Miracle-dz/UniFAR-FTUs huggingface dataset:
Instructional Procedure and Strategy of Primary School Programming Education Focusing on Cultivating Computational Thinking

Based on the analysis of some famous instructional cases in programming education and in-depth interviews with primary school teachers who are engaged in programming education, this paper focuses on the instructional procedure and strategies of primary school programming education for cultivating students’ computational thinking. This examination reveals the current situation and possible problems in the practice of cultivating computational thinking and provide some useful references. Continuous comparison are used to code and analyze the interview data. The article founds that the instructional procedure of cultivating computational thinking in programming education generally involves five major stages: knowledge review, new knowledge exploration, problem analysis, programming on computer, and the stage of reflection. Instructional strategies accordingly include: mind map-supported, unplugged programming, flow chart use, pair programming, log reflection, and more. Finally, the paper concludes with the problems that appeared in teaching, especially in the stage of new knowledge exploration, unplugged programming and pair-programming.
Decision Support System for Employee Recruitment of A Company Using Multi Attribute Utility Theory

Recruitment is one of the important things in the human resource management of a company. Selection is part of the recruitment process to decide which applicant or candidate suitable for a certain position. The problem occurs in the lack of service to consumers which results in the company's sales target not being achieved. This is due to the decline in employee performance and the selection of less competent employees who cannot meet the requirements of the position. This research proposes a Decision Support System (DSS) using the MultiAttribute Utility Theory (MAUT) approach in the process of selecting the candidate employee to make the assessment section simplify in the decision making. There are four criteria used in this research namely administration test, psychological test, interview, and medical test. The decision support system can help the stakeholder in the decision making of employee selection with an accurate and objective result. To measure the accuracy, we compare our results with the old system measurements of selection data in this company. The result is compared against the available data and an accuracy of 93.33 % is achieved.
Two Novel Spectrum Sensing Algorithms Based On Eigenvalue under Different Noise

In the field of spectrum sensing for cognitive radio networks, there exists a category of eigenvalue based algorithms by analyzing the limiting eigenvalue distribution of the sample covariance matrix. In this paper, an novel algorithm based on the ratio of the maximum eigenvalue to signal energy (MEE) is proposed under Gaussian white noisy environment. The corresponding probabilities of detection and the detector robustness in low signal to noise ratio (SNR) are derived. Moreover, an improved difference between the maximum eigenvalue and the minimum eigenvalue (DMM) algorithm in alpha-stable distribution noise is proposed. The relation between the probability of false alarm and the decision threshold is derived as well. Both the theoretical analysis and numerical simulations demonstrate that the proposed MEE algorithm can effectively overcome the noise uncertainty without the prior knowledge of the signal transmitted and has better detection performance. The improved DMM algorithm outperforms the original counterpart under alpha-stable distribution noise in terms of suppression impulse noise.
A Streamlined scRNA-Seq Data Analysis Framework Based on Improved Sparse Subspace Clustering

One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering. However, cell clustering based on single-cell RNA sequencing is challenging due to the high transcript amplification noise, sparsity and outlier cell populations. In this study, we propose a novel sparse subspace clustering method called Structured Sparse Subspace Clustering and Completion for single-cell RNA sequencing analysis by assuming the cells related together are in the same subspace, and so the relationships among cells can be described within a subspace instead of between cell pairs. The proposed optimization model is solved by the Linearized Alternating Direction Method of Multipliers, in which data completion and spectral clustering are combined as a whole by mutual constraint. It is worth noting that random walk is used in the model to make the coefficient matrix more diagonal in the optimum iterative procedure, and the effect is significant. Our model is applied and compared with 5 state-of-the-art clustering methods on 6 public single cell datasets and a simulated data set with cell numbers varying from 56 to over 3000. As a result, our model outperforms the other clustering methods in clustering accuracy as evaluated by Adjusted Rand Index, Normalized Mutual Information, Homogeneity and Completeness, especially compared with the other improved sparse subspace clustering methods.
Continuous Estimation of Knee Joint Angle during Squat from sEMG using Artificial Neural Networks

