Miracle-dz/UniFAR-FTUs huggingface dataset:
Using the Cognitive Apprenticeship Model with a Chat Tool to Enhance Online Collaborative Learning

In Colombia, many institutions are in the firm quest of virtual learning environments to improve instruction, and making the most of online tools is clearly linked to offering quality learning. Thus, the purpose of this action research was to identify how the Cognitive Apprenticeship Model enhances online collaborative learning by using a chat tool. To describe the effectiveness of this model, five of its teaching methods were implemented in an eight-week period over one semester. Twelve beginning online English students enrolled in Colombia’s national vocational and technical training center participated in the study. Data was collected from surveys, chat transcripts, interviews and checklists, and analyzed through content analysis. Results reveal that modeling, coaching, scaffolding, exploration, and reflection may be implemented in a chatroom, developing a sense of collaboration. Learners also moved from guided instruction (modeling) to more independent learning (articulation), assuming the roles of experts. In conclusion, the six teaching methods of the Cognitive Apprenticeship Method enhance online collaborative learning not only because students work together to reach a common goal, but also because they can support each other’s learning through synchronous interactions when using a chatroom for this purpose.
Analysis and design of a high performance Infrared detector readout circuit

This paper presents a high performance readout circuit for Infrared detector. The circuit is composed of capacitor trans-impedance amplifier (CTIA) and correlation double sampling (CDS) circuit. The CTIA structure is used to convert the photo-current into voltage, and could obviously improve the readout accuracy of weak current signal. And the CDS structure is used to reduce the fixed pattern noise (FPN) of CTIA. Thus, the signal to noise ratio (SNR) of the designed readout circuit is improved. Meanwhile the small signal models of CTIA and CDS are established, and the converting accuracy and speed are fully discussed. The final chip is fabricated with Chartered 0.35um standard CMOS process, and occupies a 45µm×400µm area. Under the condition that the power supply is 3.3 V, operation of the fabricated chip is verified, and the output dynamic range is measured as 0.3 to 2.48V. Testing results show that the linearity of CTIA is 99%, and that the readout accuracy is 10-bit, while the detecting current varies from 20pA to 10nA.
Research Statement Identifying the Source of Encrypted Http Connections

The goal of my research is to develop and improve low-latency anonymous communication systems for the Internet. These systems enable anonymous and private communication in such scenarios as web browsing and Internet telephony. Anonymity allows users to communicate over the Internet for such sensitive and important reasons as whistleblowing, reporting human rights abuses, or researching an illness. The subject of my work is the analysis and careful empirical study of low-latency anonymity systems. Rather than examining the traditional theoretical basis of such systems, I examine the problems and opportunities that appear when these systems are implemented. In particular, I focus on identification and mitigation of network-level attacks upon these systems and on enabling new classes of service. Below, I describe these these challenges and opportunities, and I discuss how my research addresses them.
Heterologous Prime-Boost Vaccination with MF59-Adjuvanted H5 Vaccines Promotes Antibody Affinity Maturation towards the Hemagglutinin HA1 Domain and Broad H5N1 Cross-Clade Neutralization

