Miracle-dz/UniFAR-FTUs huggingface dataset:
The National Qualifications Framework in private training establishments : a patch of evaluation in the seamless system : a thesis submitted in partial fulfilments of requirements for the degree of Master of Educational Administration at Massey University

Education sectors have been grappling with the controversial National Qualifications Framework (NQF) for nearly a decade. Unlike other sectors, the majority of private training establishments (PTEs) have chosen to adopt the unit standards-based qualifications of the NQF. Although responsible for only a small percentage of all tertiary enrolments, PTEs have awarded more NQF qualifications than any other educational sector. This study sought to explain this remarkable record through evaluating the extent to which the NQF was compatible with the organisational context of PTEs. Three PTEs situated in greater Auckland were selected for a collection of case studies evaluating compatibility of organisational context with the NQF. In each PTE administrators, tutors and students were interviewed and surveyed, and documents were reviewed. Following a framework developed by the researcher, compatibility of organisational context with the NQF was evaluated in terms of assumptions, objectives and approaches. The study found there was general readiness for change in the PTEs studied, and that PTE organisational assumptions and objectives were highly compatible with assumptions and objectives of the NQF. However, investigation of approaches to curriculum, teaching and assessment revealed gaps between NZQA intentions for the NQF and the realities of implementation. Gaps were particularly apparent in the areas of curriculum and assessment. Concerns were expressed about the increase in tutor
Implementation and acceleration scheme of Heart sound classification Algorithm based on SOC-FPGA

ABSTRACT-Widespread screening of congenital heart disease is of time-consuming, labor-consuming, and difficult for rural doctors to master the skill of cardiac auscultation. A kind of machine-assisted diagnosis method was put forwarded in this paper to solve the above problems. In which a heart sound classification algorithm and acceleration plan of CNN was implemented on a small scale SoC-FPGA chip with fewer resources. In this method, heart sounds were denoised and segmented into cardio cycles first. Then STFT transformation was done for time-frequency feature extraction. The time-frequency features were used to train the CNN model to extract network model parameters. In hardware implementation, the parallelism of CNN was corresponding to FPGA parallel hardware. In order to make acceleration of the algorithm, loop unrolling, fixed-point of model parameter, and reducing global memory access were done. The experimental results show that the classification speed is 3.13 times as much as one of CPU at the same conditions with the classification accuracy not any dropping significantly. It provides an offline solution for the machine-assisted screening of congenital heart disease.
To PIM or not for emerging general purpose processing in DDR memory systems

As Processing-In-Memory (PIM) hardware matures and starts making its way into normal compute platforms, software has an important role to play in determining what to perform where, and when, on such heterogeneous systems. Taking an emerging class of PIM hardware which provisions a general purpose (RISC-V) processor at each memory bank, this paper takes on this challenging problem by developing a software compilation framework. This framework analyzes several application characteristics - parallelizability, vectorizability, data set sizes, and offload costs - to determine what, whether, when and how to offload computations to the PIM engines. In the process, it also proposes a vector engine extension to the bank-level RISC-V cores. Using several off-the-shelf C/C++ applications, we demonstrate that PIM is not always a panacea, and a framework such as ours is essential in carefully selecting what needs to be performed where, when and how. The choice of hardware platforms - number of memory banks, relative speeds and capabilities of host CPU and PIM cores, can further impact the "to PIM or not" question.
Fusion of DET and Time-Frequency Analysis for Obstructive Sleep Apnea Screening

Obstructive Sleep Apnea (OSA) is a common disease, whose main feature is repeated episodes of apnea and hypopnea during sleep, and always leading to waking up from sleep. Traditional Heart Rate Variability (HRV) analysis methods for OSA screening are divided into time domain analysis and frequency domain analysis. The time domain analysis has been widely used, it uses the statistical discrete trend analysis method to analyze the variation of heart rate and RR. The law of frequency domain analysis is to use the time domain signal of HRV to do spectrum analysis. However, traditional HRV analysis methods are linear analysis methods, and there are shortcomings such as ignoring the short-term volatility of data and being unable to measure complexity. Therefore, this experiment proposes to perform time domain analysis, frequency domain analysis, and nonlinear DET analysis on the PPG signals of 15 OSA patients and 15 normal people, a total of 30 subjects. Significance, specificity, and accuracy are contrasted. Finally, when the embedding dimension m=6 and the scale factor s=4, the DET method gets better results than the traditional time-domain and frequency-domain analysis methods on significance, accuracy and specificity of OSA screening. This provides a fresh perspective for OSA screening.
FFCCD: fence-free crash-consistent concurrent defragmentation for persistent memory

