Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Executive Processing in Children's Arithmetic

This project examined the role of executive processing in the development of arithmetic addition in 163 children aged from 6 to 9 years. Executive processing was conceptualised as involving working memory, inhibitory control, and relational processing. Further distinctions were made within each construct. Working memory was operationalized as simultaneous storage and processing, and as updating. Three types of inhibitory control (switching, proactive interference, inhibition of irrelevant information) were assessed. Relational processing was assessed at binary-and ternary-relational levels of complexity. Thus, the assessment of executive processing is broader than in most existing research which has adopted narrower interpretations of executive processing. Arithmetic competency involves procedural skill and conceptual understanding, so both aspects were investigated. Procedural skills were assessed using 48 addition problems that varied in terms of their working memory, inhibitory control, and relational processing demands as well as their presentation format (object, word, numeral). Children’s understanding of four conceptual principles (identity, commutativity, associativity, addition-subtraction complement) was assessed using a method similar to that of Canobi, Reeve, and Pattison (1998). There were four main research questions. The findings are summarised in relation to each. First, to what extent does procedural performance vary as a function of executive processing demands, age and task format? The findings showed that arithmetic addition problems that imposed higher working memory loads, higher inhibitory control loads, and higher relational processing loads were more difficult than comparable problems that imposed lower loads. However, the magnitude of these effects varied according to task format (object, word, numeral) and age. In most cases older children were less sensitive to cognitive load than younger children were.
Diffusion effects : Control group contamination threats to the validity of teacher-administered interventions

Within-class experimental designs (with experimental and control groups in the same classroom) are subject to diffusion effects whereby both experimental and control students benefit from the intervention, thereby contaminating the control group and biasing evaluations of intervention effects. Although the problem has been recognized, most previous demonstrations have been anecdotal. In support of diffusion effects, we show that a classroom intervention resulted in higher academic self-concepts for internal (within-class) controls compared with external (between-class) controls. The construct validity of the interpretation of this difference as a diffusion effect was supported by observer and teacher comments and ratings of teacher success in focusing the intervention on experimental students and by different patterns of results for teachers who were more or less successful in maintaining this focus. Potential dangers in sole reliance on internal within-class control groups may outweigh advantages of this expedient experimental design.
“When I went to Canada, I saw the madre": evaluating two theories' predictions about codeswitching between determiners and nouns using Spanish-English and Welsh-English bilingual corpora

Abstract Previous work on intrasentential codeswitching has noted that switches between determiners and their noun complements are frequent in both Spanish–English and Welsh–English data. Two major recent theories of codeswitching, the Matrix Language Frame model and a Minimalist Program approach, make potentially competing predictions regarding the source language of the determiner in these mixed nominal constructions. In this paper we evaluate the predictions of each theory with reference to comparable sets of Spanish–English and Welsh–English codeswitching data. Mixed nominal constructions are extracted to test the compatibility of these data with the predictions, taking into account coverage and accuracy. We find that the data are broadly consistent with each set of predictions but do not find statistically significant differences between the accuracy of the predictions of the two theories. We examine in detail the counterexamples to the predictions of each theory to see what further factors may influ.
Scalable High-Speed Communications for Neuromorphic Systems

Field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), and other chip/multi-chip level implementations can be used to implement Dynamic Adaptive Neural Network Arrays (DANNA). In some applications, DANNA interfaces with a traditional computing system to provide neural network configuration information, provide network input, process network outputs, and monitor the state of the network. The present host-toDANNA network communication setup uses a Cypress USB 3.0 peripheral controller (FX3) to enable host-to-array communication over USB 3.0. This communications setup has to run commands in batches and does not have enough bandwidth to meet the maximum throughput requirements of the DANNA device, resulting in output packet loss. Also, the FX3 is unable to scale to support larger single-chip or multi-chip configurations. To alleviate communication limitations and to expand scalability, a new communications solution is presented which takes advantage of the GTX/GTH high-speed serial transceivers found on Xilinx FPGAs. A Xilinx VC707 evaluation kit is used to prototype the new communications board. The high-speed transceivers are used to communicate to the host computer via PCIe and to communicate to the DANNA arrays with the link layer protocol Aurora. The new communications board is able to outperform the FX3, reducing the latency in the communication and increasing the throughput of data. This new communications setup will be used to further DANNA research by allowing the DANNA arrays to scale to larger sizes and for multiple DANNA arrays to be connected to a single communication board.
Understanding Dynamic Software Behavior with Tools for Retroactive Investigation