In an open label clinical study (2007), MF59-adjuvanted hemagglutinin (HA) vaccine from H5N1-A/Vietnam/1194/2004 (clade 1) was administered to subjects previously vaccinated (primed) with clade 0 H5N3 (A/duck/Singapore/97) vaccine at least 6 years earlier (in 1999 or 2001). The primed individuals responded rapidly and generated high neutralizing antibody titers against the H5N1-Vietnam strain within 7 days of a single booster vaccination. Furthermore, significant cross-neutralization titers were measured against H5N1 clade 0, 1, and 2 viruses. In the current study, the impact of MF59 adjuvant during heterologous priming on the quality of humoral polyclonal immune response in different vaccine arms were further evaluated using real time kinetics assay by surface plasmon resonance (SPR). Total anti-H5N1 HA1 polyclonal sera antibody binding from the heterologous prime-boost groups after a single MF59-H5N1 boost was significantly higher compared with sera from unprimed individuals that received two MF59-H5N1 vaccinations. The antigen-antibody complex dissociation rates (surrogate for antibody affinity) of the polyclonal sera against HA1 of H5N1-A/Vietnam/1194/2004 from the MF59-H5N3 primed groups were significantly higher compared to sera from unadjuvanted primed groups or unprimed individuals that received two MF59-H5N1 vaccines. Furthermore, strong inverse correlations were observed between the antibody dissociation off-rates of the immune sera against HA1 (but not HA2) and the virus neutralization titers against H5 vaccine strains and heterologous H5N1 strains. These findings supports the use of oil-in-water-adjuvanted pandemic influenza vaccines to elicit long term memory B cells with high affinity BCR capable of responding to potential variant pandemic viruses likely to emerge and adapt to human transmissions.
TRADING SYSTEM FOR AUSTRALIAN DOLLAR USING MULTIPLE MOVING AVERAGES AND AUTO- REGRESSIVE MODELS.

This paper tested two of the simplest and most popular trading rules - Auto- Regressive Models and Moving Averages - by utilising the Australian Dollar relative to US Dollar from 1 Jan 1986 to 9 June 1999. This data set was used by Tan (1995, 1997) in his study in comparing the profitability of systems based on Artificial Neural Networks and ARIMA models. Similar works were done earlier (LeBaron et al. 1995) which utilised Dow Jones index from 1897 to 1986. This paper did not utilise any index data due to the inconsistency of its composite stocks from time to time. The main reason for using the techniques was that they were simple to interpret and calculate, and seemed to work quite well in trending markets. Trading rules were derived from the short and long-term moving averages with the trading signals based on the differences between the two. Combination of relatively longer period moving averages generally outperformed the shorter period moving averages. This was probably the result of eliminating unprofitable whipsaw trades. Buying (selling) signals were generated if the short (long) period moving average crossed above (below) the long (short) period moving average. Certain bands or filters were introduced to reduce the number of unnecessary trades that signals were only generated if the differences between the moving averages exceeded the interest rate differentials and foreign exchange spreads. Periods used were 5, 10, 15, 20 and 25 days for the short-term and 50 to 100 days for the long-term periods. Extensive tests to compare each and every moving average periods to find the best profit were carried out and the highest percentage of winning trades over the test period. The work was extended to utilising support and resistance line as a filter to the buying or selling signals. Trading signals were generated only if the period tested was at the local minimum or maximum, or in other words, identifying key reversal areas. Results confirmed that the use of two-period moving averages with auto-regressive models outperform the simple single-period moving averages. The use of support and resistance lines as part of the filter rules will help a trading system to eliminate unnecessary trading, even though the overall performance does not outperform the previous two models.
Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological

Segmentation is a key process of 2-D barcode identification. In this study we propose a fast adaptive segmentation method that is based on morphological method which is suitable for kinds of 2-D barcode images with different scale, angle and sort. The algorithm is based on mathematical morphology, the basic idea of the algorithm is to use Multi-scale open reconstruction of mathematical morphology to transform the image continuously, then choose whether to terminate by the results of the adjacent image transformation and finally get the final segmentation results by further processing of the images obtain from termination.The proposed approach is applied in experiments on 2-D barcodes with complicated background. The results indicated that the proposed method is very effective in adaptively 2-D barcode image segmentation.
Information Self-Structuring: Key Principle for Learning and Development

Intelligence and intelligence-like processes are characterized by a complex yet balanced interplay across multiple time scales between an agent's brain, body, and environment. Through sensor and motor activity natural organisms and robots are continuously and dynamically coupled to their environments. We argue that such coupling represents a major functional rationale for the ability of embodied agents to actively structure their sensory input and to generate statistical regularities. Such regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. We show how information theoretical measures can be used to quantify statistical structure in sensory and motor channels of a robot capable of saliency-driven, attention-guided behavior. We also discuss the potential importance of such measures for understanding sensorimotor coordination in organisms (in particular, visual attention) and for robot design
The limit of dynamic voltage scaling and insomniac dynamic voltage scaling