The purpose of this research was to continuous knee joint angle estimation from sEMG during squat using artificial neural networks. sEMG signals of vastus medialis, rectus femoris, biceps femoris and 3D kinematics of lower extremity joints for four participants during squat were captured at 1500 Hz and 100 Hz, respectively. sEMG signals were preprocessed and RMS and variance were extracted as input features. The processed input data was given to a three-layer feed forward neural network with one hidden layer. The proposed network was trained by the Levenberg-Marquardt algorithm. The root mean square error (RMSE) and correlation coefficient (CC) were used to evaluate the accuracy of estimation. The results showed that this network is able to continuously estimate the knee joint angle with global RMSE of 5.0041° ± 0.9963° and CC of 0.9898 ± 0.0039. It concludes that a multilayer neural network with a simple structure has the ability to continuously estimate the joint angle from sEMG data while performing an athletic movement under real loading situation.
A Parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR Image Based On Enhance Learning and Wavelet

To take the advantages of a variety of remote sensing data, the application of remote sensing image change detection is a very important choice. Remote sensing image change detection is large in computing capacity and time-consuming, and with the development of modern remote sensing technology, however, the amount of various remote sensing data obtained is getting larger and larger to find a change detection in the Synthetic Aperture Radar(SAR) images accurately for effective computing power. As a basis for parallelization which is a parallel change detection methods of multi- temporal SAR image based on wavelet transform is proposed. In the methods, platform based on parallel computing enhance learning. According to the statistical characteristics of SAR images and the semantics of Convolutional Neural Networks (CNN) analysis, an efficient Change detection methods based on Enhance Learning Semantic Analysis and wavelet transform is proposed to achieve precision of change detection, the probability and Statistics Characteristics of conjugate multi-distribution function which the pixel and task were pre-handled for parallel computing. Then, it's superior to traditional change detection results that is obtained Multi-sample training set. To establish Bellman equation model, and iterate the threshold-change area to calculate statistical characteristics. The experiments show that the improving methods and computing power on parallel change detection platform will achieve better results, and it is in remote sensing applications with enhance learning of behavior science methods to solve the image threshold area change detection in enhance learning for image processing for hybrid parallel computing.
Economical Approach for SVC Systems to Improve Power Quality using Fully Indigenous Technology

The question of power quality issues are rapidly increasing and require new technologies to deal with these power quality issues in industries. Flexible alternating current transmission systems (FACTS) are modern devices in power system for maintaining power factor and stability. This paper deals with a modified switching and design of static var compensator (SVC). The proposed SVC model is designed which is economical, highly reliable, and uses fully indigenously developed technology. The design of SVC with harmonic filters has been discussed with a new switching concept.
Miniature Electrodynamic Wireless Power Transmission Receiver Using a Micromachined Silicon Suspension

We present the design, modeling, fabrication, and experimental characterization of an electrodynamic wireless power transmission (EWPT) receiver for low-frequency (< 1 kHz), near-field wireless power transmission. The device utilizes a bulk-micromachined silicon serpentine suspension, two NdFeB magnets and two precision-manufactured coils. The architecture of the transducer is designed to maximize the electrodynamic coupling coefficient while maintaining a low mechanical resonant frequency in order to maximize the power density for low-frequency wireless power transmission. An equivalent lumped-element circuit model is established to parameterize the system and to predict the output performance of the proposed system. A prototype device is fabricated, assembled and tested, and the results are compared with the model prediction. The 0.31 cm<sup>3</sup> device generates 2.46 mW average power (7.9 mW <inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula> cm<sup>−3</sup> power density) at 4 cm distance from a transmitter coil operating at 821 Hz and safely within allowable human exposure limits. This data corresponds to a normalized power density of 21.9 mW <inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-3}\,\,\cdot $ </tex-math></inline-formula> mT<sup>−2</sup>, which is 44% higher than similar reported devices. Based on these results, this device shows great suitability for wirelessly charging mobile, wearable and bio-implantable devices. [2020-0161]
Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) Data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads.
A SI-SDR Loss Function based Monaural Source Separation

Deep neural network is very popular for performing various tasks in the field of speech signal processing nowdays. This work mainly studies the problem of source separation under single channel (monaural) conditions, which is more challenging because only a single channel of information is available, and without any constraints, an infinite number of solutions are possible. We introduce deep recurrent neural network (DRNN) as a regression model to discover the deep structure and regularity of signal reconstruction from a mixture containing two sources. To alleviate the problem of interference between different sources, we integrate scale-invariant signal-to-distortion ratio (SI-SDR) loss function and discriminative network training criterion together to generate a new training objective function. Experimental results show that the proposed method produces a superior performance compared with other approaches.
Low-resource Sinhala Speech Recognition using Deep Learning