The web is a widely-available open application platform, where anyone can freely inspect a live program's client-side source code and runtime state. Despite these platform advantages, understanding and debugging dynamic behavior in web programs is still very challenging. Several barriers stand in the way of understanding dynamic behaviors: reproducing complex interactions is often impossible; finding and comparing a behavior's runtime states is time-consuming; and the code that implements a behavior is scattered across multiple DOM, CSS, and JavaScript files. This dissertation demonstrates that these barriers can be addressed by new program understanding tools that rely on the ability to capture a program execution and revisit past program states within it. We show that when integrated as part of a browser engine, deterministic replay is fast, transparent, and pervasive; and these properties make it a suitable platform for such program understanding tools. This claim is substantiated by several novel interfaces for understanding dynamic behaviors. These prototypes exemplify three strategies for navigating through captured program executions: (1) by visualizing and seeking to input events – such as user interactions, network callbacks, and asynchronous tasks; (2) by retroactively logging program states and reverting execution back to log-producing statements; and (3) by working backwards from differences in visual output to the source code responsible for inducing output-affecting state changes. Some of these capabilities have been incorporated into the WebKit browser engine, demonstrating their practicality.
PERCEPATAN PERGERAKAN TANAH MAKSIMUM DAERAH CEKUNGAN BANDUNG: STUDI KASUS GEMPA SESAR LEMBANG

The highly populated Bandung Basin needs a seismic hazard map, especially due to the existence of Lembang Fault on the north of the area. In this paper, the seismic hazard map was constructed based on calculation of peak ground acceleration (PGA) using Boore’s (1997) empirical attenuation relationship. The model was an assumed earthquake with moment magnitude (Mw) of 6 that was caused by Lembang Fault activity. Data input consists of velocity of seismic wave S, that was obtained through geological unit classification. The result shows three zones with highest PGA values: near the seismic source, lowland at the south of Bandung, and a small area at the west of Bandung.The most significant factors on high PGA values are the distance from the source of an earthquake and the geological unit of an area. Keywords: peak ground acceleration, Lembang Fault, Bandung Basin
Android Malware Detection Using Network-based Approaches

This thesis is focused on the use of networks to identify potentially malicious Android applications. There are many techniques that determine if an application is malicious, and they are ever-changing. Techniques to identify malicious applications must be robust as the schemes of creating malicious applications are changing as well. We propose the use of a network-based approach that is potentially effective at separating malicious from benign apps, given a small and noisy training set. The applications in our data set come from the Google Play Store and have been scanned for malicious behavior using Virus Total to produce a ground truth dataset. The apps in the resulting dataset have been represented as binary feature vectors (where the features represent permissions, intent actions, discriminative APIs, obfuscation signatures, and native code signatures). We use the feature vectors corresponding to apps to build a weighted network that captures the “closeness” between applications. We propagate labels, benign or malicious, from the labeled applications that form the training set to unlabeled applications (which we aim to label), and evaluate the effectiveness of the proposed approach in terms of precision, recall and F1-measure. We outline the algorithms for propagating labels that were used in our research and discuss the fine tuning of hyper-parameters. We compare our results to known supervised learning algorithms, such as k-nearest-neighbors and Naive Bayes, that can be used to learn classifiers from the training labeled data and subsequently use the classifiers to label the unlabeled test data. We discuss potential improvements on our methods and ways to further this research.
Research and Comparison of SQL Optimization Techniques based on MapReduce

MapReduce [1] is a software framework for distributed computing. It has been widely used in data processing and analysis. SQL-like queries are also playing important roles in data analysis. In this paper, we study different approaches to optimize SQL-like queries in MapReduce and evaluate them on benchmark dataset in a Hadoop cluster. The result shows that these optimizations including adjust join order, push down predicates and merge different MapReduce tasks can significantly speed up analytical queries.
Lead Independent Directors: Good Governance or Window Dressing?

