Miracle-dz/UniFAR-FTUs huggingface dataset:
Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

In the knowledge age, the use of the information technology (IT) tools including learning management systems (LMS) has became an imperative. The adoption rate of LMS in academic and training institutions is very promising worldwide. Learning Management system includes several tools that provide academic and training institutions efficient and effective means to support distance education and supplement their traditional way of teaching. LMS also provide academic insinuations mechanisms and tools to store, manage, and share its academic resources and knowledge. Instructors' acceptance is essential for the deployment of LMS. The success of LMS in any institution starts by instructors' acceptance, which in turns initiates and promotes learners' utilization of LMS. Consequently, the objective of this paper is to develop a theoretical framework for evaluating instructors' acceptance of LMS based on the Technology Acceptance Model. This framework provides a comprehensive look of the critical factors that influence the instructors' perceived ease of use and perceived usefulness of LMS and consequently the actual use. These critical factors are related to the instructor, organization, and technology. Instructor factors include self efficacy, attitude toward LMS, experience, teaching style and personal innovativeness. Organization factors include motivators, technology alignment, organization support, technical support and training. Technology factors include system quality, information quality and service quality.
Revisiting Multi-Objective MDPs with Relaxed Lexicographic Preferences

We consider stochastic planning problems that involve multiple objectives such as minimizing task completion time and energy consumption. These problems can be modeled as multi-objective Markov decision processes (MOMDPs), an extension of the widely-used MDP model to handle problems involving multiple value functions. We focus on a subclass of MOMDPs in which the objectives have a relaxed lexicographic structure, allowing an agent to seek improvement in a lower-priority objective when the impact on a higherpriority objective is within some small given tolerance. We examine the relationship between this class of problems and constrained MDPs, showing that the latter offer an alternative solution method with strong guarantees. We show empirically that a recently introduced algorithm for MOMDPs may not offer the same strong guarantees, but it does perform well in practice.
8. Conclusions 8.1 Conclusions Relating to the Integer Programming Heuristics 8.2 Conclusions Relating to the Engineering Impact of Network Design Alternatives

We consider the design and capacity expansion of ATM networks as an optimization problem in which flows representing end-to-end variable bit-rate services of different classes are to be multiplexed and routed over ATM trunks and switches so as to minimize the costs of additional switches and transport pipes while meeting service quality and survivability constraints. After discussing the underlying fractional Brownian motion models for flows, a non-linear multicommodity optimization problem is formulated and heuristics for its approximate solutions are described. Finally, computational results are produced that demonstrate realistic size problems can be solved with the proposed method to shed light on key economic characteristics of ATM traffic, such as safe levels of statistical multiplexing, as well as robust and efficient design alternatives.
‘Kitchen and cooking,’ a serious game for mild cognitive impairment and Alzheimer’s disease: a pilot study

Recently there has been a growing interest in employing serious games (SGs) for the assessment and rehabilitation of elderly people with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and related disorders. In the present study we examined the acceptability of ‘Kitchen and cooking’ – a SG developed in the context of the EU project VERVE (http://www.verveconsortium.eu/) – in these populations. In this game a cooking plot is employed to assess and stimulate executive functions (such as planning abilities) and praxis. The game is installed on a tablet, to be flexibly employed at home and in nursing homes. Twenty one elderly participants (9 MCI and 12 AD, including 14 outpatients and 7 patients living in nursing homes, as well as 11 apathetic and 10 non-apathetic) took part in a 1-month trail, including a clinical and neuropsychological assessment, and 4-week training where the participants were free to play as long as they wanted on a personal tablet. During the training, participants met once a week with a clinician in order to fill in self-report questionnaires assessing their overall game experience (including acceptability, motivation, and perceived emotions). The results of the self reports and of the data concerning game performance (e.g., time spent playing, number of errors, etc) confirm the overall acceptability of Kitchen and cooking for both patients with MCI and patients with AD and related disorders, and the utility to employ it for training purposes. Interestingly, the results confirm that the game is adapted also to apathetic patients.
Revisiting Clopper -

This paper adds one more contribution to the large literature on binomial confidence intervals for small samples (e.g., Clopper and Pearson 1934; Pearson and Hartley 1954; Sterne 1954; Crow 1956; Blyth and Still 1983; Blyth 1986; Santner and Duffy 1989; Agresti and Coull 1998; Henderson and Meyer 2001). Clopper and Peason (1934) gave the first two-sided binomial confidence intervals, in graphical form, with . 95 and . 99 coefficients. Their intervals satisfy the condition of equi-sized tails, so that each of the two corresponding one-sided tests has significance level ≤ α/2. Several authors (e.g., Sterne 1954; Crow 1956; Blyth and Still 1983) attempted to weaken the Clopper-Pearson restriction by aiming just at interval-valued confidence regions of confidence level ≥ 1−α. In particular, Sterne (1954) attempted to construct intervals by sequentially selecting most-probable variates for the coresponding two-sided acceptance regions, until their total probability is ≥ 1 − α. Crow (1956) pointed out
High-endurance and performance-efficient design of hybrid cache architectures through adaptive line replacement

