Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Secret Sharing & Review on Extended Visual Cryptography Scheme

Visual cryptography (VC) schemes hide the secret image into two or more images which allows the encoding of a secret image into shares distributed to participants. The secret image can be recovered simply by stacking the shares together without any complex computation involved. An extended visual cryptography scheme (EVCS) is a kind of VCS which consists of meaningful shares (compared to the random shares of traditional VCS. In this paper, a color visual cryptography scheme producing meaningful shares is proposed. These meaningful shares will not arouse the attention of hackers. The proposed scheme utilizes the halftone technique, cover coding table and secret coding table to generate two meaningful shares show that the proposed embedded EVCS has competitive visual quality compared with many of the well-known EVCSs in the . Comparative analysis have demonstrated that the new scheme is perfectly applicable and achieves a high security level.
Automated Color Logo Recognition System based on Shape and Color Features

This paper proposes an automated system for rotation and scale invariant color logo recognition. Colored logo images are recognized using one shape feature namely Moments Invariant and a color feature namely Color Moments. Shape of the logo is modeled using the first two central normalized Hu’s invariant moments while color is modeled using the mean, standard deviation, skewness and kurtosis calculated from the color channels. Each image is scaled and rotated by arbitrary amounts before being submitted to the recognition engine. Classification is done using Manhattan and Euclidean distances. Experimental verification is obtained using a data set of 900 images divided into 75 classes. The proposed approach is highly scalable and robust providing accuracy results comparable to the state of the art. General Terms Image Processing, Pattern Recognition, Color and Shape features.
Constrained Least Squares Filtering for Ultrasound Image Deconvolution

This paper uses a new tissue model, which incorporates random fluctuations of the tissue signal within the received ultrasound radio frequency (RF) echo signal, to propose an ultrasound image deconvolution algorithm for improving the quality of ultrasound images. Using this model, we propose a modified version of the classical regularization method which combines optimal Wiener filter and constrained least squares (LS) filtering algorithm for estimation of the tissue signal. The performance of the algorithm is evaluated by simulation. The deconvolved images can show significant improvement in both the speckle reduction and the contrast ratio
Hysteresis of intrinsic I/sub DDQ/ currents

Empirical analyses of the IDDQ signatures of 0.18 p m devices indicate that IDDQ currents exhibit hysteresis. A newly proposed test method, SPIRIT (Single Pattern Iteration lDDQ Test), demonstrates that the test pattern and the device clock speed before measurements must be maintained to assure the integrity of IDDe measurements, which is the fundamental assumption of IDDQ applications: testing, diagnosis, monitoring, and static power estimation. Newly proposed IDDQ signature and hysteresis models show that hysteresis phenomena are caused by the global transient threshold voltage shifts induced by the direct tunnel charges to the pre-existing border traps under nominal operating conditions.
On the Cauchy problem for Hartree equation in the Wiener algebra

We consider the mass-subcritical Hartree equation with a homogeneous kernel, in the space of square integrable functions whose Fourier transform is integrable. We prove a global well-posedness result in this space. On the other hand, we show that the Cauchy problem is not even locally well-posed if we simply work in the space of functions whose Fourier transform is integrable. Similar results are proven when the kernel is not homogeneous, and is such that its Fourier transform belongs to some Lebesgue space.
Hierarchical Decoupling Principle of a MIMO-CDMA Channel in Asymptotic Limits

We analyze an uplink of a fast flat fading MIMO-CDMA channel in the case where the data symbol vector for each user follows an arbitrary distribution. The maximum spectral efficiency of the channel with CSI at the receiver is evaluated analytically with the replica method. The main result is that the hierarchical decoupling principle holds in the MIMO-CDMA channel, i.e., the MIMO-CDMA channel is decoupled into a bank of single-user MIMO channels in the many-user limit, and each single-user MIMO channel is further decoupled into a bank of scalar Gaussian channels in the many-antenna limit for a fading model with a limited number of scatterers.
Bounding dissipation in stochastic models

We generalize to stochastic dynamics the exact expression for average dissipation along an arbitrary non-equilibrium process, given in Phys. Rev. Lett. 98, 080602 (2007). We then derive lower bounds by various coarse-graining procedures and illustrate how, when and where the information on the dissipation is captured in models of over- and underdamped Brownian particles.
The double Compton emissivity in a mildly relativistic thermal plasma within the soft photon limit

