Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive educational hypermedia systems in technology enhanced learning: a literature review

This literature review focuses on the educational benefits afforded to learners by Technology Enhanced Learning Environments (TELE) which adapt and personalize the learning experience. More specifically it focuses on Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles. Adaptive approaches to learning offer alternatives to the traditional "One-size-fits-all" approach and have driven the development of dynamic educational environments. The use of such environments can deliver educational benefits as educational offerings are personalised based upon various characteristics of individual learners.
Providing QoS for Real-time Applications

This paper describes a framework for packetswitched networks that balances the needs of real-time and data applications. Routers use virtual queues to mark packets reflecting a measure of congestion. These marks are then used to control the admission of time-sensitive traffic and to regulate other traffic. We present a theoretical analysis of this framework. We evaluate the performance characteristics of this approach in terms of overall link utilization and quality of service of the time-sensitive traffic. We conclude with a discussion of simulation results and of an implementation on a test network.
A Matlab Based GUI for Multiframe Super Resolution Reconstruction of Different Gray Scale Images with and without Adaptive Filter

A Robust Multi frame image Super Resolution Reconstruction (SRR) is a process which produces a better or superior quality, High Resolution (HR) image from multiple numbers of blurred noisy low resolution (LR) images of the similar scene, acquired under different conditions. It produces a high quality super resolution image with higher spatial frequency, reduced noise and image blur as compared to the original images. The inputs images can be in the form of medical images, surveillance footage, digital video, satellite terrain imagery, or images from many other sources. In many visual applications like military and civilian areas the imaging sensors used are having very poor resolution outputs. Due to some limitations i.e. cost or hardware, resolution cannot be improved by replacing imaging sensors, hence super resolution algorithms plays a crucial role in improving the resolution of the output image. The Super resolution (SR) algorithms can be implemented in such cases as SR algorithms is a low cost algorithm and easy to implement. Super Resolution Reconstruction (SRR) is a computationally intensive process. The purpose of this paper is to develop a super resolution reconstruction algorithm which can be useful to real time data and data generated synthetically. A fast and robust technique of Multi frame Super Resolution Reconstruction is presented where frame to frame motion estimation and image registration is developed iteratively using Lucas Canada Pyramidal optical flow algorithm. For the iterative approach, the high resolution image estimate obtained is the solution of Median Shift and Add method, along with Gaussian filter and down sampling. Further iteratively gradient back projection and gradient regularization is carried out to obtain High Resolution (HR) image. A MATLAB based GUI for Super Resolution Reconstructed image is developed which calculates the image quality parameters such as PSNR, RMSE, SD, SSIM and percentage change in pixels value in HR image with respect to original frame. A PSNR plot for different test vectors is plotted with respect to number of frames and number of iterations. A MATLAB based tool is also developed for synthetic data generation. The HR image of different test vectors (real and synthetic) are shown in the form of MATLAB based GUI developed with and without adaptive filters. The change in the image quality parameters like SD and SSIM with and without adaptive filters is also shown in the form of table. The proposed method and its experimental results show that there is significant improvement in image quality, maintaining the edge information and has a higher PSNR and better visual effect.
CLOUD DATA MANAGEMENT: SYSTEM INSTANCES AND CURRENT RESEARCH

With the development of Internet technology and cloud computing, cloud data management technology has emerged while the technology of traditional database management cannot meet the requirement. In this paper, we will compare the features and analyze the difference among the new cloud data management systems from data model, data partition schema, fault-tolerant mechanism of system, the load balancing mechanism of system and the model of data consistency and availability. And then we analyze and verified the system performance between the two open-source systems. Finally, research statuses of the cloud data management technology are analyzed and the key technologies in the research of cloud data management technology are summarized.
Kinetic limits for pair-interaction driven master equations and biological swarm models

We consider a class of stochastic processes modeling binary interactions in an N-particle system. Examples of such systems can be found in the modeling of biological swarms. They lead to the definition of a class of master equations that we call pair interaction driven master equations. We prove a propagation of chaos result for this class of master equations which generalizes Mark Kac's well know result for the Kac model in kinetic theory. We use this result to study kinetic limits for two biological swarm models. We show that propagation of chaos may be lost at large times and we exhibit an example where the invariant density is not chaotic.
International Union of Pharmacology. LXX. Subtypes of γ-Aminobutyric AcidA Receptors: Classification on the Basis of Subunit Composition, Pharmacology, and Function. Update