In this paper, we propose a run-time strategy for managing writes onto last level cache in chip multiprocessors where STT-RAM memory is used as baseline technology. To this end, we assume that each cache set is decomposed into limited SRAM lines and large number of STT-RAM lines. SRAM lines are target of frequently-written data and rarely-written or read-only ones are pushed into STT-RAM. As a novel contribution, a low-overhead, fully-hardware technique is utilized to detect write-intensive data blocks of working set and place them into SRAM lines while the remaining data blocks are candidates to be remapped onto STT-RAM blocks during system operation. Therefore, the achieved cache architecture has large capacity and consumes near zero leakage energy using STT-RAM array; while dynamic write energy, acceptable write latency, and long lifetime is guaranteed via SRAM array. Results of full-system simulation for a quad-core CMP running PARSEC-2 benchmark suit confirm an average of 49 times improvement in cache lifetime and more than 50% reduction in cache power consumption when compared to baseline configurations.
Investigating the use of Echo State Networks for prediction of wind power generation

This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle of training them. The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. These models use ESNs with spectral radius greater than 1 and even then they can make predictions with good results. The forecast horizons presented here fall in medium-term forecasts, up to five days ahead, which is an appropriate horizon to subsidize the operation planning of power systems. Models that directly predict the wind power generation with ESNs showed promising results.
Privacy-Preserving Egocentric Activity Recognition from Extreme Low Resolution

Privacy protection from video taken by wearable cameras is an important societal challenge. We desire a wearable vision system that can recognize human activities, yet not disclose the identity of the participants. Video anonymization is typically handled by decimating the image to a very low resolution. Activity recognition, however, generally requires resolution high enough that features such as faces are identiﬁable. In this paper, we propose a new approach to address such contradicting objectives: human activity recognition while only using extreme low-resolution (e.g., 16x12) anonymized videos. We introduce the paradigm of inverse super resolution (ISR) , the concept of learning the optimal set of image transformations to generate multiple low-resolution videos from a single video. Our ISR learns different types of sub-pixel transformations optimized for the activity classiﬁcation, allowing the classiﬁer to best take advantage of existing high-resolution videos (e.g., YouTube videos) by generating multiple LR training videos tailored for the problem. We experimentally conﬁrm that the paradigm of inverse super resolution is able to beneﬁt activity recognition from extreme low-resolution videos (e.g., 16x12 and 32x24), particularly in ﬁrst-person scenarios.
Combined association and linkage analysis applied to the APOE locus

Combined association and linkage analysis is a powerful tool for pinpointing functional quantitative traits (QTLs) responsible for regions of significant linkage identified in genome‐wide scans. We applied this technique to apoE plasma levels and the APOEε2/ε3/ε4 polymorphism in two Dutch twin cohorts of different age ranges. Across chromosome 19, short tandem repeats and the APOEε2/ε3/ε4 polymorphism were genotyped in adolescent (aged 13–22 years) and adult (aged 34–62 years) Dutch twins. In both samples, evidence for indicative linkage with plasma apoE levels was found (maximum LOD score (MLS)=0.8, MLS=2.5, respectively) at 19q13.32. These linkage regions included the APOE locus. As expected, the APOEε2/ε3/ε4 polymorphism was strongly associated with apoE plasma levels in both samples. An extension of the between/within families association test developed by Fulker et al. ([1999] Am. J. Hum. Genet. 64:259–267) showed that these associations were not due to population stratification. The combined association and linkage analyses revealed that the association of the APOEε2/ε3/ε4 polymorphism with apoE plasma levels completely explained the linkage in the adolescent twins and partly in the adult twins. © 2004 Wiley‐Liss, Inc.
Accelerating volume animation by space-leaping

In this work we present a method for speeding the process of volume animation. It exploits coherency between consecutive images to shorten the path rays take through the volume. Rays are provided with the information needed to leap over the empty space and commence volume traversal at the vicinity of meaningful data. The algorithm starts by projecting the volume onto a C-buffer (coordinates-buffer) which stores the object-space coordinates of the first non-empty voxel visible from a pixel. Following a change in the viewing parameters, the C-buffer is transformed accordingly. Next, coordinates that possibly became hidden are discarded. The remaining values serve as an estimate of the point where the new rays should start their volume traversal. This method does not require 3-D preprocessing and does not suffer from any image degradation. It can be combined with existing acceleration techniques and can support any ray traversal algorithm and material modeling scheme. <<ETX>>
Kinect-based framework for motor rehabilitation

