Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring AWE-supported writing process: An activity theory perspective

Despite the growing interest in investigating the pedagogical application of Automated Writing Evaluation (AWE) systems, studies on the process of AWE-supported writing are still scant. Adopting activity theory as the framework, this qualitative study aims to examine how students incorporated AWE feedback into their writing in an English as a foreign language setting. We conducted semi-structured interviews with four Chinese students sampled from two classes and collected their AWE submissions and feedback for data analysis. Our findings demonstrate that AWE-supported writing is a tool-mediated, purposive, and collective activity shaped by individual and contextual factors. Students used various strategies to attain their learning goals and to address the tensions arising from their activity systems. This study contributes to the research on the effectiveness of AWE by assuming a process-oriented approach that was informed by activity theory. Our findings also shed light on the complex process of second language writing mediated by new technology innovations. Pedagogical implications of our findings are discussed in the conclusion.
ConnectorX: Accelerating Data Loading From Databases to Dataframes

Data is often stored in a database management system (DBMS) but dataframe libraries are widely used among data scientists. An important but challenging problem is how to bridge the gap between databases and dataframes. To solve this problem, we present ConnectorX, a client library that enables fast and memory-efficient data loading from various databases to different dataframes. We first investigate why the loading process is slow and consumes large memory. We surprisingly find that the main overhead comes from the client-side rather than query execution or data transfer. We integrate several existing and new techniques to reduce the overhead and carefully design the system architecture and interface to make ConnectorX easy to extend to various databases and dataframes. Moreover, we propose server-side result partitioning that can be adopted by DBMSs in order to better support exporting data to data science tools. We conduct extensive experiments to evaluate ConnectorX and compare it with popular libraries. The results show that ConnectorX significantly outperforms existing solutions. ConnectorX is open sourced at: https://github.com/sfu-db/connector-x.
Particle Swarm Optimization from Animation to Recent Trends: A Systematic Review

: Particle Swarm Optimization (PSO) is a natural phenomenon computing technique which came into existence in mid-1990 and belongs to swarm intelligence (SI) family. Many researchers tried to modify the core algorithm while other tried hybridization by combining best features of other algorithm. In this comprehensive investigation paper, a systematic and chronological effort has been made for literature review from 1983 to 2019 which fits the need of researcher from starting with all the developments like historical development, addition of new parameters, tuning or refinement of parameters and its variants for different optimization problems with constraints, multi-objectives. In addition this paper also covers the literature survey of parallel PSO, its hybrids, communication topology and for multi-objective problems strategy used for parallel computing is covered in detail. This review will give a correct direction for future study and open problems to many researchers.
Colors of c-type RR Lyrae Stars and Interstellar Reddening

RR Lyrae stars pulsating in the fundamental mode have long been used to measure interstellar reddening, based on their observed uniformity of $B-V$ color at minimum light after small corrections for metallicity and period are applied. However, little attention has been paid to the first overtone pulsators (RRc or RR1). We present new $V-I$ observations of field RRc stars, supplemented with published data from uncrowded RRc in globular clusters. Preliminary results indicate the RRc colors are correlated with period, but appear to be independent of the stars' metallicity. The scatter around the period-color relation is slightly larger than a comparable relation for RRab. Thus, RRc can be useful indicators of line of sight reddening toward old stellar systems, particularly when multiple stars are available as in Oosterhoff II globular clusters and metal-poor galaxies.
Economic-Environmental Optimal Management of Smart Residential Micro-Grid Considering CCHP System

Abstract Due to issues such as air pollution and global warming, Distributed Energy Resources (DERs) including Distributed Generations (DGs), Energy Storage Systems (ESSs) and Demand Response Programs (DRPs) are gaining increasing attention to reduce dependency on fossil fuels as well as environmental pollution. In this article, a multi-objective optimization of economic environmental scheduling of DERs in a smart residential Micro-Grid (MG) considering numerous constraints and Demand Response Programs (DRPs) has been presented. To optimize the aforementioned objective functions, a new algorithm called Adaptive Fuzzy Multi-Objective Particle Swarm Optimization (AFMOPSO) has been proposed. This algorithm has a great capability to avoid local optimal solutions by finding a compromise between the general and local search within the decision making area. In addition, a comprehensive Combined Cooling Heating and Power (CCHP) system based on various thermodynamic equations has been proposed. The results demonstrate the efficient performance of the proposed algorithm compared to the other traditional optimization methods via some diversity and spacing criteria.
: Multi-Source Behavioral Aggregation of Natural Language Cyber Threat Intelligence