Automatic speech recognition has progressed considerably in the past several decades for most of the European languages, but still, it is a prominent research area for most of the low-resourced languages. This project presents a study to build an automatic speech recognition (ASR) system using the Kaldi toolkit [1] for the Sinhala language which is one of the low-resourced languages with a large lexical variety.We experiment on different deep neural architectures such as pre-trained DNN, DNN, TDNN, TDNN+LSTM to enhance the acoustic modeling process and each of their performances is investigated in this document. A statistical model of GMM-HMM is also trained on the same dataset keeping it as the baseline model for comparing the effectiveness of a deep learning approach. For the language model, a corpus containing more than 20K sentences taken from UCSC LTRL [2] is used to generate the 220K extended lexicon. The experiments are conducted using a phonetically balanced training dataset consisting of twenty-five hours of speech data collected from fifty females and twenty males and tested on 1.6 hours of speech data. The results obtained show that deep neural network architectures exceed the performance of the statistical baseline model by giving a lower Word Error Rate (WER) of 7.48% on the test data set. The best observed lowest WERs were produced by the TDNN architectures.
Permission Blockchain based Smart Contract Utilizing Biometric Authentication as a Service: A Future Trend

Smart contracts are the triggered self executing contracts which are termed and conditioned among the trusted parties. Being independent of trusted third party, smart contract rely on blockchain and the underlying consensus submitted by participants for the security. Hyper ledger fabric provides peer-to-peer-network based distributed operating environment. This scenario highlights the need of identification of peer, more precisely on the ‘who the user is’ rather than ‘what does the user possess’. This paper proposes an architecture of the integration of multimodal biometric authentication system incorporation with the permissioned blockchain based smart contract. Every peer has to be authenticated before getting involved in smart contract. Paper also informs about the surveyed ways of multimodal recognition which ensures the major concern of various applications like insurance settlement, market predictions and internet on things, etc. i.e. liveliness detection.
Cost Effective IoT Framework for Connected Sensors and Devices

Internet of Things [IoT]used in manufacturing industry is not very new, however it is evident from different sources, the implemented IoT solution comes with a full suite including cloud and its corresponding ecosystem. Multiple cloud based IoT solutions have been explored during the analysis phase of this project, for example Kosmos Cloud, iMETLand Project etc. After rigorous analysis it has been deduced that majority of the Cloud based IoT solutions are advantageous but costly and coupled closely with their own cloud infrastructure with limited opportunities for the creation of hybrid or multiple cloud model. They also exhibit difficulty for integration with the legacy systems. The pricing models of existing solutions are either based on the number of connected devices, volume of messages or upon more complex calculations. This study has endeavored to state a vision statement for an architectural development of cost effective IoT framework that could connect various types of sensors and devices for an automated unit operation with the help of representational state transfer (REST) API, developed by using windows communication foundation (WCF) framework.
Securing CACC: Strategies for Mitigating Data Injection Attacks

Cooperative Adaptive Cruise Control (CACC) is a typical example for cooperative maneuvers of connected, automated vehicles. Safe operation of any such vehicle highly depends on information emitted by the surrounding vehicles as it is processed by the vehicle's longitudinal controller. This opens an attack surface affecting vehicle safety. This paper studies how CACC controllers can be protected against such attacks to ensure the safety of the vehicle and its passengers. This raises the question of how to mitigate injection of false data into the controller by attacks and other forms of vehicle misbehavior. Based on a literature analysis, this paper defines a new categorization for mitigation strategies, discussing their individual strengths, weaknesses and their potential for attack mitigation. This categorization can be used to derive novel ideas for mitigations, for example, an approach with a parameter-dependent reaction of the controller based on a suspiciousness parameter signaled by the Misbehavior Detection System (MDS). Through a simulation study, we show that this approach improves safety and efficiency of the platoon under attack but also identify a fundamental trade-off between these two design goals.
Proactive Supply Noise Mitigation with Low-Latency Minor Voltage Regulator and Lightweight Current Prediction