Dynamic voltage scaling (DVS) is a popular approach for energy reduction of integrated circuits. Current processors that use DVS typically have an operating voltage range from full to half of the maximum V/sub dd/. However, there is no fundamental reason why designs cannot operate over a much larger voltage range: from full V/sub dd/ to subthreshold voltages. This possibility raises the question of whether a larger voltage range improves the energy efficiency of DVS. First, from a theoretical point of view, we show that, for subthreshold supply voltages, leakage energy becomes dominant, making "just-in-time computation" energy-inefficient at extremely low voltages. Hence, we introduce the existence of a so-called "energy-optimal voltage" which is the voltage at which the application is executed with the highest possible energy efficiency and below which voltage scaling reduces energy efficiency. We derive an analytical model for the energy-optimal voltage and study its trends with technology scaling and different application loads. Second, we compare several different low-power approaches including MTCMOS, standard DVS, and the proposed Insomniac (extended DVS into subthreshold operation). A study of real applications on commercial processors shows that Insomniac provides the best energy efficiency. From these results, we conclude that extending the voltage range below V/sub dd//2 will improve the energy efficiency for many processor designs.
A Window Into Bilingual Reading: The Bilingual Reading Practices of Fourth-Grade, Mexican American Children Who Are Emergent Bilinguals

A qualitative think-aloud study, informed by social literacies and holistic bilingual perspectives, was conducted to examine how six emergent bilingual, Mexican American, fourth graders approached, interacted with, and comprehended narrative and expository texts in Spanish and English. The children had strong Spanish reading test scores, but differed in their English reading and oral proficiency test scores. All but one of them varied their cognitive and bilingual strategy use according to the demands and genre of the text and their oral English proficiency. The most frequent bilingual strategies demonstrated were translating and code-mixing. Only two children used cognates. The children often employed one language to explain their reading in the other language. They displayed a wider range of strategies across two languages compared with a single language, supporting the use of a holistic bilingual perspective to assess their reading rather than a parallel monolingual perspective. Their reading profiles in the two languages were similar, suggesting cross-linguistic transfer, although the think-aloud procedures could not determine strategy transference. The findings supported a translanguaging interpretation of their bilingual reading practices. Future research on how emergent bilingual children of different ages develop translanguaging and use it to comprehend texts was recommended.
An evidence-based technological caregiver skill building intervention for dementia family caregivers: pilot study

Family caregiving is a growing public health concern and caregivers of persons with Alzheimer’s disease or other related dementias (ADRD) need appropriate, timely, and ongoing education and support to successfully meet their caregiving responsibilities. Well-designed interventions can significantly reduce risk concerning caregiver mental and physical health, and improve care recipient outcomes. Few interventions are translated into everyday practice; and most rely on in-person, professionally-led delivery methods, making them inaccessible to many family caregivers. The product developed and tested in this pilot study included six evidence-based Caregiver Skill Building Intervention (CSBI) modules, available via a web-based online format for ADRD caregivers. On-demand interactive training modules made up an integrated web-based product that taught family caregiver skills concerning how to care for their impaired family member. Product strengths include: a) an evidence-based intervention, developed from a prior group-based randomized clinical trial (RCT); b) a focus on caregiver management skill, rather than merely teaching facts; c) an interactive approach, enabling caregivers to apply information to realistic scenarios and receive immediate feedback to their responses; d) content which addresses key caregiving topics; and e) a convenient approach, available 24 hours/day/7 days/week from any location with internet access. The project enrolled 100 family caregivers and established feasibility of translating an RCT group intervention to an individualized web-based format. Caregivers positively evaluated this eLearning process (n=53); significantly increased caregiving skill at 6and 12-weeks, (p=0.01 and 0.05, respectively); and improved caregiver depressive symptoms (p=0.01) and positive affect (p=0.05) at six weeks. Correspondence to: Carol J Farran, DNSc, RN, FAAN, Emeritus Professor and The Nurses Alumni Association Endowed Chair in Health and the Aging Process, Rush University College of Nursing, Adult Health and Gerontological Nursing, 600 South Paulina Street, Armor Academic Center, 1080 AAC, Chicago, IL, USA, Tel: (708) 386-5395, Fax:312-942-3287; E-mail: Carol_J_Farran@rush.edu
Service Identification Strategies in Legacy-to-SOA Migration