Persistent Memory (PM) is increasingly supplementing or substituting DRAM as main memory. Prior work have focused on reusability and memory leaks of persistent memory but have not addressed a problem amplified by persistence, persistent memory fragmentation, which refers to the continuous worsening of fragmentation of persistent memory throughout its usage. This paper reveals the challenges and proposes the first systematic crash-consistent solution, Fence-Free Crash-consistent Concurrent Defragmentation (FFCCD). FFCCD resues persistent pointer format, root nodes and typed allocation provided by persistent memory programming model to enable concurrent defragmentation on PM. FFCCD introduces architecture support for concurrent defragmentation that enables a fence-free design and fast read barrier, reducing two major overheads of defragmenting persistent memory. The techniques is effective (28--73% fragmentation reduction) and fast (4.1% execution time overhead).
(A)I Will Teach You to Play Gomoku: Exploring the Use of Game AI to Teach People

Artificial intelligence systems such as AlphaGo, AlphaGo Zero and AlphaZero, have demonstrated their advantages and competency over human players. However, little research has explored the possibility of applying such algorithms for educational purposes, such as teaching people to play strategy games. To investigate this gap, we designed and developed a Gomoku tutor that can provide instant/delayed feedback to users. We trained an expert model for Gomoku from scratch by using an open-source AlphaZero implementation and embedded this model into our Gomoku tutoring system. We plan to use this tutor to investigate two main research questions: 1) Can Game AI models, which are inhuman in their expertise, provide guidance that improves human learning? 2) How do different types of Game AI derived feedback affect people's learning outcomes? In this paper, we outline our experimental plans to investigate these questions.
Regularity of Heart Rate Fluctuations Analysis in Congestive Heart Failure Patients Using Information-Based Similarity

Congestive Heart Failure (CHF) is a chronic progressive condition that affects the pumping power of your heart muscle. There are a number of works investigating Obstructive Sleep Apnea (OSA) detection based on heart rate variability and obtain outstanding results. Therefore, using HRV analysis for the screening of CHF patients has great potential. This study included 30 electrocardiogram (ECG) recordings (15 CHF recordings and 15 normal recordings) from the PhysioNet database. These recordings included 24h RR interval data and were divided into 5-minnute segments. Comparing with traditional time-domain analysis and frequency-domain analysis, information-based similarity (IBS) has a better performance on showing significant differences between normal group and CHF group (p < 0.001). The accuracies of time-domain analysis and frequency-domain analysis in the CHF detection are 86.7% and 83.3%, respectively, while IBS performed an accuracy of 86.7% with a better balance between sensitivity and specificity. This research find that the similarity of heart rate decreased in CHF group because of the low-level similarity of adjacent RR segments. This finding is probably the reason that CHF patients have arrhythmia. Consequently, this IBS method has certain clinical significance, and could be used to detect CHF.
Modeling of Evolutionary Progress of Nonstructure Protein 1 Family from Influenza A Virus

The evolutionary progress is mathematically how an indicator changes over time such as the brain weight changes over time. Thus, this can present in x-y coordinates, on where we can use mathematical and statistical methods to build the time-dependent relationships. Of mathematical methods, the mass-balance styled differential equation is most useful because we can use it to predict an evolutionary indicator in the future. We are particularly interested in the evolution of proteins from influenza A virus not only because influenza A virus causes influenza in humans and animals but also the proteins from influenza A virus have the longest records among viruses. Of 10 proteins from influenza A virus, the nonstructural protein 1 (NS1) gets much less attention than others. However, this ignorance does not minimize its importance because more and more functions have been found in NS1.This requires converting a protein into a numeric scale because the y-axis is better to set in a quantitative scale rather than a qualitative scale. Of 540-plus methods to convert amino acids into digital numbers, we developed the amino-acid pair predictability to do the job. In this communication, 2729 NS1 proteins of influenza A viruses filtered from 7826 full-length NS1 proteins of influenza A virus to eliminate identical sequences were employed. (1) We converted them into numeric format using the amino-acid pair predictability. (2) We presented these proteins in the y-axis according to their sampling time over the x-axis. (3) We determined upward and downward half-life as initial estimates for the analytical solution of mass-balance styled differential equations. (4) We used the analytical solution to fit the evolutionary progress of NS1 protein family. (5) We tested the goodness-of-fit to verify the fittings. The results demonstrated the positive possibility of such studies, and paved the pathway to simulate and predict the evolutionary progress of proteins in the future.
Forecasting CO2 Emission from Thermal Power Production in Beijing-Tianjin-Hebei by Using GM (1,1) Model

