", which consists of small multi-scale versions of the patch and its immediate surrounding region. While the patch at the finest image scale is severely degraded, the degradation decreases dramatically in coarser needle scales, revealing reliable information for matching. We show that the Needle is robust to many types of image degradations, leads to matches faithful to the underlying high-quality patches, and to improvement in existing patch-based methods.
The Formation and Fragmentation of Primordial Molecular Clouds

Many questions in physical cosmology regarding the thermal history of the intergalactic medium, chemical enrichment, reionization, etc., are thought to be intimately related to the nature and evolution of pregalactic structure. In particular, the efficiency of primordial star formation and the primordial initial mass function are of special interest. We present results from high-resolution three-dimensional adaptive mesh refinement simulations that follow the collapse of primordial molecular clouds and their subsequent fragmentation within a cosmologically representative volume. Comoving scales from 128 kpc down to 1 pc are followed accurately. Dark matter dynamics, hydrodynamics, and all relevant chemical and radiative processes (cooling) are followed self-consistently for a cluster-normalized cold dark matter (CDM) structure formation model. Primordial molecular clouds with ~105 solar masses are assembled by mergers of multiple objects that have formed hydrogen molecules in the gas phase with a fractional abundance of ≲10-4. As the subclumps merge, cooling lowers the temperature to ~200 K in a "cold pocket" at the center of the halo. Within this cold pocket, a quasi-hydrostatically contracting core with mass ~200 M☉ and number densities ≳105 cm-3 are found. We find that less than 1% of the primordial gas in such small-scale structures cools and collapses to sufficiently high densities to be available for primordial star formation. Furthermore, it is worthwhile to note that this study achieved the highest dynamic range covered by structured adaptive mesh techniques in cosmological hydrodynamics to date.
Retrieval of video scenes by structural descriptors

Structural information, on the form of shot patterns and timing, plays an important role in scripted video, being consistently used by filmmakers as a way to convey meaning and significance into their work. For this reason, a video retrieval system can benefit from exploiting this kind of information. In this paper we propose a novel method for the retrieval of video scenes according to their structural similarity, based on graph-theoretical measures and vector quantization techniques. We show the results of our method in some scene retrieval experiments on a data set of 802 movie scenes, extracted from a set of 180 mainstream movies.
RSC Chromatin-Remodeling Complex Is Important for Mitochondrial Function in Saccharomyces cerevisiae

RSC (Remodel the Structure of Chromatin) is an ATP-dependent chromatin remodeling complex essential for the growth of Saccharomyces cerevisiae. RSC exists as two distinct isoforms that share core subunits including the ATPase subunit Nps1/Sth1 but contain either Rsc1or Rsc2. Using the synthetic genetic array (SGA) of the non-essential null mutation method, we screened for mutations exhibiting synthetic growth defects in combination with the temperature-sensitive mutant, nps1-105, and found connections between mitochondrial function and RSC. rsc mutants, including rsc1Δ, rsc2Δ, and nps1-13, another temperature-sensitive nps1 mutant, exhibited defective respiratory growth; in addition, rsc2Δ and nps1-13 contained aggregated mitochondria. The rsc2Δ phenotypes were relieved by RSC1 overexpression, indicating that the isoforms play a redundant role in respiratory growth. Genome-wide expression analysis in nps1-13 under respiratory conditions suggested that RSC regulates the transcription of some target genes of the HAP complex, a transcriptional activator of respiratory gene expression. Nps1 physically interacted with Hap4, the transcriptional activator moiety of the HAP complex, and overexpression of HAP4 alleviated respiratory defects in nps1-13, suggesting that RSC plays pivotal roles in mitochondrial gene expression and shares a set of target genes with the HAP complex.
Minimum duration outage of wireless Rayleigh-fading links using selection combining

Resilience is a major cornerstone in the development of next generation wireless networks. Important aspects of resilience are the availability and survivability of wireless connections. In order to characterize these properties, we present elementary models for Rayleigh-fading links. An important part of the survivability model is an approximation of the fade duration distribution by an exponential distribution. We utilize this approximation to derive the minimum duration outage of multiple selection combined links, a performance metric for survivability which also captures the channel correlation of time-varying channels. Since the findings are given in closed form, they are much simpler than existing expressions containing infinite series and Bessel functions. Finally, numerical evaluation confirms the accuracy of the approximation and the expressions derived.
Applying image processing technique to detect plant diseases