Aims. We provide simple and accurate analytic approximations for the low frequency double Compton emission coefficient that are applicable in a broad range of physical situations up to mildly relativistic temperatures. These approximations may be useful for checking in which circumstances the double Compton process is important. Methods. We perform series expansions of the double Compton emission integral for low energies of the incident photon and electron and compare the derived analytic expressions with the results obtained by numerical integrations of the full double Compton cross section. Results. We explicitly derived analytic approximations to the low frequency double Compton emission coefficient for initial monochromatic photons and Wien spectra. We show that combining the analytic approximations given in this paper, an accuracy of better than 5% over a very broad range of temperatures and under various physical conditions can be achieved. The double Compton emissivity strongly depends on the ratio of the incoming photon and electron energies: for hard photons and cold electrons the emission is strongly suppressed compared to the case of similar photon and electron energy, whereas in the opposite situation, i.e. hot electrons and soft initial photons, the emission is enhanced. For photons and electrons close to thermodynamic equilibrium the double Compton emissivity increases less rapidly with temperature than in the Lightman-Thorne approximation and the corrections exceed ∼ 10% for temperatures above $4\,$keV.
The Role and Regulation of Adenosine in the Central Nervous System

Adenosine is a modulator that has a pervasive and generally inhibitory effect on neuronal activity. Tonic activation of adenosine receptors by adenosine that is normally present in the extracellular space in brain tissue leads to inhibitory effects that appear to be mediated by both adenosine A 1 and A 2A receptors. Relief from this tonic inhibition by receptor antagonists such as caffeine accounts for the excitatory actions of these agents. Characterization of the effects of adenosine receptor agonists and antagonists has led to numerous hypotheses concerning the role of this nucleo-side. Previous work has established a role for adenosine in a diverse array of neural phenomena, which include regulation of sleep and the level of arousal, neuroprotec-tion, regulation of seizure susceptibility, locomotor effects, analgesia, mediation of the effects of ethanol, and chronic drug use.
Mentoring Emerging Leaders: Perspectives of Mentees on Dispositions and Conditions for Leadership Learning

Although mentoring is believed to improve the craft of beginning principal, the context of collective engagement between mentor and mentee principals that optimize their leadership learning are rarely investigated. This study explored co-creating dispositional values, knowledge conversion modes, and enabling conditions as contexts that favor the creation and transmission of explicit and tacit knowledge for leadership learning for the mentee principal.
Terms, Topics & Tasks: Enhanced User Modelling for Better Personalization

Given the distinct preferences of different users while using search engines, search personalization has become an important problem in information retrieval. Most approaches to search personalization are based on identifying topics a user may be interested in and personalizing search results based on this information. While topical interests information of users can be highly valuable in personalizing search results and improving user experience, it ignores the fact that two different users that have similar topical interests may still be interested in achieving very different tasks with respect to this topic (e.g. the type of tasks a broker is likely to perform related to finance is likely to be very different than that of a regular investor). Hence, considering user's topical interests jointly with the type of tasks they are likely to be interested in could result in better personalised We present an approach that uses search task information embedded in search logs to represent users by their actions over a task-space as well as over their topical-interest space. In particular, we describe a tensor based approach that represents each user in terms of (i) user's topical interests and (ii) user's search task behaviours in a coupled fashion and use these representations for personalization. Additionally, we also integrate user's historic search behavior in a coupled matrix-tensor factorization framework to learn user representations. Through extensive evaluation via query recommendations and user cohort analysis, we demonstrate the value of considering topic specific task information while developing user models.
A Simple Method to Detect Recovery of Glomerular Filtration Rate following Acute Kidney Injury

In acute kidney injury (AKI), elevated plasma creatinine is diagnostic of an earlier loss of glomerular filtration rate (GFR) but not of the concomitant GFR. Only subsequent creatinine changes will inform if GFR had already recovered or not. We hypothesized that the creatinine excretion rate to production rate ratio would provide this information. A retrospective analysis of 482 critically ill patients from two intensive care units (ICU) is shown. Plasma creatinine was measured on ICU entry and 12 hours later. Four-hour creatinine excretion rates (E) were measured on entry. Creatinine production rates were estimated (eG). The ability of the ratio E/eG to predict a decrease in plasma creatinine concentration, identify recovered AKI (≥0.3 mg/dL decrease), and predict AKI (≥0.3 mg/dL increase) was assessed by the area under the receiver operator characteristic curves (AUC). There was a linear relationship between reduced creatinine concentration and E/eG (r 2 = 0.15; P < 0.0001). E/eG predicted a decrease in creatinine (AUC 0.70 (0.65 to 0.74)), identified recovered AKI (0.75 (0.67 to 0.84)), and predicted AKI (0.80 (0.73 to 0.86)). A ratio of the rates of creatinine excretion to estimated production much less than 1 indicated a concomitant GFR below baseline, whereas a ratio much more than 1 indicated a recovering or recovered GFR.
Development of a 2-D Analytical Model for the Electromagnetic Computation of Axial-Field Magnetic Gears