In this review we attempt to summarize experimental evidence on the existence of defined native GABAA receptor subtypes and to produce a list of receptors that actually seem to exist according to current knowledge. This will serve to update the most recent classification of GABAA receptors (Pharmacol Rev 50:291–313, 1998) approved by the Nomenclature Committee of the International Union of Pharmacology. GABAA receptors are chloride channels that mediate the major form of fast inhibitory neurotransmission in the central nervous system. They are members of the Cys-loop pentameric ligand-gated ion channel (LGIC) superfamily and share struc-tural and functional homology with other members of that family. GABAA receptors are assembled from a family of 19 homologous subunit gene products and form numerous, mostly hetero-oligomeric, pentamers. Such receptor subtypes with properties that depend on subunit composition vary in topography and ontogeny, in cellular and subcellular localization, in their role in brain circuits and behaviors, in their mechanisms of regulation, and in their pharmacology. We propose several criteria, which can be applied to all the members of the LGIC superfamily, for including a receptor subtype on a list of native hetero-oligomeric subtypes. With these criteria, we develop a working GABAA receptor list, which currently includes 26 members, but will undoubtedly be modified and grow as information expands. The list is divided into three categories of native receptor subtypes: “identified,” “existence with high probability,” and “tentative. ”
Limbic and prefrontal activity during conformity and violation of norms in a coordination game

Previous neuroimaging studies of scenarios such as the prisoner’s dilemma and ultimatum game show how brain systems which evolved to process rewarding and aversive stimuli are activated differentially during adherence to and breaches of norms. One theory of how shared social norms evolve is that they arise from mutually rewarding conventions which, through repeated execution, acquire a normative value that sustains social cohesion even when individual self-interest is not served. The authors report an fMRI study of a 2-player coordination game in which players must coordinate on an arbitrary convention (a left or right button press) to obtain monetary rewards. Once this convention has been established, one of the participants is incentivized to deviate from the equilibrium with the offer of extra reward. During the period prior to decisions to violate the convention, activity was observed in regions associated with reward processing, such as the midbrain, caudate, and orbitofrontal cortex. Activations in advance of decisions to continue coordinating included the amygdala and anterior insula–inferior frontal gyrus. The data are discussed in the light of theories which propose the existence of multiple interacting value-based decision-making systems in the human brain.
Understanding IT Innovations through Discourse Analysis

The dynamic information field is characterized by the constant ebbs and flows of innovations in information technologies (IT). Accordingly, managing information and formulating policies in the iField require understanding IT innovations – what they are and will be, who develops and/or adopts them, and how innovations may be effectively developed, implemented, and used. Despite a relatively sustained research literature on IT innovations [2], our knowledge of innovations is still inadequate to effectively inform strategic information management and policy-making in the iField. For instance, the field is filled with numerous buzzwords and acronyms, making it hard to differentiate true progress from mere change. And most research and practice are focused on highly popular innovations such as Web 2.0 and cloud computing; little is known about why only some innovations come to be popular while others do not. The lack of understanding is in part caused by limited research designs that focus on only one or a few innovations, owing to the difficulty in analyzing large-scale data on multiple innovations.
Light-Weight Forwarding Protocols in Energy Harvesting Wireless Sensor Networks

Due to the unavoidable battery replacement or replenishment, diverse energy harvesting techniques have been integrated with Wireless Sensor Networks (WSNs) to overcome limited battery power and extend the network lifetime. However, variable transmission power levels based on non-uniform energy harvesting rates can incur asymmetric links. In this paper, we propose light-weight forwarding protocols to reliably deliver sensory data to a sink over time-varying asymmetric links in energy harvesting WSNs. A Weighted Confirmation (WCFM) scheme is proposed to differentiate multiple paths between a data source and a sink by assigning multiplicative weights on the paths. A Lazy Confirmation (LCFM) scheme is also proposed to assure a reverse path by waiting for extended communication range. In addition, an Asymmetric Link Aware Backoff mechanism is proposed to avoid possible packet contentions and collisions. We evaluate the proposed techniques through extensive simulation experiments and their results indicate that the proposed forwarding protocols can be a viable approach in energy harvesting WSNs.
Knowledge Acquisition for Expanding Semantic Network

This article presents the issues of knowledge management, in particular knowledge acquisition. The article summarizes research work started with the SeiPro2S (Semantically Enhanced Intellectual Property Protection System) system designed to protect resources from the unauthorized use of intel¬lectual property. The system implements semantic network as a structure of knowledge repre¬sentation and a new idea of semantic compression. As the author proved that semantic compression is viable concept for English, he decided to focus on potential applications. An algorithm is presented that employ¬ing semantic network WiSENet for knowledge acquisition with flexible rules that yield high precision results. Detailed discussion is given with description of devised algorithm, usage examples and results of experi¬ments.
Broad-band fading channels: Signal burstiness and capacity