The current state-of-the-art in extracting machine-readable attack behavior graphs from natural language cyber threat intelligence (CTI) reports relies on one prominent CTI source such as a high-profile advanced persistent threat (APT) report. This thesis hypothesizes that multiple CTI sources offer complementary fragments of attack behavior due to factors such as variation in analysis details of an attack, polymorphic nature of malicious behavior manifestations, and experience and resources available to the analyst(s) who produce a CTI report. To test this hypothesis, this work proposes a systematic attack behavior graph aggregation approach, called CYTAG, that significantly enhances the fidelity of an attack graph given multiple CTI sources about a given attack such as an APT. CYTAG achieves this while preserving attack semantics and minimizing redundancy of nodes and edges in the aggregated attack graph. Evaluation of CYTAG on CTI reports covering multiple years and comparing its attack graph aggregation results with state-of-the-art attack behavior extraction approach suggests that CYTAG significantly improves the detection and forensics arsenal of cyber threat hunters with reasonable aggregation performance overhead.
EVAULATION OF THE P-WAVE SEISMOGRAM APPROACH TO ESTIMATE V s 30 TECHNICAL

The P-wave seismogram method is utilized to estimate the 𝑉 𝑆30 of 194 stations in California. Comparison between the 𝑉 𝑆30 measurement shows that the measured and estimated 𝑉 𝑆30 of 77% stations are within ±50% range of measurement, which validates the performance of the P-wave seismogram method in estimating 𝑉 𝑆30 . However, stations with average measured 𝑉 𝑆30 greater than 400 m/s may be potentially overestimated by the P-wave seismogram method. Comparison between the residual obtained by this study and by the geology and slope based 𝑉 𝑆30 map suggests that less dispersion relative to measured 𝑉 𝑆30 is achieved by the P-wave seismogram method than the proxy-based method, but the 𝑉 𝑆30 predicted by the P-wave seismogram method may be more overestimated when the estimated 𝑉 𝑆30 is greater than about 400 m/s. The effect of using a more detailed crustal model is examined, and the result shows that the 𝑉 𝑆30 tends to be underestimated for events with focal depth smaller than 2.5 km when the more detailed crustal model is used, which is because of the layers with low P-wave velocity and steeper gradient of P-wave velocity at shallow depths in the more detailed crustal model.
A Machine Learning Approach for Conflict Resolution in Dense Traﬀic Scenarios with Uncertainties

—With the continuous growth in the air transportation demand, air trafﬁc controllers will have to handle increased trafﬁc and consequently more potential conﬂicts. That gives rise to the need for conﬂict resolution tools that can perform well in high-density trafﬁc scenarios given a noisy environment. Unlike model-based approaches, learning-based or machine learning approaches can take advantage of historical trafﬁc data and ﬂexibly encapsulate the environmental uncertainty. In this study, we propose an artiﬁcial intelligent agent that is capable of resolving conﬂicts, in the presence of trafﬁc and given uncertainties in conﬂict resolution maneuvers, without the need of prior knowledge about a set of rules mapping from conﬂict scenarios to expected actions. The conﬂict resolution task is formulated as a decision-making problem in large and complex action space, which is applicable for employing the reinforcement learning algorithm. Our work includes the development of a learning environment, scenario state representation, reward function, and learning algorithm. As a result, the proposed method, inspired from Deep Q-learning and Deep Deterministic Policy Gradient algorithms, can resolve conﬂicts, with a success rate of over 81%, in the presence of trafﬁc and varying degrees of uncertainties.
The Impact of Country Risk on Expected Return and Volatility: Evidence from Emerging Oil & Gas Stock Markets

While many studies examined the impact of country risks on the predictability and volatility of national stock market returns, most of these studies have ignored Oil & Gas (O&G) emerging stock markets. Given the importance of the O&G economies in the global economy and the increased level of investors’ interest in O&G emerging stock markets, this paper investigates the potential impact of country risk on the O&G emerging stock market return predictability and volatility. A sample of 10 emerging O&G stock markets are included in this study to examine the behaviour of these countries’ monthly stock markets return in response to the changes in their related country political, financial, economic and credit risk rating over the period from January 2000 to December 2013. the common believe that stock returns depend on a single factor as suggested by the well-known single factor CAPM or that international stock returns depend only on the global market portfolio as suggested by the ICAPM, this study shows strong and significant evidences that country-specific political, financial, economic and credit risk drive emerging O&G stock market return and volatility. Results show that political risk an essential rule in predicting stock market return and volatility of all O&G stock markets under investigation. Portfolio managers and investors must therefore take country risk into consideration in addition to the conventional variables that are often used to analyse equity investments when making portfolio investment decisions in emerging markets. show strong evidence that local country factors and information tend to have more predictive power of the national stock market return than the world factors and information. Evidences were provided from developed markets as well as emerging markets. It was shown by El-Sady et al. (2003a,b,c) provide evidences from emerging and developed markets showing that specific country political, financial and economic risk factors have more predictive power to estimate its stock market return and volatility than world factors used by the regular ICAPM. Our study adopts the approach of El-Sady et al. who tested the relationship between country specific risk and stock market returns.
Using a static naming approach to implement remote scope promotion