Legacy-to-SOA migration has been extensively researched in the last decade. Numerous approaches have been proposed. However, some of the issues still remain, such as candidate service identification in legacy code, and tool supported (semi-)automated and programming language independent service extraction. In this research, such existing issues of legacy-toSOA migration approaches are addressed. The research initially proposes a consolidated legacy-to-SOA migration method, which combines the migration feasibility and supporting technology aspects. The research, then aims at investigating the candidate service identification strategies through architectural reconstruction and source code visualization, detection of design patterns and concept analysis. Finally, the research aims at extracting those identified services using code-query technologies enabling the programming language independent service extraction. The overall result of this research is a set of techniques and tool-sets that facilitates the (semi-)automated legacy-to-SOA migration.
Reduced-order predictive outage compensators for networked systems

Control systems utilizing wireless sensor and actuator networks can be severely affected by the properties of the wireless links. Radio fading and interference may cause communication outage of several samples in situations when the radio environment is noisy and low transmission power is desirable. We propose a method to compensate for outages by introducing a predictive outage compensator (POC), which is a filter to be implemented at the receiver sides of networked control systems and that generates artificial samples during the outage. The main contribution of the paper is to show that a POC can be derived based on a Kalman filter formulation and that it is possible to achieve good performance with a low-order implementation based on Hankel norm approximation. Tradeoffs between achievable closed-loop performance, outage length, and POC order are discussed. The results are illustrated on a simulated example of a multiple-tank process.
YSmart: Yet Another SQL-to-MapReduce Translator

MapReduce has become an effective approach to big data analytics in large cluster systems, where SQL-like queries play important roles to interface between users and systems. However, based on our Face book daily operation results, certain types of queries are executed at an unacceptable low speed by Hive (a production SQL-to-MapReduce translator). In this paper, we demonstrate that existing SQL-to-MapReduce translators that operate in a one-operation-to-one-job mode and do not consider query correlations cannot generate high-performance MapReduce programs for certain queries, due to the mismatch between complex SQL structures and simple MapReduce framework. We propose and develop a system called Y Smart, a correlation aware SQL-to-MapReduce translator. Y Smart applies a set of rules to use the minimal number of MapReduce jobs to execute multiple correlated operations in a complex query. Y Smart can significantly reduce redundant computations, I/O operations and network transfers compared to existing translators. We have implemented Y Smart with intensive evaluation for complex queries on two Amazon EC2 clusters and one Face book production cluster. The results show that Y Smart can outperform Hive and Pig, two widely used SQL-to-MapReduce translators, by more than four times for query execution.
A browsing system with learning capability for internet of things

IoT browser is a useful tool for the Internet of Things (IoT), which provides a novel way for people to interact with daily life objects through the Internet. Comparing with traditional web browsing environment, IoT browsing environment has some distinctive features such as the way of interacting with the physical objects, the importance of spatial-temporal information of objects, and the necessity of resource reuse. In this demonstration, we present a prototype of a proposed IoT browsing system architecture with a semantic-based learning-capable middleware for IoT browser and demonstrate the applicability of the system in a browsing scenario. The learning scheme is able to suggest users the next possible events when the users design their own service flows through web interface. The prototype shows that with the help of the IoT browsing system, heterogeneous devices can cooperate with each other to provide IoT services.
Ca(2+)‐dependent inactivation of Ca2+ current in Aplysia neurons: kinetic studies using photolabile Ca2+ chelators.