Medard and Gallager (2002) showed that very large bandwidths on certain fading channels cannot be effectively used by direct sequence or related spread-spectrum systems. This paper complements the work of Medard and Gallager. First, it is shown that a key information-theoretic inequality of Medard and Gallager can be directly derived using the theory of capacity per unit cost, for a certain fourth-order cost function, called fourthegy. This provides insight into the tightness of the bound. Secondly, the bound is explored for a wide-sense-stationary uncorrelated scattering (WSSUS) fading channel, which entails mathematically defining such a channel. In this context, the fourthegy can be expressed using the ambiguity function of the input signal. Finally, numerical data and conclusions are presented for direct-sequence type input signals.
A Feature Selection Method for Multivariate Performance Measures

Feature selection with specific multivariate performance measures is the key to the success of many applications such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimensional data. Hence, a two-layer cutting plane algorithm is proposed to solve this problem, and the convergence is presented. In addition, we adapt the proposed method to optimize multivariate measures for multiple-instance learning problems. The analyses by comparing with the state-of-the-art feature selection methods show that the proposed method is superior to others. Extensive experiments on large-scale and high-dimensional real-world datasets show that the proposed method outperforms l1-SVM and SVM-RFE when choosing a small subset of features, and achieves significantly improved performances over SVMperl in terms of F1-score.
Adding Slow-Silent Virtual Channels for Low-Power On-Chip Networks

In this paper, we introduce the use of slow-silent virtual channels to reduce the switching power of on-chip networks while keeping the leakage power small. Adding virtual channels to a network improves the throughput until each link bandwidth is saturated. This enables us to reduce the switching power of on-chip networks by decreasing their operating frequency and supply voltage. However, adding virtual channels increases the leakage power of routers as well as the area due to their large buffers; so the runtime power gating is applied to individual virtual channels to eliminate this problem. We evaluate the performance of slow-silent virtual channels by using real application traces, and their power consumption (switching and leakage) is evaluated based on the detailed design of a virtual-channel router placed and routed with a 90 nm technology. These evaluation results show that a network with three or four virtual channels achieves the best energy efficiency in a uniform traffic. In the cases of neighboring communications, a network with two virtual channels is better than the other networks with more virtual channels, because the performance improvement from no virtual channel to two virtual channels is the largest and their frequency and supply voltage can also be reduced well in these cases.
A stream-based selectivity estimation technique for forward XPath

The Extensible Markup Language (XML) rapidly establishes itself as the de facto standard for presenting, storing, and exchanging data on the Internet. However, querying large volume of XML data represents a bottleneck for several computationally intensive applications. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query performance. Recently proposed techniques are all based on some forms of structure synopses that could be time-consuming to build and not effective for summarizing complex structure relationships. To overcome this limitation, we propose an innovative selectivity estimation algorithm, which consists of (1) the path tree synopsis data structure, a succinct description of the original document with low computational overhead and high accuracy for processing tasks like selectivity estimation, (2) a streaming selectivity estimation algorithm which is efficient for path tree traversal. Extensive experiments on both real and synthetic data sets show that our technique achieves better accuracy and less construction time than existing approaches.
idMesh: graph-based disambiguation of linked data

We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities -- represented by globally identifiable declarative artifacts -- self-organize in a dynamic and probabilistic manner. Our solution has the following two desirable properties: i) it lets end-users freely define associations between arbitrary entities and ii) it probabilistically infers entity relationships based on uncertain links using constraint-satisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer applications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efficient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines.
Effect of Alcohol Fermented Feed on Lactating Performance, Blood Metabolites, Milk Fatty Acid Profile and Cholesterol Content in Holstein Lactating Cows