: GPUs employ simple coherence mechanisms and require explicit use of costly synchronization operations for data integrity. Local-scoped synchronization can be utilized to lower the performance penalty of synchronization when sharing is within a subgroup of threads. Unfortunately, in asymmetric sharing (which is an important dynamic sharing pattern), it is necessary to use global-scoped synchronization due to possible accesses by remote sharers. Remote Scope Promotion (RSP) was introduced to take advantage of local-scoped synchronization at regular accesses while using scope promotion at occasional remote accesses. First implementation of RSP makes use of a simple approach that performs costly cache operations on all L1 data caches when implementing scope promotion, and therefore, it performs poorly on large scale GPU systems. We present nRSP which utilizes a static naming mechanism to identify regularly accessing agent in asymmetric sharing and avoids applying costly coherence actions on every L1 data cache when implementing scope promotion. We evaluate nRSP using timing detailed Gem5-APU simulator modeling a GPU system with 128 Compute Units and show that nRSP lowers remote synchronization overhead greatly and improves performance considerably. On average, nRSP provides around 28% speedup on a 128 Compute Unit GPU device.
Structural Equation Model on Professional Development of Public School Teachers in Relation to their Time Management, Work Task Motivation and Aspect of Identity Orientation

— The study aimed to determine the best fit model for the professional development of public-school teachers in region XII. This study utilized descriptive-correlational design and employed structural equation modeling. Using a stratified random sampling technique, the researcher chose 500 public school teachers across region XII as the respondents. To gather factual data, the researcher used pilot-tested and enhanced adapted questionnaires to determine the level of time management, work task motivation, aspect of identity orientation, and the professional development of public-school teachers through a google form. Results revealed that the time management of public-school teachers in region XII is very high, whereas the work task motivation and aspect of identity orientation are high. In addition, time management, work task motivation, and aspect of identity orientation all show a significant positive correlation to professional development. Further, results showed that work task motivation and aspects of identity orientation influenced professional development. However, time management does not predict professional development. Lastly, the structural equation model 5 revealed that professional development in terms of thematic knowledge, learning environment, educational technology, planning, evaluation, and development of human resources of public-school teachers in the region is best anchored on their time management in terms of its importance, self-conscious, coping, and implementation efforts and supported by their aspect of identity orientation in terms of personal and collective identity orientation.
How fast do you heal? A taxonomy for post-compromise security in secure-channel establishment

Post-Compromise Security (PCS) is a property of secure-channel establishment schemes, which limits the security breach of an adversary that has compromised one of the end-point to a certain number of messages, after which the channel heals . An attractive property, especially in view of Snowden’s revelation of mass-surveillance, PCS was pioneered by the Signal messaging protocol, and is present in OTR. In this paper, we introduce a framework for quantifying and comparing PCS security, with respect to a broad taxonomy of adversaries. The generality and flexibility of our approach allows us to model the healing speed of a broad class of protocols, including Signal, but also an identity-based messaging protocol named SAID, and even a composition of 5G handover protocols.
INTEGRATION OF RESTFUL WEB OF THINGS FOR SMART HOME ENERGY MANAGEMENT

- Internet of Thing (IoT) concept has matured with the evolution of smart things. Smart home notion is highly favored with the extensive attention on IoT. However, realistic implementation of energy efficient smart home architectures is hindered by platform incompatibilities among heterogeneous smart devices. Web of Things (WoT) expose these connected devices over the web in a more transparent and effective way. Thus, we proposed a WoT based sensor network to collect and share data of a smart home employing representational state transfer (REST) architecture. The smart home scenario is tested through computer simulation. The simulation results revealed the performance enhancement in terms of energy efficiency of domestic appliances, device discovery performance, response time, and failed transmissions.
Transformational Learning in Teacher Context

