Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Learning Cycle to Deepen Chinese Primary Students' Concept Learning of the "Phases of the Moon".

Background:This study focuses on the internal conditions of students’ concept learning and builds a learning cycle of “phases of the Moon” (MP) to deepen students’ understanding.Material and methods:The learning cycle of MP developed for this study includes three basic learning links, which are: cognitive conflict, abstraction and generalization, and transfer practices. The three basic learning links are named CAT for short. A teaching courseware based on CAT for MP is developed and tested in real classes. 667 students and 21 science teachers use this courseware in 9 primary schools in Beijing. Three approaches for collecting data are incorporated, including: questionnaire surveys for students, interviews for students and teachers, as well as classroom observations by teachers.Results:Findings indicate that CAT for MP is useful to engage students’ thinking and deepen students’ understanding of MP. Findings also suggest that CAT classroom instruction should be learner-centered and inquiry-oriented. Teachers should use the “nondirective language” to guide and boost students’ learning.Conclusions:The CAT learning cycle is worth further promoting to be applied in students’ concept learning. The mechanism and application strategies of CAT, the students’ learning achievements, need to be further studied, so as to make CAT more useful for science classes and learners.
CSR-Contingent Executive Compensation Incentive and Earnings Management

This paper empirically studies the connection between earnings management and corporate social performance, conditional on the existence of CSR-contingent executive compensation contracts, an emerging practice to link executive compensation to corporate social performance. We find that executives are more likely to manipulate earnings to achieve their personal compensation goals when CSR rating is low, as well as their CSR-contingent compensation. Because of public pressure on their excessive total compensation, corporate executives see no need to manipulate earnings to increase compensation when their CSR-contingent compensation is already high. Our results suggest that earnings management and CSR-contingent compensation are substitute tools to serve the interests of executives, which is an agency problem that was never previously studied. Additionally, we explore how managerial characteristics affect earnings management, driven by the incentive effects of CSR-linked compensation.
Design and Test of Embedded SRAMs

Embedded SRAMs can occupy the majority of the chip area in SoCs. The increased process spreads of modern scaled-down technologies and non-catastrophic defect-related sensitivity to environmental parameters can compromise the stability of SRAM cells, which is quantified by a low Static Noise Margin (SNM). A Stability Fault (SF) can present itself in a cell whose SNM is so small that it can accidentally flip in the worst-case operating conditions. In this work, we conduct a comprehensive SRAM SNM sensitivity analysis and identify the major factors causing low SNM. Based on this study, we propose a Weak Cell Fault Model, which can be used in fault simulations to mimic an SRAM cell with a compromised SNM. Furthermore, we have derived an analytical expression for the SNM of the recently proposed loadless 4T SRAM cell. Reading a 6T SRAM cell with bit lines precharged to VDD may not detect several types of defects in the pull-up path of the cell. Such defects can cause the SFs. Regular SRAM March Tests are shown to have extremely limited ability to detect SRAM cells with potential SFs. The traditional Data Retention Test (DRT) is costly in terms of the test time and fails to detect open defects in the cell’s pull-up path of less than 50MΩ, even if provided unlimited pause time at 150C (0.13μm technology). These factors show the disparity between the existing SRAM test practices and the need for an economical and low PPM cell stability tests. We introduce the SRAM Cell Stability Detection Concept explaining the mechanism of the weak cell detection. Based on this concept, we propose three novel programmable Design for Testability (DFT) techniques capable of detecting the SFs and replacing the DRT. For verification of the proposed techniques, we have designed two fully functional SRAM test chips: an asynchronous SRAM (CMOS 0.18μm technology) and a synchronous SRAM (CMOS 0.13μm technology). The simulation and measurement results have proven
Visual Imagery Styles of Architecture Students in the Context of Library Instruction

Within the context of library instruction, this study examined visual imagery styles of first-year architecture students at the point of standard library instruction sessions. There are two types of mental imagery used by individuals: object and spatial. Object imaging involves forming colorful, pictorial representations of objects. Spatial imaging forms schematics of parts in relation to one another. The Object Spatial Imagery Questionnaire was administered to the students and the scores on the imagery scales were assessed. Elements of spatial and object imagery in the library instruction session were identified. The goal of the study was to determine if there were distinctive visualization characteristics among the architecture students and if those characteristics might have future implications for designing effective library instruction.
Subtopic Taxonomy-Aware Framework for Diversity Evaluation