China aims to reach the carbon peak by 2030 and carbon neutrality by 2060. However, China's economy over relies on thermal power industry, which make this goal difficult to achieve. Beijing-Tianjin-Hebei region is one of China's most important economic bases and is the main carbon dioxide (CO2) emitter in China. Therefore, CO2 emission of thermal power production in Beijing-Tianjin-Hebei region is predicted in this paper. GM (1,1) model is adopted to forecast CO2 emission due to the obvious advantage of dealing with small sample. The mean relative error and the posterior error test indicate that GM (1,1) prediction performance is satisfactory. The prediction results indicate that CO2 emission of thermal power production in Beijing-Tianjin-Hebei region will continuedly and steadily increase in next five years. This prediction results can provide local government with policy guidance on low-carbon development.
The Application of EMG and Machine Learning in Human Machine Interface

Myocontrol is the intuitive control of a neural prosthetic via the user's voluntary muscular activations detected in the form of surface EMG(electromyography) signals. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a machine learning model that can decipher these muscular electrical activations. EMG myocontrol is now widely used in multiple fields such as clinical medicine and human-computer interfacing. However, the usefulness of myocontrol depends greatly on the accuracy of the collected EMG signal. Some of the factors that cause inaccuracy include the interference of adjacent muscle, the inherent instability of the signals acquired from the human body (e.g. unexpected changes in the sEMG caused by sweating, electrodes displacement, muscle fatigue or specific postures and motions of the body segments ). Today, researchers focus on finding problems and propose new concepts of models that could make improvements. This review examines the application of EMG myocontrol in hand gesture recognition and neural prostheses, the shortages of EMG and surface EMG(sEMG) signal, as well as the solutions to some current issues.
Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality

Peer review has been recognised as a beneficial approach that promotes higher-order learning and provides students with fast and detailed feedback on their work. Still, there are some common concerns and criticisms associated with the use of peer review that limits its adoption. One of the main points of concern is that feedback provided by students may be ineffective and of low quality. Previous works supply three explanations for why students may fail to provide effective feedback: They lack (1) the ability to provide high-quality feedback, (2) the agency to monitor their work or (3) the incentive to invest the required time and effort as they think the quality of the reviews are not reviewed. To help mitigate these shortcomings, this paper presents a complementary peer review approach that integrates training, self-monitoring and AI quality-control assistance to improve peer feedback quality. In particular, informed by higher education research, we built a set of training materials and a self-monitoring checklist for students to consider while writing their reviews. Also, informed by work from natural language processing, we developed quality control functions that automatically assess feedback submitted and prompt students to improve, if necessary. A between-subjects field experiment with 374 participants was conducted to investigate the approach's efficacy. Findings suggest that offering training, self-monitoring, and quality control functionalities to students assigned to the complementary peer review approach resulted in longer feedback that was perceived as more helpful than those who utilised the regular peer review interface. However, this complementary approach does not seem to affect students judgement (leniency or harshness) or confidence in grading. Directions are suggested to further evaluate and refine peer review systems.
A Scenario-based Exploration of Expected Usefulness, Privacy Concerns, and Adoption Likelihood of Learning Analytics

Learning analytics has become a robust research area in the last decade, as innovative analytic models of learning data have been created with the goal of enhancing teaching and learning. However, barriers to large scale adoption of such technologies in higher education still exist. In recent years, a strand of research has begun to investigate stakeholders' expectations of learning analytics, hoping to find ways to integrate the innovations into everyday teaching practices. For instance, studies have investigated instructors' ideas about how learning analytics might be helpful, as well as concerns about student data privacy. However, most studies have taken a general approach rather than considering instructors' day-to-day experiences. Using survey methods, we presented instructors with hypothetical scenarios of learning analytics in use across disciplines, class sizes, teaching activities, and types of student data. We asked for ratings of both usefulness and privacy concerns for each proposed teaching situation. Our respondents considered scenarios involving learning outcomes-related data (e.g. grades) to be more useful than those that involve student interactions (e.g. language, social activity). In contrast, privacy concerns were lower for outcomes-oriented scenarios than interactions-focused scenarios. An interesting new finding was a negative correlation of usefulness and privacy; we discuss this in the context of instructors' possible cost-benefit reasoning. We reflect on our findings with respect to future efforts in developing and fielding learning analytics tools.
The Effect of Ethanol on Mutant Human Prion Protein using Molecular Dynamics Simulations

As you know that monohydric alcohols have a great effect on protein structure, and human prion diseases are caused by conformational changes from their natural form to a misfolded state. To illustrate the effect of monohydric alcohols on the structural stability of the mutation V210I, we used the molecular dynamics method to study the dynamic features of single point mutation V210I and mutation V210I with ethanol solution. Our findings show that, while the models' global three-dimensional structure remains relatively unchanged over simulation, the mutation V210I causes local structural and secondary structure alterations, as well as global flexibility modifications. On the other hand, the existence of ethanol molecules results in overall stability compared to others simulations, exhibiting favourable dynamical properties in the secondary structure, solvent accessible surface, and salt bridges, and inhibiting the misfolding of the human prion protein. In conclusion, the simulation suggests that alcohol may provide a feasible idea in the future in-depth study of the transformation mechanism of the prion protein.
PPMLAC: high performance chipset architecture for secure multi-party computation