Power supply noise induces extra timing delay or even malfunctions in modern power-demanding VLSI chips. Traditional reactive noise mitigation is often too late to suppress emergent supply noise due to the long latency of voltage boosting. This paper proposes a proactive method for mitigating emergent supply noises and avoiding unexpected failures in power-hungry VLSI designs with two contributions. First, a major-minor voltage regulator (MMVR) structure, which enables quick and widerange voltage scaling with small ripples, is proposed. Second, a lightweight current predictor consisting of a six-layer decision tree regressor achieves over 0.98 correlation for 50-cycle-ahead prediction in 25 RISC-V benchmark programs. Experimental results with a multi-core RISC-V design show that the proposed method mitigates the supply noise within 30 mV while the noise exceeds 70 mV with the conventional reactive mitigation. Also, the average supply voltage is compensated during the powerdemanding operation.
QMA-hardness of Consistency of Local Density Matrices with Applications to Quantum Zero-Knowledge

We provide several advances to the understanding of the class of Quantum Merlin-Arthur proof systems (QMA), the quantum analogue of NP. Our central contribution is proving a longstanding conjecture that the Consistency of Local Density Matrices (CLDM) problem is QMA-hard under Karp reductions. The input of CLDM consists of local reduced density matrices on sets of at most $k$ qubits, and the problem asks if there is an n-qubit global quantum state that is locally consistent with all of the k-qubit local density matrices. The containment of this problem in QMA and the QMA-hardness under Turing reductions were proved by Liu [APPROX-RANDOM 2006]. Liu also conjectured that CLDM is QMA-hard under Karp reductions, which is desirable for applications, and we finally prove this conjecture. We establish this result using the techniques of simulatable codes of Grilo, Slofstra, and Yuen [FOCS 2019], simplifying their proofs and tailoring them to the context of OMA. In order to develop applications of CLDM, we propose a framework that we call locally simulatable proofs for QMA: this provides QMA proofs that can be efficiently verified by probing only $k$ qubits and, furthermore, the reduced density matrix of any $k$-qubit subsystem of a good witness can be computed in polynomial time, independently of the witness. Within this framework, we show several advances in zero-knowledge in the quantum setting. We show for the first time a commit-and-open computational zero-knowledge proof system for all of QMA, as a quantum analogue of a “sigma” protocol. We then define a Proof of Quantum Knowledge, which guarantees that a prover is effectively in possession of a quantum witness in an interactive proof, and show that our zero-knowledge proof system satisfies this definition. Finally, we show that our proof system can be used to establish that QMA has a quantum non-interactive zero-knowledge proof system in the secret parameter setting. 11The full version of this work can be found in https://arxiv.org/abs/1911.07782.
A Parallel Implementation of Q-learning

Q-learning has been one of the most used algorithms in reinforcement learning. There have been a lot of improvements in the speed of convergence of algorithm and many other improvements for various types of environments. Parallelizing the algorithm is one of the very useful improvements for Q-learning. Parallelizing the algorithm can mean the algorithm can run at multiple times the speed on capable systems. In this paper a technique to parallelize the algorithm is proposed. Experimental results are presented with the improvements achieved and a theoretical reasoning of the algorithm’s merit in comparison with other works done in the field are also written.
Learning Wasserstein Isometric Embedding for Point Clouds

The Wasserstein distance has been employed for determining the distance between point clouds, which have variable numbers of points and invariance of point order. However, the high computational cost associated with the Wasserstein distance hinders its practical applications for large-scale datasets. We propose a new embedding method for point clouds, which aims to embed point clouds into a Euclidean space, isometric to the Wasserstein space defined on the point clouds. In numerical experiments, we demonstrate that the point clouds decoded from the Euclidean averages and the interpolations in the embedding space accurately mimic the Wasserstein barycenters and interpolations of the point clouds. Furthermore, we show that the embedding vectors can be utilized as inputs for machine learning models (e.g., principal component analysis and neural networks).
Multi-defect detection for magnetic tile based on SE-U-Net

Vision-based on-line full detection of surface defects is of great significance to the production efficiency and quality of magnetic tiles. In this work, we introduce a pixel-wise surface defect detection model called SE-U-Net, which simultaneously realizes detection and classification. The image augmentation based on instance transfer effectively reduces the imbalance between the background and the defect. The squeeze-and-excitation module empowers U-Net to adaptively learn the shallow information matched with deep features in the skip path, enhancing the ability to identify small defects with a few of additional network parameters. Experiments show that the recognition accuracy of this model exceeds the existing methods, and the mean pixel-accuracy reaches 0.97.