We document the emergence of the lead independent director (LID) role in a sample of U.S. firms in 1999-2009. We find that firms with an LID are valued higher, provide stronger risk taking incentives for CEOs, and are more likely to terminate poorly performing CEOs. We also find that CEOs of firms with an LID receive lower excess cash compensation. These results hold after applying various econometric techniques such as instrumental variables analysis, firm fixed effects and first differences regressions. Taken as a whole, the results suggest that LIDs enhance corporate governance.
Research of software defect prediction based on GRA-SVM

The accuracy and applicability of most traditional software defect prediction is not very high. This paper puts forward a software defect prediction model based on grey relational analysis (GRA) and support vector machine (SVM) to solve the problem. In this paper, firstly, use grey relational analysis to reduce the dimensions of software defect metrics, and extracted the data which was irrelevant with software defect prediction in order to improve the speed of operation. Then, the software defect prediction model is established by using SVM, and the 10-fold CV is adopted to validate the model.The accuracy and applicability of most traditional software defect prediction is not very high. This paper puts forward a software defect prediction model based on grey relational analysis (GRA) and support vector machine (SVM) to solve the problem. In this paper, firstly, use grey relational analysis to reduce the dimensions of software defect metrics, and extracted the data which was irrelevant with software defect prediction in order to improve the speed of operation. Then, the software defect prediction model is established by using SVM, and the 10-fold CV is adopted to validate the model.
Application of Optical Flow Techniques to Rainfall Nowcasting

References: • Bruhn A, Weickert J, Feddern C, Kohlberger T, Schnörr C. 2003. Real-time optic flow computation with variational methods. In N. Petkov, M. A. Westenberg (Eds.): Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, Vol. 2756, Springer, Berlin, 222-229. • Cheung P, Yeung HY. 2012. Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong. The 3rd WMO International Symposium on Nowcasting and Very Short-Range Forecasting (WSN12). 6-10 August, Rio de Janeiro.
Pruning methods for rule induction

Machine learning is a research area within computer science that is mainly concerned with discovering regularities in data. Rule induction is a powerful technique used in machine learning wherein the target concept is represented as a set of rules. The attraction of rule induction is that rules are more transparent and easier to understand compared to other induction methods (e.g., regression methods or neural network). Rule induction has been shown to outperform other learners on many problems. However, it is not suitable to handle exceptions and noisy data in training sets, which can be solved by pruning. This thesis is concerned with investigating whether preceding rule induction with instance reduction techniques can help in reducing the complexity of rule sets by reducing the number of rules generated without adversely affecting the predictive accuracy. An empirical study is undertaken to investigate the application of three different rule classifiers to datasets that were previously reduced with promising instance-reduction methods. Furthermore, we propose a new instance reduction method based on Ant Colony Optimization (ACO). We evaluate the effectiveness of this instance reduction method for k nearest neighbour algorithms in term of predictive accuracy and amount of reduction. Then we compared it with other instance reduction methods. We show that pruning classification rules with instance-reduction methods lead to a statistically significant decrease in the number of generated rules, without adversely affecting performance. On the other hand, applying instance-reduction methods enhances the predictive accuracy on some datasets. Moreover, the results provide evidence that: (1) our proposed instance reduction method based on ACO is competitive with the well-known k-NN algorithm; (2) the reduced sets computed by our method offers better classification accuracy than those obtained by the compared algorithms.
INSIDE THE BLACK BOX: TRACKING DECISION-MAKING IN AN ACTION RESEARCH STUDY

Action research has been described as “designing the plane while flying it” (Herr & Anderson, 2005, p. 69). A black box documented the researcher’s decisions while facilitating leadership development sessions with teacher leaders. Ten process folio steps informed the study through six iterations. Planning steps included a design frame, researcher’s action research cycle, session agenda, facilitator’s guide and researcher’s checklist. Observation steps included videotape transcriptions and written participant and researcher reflections. Analytical steps included a logic model, session summary and cumulative themes. Secondary analysis across the data sets crystalized the research contributions. The black box effectively documented complex action research.
The use of modern tools for modelling and simulation of UAV with Haptic

I PREFACE . II ACKNOWLEDGEMENTS . IV TABLE OF CONTENTS . V LIST OF FIGURES . VIII LIST OF TABLES . X LIST OF SYMBOLS . XI ACRONYMS . XII .
Mining Important Comments of Micro-Blog Based on Feature Weighting

Important comments of micro-blog not only reflect the views of users but also can influence the public’s opinion towards a particular topic. This paper presents a method of feature weighting based k-Nearest Neighbors for mining important comments of hot topics on Sina Weibo. By using feature weighting method, each selected feature is assigned to a corresponding weight. The comprehensive experimental results demonstrate that the presented method can better predict important comments than traditional k-Nearest Neighbors method. Furthermore, we show the presented method significantly outperforms the state-of-the-art classifiers.
Control of a nonanthropomorphic exoskeleton for multi-joint assistance by contact force generation