To evaluate search result diversification,which is supposed to meet different needs behind a same query,a number of evaluation frameworks are proposed.Although most of these frameworks take the probability distribution of information needs(usually called subtopics) underlying a query topic into account,they usually do not consider the subtopic taxonomy information.In this paper,the decay function is first introduced to take the subtopic taxonomy information into account.And then based on the decay function,a novel framework called the subtopic taxonomy-aware(STA) framework is proposed to define the structure that the taxonomy-aware diversity evaluation metrics would have.The decay functions used for the informational and navigational subtopics are also discussed.Experiments based on TREC and NTCIR test collections show the effectiveness of the proposed method.
Socio-Demographic Predictors of Food Waste Behavior in Denmark and Spain

Food waste generated at the household level represents about half of the total food waste in high-income countries, making consumers a target for food waste reduction strategies. To successfully reduce consumer food waste, it is necessary to have an understanding of factors influencing food waste behaviors (FWB). The objective of this study was to investigate socio-demographic predictors of FWB among consumers in two European countries: Denmark and Spain. Based on a survey involving 1518 Danish and 1511 Spanish consumers, we examined the associations of age, sex, education, marital status, employment status, and household size with FWB. By using structural equation modeling based on confirmatory factor analysis, we created the variable FWB from self-reported food waste and two activities that have been correlated with the amount of food wasted in previous studies: namely, shopping routines and food preparation. Results show that being older, unemployed, and working part-time were associated with less food waste behavior in both countries. In Denmark, being male was associated with more food waste behavior, and living in a household with four or more people was associated with less food waste behavior. These results underscore the modest role of socio-demographic characteristics in predicting food waste behavior in Europe.
Deliberative democracy: answering the practical challenges

Supervisory Committee Colin Macleod, Department of Philosophy Supervisor Marsha Hanen, Department of Philosophy Departmental Member Scott Woodcock, Department of Philosophy Departmental Member Sceptics about deliberative democracy point out that some issues involve material that is inaccessible to the untrained public, while other issues highlight fundamental value differences that cannot be resolved by public discussion. Value pluralism and public incompetence present serious challenges that threaten to limit the practical scope of deliberative democracy. This project aims to meet these challenges by considering cases that illustrate the successes and failures of public interaction. I draw upon the Oregon experiment, the BC Citizens‘ Assembly, the Quebec kirpan controversy, and Turkey‘s ongoing headscarf debate to outline useful procedures and institutional elements for a more robust deliberative theory. Provided these procedural suggestions, I argue that deliberative democracy can be a functional theory under non-ideal social conditions.
Methods of Leak Search from Pipeline for Acoustic Signal Analysis

Objectives: The article describes the method of defect location determination in the pipelines according to the analysis of their vibrio-acoustic signals. Methods: In particular, the timely detection of transported product leaks will prevent accidents and reduce environmental damage. The graphic programming environment Lab VIEW was used to implement the algorithm of leak search and identification as a working tool. Results: The description and the operation method are provided for the software developed by the authors to record and process the acoustic signals. Conclusion: The aim of the research is to develop methods of transported product leak search according to the analysis of acoustic signals. This work was supported by RFBR grant №. 16-38-00261.
Relationship of the Australian Stock Market with Its Major Trading Partners: A Simple Exposition

This paper examines the relationship between Australian stock market and the equity markets of its major trading partners namely, UK, USA, Canada, Germany, France, and Japan using 1945 to 2002 annual data series. The analysis of this paper considers both the foreign exchange risk element and the notion of share-return parity into its modified structural model which is estimated by applying both OLS and GMM methods. The UK, Canadian and French markets are found significant from OLS estimation; while GMM estimation suggests four markets including the German are significant at 5% level. Accordingly, this paper concludes that Australian stock market is related to that of its trading partners; and it is being influenced by 3 to 4 markets of which the UK is the most significant. The outcomes of this paper seem beneficial to Australian investors for effective diversifications of their international portfolio risks.
Research on Extraction and Integration of Developing Event Based on Analysis of Space-time Information

