Miracle-dz/UniFAR-FTUs huggingface dataset:
Early In-trouble Student Identification Based on Temporal Educational Data Clustering

Temporal educational data are ubiquitous with hidden knowledge helpful for early in-trouble student identification in an academic credit system. Exploiting such data for this challenging popular task has not yet been done much in the educational data mining field. In this paper, we propose the information retrieval-based approach to return an in-trouble student list from the ranking of all the students after temporally grouping them together. In this approach, a temporal educational data clustering method is proposed as an ensemble method by stacking the temporal clusters from a dynamic topic model on temporal data with kernel k-means. The ensemble learning process is further optimized with an internal validation scheme using S_Dbw. The resulting method, DTM_Kernel, can provide better clusters for final relevance-based retrieval as examining the students with all of their study performance over time. Its effectiveness has been confirmed with the better experimental results on three different real datasets over three academic years. Indeed, it has higher Recall and F-measure than several other unsupervised and supervised learning-based methods for correct prediction of in-trouble students. It also outperforms some other direct retrieval methods for a resulting list of in-trouble students. As a result, our approach has formed an effective solution to the early in-trouble student identification task.
Approaching the power limit of an electrodynamic WPTS with nearly coupling-independent operation

This paper further develops a theoretical analysis on the limitation on transmitted power for a low-frequency electrodynamic wireless power transfer system (WPTS). Based on an equivalent circuit model that was experimentally verified in a previous work, we derive an explicit-form solution of the maximum possible power that can be transferred to an electrical load under a given constant B - field amplitude. The obtained results reveal the essential role of the quality factor Q of the mechanical resonator and the electromagnetic receiver. At the short-circuit (resonance) frequency, we denote these two values as Qo and QLOrespectively. In particular, the maximum delivered power is a fractional function of the effective figure of merit characterized by $Me = k_{e}^{2} Q_{0}QL_{0}$, where $k_{e}$ is the generalized electrodynamic coupling coefficient. Larger Me results in higher maximum output power, which then reaches and levels out at its fundamental limit when Me » 1. Motivating from that observation, we propose a simple method to improve the WPTS performance while keeping the operating frequency well below 1 kHz. This approach is referred to as a nearly coupling-independent operation.
Research and Application of Fast and Elitist Non-Dominated Sorting Generic Algorithm in Coal Blending Optimization

In view of coal blending optimization in thermal power plants, a multi-objective coal blending optimization model is proposed. The model uses coal quality indexes as constraint condition to construct three objective functions of economy, environmental protection and safety. The fast and elitist non-dominated sorting generic algorithm is used as the algorithm of solving the model. Combined with actual coal blending problem in domestic thermal power plants, the Pareto solution set is obtained. Results of example show that the algorithm has a good optimization effect and provides effective technical support for actual coal blending technology.
Hybrid Xception Model for Human Protein Atlas Image Classification

In order to automate the process of Protein Subcellular Localisation using annotated images from Human Protein Atlas, two different approaches have been adopted. The first one is a conventional approach in which various features are extracted from the image and fed into a classifier such as a Random Forest Classifier. The second approach uses a Convolutional Neural Network (CNN) to extract features and classify the image. Two different CNN architectures- Xception and ResNet50 are tried. With the addition of features like, correlation and mean square error between different filtered images, a Hybrid Xception model has been implemented that provides optimal solution. The F1 Score obtained for 15 classes is 0.69 by the Hybrid Xception model as opposed to the conventional approach that yields a value of 0.61 and ResNet50 that gives a score of 0.41.
Intrusion Detection Based on Machine Learning Using Fractal Properties of Traffic Realizations

In this paper, we consider the problem of intrusion detection in computer networks by the realizations of network traffic. To solve this problem, time series analysis methods, fractal methods, and data mining are used. The verification of anomaly detection methods was performed using experimental data sets. Using these data sets, realizations of multifractal stochastic binomial cascades were generated with a change of the multifractal characteristics. In the work, numerical experiments were conducted to detect intrusions using the example of detecting typical DDoS attacks, which showed that the Random Forest method using regression decision trees can effectively detect anomalies in network traffic.
Two-Stage Sketch Colorization With Color Parsing