The number of people suffering from motor impairment is increasing as incidence involving limb injuries can easily happen. These injuries may be traumatic injuries, congenital deformities, neurological and arthritic conditions or regional pain syndrome. Some of these injuries need operative procedure, whilst others use interventive methods. Regardless of the treatment performed, a vital component in the road to recovery should include physical rehabilitation. This paper proposes the design of a Kinect-based framework for motor rehabilitation. Originally introduced as an add-on for the Xbox gaming console, Kinect offers the capability to track the motion of a human body in real time. This research makes use of that capability to combine the Kinect with an easily modifiable application to produce an individually customized home rehabilitation system that will motivate, provide feedback and track the progress of the rehabilitation patient. This paper also proposes an evaluation framework to evaluate the Kinect based home rehabilitation system based on the technology acceptance, the motivation of the patient, and the patient's learning style.
A morphological segmentation and curve-let features extraction on text region classification using SVM

Text Extraction from different background is always a challenging problem in these emerging field of research area of image processing. In our project we have implemented upon Noise Removal segmentation, Curve-let Transformation features and Support Vector Machine classifier. In that we calculated on successful result purpose on still images and some real time images for different background location image on features extraction and classified techniques. Previously, Different methodologies have been used for texture feature extraction. The method which we proposed gives a better accuracy and efficiency towards extract texture feature analysis as with wavelet transform. Curve-let, Gradient Edge Operator on Support Vector Machine Classifier analysis and performance measure is completely new in this paper.
Morphology Based Enhancement and Skull Stripping of MRI Brain Images

Brain is an important part of human body. Many complex human body functions are controlled by brain. Brain imaging is a widely applicable method for diagnosing many brain abnormalities like brain tumor, stroke, paralysis etc. Magnetic Resonance Imaging (MRI) is a recently emerged method for brain imaging. Two major limitations of brain MR Images are low contrast image and presence of skull region in the image. So before detailed analysis of an image, as a preprocessing stage these two problems must be resolved. In this study we propose a method for MR Image contrast enhancement and skull stripping based on the Morphological image processing technique. The proposed method works on T1, T2 and FLAIR axial images. Experimental results show that the proposed method efficiently works for enhancing and skull removal of brain MR Images.
Bicentennial Decrease of the Total Solar Irradiance Leads to Unbalanced Thermal Budget of the Earth and the Little Ice Age

Temporal changes in the power of the longwave radiation of the system Earth-atmosphere emitted to space always lag behind changes in the power of absorbed solar radiation due to slow change of its enthalpy. That is why the debit and credit parts of the average annual energy budget of the terrestrial globe with its air and water envelope are practically always in an unbalanced state. Average annual balance of the thermal budget of the system Earth-atmosphere during long time period will reliably determine the course and value of both an energy excess accumulated by the Earth or the energy deficit in the thermal budget which, with account for data of the TSI forecast, can define and predict well in advance the direction and amplitude of the forthcoming climate changes. From early 90s we observe bicentennial decrease in both the TSI and the portion of its energy absorbed by the Earth. The Earth as a planet will henceforward have negative balance in the energy budget which will result in the temperature drop in approximately 2014. Due to increase of albedo and decrease of the greenhouse gases atmospheric concentration the absorbed portion of solar energy and the influence of the greenhouse effect will additionally decline. The influence of the consecutive chain of feedback effects which can lead to additional drop of temperature will surpass the influence of the TSI decrease. The onset of the deep bicentennial minimum of TSI is expected in 2042±11, that of the 19 th Little Ice Age in the past 7500 years – in 2055±11.
Brain Gαi2-subunit proteins and the prevention of salt sensitive hypertension

To counter the development of salt-sensitive hypertension, multiple brain G-protein-coupled receptor (GPCR) systems are activated to facilitate sympathoinhibition, sodium homeostasis, and normotension. Currently there is a paucity of knowledge regarding the role of down-stream GPCR-activated Gα-subunit proteins in these critically important physiological regulatory responses required for long-term blood pressure regulation. We have determined that brain Gαi2-proteins mediate natriuretic and sympathoinhibitory responses produced by acute pharmacological (exogenous central nociceptin/orphanin FQ receptor (NOP) and α2-adrenoceptor activation) and physiological challenges to sodium homeostasis (intravenous volume expansion and 1 M sodium load) in conscious Sprague–Dawley rats. We have demonstrated that in salt-resistant rat phenotypes, high dietary salt intake evokes site-specific up-regulation of hypothalamic paraventricular nucleus (PVN) Gαi2-proteins. Further, we established that PVN Gαi2 protein up-regulation prevents the development of renal nerve-dependent sympathetically mediated salt-sensitive hypertension in Sprague–Dawley and Dahl salt-resistant rats. Additionally, failure to up-regulate PVN Gαi2 proteins during high salt-intake contributes to the pathophysiology of Dahl salt-sensitive (DSS) hypertension. Collectively, our data demonstrate that brain, and likely PVN specific, Gαi2 protein pathways represent a central molecular pathway mediating sympathoinhibitory renal-nerve dependent responses evoked to maintain sodium homeostasis and a salt-resistant phenotype. Further, impairment of this endogenous “anti-hypertensive” mechanism contributes to the pathophysiology of salt-sensitive hypertension.
Constructing family identity close to death