Technology of information extraction(IE) can provide high-quality service for retrieval.Targeting at events in web news,this paper conducts a system that can extract and integrate key information of event that interests people.Methodologies and strategies of the system are as follows:(1) Extraction rules are built in terms of sentence patterns,then event information is directly extracted from the text in which temporal phrases(TP) and space phrases(SP) are recognized and normalized.The extraction system can thus be easily implemented owing to skipping complex syntax parsing. (2) The same event in different documents is linked by normalized TP and SP of event,and the information associated with an event is merged. (3) When new event appears in a text,the text is segmented.So isolative information for an event in same segment can be merged into its owner.Preliminary experiments show that methodologies and strategies in this paper are feasible.
Electricity Load Forecasting Using Machine Learning Techniques

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques
A Embedded System-based Computing Platform for Tolerating Untrusted Component

Considering that there are many untrusted components on the legacy OS,which leads to security operation's hidden troubles on the computing terminal,the paper proposes a new computing platform,which is based on the embedded system and tolerates untrusted components.In addition,the platform proposed in this paper doesn't need change hardware structure and the OS on the legacy terminal system.Secondly,the model assures the existence of untrusted components can't bring serious information security threats.At the same time,the paper also proposes a new integrity measurement method which integrates IMA and SB methods,offers effective security protection for the booting of the platform,the loading of the process and the running of the procedure.
USE OF FACEBOOK GROUP AS BLENDED LEARNING AND LEARNING MANAGEMENT SYSTEM IN WRITING

In this paper, I will report qualitative research f indings on how undergraduate students at a small-sized university in Thailand perceive Faceboo k group usage in a writing class. The findings suggest that the Facebook group can be use d as blended learning (a hybrid instructional model) and learning management system (posting announcements and comments relevant to the class and their writing assignments ) for the students to learn with, not to learn from, as facilitated by the instructor. The reasons were that they positively viewed the Facebook group usage as a practical, trendy and ben eficial teaching tool, which helped to motivate them to learn English virtually and enhanc e their positive attitudes towards learning the language. When they encounter virtual communica tion apprehension, the Facebook group usage appeared to be a booster neutralizing their p os tive attitudes towards virtual interactions. Such positive results may derive from the fact that e students grew up as digital natives and the Millennial Generation using Facebook, the promi nent social network site, as parts of their daily life. Teachers of English, who may be seen as digital immigrants or digital immigrant accent, may need to consider use of learning techno logies in their writing class.
Digital neuroimplementations of visual motion-tracking systems

The paper describes the implementation of neural systems for visual motion tracking on a digital neurocomputing platform, i.e., the NLX420 Neural Processing Slice. The chip architecture and the problem model considered greatly facilitate the implementation task, which involves the fulfilment of critical real-time constraints. Experimental results confirm the approach validity in terms of both speed and prediction accuracy; training adjustment techniques are also examined.
LDA-based Model for Online Topic Evolution Mining

A computational model for online topic evolution mining was established through a latent semantic analysis process on textual data.Topical evolutionary analysis was achieved by tracking the topic trends in different time-slices.In this paper,Latent Dirichlet Allocation(LDA)was extended to the context of online text streams,and an online LDA model was proposed and implemented as well.The main idea is to use the posterior of topic-word distribution of each time-slice to influence the inference of the next time-slice,which also maintains the relevance between the topics.The topic-word and document-topic distributions are inferenced by incremental Gibbs algorithm.Kullback Leibler(KL)relative entropy is uesd to measure the similarity between topics in order to identify topic genetic and topic mutation.Experiments show that the proposed model can discover meaningful topical evolution trends both on English and Chinese corpus.
Software Metrics. A Rigorous Approach

This Wiley Series is already notable for a number of very interesting texts. This is another well produced, detailed volume, though one can question why in a 1991 publication, in an area as fast moving and voluminously written about as information systems, except in the section on telework, it is difficult to find a reference after 1987. The book is organized into four sections. The first covers fundamental issues, including the organizational scan and scope of DIS, the nature of office technology, and the integration of business systems, the strategic use of DIS and the likely impacts of DIS. Much of this is well-trodden ground but is clearly discussed, with interesting case examples. Part Two covers operational issues in OIS, namely planning and needs analysis for OIS, an.d implementing and evaluating OIS. The needs analysis section provides the more original material here, but the section on evaluation comes in at just over two pages and is far too brief to be helpful. Part Three lo.oks at using computers to monitor performance, flexible work and OIS, and end-user computing. A managerial perspective is assumed throughout, and key management questions are usefully highlighted. Part Four looks at OIS and the future, with the authors suggesting that IS needs to be much more focussed on organizational processes and required organizational goals. OIS applied without a clear understanding of the business 'is a recipe for disaster'. The authors provide an informative introduction to the practitioners and students they address, and on a few subjects they provide something more than that.
THREE MODELS OF US FOREIGN AID POLICY: A COMPARATIVE TEST