Privacy issue is a main concern restricting data sharing and cross-organization collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party Computations (MPC), Homomorphic Encryption, and Federated Learning are proposed to solve this problem, no solution exists with both strong security and high performance to run large-scale, complex machine learning models. This paper presents PPMLAC, a novel chipset architecture to accelerate MPC, which combines MPC's strong security and hardware's high performance, eliminates the communication bottleneck from MPC, and achieves several orders of magnitudes speed up over software-based MPC. It is carefully designed to only rely on a minimum set of simple hardware components in the trusted domain, thus is robust against side-channel attacks and malicious adversaries. Our FPGA prototype can run mainstream large-scale ML models like ResNet in near real-time under a practical network environment with non-negligible latency, which is impossible for existing MPC solutions.
Determinant factors for consumers’ intention in choosing a shopping center: An extended Theory of Planned Behavior approach

Shopping centers play a significant role in consumers' life cycle, from shopping to social and recreational needs. With the growing business in the shopping center industry, the researchers aimed to determine the factors affecting the consumers' intention in choosing a shopping center. This study utilized an extension of the Theory of Planned Behavior (TPB) with service quality. A total of 317 respondents answered the online questionnaire. The respondents mainly consisted of aged between 21 to 26 years old. The Structural Equation Modeling (SEM) revealed that attitude toward behavior has the most significant effect in predicting the consumers' intention to choose a shopping center, followed by the service quality and perceived behavioral control. Interestingly, the subjective norm was found not significantly affect behavioral intentions. The findings of this study could serve as a theoretical basis in enhancing consumers' purchase intention, frequency of visits, and choosing the shopping center's attributes.
Semiclassical expansion of quantum characteristics for many‐body potential scattering problem

In quantum mechanics, systems can be described in phase space in terms of the Wigner function and the star‐product operation. Quantum characteristics, which appear in the Heisenberg picture as the Weyl's symbols of operators of canonical coordinates and momenta, can be used to solve the evolution equations for symbols of other operators acting in the Hilbert space. To any fixed order in the Planck's constant, many‐body potential scattering problem simplifies to a statistical‐mechanical problem of computing an ensemble of quantum characteristics and their derivatives with respect to the initial canonical coordinates and momenta. The reduction to a system of ordinary differential equations pertains rigorously at any fixed order in ħ. We present semiclassical expansion of quantum characteristics for many‐body scattering problem and provide tools for calculation of average values of time‐dependent physical observables and cross sections. The method of quantum characteristics admits the consistent incorporation of specific quantum effects, such as non‐locality and coherence in propagation of particles, into the semiclassical transport models. We formulate the principle of stationary action for quantum Hamilton's equations and give quantum‐mechanical extensions of the Liouville theorem on conservation of the phase‐space volume and the Poincaré theorem on conservation of 2p‐forms. The lowest order quantum corrections to the Kepler periodic orbits are constructed. These corrections show the resonance behavior.
Approximate record length constraints for experimental identification of dynamical fractals

The ambiguity that can exist, for short datasets, between the observational power spectra of dynamical fractals and low‐order linear memory processes is demonstrated and explained. It is argued that it could be broadly useful to have a highly practical rule‐of‐thumb for assessing whether a data record is sufficiently long to permit distinguishing the two types of processes, and if it is not, to produce an approximate estimate of the amount of additional data that would be required to do so. Such an expression is developed using the AR(1) process as a loose benchmark. Various aspects of the technique are successfully tested using synthetic time series generated by a range of prescribed models, and its application and relevance to observational datasets is then demonstrated using examples from mathematical ecology (wild steelhead population size), geophysics (river flow volume), and econophysics (stock price volatility).
Body position and technique effects on displacement in the dyno maneuver in rock climbing

Abstract This study compared vertical displacement of rock climbers performing a dyno maneuver using a squat jump (SJ) and a counter-movement (CM) technique. These techniques were tested at three different starting positions, varying in distance between hand and foot holds. Thirteen recreational rock climbers performed two SJ and two CM dynos at each starting position. Vertical displacement was measured through video analysis. Results showed that there were no significant differences in vertical displacement of the hand (VDH) between jump techniques (p > 0.05). There was, however, a significant improvement in VDH as the subjects moved from the low-to-medium and medium-to-high starting positions (p < 0.05). Therefore, a climber should base their technique on preference or experience while leaving a sufficient amount of space between the hands and feet if possible.