This paper describes a two-dimensional (2-D) analytical model to predict the magnetic field distribution in axial-field magnetic gears by using the sub-domain method. The sub-domain method consists in solving the partial differential equations linked to the Maxwell's equations in each rectangular region (magnets, air gaps, and slots) by the separation of variables method. The proposed model is based on a two-dimensional approximation for the magnetic field distribution (mean radius model) i.e. the problem is solved in 2-D Cartesian coordinates. One of the main contributions of the paper concerns the analytic solution of the magnetic field in a slot open on the two sides (space between the ferromagnetic pole-pieces). Moreover, it is shown that the analytical model and the 3-D finite elements simulations follow the same trends in the determination of the optimum values for the geometrical parameters. As the analytical model takes less computational time than 3-D numerical model, it can be used as an effective tool for the first step of design optimization.
Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data

We produced a landscape scale map of mean tree height in mangrove forests in Everglades National Park (ENP) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data was calibrated using airborne lidar data and a high resolution USGS digital elevation model (DEM). The resulting mangrove height map has a mean tree height error of 2.0 m (RMSE) over a pixel of 30 m. In addition, we used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The estimation showed that most of the mangrove standing biomass in the ENP resides in intermediate-height mangrove stands around 8 m. We estimated the total mangrove standing biomass in ENP to be 5.6 � 10 9 kg.
Implicit Feature Selection with the Value Difference Metric

The nearest neighbour paradigm provides an effective approach to supervised learning. However, it is especially susceptible to the presence of irrelevant attributes. Whilst many approaches have been proposed that select only the most relevant attributes within a data set, these approaches involve pre-processing the data in some way, and can often be computationally complex. The Value Difference Metric (VDM) is a symbolic distance metric used by a number of different nearest neighbour learning algorithms. This paper demonstrates how the VDM can be used to reduce the impact of irrelevant attributes on classification accuracy without the need for pre-processing the data. We illustrate how this metric uses simple probabilistic techniques to weight features in the instance space, and then apply this weighting technique to an alternative symbolic distance metric. The resulting distance metrics are compared in terms of classification accuracy, on a number of real-world and artificial data sets.
Extending the Technology Acceptance Model to Investigate the Utilization of ERP Systems

The implementation of Enterprise Recourse Planning ERP systems has grown rapidly, but limited research has been conducted to investigate the utilization of ERP systems. By extending the Technology Acceptance Model, this paper provides a research model for examining the impact of computer self-efficacy and ERP systems design features on the utilization of ERP systems. To test the proposed research model, data are collected through a questionnaire survey distributed among employees in different organizations that have implemented an ERP system in the United Arab Emirates. Structural equation modeling techniques are used in this study to verify the causal relationships between the variables. The results strongly support the extended TAM in understanding employees' utilization of ERP systems. The implications of this study and further research opportunities are also discussed.
Moving from talking heads to newlyweds: exploring video chat use during major life events

Video chat programs for home and personal use (e.g., Skype) are becoming increasingly popular for doing more than simply conversing with a remote friend or family member. This creates a need to understand the broader use of video chat that moves "beyond talking heads. " In this paper, we investigate one emergent scenario: major life events where video chat is used to connect remote participants to a ritual gathering (e.g., a wedding, a funeral). To explore this scenario, we conducted an online survey with 87 people who reported on their usage of video chat for viewing or sharing major life events. Our results show that major life events, as an example of a burgeoning set of video chat scenarios, bring unique socio-technical contexts and challenges. Asymmetry characterized much of the findings: we find differences between local and remote group sizes, environments, atmosphere, and emotionality. We discuss these situations and identify ways to improve the design of video chat to better support shared experiences.
Optimal Spectrum Sensing Threshold for Unequal Priors Case

Classical spectrum sensing techniques utilize maximum likelihood (ML) detection for identification of spectrum holes. The approach is sub-optimal for the case of un-equal priors where the probabilities of channel occupation and vacancy are not the same. Such situations are bound to occur in most commercial bands such as GSM etc and hence are of more interest. The loss in performance has been disregarded as negligible in most of the work done on spectrum sensing techniques. This paper quantifies the effects of changing priors on classical energy detection and infers that the loss in spectrum sensing performance is not negligible. The deterioration is especially considerable at low SNR values and at low probabilities of channel occupation. An optimum threshold for achieving minimum probability of error has been derived in this work for unequal prior case. Detection based on the proposed threshold out-performs classical detectors under the assumption that priors are known at the receiver.
Fijian culture and the environment: a focus on the ecological and social interconnectedness of tourism development