: The present work proposes a methodology for detecting plant diseases early and accurately, using diverse image processing techniques and artificial neural network (ANN). Farmers experience great difficulties in changing from one disease control policy to another. Relying on pure naked-eye observation to detect and classify diseases can be expensive various plant diseases pose a great threat to the agricultural sector by reducing the life of the plants. the present work is aimed to develop a simple disease detection system for plant diseases. The work begins with capturing the images. Filtered and segmented using Gabor filter. Then, texture and color features are extracted from the result of segmentation and Artificial neural network (ANN ) is then trained by choosing the feature values that could distinguish the healthy and diseased samples appropriately. Experimental results showed that classification performance by ANN taking feature set is better with an accuracy of 91%.
Calcium Dynamics in Root Cells of Arabidopsis thaliana Visualized with Selective Plane Illumination Microscopy

Selective Plane Illumination Microscopy (SPIM) is an imaging technique particularly suited for long term in-vivo analysis of transparent specimens, able to visualize small organs or entire organisms, at cellular and eventually even subcellular resolution. Here we report the application of SPIM in Calcium imaging based on Förster Resonance Energy Transfer (FRET). Transgenic Arabidopsis plants expressing the genetically encoded-FRET-based Ca2+ probe Cameleon, in the cytosol or nucleus, were used to demonstrate that SPIM enables ratiometric fluorescence imaging at high spatial and temporal resolution, both at tissue and single cell level. The SPIM-FRET technique enabled us to follow nuclear and cytosolic Ca2+ dynamics in Arabidopsis root tip cells, deep inside the organ, in response to different stimuli. A relevant physiological phenomenon, namely Ca2+ signal percolation, predicted in previous studies, has been directly visualized.
H I Shells behind the Coalsack

We report the discovery of two new large H I shells in the direction of the Coalsack Nebula. Both shells were observed with the Parkes Radio Telescope as part of the Southern Galactic Plane Survey. The largest shell, GSH 304-00-12, is at a distance of ~1.2 kpc and has derived physical dimensions of 280 × 200 pc. The second shell, GSH 305+01-24, is at a distance of ~2.2 kpc and has derived dimensions of 280 × 440 pc. We present a simple numerical model to show that GSH 305+01-24 most likely formed from stellar winds in the Centaurus OB1 stellar association. There is associated radio, infrared, and Hα continuum emission. Both shells are situated in the Sagittarius-Carina arm, with GSH 305+01-24 more distant. The far edge of GSH 304-00-12 is at the near side of the arm and opens into the interarm region. We find no evidence of closure at the near side of the shell and therefore describe the geometry as conical. Emission from the near side of the shell may be lost in absorption by the Coalsack Nebula.
Distributed model predictive control and application to irrigation canal

Most of the literatures use game approach to design distributed controller such as Feasible Cooperation MPC and Nash-bargaining MPC that contains the weighting for linear convex combination of objective functions. In this paper, we propose a scheme to determine the weighting values that proportional to the control load for each subsystem. To observe the effect of our weighting scheme, we apply the weighting scheme to control the irrigation canal using Feasible-Cooperation MPC and Nash-bargaining MPC. We choose the weighting values are proportional to the volume for each subsystem. We compare the result with weighting that are same for all subsystems. From the numerical simulation results, the total cost of our weighting give better result.
Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms

— Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
Genetic Influences on Cost-Efficient Organization of Human Cortical Functional Networks

The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interindividual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09–0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization.
Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

Introduction: Retinal capillary nonperfusion (CNP) is one of the retinal vascular diseases in diabetic retinopathy (DR) patients. As there is no comprehensive detection technique to recognize CNP areas, we proposed a different method for computing detection of ischemic retina, non-perfused (NP) regions, in fundus fluorescein angiogram (FFA) images. Methods: Whilst major vessels appear as ridges, non-perfused areas are usually observed as ponds that are surrounded by healthy capillaries in FFA images. A new technique using homomorphic filtering to correct light illumination and detect the ponds surrounded in healthy capillaries on FFA images was designed and applied on DR fundus images. These images were acquired from the diabetic patients who had referred to the Nikookari hospital and were diagnosed for diabetic retinopathy during one year. Our strategy was screening the whole image with a fixed window size, which is small enough to enclose areas with identified topographic characteristics. To discard false nominees, we also performed a thresholding operation on the screen and marked images. To validate its performance we applied our detection algorithm on 41 FFA diabetic retinopathy fundus images in which the CNP areas were manually delineated by three clinical experts. Results: Lesions were found as smooth regions with very high uniformity, low entropy, and small intensity variations in FFA images. The results of automated detection method were compared with manually marked CNP areas so achieved sensitivity of 81%, specificity of 78%, and accuracy of 91%. The result was present as a Receiver operating character (ROC) curve, which has an area under the curve (AUC) of 0.796 with 95% confidence intervals. Conclusion: This technique introduced a new automated detection algorithm to recognize non-perfusion lesions on FFA. This has potential to assist detecting and managing of ischemic retina and may be incorporated into automated grading diabetic retinopathy structures.