1. The kinetics and sensitivity of the Ca(2+)‐dependent inactivation of calcium current (ICa) were examined in intact cell bodies from the abdominal ganglion of Aplysia californica under two‐electrode voltage clamp. 2. Rapid changes in the level of intracellular free calcium ([Ca2+]i) were generated at the cell surface by photolytic release of Ca2+ (nitr‐5 and dimethoxy nitrophen) or Ca2+ buffer (diazo‐4). 3. Diazo‐4 increased ICa by 10‐15% and slowed the rate of ICa decay when photolysed before a test pulse or between a prepulse and a test pulse. The predominant effect of further light flashes was to increase the amount of non‐inactivating current (I infinity) remaining at the end of long (> 1 s) depolarizing pulses. 4. A rapid increase in [Ca2+]i buffering during ICa inactivation did not cause a rapid recovery of current but merely reduced the rate and extent of subsequent inactivation. This effect was not seen when Ba2+ was the charge carrier. 5. Photolytic release of Ca2+ from nitr‐5 produced estimated Ca2+ jumps of 3‐4 microM at the front surface of the cell but failed to augment inactivation either before or during ICa. In contrast, photolysis of DM‐nitrophen 10‐90 ms before the test pulse decreased peak ICa by about 30%. A flash given during ICa rapidly blocked 41 +/‐ 3% of peak current with a time constant of 3‐4 ms at 17 degrees C. Similar results were seen with the barium current (IBa). 6. Microinjection of the potent phosphatase inhibitor microcystin‐LR (5 microM) had variable effects on ICa inactivation and augmented the cyclic AMP‐induced depression of the delayed rectifier (IK(V) by forskolin (100 microM) and 3‐isobutyl‐1‐methylxanthine (IBMX; 200 microM). 7. Full recovery from inactivation measured in two‐pulse experiments took at least 20 s. This slow recovery process was unaffected by increases in intracellular cyclic AMP elicited by direct injection or by bath application of forskolin and IBMX. It was also unaffected by decreases in cyclic AMP induced by injecting 2',5'‐dideoxyadenosine (1 mM) or bath application of the Rp isomer of cyclic adenosine 3',5'‐monophosphothioate (Rp‐cAMPS; 200 microM). 8. A 'shell’ model relating submembrane Ca2+ to inactivation was inconsistent with the experimental results since it greatly overestimated the effects of diazo‐4 and predicted significant inactivation by nitr‐5 photolysis. 9. A model linearly relating [Ca2+]i in a single Ca2+ channel ‘domain’ to inactivation more closely matched the experimental results with diazo‐4 and DM‐(ABSTRACT TRUNCATED AT 400 WORDS)
Short-term price forecasting considering distributed generation in the price-sensitive environment of smart grids

In smart grids environment, all participants, including different load types, are able to utilize the network. Advances in measurement tools in these networks make it easy to move towards dynamic pricing and non-fixed electricity tariffs. In this environment, the forecasted electricity price is declared to wholesale and retail consumers via Advanced Measurement Instruments (AMI). Therefore, motivated by different factors such as optimizing the economic/environmental issues or increasing the reliability, the customers are able to react to prices and manage their consumption. This pattern of reaction brings about extensive changes in the load and price curves of the network. On the other hand, environmental concerns resulting from the use of fossil fuels have increased the exploitation of renewable energies. But, as the penetration of renewable energy sources increases, serious improvements and modifications for the existing electric grid are needed to accommodate and integrate these intermittent sources. In this paper, a hybrid model is presented for simultaneous short term forecasting of electricity prices considering Distributed Generation (DG) in the price-sensitive environment of smart grids. The proposed model combines the Support Vector Regression (SVR) network with an Adaptive Neuro Fuzzy Inference System (ANFIS) network, and it is capable of tracking customers' reaction to declared prices. This model is applied on the data of power markets of Nordpool regrion, Denmark, where the smart grids are very active. The results of short term price forecasting for a target day (1/1/2016) shows the accuracy of the model.