ABSTRACT Understanding the complex and adaptive nature of Pacific Island communities is a growing yet relatively unexplored area in the context of tourism development. Taking an ethnographic research approach, this study examines how over 40 years of tourism development have led to complex and multi-scale changes within an Indigenous Fijian village. The study establishes that tourism development has brought a range of ecological shifts that have, over time, spurred far-reaching changes within the embedded sociocultural constructs of the community. The development of the Naviti Resort, a water catchment dam, a causeway and a man-made island have created substantial changes in totemic associations, livelihood approaches, and traditional knowledge structures within Vatuolalai village. The emergence of internal adaptive cycles, and new behaviours, practices and values that redefine the cultural landscape will be discussed. This paper demonstrates the interconnectivity of nature, society and culture within Indigenous communal systems and asserts that ecological changes introduced in one part of a community stimulate complex, non-linear responses in other elements of the socio-ecological system of a Fijian village.
Low Power Characteristics of Single-Core and Multi-core Systems

Power and thermal reduction techniques are presented for hardware level, operating system level and application level. While the majority of control resides within the hardware level, it is shown that allowing all three to operate harmoniously and giving control to the level that has the best understanding of the current process will yield the best results. Topics such as DVFS, Clock gating, power metrics and custom component design are covered.
Spectral and Fractal Analysis of ECoG in Animal Model of Aluminium Intoxication

This paper presents an overview of rat animal model of Alzheimer’s disease. Model is based on the toxicity, oxidative stress and neurodegeneration. The model is obtained by treatment of rats with intraperitoneal aluminum. Electrocortical group neuron activity describes changes in neurotransmission caused by different factors. Such changes could be qualitatively described by spectral and fractal analysis of electrocortical activity as a variation of the relative spectral power. Both animals and patients with Alzheimers demention have increased relative spectral power in delta range. By fractal analysis we described changes in electrocortical activity of aluminum intoxication animals compare to physiological control. We used change in delta range to calculate fractal dimension. Also we used fractal dimension to compare treated animals with control ones to quantitatively describe the degree of pathophysiological state. All changes are correlated with an increase in the relative spectral power in the delta range and can be quantitatively described by fractal dimension. Results are presented as the effect and can also be applied to the human model of neurotoxicity and neurodegeneration like Alzheimer’s dementia. The model itself may be used for diagnostic and prognostic purposes since it describes the parameters underlying Alzheimer’s disease. However in definition of disease should be consider a state of dementia and menthal activity.
A brief cognitive test battery to differentiate Alzheimer's disease and frontotemporal dementia

Objectives: To validate a simple bedside test battery designed to detect mild dementia and differentiate AD from frontotemporal dementia (FTD). Methods: Addenbrooke's Cognitive Examination (ACE) is a 100-point test battery that assesses six cognitive domains. Of 210 new patients attending a memory clinic, 139 fulfilled inclusion criteria and comprised dementia (n = 115) and nondementia (n = 24) groups. The composite and the component scores on the ACE for the two groups were compared with those of 127 age- and education-matched controls. Norms and the probability of diagnosing dementia at different prevalence rates were calculated. To evaluate the ACE's ability to differentiate early AD from FTD, scores of the cases diagnosed with dementia with a Clinical Dementia Rating < 1 (AD = 56, FTD = 24, others = 20) were compared. Results: Two cut-off values for the ACE composite score (88 and 83) were of optimal utility depending on the target population. The ACE had high reliability, construct validity, and sensitivity (93%, using 88 as cut-off). Using the lower cut-off of 83, the ACE had a higher sensitivity (82%) and predictive value than the Mini-Mental State Examination for a wide range of dementia prevalence. The ACE differentiated AD from FTD, and the VLOM ratio (derived using component scores: [verbal fluency + language]/[orientation + memory]) of <2.2 for FTD and >3.2 for AD was highly discriminating. Conclusion: The ACE is a brief and reliable bedside instrument for early detection of dementia, and offers a simple objective index to differentiate AD and FTD in mildly demented patients.
Control-Lyapunov Functions for Systems Satisfying the Conditions of the Jurdjevic-Quinn Theorem

For a broad class of nonlinear systems satisfying the Jurdjevic-Quinn conditions, we construct a family of smooth control-Lyapunov functions whose derivatives along the trajectories of the systems can be made negative definite by smooth control laws that are arbitrarily small in norm. We also design state feedbacks of arbitrarily small norm that render our systems integral-input-to-state stable to actuator errors.
Persistent Homological Structures in Compressed Sensing and Sparse Likelihood

In this paper, we explore hidden persistent homological structures in sparse regressions. Sparse regressions are usually parameterized by tuning parameters that determine the sparsity of solutions. By treating the tuning parameters as additional dimension, we can have multi-scale representations. We can show there exist hidden persistent homological structures in these dimensions. By exploiting the hidden topological structures further, it is possible to completely bypass the computational bottlenecks that usually occur in solving the sparse regressions.