In this article, a novel controller for a nonanthropomorphic exoskeleton robot was designed to reduce joint torque of its operator using the contact force between them. Since the joints of the nonanthropomorphic exoskeletons are not directly connected to those of the operator due to the difference between their kinematic structure, joint assistance is performed by transmitting the contact force on their coupling parts instead of transmitting the joint torque of the nonanthropomorphic exoskeleton directly into the human joint. Most of the previous studies have focused on reducing the measured contact force by moving the coupling parts or commanding the robot joint torque. On the contrary, the proposed method focuses on reducing the human joint torque, which is estimated by formulating inverse dynamics, by obtaining possible contact force solutions. The commanding torque of the nonanthropomorphic exoskeleton was calculated by inverse dynamics based on the model information. To verify the control performance of the proposed method, we have developed a simulation environment for a lower-limb nonanthropomorphic exoskeleton. When the coupling part was implemented to be rigid for an ideal case, the joint torque of the human model to perform the same motion was successfully reduced by the given torque reduction ratio. For a more realistic condition, a nonrigid coupling was also implemented as a virtual spring-damper system, and its effect on the control performance was demonstrated in the simulation.
A dynamic DRR scheduling algorithm for flow level QOS assurances for elastic traffic

A DYNAMIC DRR SCHEDULING ALGORITHM FOR FLOW LEVEL QOS ASSURANCES FOR ELASTIC TRAFFIC Sıla Kurugöl M.S. in Electrical and Electronics Engineering Supervisor: Assoc. Prof. Dr. Nail Akar September 2006 Best effort service, used to transport the Internet traffic today, does not provide any QoS assurances. Intserv, DiffServ and recently proposed Proportional DiffServ architectures have been introduced to provide QoS. In these architectures, some applications with more stringent QoS requirement such as real time traffic are prioritized, while elastic flows share the remaining bandwidth. As opposed to the well studied differential treatment of delay and/or loss sensitive traffic to satisfy QoS constraints, our aim is satisfy QoS requirements of elastic traffic at the flow level. We intend to maintain different average rate levels for different classes of elastic traffic. For differential treatment of elastic flows, a dynamic variant of Deficit Round Robin Scheduler (DRR) is used as oppose to a FIFO queue. In this scheduling algorithm, all classes are served in a round robin fashion in proportion to their weights at each round. The main difference of our scheduler from the original DRR scheduler is that, we update the weights, which are called quantums of the scheduler at each round in response to the feedback from the network, which is in terms of the rate of phantom connection sharing capacity fairly with the other flows in the same queue. According to the rate measured in
Physical therapy interventions for the correction of equinus foot deformity in post-stroke patients with triceps spasticity: A scoping review

Objective Equinus foot deformity (EFD) is the most common deformity following a stroke. Several approaches have been suggested for its correction, including pharmacological, surgical, and physical therapy (PT) interventions. This scoping review aims to map and synthesize the available evidence focusing on physical therapy treatments for EFD caused by triceps surae (TS) spasticity. Methods Scoping review methodological frameworks have been used. Pubmed, Cinahl, and Cochrane databases were searched for primary literature. Studies focusing on the treatment of EFD in adult stroke patients were included only when the intervention involved PT treatments and presented at least one outcome measure for the functional and/or structural condition of the TS. Data were systematically collected and reported in tables inclusive of type of intervention, sample characteristics, dosage, comparators, outcomes, follow-up timeline, and treatment efficacy. A narrative synthesis was also added. Results Of the 642 experimental or observational screened studies, 53 were included, focusing on stretching exercises, shock waves, electrical stimulation, dry needling, TENS, vibration therapy, ultrasounds, cryotherapy, and active physiotherapy. Patients with EFD benefited from specific physical therapy treatments. These usually resulted in Modified Ashworth Scale reduction, typically by 1 point, and an increase in ROM. Interventions consisting of shock waves, dry needling, and electrostimulation showed the best results in reducing EFD. Heterogeneous dosage and delivery mode generally limited conclusions. Conclusions This scoping review summarized available primary literature based on PT treatments for the correction of EFD. By highlighting the remaining gaps in knowledge, it provides a reference for future studies on this pathology. Further investigations are necessary to pinpoint the best dosage and delivery methods. Future studies should investigate whether early rehabilitation programs started during the acute phase might help prevent or limit the development of secondary deformities.