In today’s context, teachers not only teach, but also help to develop students to become useful citizens. The principles of transformational learning require teachers to change their beliefs, behaviours and emotions. At the end, teachers should be able to reflect on their experiences to help students holistically and thus this article will help to provide comprehensive overview of transformational learning among teachers. The scope of this review paper covers articles written between 2015 and 2020. Among them are peer coaching, implementation of adaptive change models (ACM), conflict, training, service learning pedagogy and approaches relating to culture. Previous studies have shown that transformational learning theory can enhance teacher effectiveness and bring about dissemination of changes. Therefore, there is a need to explore the relationship and the inconsistencies between Mezirow’s transformational learning theory and that of teaching theories practised by teachers. It is noted that most studies show significant relationships between the two theories but some studies claim that they are not significant. It is hoped that this study will be of benefit to practicing teachers to develop balanced and harmonious people. This article reports on the experiences of teachers who began their education in a university located in Australia. A mixed method approach, namely online surveys and semi structured interviews, was used to collect data. The data indicated that the respondents experienced several phases of Mezirow’s transformative learning theory (TLT). The results show that teachers are passionate about their future careers but all teachers show a sense of burden in their studies. As a result, teachers have experienced a dilemma in the early stages. This study focuses on how teachers adapt and how their experiences relate to TLT. A total of 72 respondents were involved in the survey and 11 respondents participated in face-to-face interviews. The majority of respondents were women comprising 86%. Those aged 21 years and over made up 47.4%. Lummis et al maps the interview questions to the TLT phase follows:
Running Head: FAKE NEWS AND IMPLICIT MEASURES 1 Exploring the Phenomenon of Fake News with Implicit Measurements

Fake news – fraudulent information which mimics the formation of online news media content – has been part of the national conversation since the 2016 US elections. Recent studies have implicated automatic processes, as opposed to controlled, in the perceived accuracy of fake news. This study explored the association between blatantly false news headlines and implicit truth evaluations by using the Implicit Association Test (IAT). Participants were either exposed to fake news headline posts with warning tags or real new headline posts with no tags, or not exposed to any stimuli. We then measured participants’ implicit truth evaluations and self-reported perceived accuracies of real and fake headlines. After controlling for political orientation, participants with prior exposure to fake news headlines had lower implicit truth evaluations and decreased perceived accuracy for fake news. This implies that prior exposure of fake news headlines with warning tags may have had a direct effect on implicit evaluations but participants were able to reject the proposition while reporting their perceived accuracy of fake news headlines. exposure to either fake or real news headlines. After controlling for political orientation, the results of the study observed an illusory truth in implicit evaluations but only for participants exposed to fake news headlines with tags. Exposure to real news headlines did not have an impact on implicit truth evaluations nor the explicit measurements of perceived accuracy. Design limitations with the political nature and the degree of plausibility of the stimuli were highlighted and future study designs were proposed.
Consensus Building in On-Line Citizen Science

A number of initiatives invite members of the public to perform online classification tasks such as identifying objects in images. These tasks are crucial to numerous large-scale Citizen Science projects in different disciplines, with volunteers using their knowledge and online support tools to, for example, identify species of wildlife or classify galaxies by their shapes. However, for complex classification tasks, such as this case study on identifying species of bumblebee, reaching an agreement between volunteers - or even between experts~-~may require consensus-building processes. Collaboration and teamwork approaches to problem solving and decision-making have been widely documented to improve both task performance and user learning in the real world. Most of these processes and projects are mediated online through feedback delivered in an asynchronous manner, and this article thus addresses a central research question: How do participants involved in species identification tasks respond to different forms of feedback provided in online collaboration, designed to support peer-learning and improve task performance? We tested four different approaches to feedback within a collaboration task, where participants reviewed their previously annotated data based on information curated from their peers on a long running online citizen science initiative. The selected interfaces have a strong foundation in social science and psychology literature and can be applied to citizen science practices as well as other online communities. Results showed that while all four approaches increased accuracy, there were differences based on the types of consensus that existed before collaboration. Such differences highlight the usefulness of different forms of feedback during collaboration for increasing data accuracy of identification and furthering users' expertise on identification tasks. We found that anonymised and goal-directed free text comments posted on social learning interfaces were most effective in improving data accuracy as well as creating opportunities for peer-learning, particularly where the species identification task was more difficult. This study has significant implications for extending the practice of citizen science across formal and informal learning environments and reaching out to a variety of users.
Sub-pixel Mapping of Sahelian Wetlands using Multi-temporal SPOT VEGETATION Images

- Africa supports some of the world’s largest swamps. These wetlands are very extensive and dynamic eco-systems. These characteristics make them suitable objects for study using satellite images with coarse spatial but high temporal resolution, such as SPOT-VEGETATION data. At coarse spatial resolutions pixels inevitably become mixed. For mixed pixels ‘soft’ classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. The basic hypothesis of the linear spectral unmixing model is that the value of each pixel can be modeled as a linear combination of the land cover spectra present in the image and their respective fractions. Multiple linear regression techniques can be used to solve these models. The resultant fraction values can be assigned to sub-pixels, based on the assumption of spatial dependence and the application of linear optimisation techniques.