This article aims to test competing explanations about the US foreign aid policy, namely interest-driven theory, humanitarian theory, and domestic political theory. Using longitudinal data on the amount of Official Development Assistance provided by the US to 155 recipient countries from 1960 to 2008, analyzed using random coefficient models, the research found that humanitarian and domestic political theories can account for the US foreign aid policy to the recipient countries for almost five decades better than the interest-driven theory. Generally, the US were more likely to send aid to poorer countries and/or countries where the US-based NGOs were actively involved. The econometric models also show that there are some cross-sectional and temporal variations in the aid. On average, countries receiving high amount of aid in the 1960s tend to have lower annual growth rate in the money they received from the US.
Foreign Direct Investment Productivity Premium and Foreign Affiliates' Heterogeneity: A Comparison between Advanced and Emerging Market Overseas Investments in the EU

This paper aims to investigate the existence of productivity heterogeneity among foreign‐owned firms (FOFs) based in the European Union (EU). Using firm‐level data for a sample of FOFs investing in the EU over the period 2006–14, we find that foreign affiliates from advanced countries (AFOFs) show a positive productivity gap compared to foreign affiliates from emerging countries (EFOFs). However, when we consider the type and the motivation of foreign direct investment, our results reveal that, while AFOFs always seem to be more productive than EFOFs in manufacturing sectors, EFOFs appear to enjoy a productivity premium compared to AFOFs in the services, when their activity occurs in the same industry as their parent and they operate in less knowledge‐intensive market sectors.
U.S. Fiscal Policy and Asset Prices: The Role of Partisan Conflict

Fiscal policy shocks exert wide-reaching effects, including movements in asset markets. U.S. politics have been characterized historically by a high degree of partisan conflict. The combination of increasing polarization and divided government leads not only to significant Congressional gridlock, but also to spells of high fiscal policy uncertainty. This paper adds to the literature on the relationships between fiscal policy and asset prices in the U.S. economy, conditional on the degree of partisan conflict. We analyze whether a higher degree of partisan conflict (legislative gridlock) reduces the efficacy of the effect and response of fiscal policy on and to asset price movements, respectively. We find that partisan conflict does not significantly affect the relationships between the fiscal surplus to GDP and housing and equity returns. Rather, if important, partisan conflict affects the actual implementation of fiscal policy actions.
Intelligent information recommendation algorithm based on user model clustering

Intelligent information recommendation service based on data mining has becoming an important research task increasingly.Aimed at the shortcomings of current intelligent information recommendation algorithm,an intelligent information recommendation al-gorithm based on user model clustering(IRUMC) is presented.IRUMC clusters similar user models,produces user clustering models,then matches user session and user clustering models,finally gains recommendation sets.It fits new users,less-time visitors and users who need fresh information.The experimental results show IRUMC is effective.
Optimizing multi-objective sequencing problem in mixed-model assembly line on just-in-time: particle swarm optimization algorithm

Abstract This paper deals with the formulation of a sequencing problem with the dual goals of varying the parts utilization at different workstations of the assembly line and varying the workload associated with each workstation; these two objectives are typically inversely correlated with each other, and therefore the simultaneous optimization of both is challenging. Owing to the NP-hardness of the problem, this paper introduces a Discrete Particle Swarm Optimization (DPSO) algorithm, a Memetic Algorithm (MA), a Weighted sum Multi-Objective Genetic Algorithm (MOGAW), and a Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a just-in-time sequencing problem where these objectives are to be optimized simultaneously. The performance of these algorithms is compared against each other on small, medium, and large problems. According to statistical analysis, experimental results show that discrete particle swarm optimization outperforms the other algorithms in respect of a comparison metric.