Daily life close to death involves physical, psychological, and social strain, exposing patients and their family members to major transitions affecting relational patterns and identity. For the individual family member, this often means sharing life with a changing person in a changing relationship, disrupting both individual identity and family identity. Our aim was to deepen the understanding of individual experiences that are important in constructing family identity close to death at home. We performed a secondary analysis of qualitative data collected through 40 interviews with persons with life-threatening illness and the family members who shared everyday life with them. The analysis resulted in interpretive descriptions which provided three patterns important for creating family identity, which we here call “we-ness” close to death. The patterns were: being an existential person, being an extension of the other, and being together in existential loneliness. Together, these three patterns seemed to play a part in the construction of family identity; we-ness, close to death. One important finding was the tension between the search for togetherness in “we-ness” while dealing with an existential loneliness, which seemed to capture an essential aspect of being a family of which one member is dying.
Dual True Random Number Generators for Cryptographic Applications Embedded on a 200 Million Device Dual CPU SoC

Implementations of a thermal noise and a chaotic True Random Number Generator (TRNG) are presented. They are embedded in a large commercial SoC and used for cryptographic applications (SSL and key generation). Their outputs are combined to improve the randomness of the bit stream. The design goal was to minimize the effect of data dependent noise injected by the supplies and substrate. The random bit rate is 2Mbit/s and passes the DIEHARD test suite. The area of the TRNG is 0.21mm2 in a 65nm CMOS process.
End Effects in Linear Tubular Motors and Compensated Position Sensorless Control Based on Pulsating Voltage Injection

The sensorless position control of permanent-magnet (PM) synchronous motors can be successfully implemented by superimposing a high-frequency voltage signal on the control voltage. In this paper, the position estimation is obtained by means of a high-frequency sinusoidal voltage signal injected along the estimated -axis. Several methods proposed in the literature obtain the position estimation by tracking the zero condition of the high-frequency current component. We propose a new approach that also exploits the -axis high-frequency current component and allows working with injected voltage signal of reduced amplitude, thus reducing noise and additional losses. The main contribution of this paper relies in the compensation of the motor end effects due to the finite length of the tubular motor armature. These effects must be taken into account in the motor modeling because they cause an error in the position estimation that varies with the motor position. The modeling of the phenomenon and a proper compensation technique are proposed in this paper. Last, a simplified integral-type controller is used to estimate motor position instead of the commonly adopted proportional-integral controller plus integrator, and this requires a low-effort design. Experiments on a linear tubular PM synchronous-motor prototype are presented to validate the theoretical analysis and evidence the feasibility of the proposed sensorless technique.
Manga Vectorization and Manipulation with Procedural Simple Screentone

Manga are a popular artistic form around the world, and artists use simple line drawing and screentone to create all kinds of interesting productions. Vectorization is helpful to digitally reproduce these elements for proper content and intention delivery on electronic devices. Therefore, this study aims at transforming scanned Manga to a vector representation for interactive manipulation and real-time rendering with arbitrary resolution. Our system first decomposes the patch into rough Manga elements including possible borders and shading regions using adaptive binarization and screentone detector. We classify detected screentone into simple and complex patterns: our system extracts simple screentone properties for refining screentone borders, estimating lighting, compensating missing strokes inside screentone regions, and later resolution independently rendering with our procedural shaders. Our system treats the others as complex screentone areas and vectorizes them with our proposed line tracer which aims at locating boundaries of all shading regions and polishing all shading borders with the curve-based Gaussian refiner. A user can lay down simple scribbles to cluster Manga elements intuitively for the formation of semantic components, and our system vectorizes these components into shading meshes along with embedded Bézier curves as a unified foundation for consistent manipulation including pattern manipulation, deformation, and lighting addition. Our system can real-time and resolution independently render the shading regions with our procedural shaders and drawing borders with the curve-based shader. For Manga manipulation, the proposed vector representation can be not only magnified without artifacts but also deformed easily to generate interesting results.