A feeding experiment with 40 lactating Holstein cows and 4 dietary treatments was conducted to investigate supplementation with different levels of alcohol fermented feed to the TMR on lactating performance, blood metabolites, milk fatty acid profile and cholesterol concentration of blood and milk. Forty Holstein lactating cows (106±24 d post-partum; mean±SD) were distributed into four groups and randomly assigned to one of four treatments with each containing 10 cows per treatment. The treatment supplemented with TMR (DM basis) as the control (CON), and CON mixed with alcohol-fermented feeds (AFF) at a level of 5%, 10% and 15% of the TMR as T1, T2 and T3, respectively. Dry matter intake and milk yield were not affected by supplementation of AFF. An increased 4% FCM in the milk occurred in cows fed T3 diet compared with CON, while T1 and T2 diets decreased 4% FCM in a dose dependent manner. Supplementation of AFF increased the concentration of albumin, total protein (TP), ammonia, and high density lipoprotein-cholesterol in serum compared with CON. In contrast, supplementation with AFF clearly decreased concentration of blood urea nitrogen (BUN) and total cholesterol (TC) compare with CON. AFF supplementation increased the proportion of C18:1n9 and C18:2n6 compared to CON. A decrease in the concentration of saturated fatty acid (SFA) for T1, T2 and T3 resulted in an increased unsaturated fatty acid (USFA) to SFA ratio compared to CON. Concentration of cholesterol in milk fat was reduced in proportion to the supplemental level of AFF. Feeding a diet supplemented with a moderate level AFF to lactating cows could be a way to alter the feed efficiency and fatty acid profile of milk by increasing potentially human consumer healthy fatty acid without detrimental effects on feed intake and milk production. A substantially decreased cholesterol proportion in milk induced by supplementation AFF suggests that alcohol fermented feed may improve milk cholesterol levels without any negative effects in lactating cows.
The Legionella pneumophila IcmSW Complex Interacts with Multiple Dot/Icm Effectors to Facilitate Type IV Translocation

Many Gram-negative pathogens use a type IV secretion system (T4SS) to deliver effector proteins into eukaryotic host cells. The fidelity of protein translocation depends on the efficient recognition of effector proteins by the T4SS. Legionella pneumophila delivers a large number of effector proteins into eukaryotic cells using the Dot/Icm T4SS. How the Dot/Icm system is able to recognize and control the delivery of effectors is poorly understood. Recent studies suggest that the IcmS and IcmW proteins interact to form a stable complex that facilitates translocation of effector proteins by the Dot/Icm system by an unknown mechanism. Here we demonstrate that the IcmSW complex is necessary for the productive translocation of multiple Dot/Icm effector proteins. Effector proteins that were able to bind IcmSW in vitro required icmS and icmW for efficient translocation into eukaryotic cells during L. pneumophila infection. We identified regions in the effector protein SidG involved in icmSW-dependent translocation. Although the full-length SidG protein was translocated by an icmSW-dependent mechanism, deletion of amino terminal regions in the SidG protein resulted in icmSW-independent translocation, indicating that the IcmSW complex is not contributing directly to recognition of effector proteins by the Dot/Icm system. Biochemical and genetic studies showed that the IcmSW complex interacts with a central region of the SidG protein. The IcmSW interaction resulted in a conformational change in the SidG protein as determined by differences in protease sensitivity in vitro. These data suggest that IcmSW binding to effectors could enhance effector protein delivery by mediating a conformational change that facilitates T4SS recognition of a translocation domain located in the carboxyl region of the effector protein.
Ocean acidification impacts on sperm mitochondrial membrane potential bring sperm swimming behaviour near its tipping point

ABSTRACT Broadcast spawning marine invertebrates are susceptible to environmental stressors such as climate change, as their reproduction depends on the successful meeting and fertilization of gametes in the water column. Under near-future scenarios of ocean acidification, the swimming behaviour of marine invertebrate sperm is altered. We tested whether this was due to changes in sperm mitochondrial activity by investigating the effects of ocean acidification on sperm metabolism and swimming behaviour in the sea urchin Centrostephanus rodgersii. We used a fluorescent molecular probe (JC-1) and flow cytometry to visualize mitochondrial activity (measured as change in mitochondrial membrane potential, MMP). Sperm MMP was significantly reduced in ΔpH −0.3 (35% reduction) and ΔpH −0.5 (48% reduction) treatments, whereas sperm swimming behaviour was less sensitive with only slight changes (up to 11% decrease) observed overall. There was significant inter-individual variability in responses of sperm swimming behaviour and MMP to acidified seawater. We suggest it is likely that sperm exposed to these changes in pH are close to their tipping point in terms of physiological tolerance to acidity. Importantly, substantial inter-individual variation in responses of sperm swimming to ocean acidification may increase the scope for selection of resilient phenotypes, which, if heritable, could provide a basis for adaptation to future ocean acidification. Highlighted Article: Sea urchin sperm motility under acidified conditions is shown to be related to reduced mitochondrial potential. Future forecasts of ocean acidification could place sea urchin sperm at their tolerance threshold.
An Empirical Identification Method of Gaussian Blur Parameter for Image Deblurring

In this paper, we propose an empirical identification method of the Gaussian blur parameter for image deblurring. The parameter estimate is chosen from a collection of candidate parameters. The blurred image is restored by these candidate parameters under the assumption that the candidate is equal to the true value. The estimate is selected to be at the maximum point of the differential coefficients of restored image Laplacian L1 norm curve. Experimental results are presented to demonstrate the performance of the proposed method.