We implement high-quality sketch colorization using two-stage conditional generative adversarial network (GAN) training based on different intermediate features. The intermediate features used in autonomous colorization are the grayscale parsing and interval pixel-level color parsing. The autonomous colorization based on grayscale parsing feature can learn the spacial topology of pixels in the first stage to guide the colorization in the second stage. The autonomous colorization based on pixel-level color parsing feature can learn the color information of few feature points in the first stage to guide the colorization of all pixels in the second stage. Additionally, we use the intermediate feature of sampling points as constraint and achieve the color reconstruction using Laplacian mesh editing as a special second stage. Furthermore, the interactive colorization uses the superpixel color parsing as the intermediate feature. Specifically, we use the simple linear iterative cluster (SLIC) to obtain a palette that maintains the edges in the first stage to guide the colorization in the second stage. As for evaluation metrics, we propose a color-coded local binary pattern (CCLBP) score based on color distances from the first-order 8 pixels to the central pixel, to measure the degrees of color blurring and mess. We also propose a light-sensitivity (LS) score based on the reversed grayscale map, to measure the degrees of auto painting and overfitting of the color hint. According to the L1 distances between the original and generated color images based on these scores, compared with state-of-the-art methods including one stage approaches such as pix2pix and PaintsChainer and two-stage approaches such as Style2Paints and DeepColor, our model can achieve the highest-quality autonomous colorization. Moreover, compared with pix2pix, PaintsChainer and Style2Paints with color hints, according to the proposed objective evaluation as well as the user visual study, our model can achieve the highest-quality interactive colorization as well.
Enhancement of Corn image quality using Very-Deep Super-Resolution (VDSR) neural network

Machine vision based quality inspection of food items is dependent on the quality of the acquired images. However, acquisition of high quality images requires costly and complex camera or other image acquisition equipment. In this paper, we have proposed a deep learning based solution which can be used to increase the resolution of input data acquired by low cost camera systems. The benefit of the proposed work lies in the fact that the input data acquired at lower resolution may be processed to obtain higher resolution suitable for detecting minute defects in agricultural products. Our proposed method was found to perform better than existing methods in terms of accuracy and visual appearance improvements.
Avoiding common scalability pitfalls in shared-cache chip multiprocessor design

Today, the most common approach to next-generation microprocessor design is to increase their core number. However, in general-purpose microprocessors, the growing performance disparity between DRAM and the cores, and the need to retain compliance with common paradigms, including coherent shared memory and unifirm memory access, lead to a number of issues as the number of cores scales up.In this work, we duscuss some of the common scalability issues in such processors, involving shared cache, on-chip network, and DRAM controller design, and propose their solutions suitable for at least 1000-core chip multiprocessors according to the results of our analysis and experiments.
Thermal Deformation Suppression Chip Based on Material Symmetry Design for Single Center Supported MEMS Devices

Structural thermal deformation is an important factor that affects the performance of MEMS devices. The mismatch of thermal expansion coefficient (CTE) between different materials is a major source. For single center supported MEMS devices, expansion difference between device and the substrate leads to the out of plane thermal deformation of the structure, resulting in the performance deterioration. This paper presents a thermal deformation suppression chip (TDSC) for single center supporting MEMS devices. It is fabricated by the MEMS process and consists of an upper plate and a lower plate. The materials of the upper and lower plates are the same as those of the device substrate and structure respectively. The principle of TDSC to suppress thermal deformation is material symmetry design. Single center anchor is adapted to connect the upper and lower plates. Besides, the center anchor can also isolate the packaging stress generated between TDSC and package shell. In this paper, center supported quadruple mass gyroscope (CSQMG) is used to verify the effect of TDSC. Finite element simulation shows that the thermal deformation suppression effect is determined by radius of TDSC anchor, and the deformation can be suppressed to 0. The conclusion is confirmed by White light interference experiment. In addition, the experiment results also show that the TDSC effectively reduce the thermal out-of-plane deformation after packaging, which is even better than that of the gyroscope without any packaging. It is further proved that TDSC can also significantly improve the temperature performance of the CSQMG. Moreover, the TDSC has simple process steps, small chip area, low cost, and is suitable for various MEMS devices with single center anchor.
Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario

Heart rate estimation from fitness plays an important role in the evaluation of fitness exercises. Conventional approaches use the photoplethysmography (PPG) sensor to consider the change of light absorption on the wrist skin for heart rate estimation. However, users are required to buy smartwatches for using this function. Various approaches based on video analysis are recently implemented for surveillance purpose. However, it is unstable for motion scenario such as fitness exercises due to the color distortion induced by movement. POS and CHROM are introduced to address this issue. Since the fixed projection planes from POS and CHROM are given in several sources of light, it is not widely applied for surveillance applications. Therefore, a novel projection plane that is adaptively changed with the lighting environment is proposed to estimate the heart rate from fitness videos in ambient light. Moreover, image and digital signal processing techniques are also applied to extract the clean pulse signal from a novel projection plane. From the experiments conducted, the proposed approach outperformed the existing approaches to be the best model for heart rate estimation from fitness videos with the accuracy up to 91.08%.
Comprehensive solution of Scheduling and Balancing Load in Cloud - A Review

Cloud computing provides powerful methods for digital resources to share computing tasks. Congruent scheduling solutions for task and workflow are necessary in order to develop profitable executions and obtain availability, load balancing and consistency in cloud computing, The problem of planning, an NP-hard issue, is solved by the use of multiple metaheuristic algorithms. This paper elaborates on the scheduling schemes for task and workflow for the cloud environment. Cloud computing is instrumental in sharing data and offering a myriad of resources to users, wherein users pay only for the resources used by them. Cloud computing sanctions storage of data and allocated resources in an open environment, thus enabling an increase in the quantity of data storage. Several types of load balancing issues such as memory, CPU and network load are inevitable in cloud environment. To solve these balancing problems by locating the overloaded nodes and diverting the load to another node in the network, load balancing employed. This helps in delegating the incoming workload to various nodes in the system to assure that none of the nodes are overloaded.
Enhancements to the Sequence-to-Sequence-Based Natural Answer Generation Models

There is a great interest shown by academic researchers to continuously improve the sequence-to-sequence (Seq2Seq) model for natural answer generation (NAG) in chatbots. The Seq2Seq model shows a weakness whereby the model tends to generate answers that are generic, meaningless and inconsistent with the questions. However, a comprehensive literature review on the factors contributing to the weakness and potential solutions are still missing. Therefore, this review article fills the gap by reviewing Seq2Seq based natural answer generation-based literature to identify those factors and proposed methods to address the weakness. This literature review identified several factors such as input question is not sufficient to determine a meaningful output, usage of cross-entropy function as the loss function during training, infrequent words in training data, language model influence which generates answers not relevant to the question, utilization of teacher forcing method during training which results in exposure bias, long sentences and inability to consider dialogue history as the factors. Additionally, this literature review also identified and reviewed the methods proposed to address the weakness such as utilizing additional embedding and encoders, using different loss functions and training approaches, as well as utilizing other mechanisms like copying source word(s) and paying attention to a certain portion of the input. For discussion, these methods are categorized into four broad categories which are Structural Modifications, Augmented Learning, Beam Search and Complementary Mechanisms. Additionally, the paper highlights unexplored areas in Seq2Seq modeling and proposes potential future works for natural answer generation.
A New Hybrid Control Strategy for the Global Attitude Tracking Problem

This paper presents a new hybrid feedback design strategy for the attitude tracking problem on SO(3) guaranteeing global asymptotic stability. The proposed hybrid controller is derived from a modified potential function on SO(3)×ℝ involving a virtual state variable with hybrid flow and jump dynamics. We propose a new resetting mechanism that keeps the state away from the undesired critical points while, at the same time, guaranteeing a decrease of the potential function. A systematic design of the instrumental potential function is provided. Numerical results are presented to illustrate the performance of the proposed hybrid controller.
Maximum Eigenvalue and Energy Combined Spectrum Sensing Algorithm

Energy detection (ED) method for spectrum sensing does not require any information of the source signal and channel, as well as it is suitable for detecting independent identically distributed signals. Covariance matrix catches the signal correlations and the maximum eigenvalue detection (MED) method is better than the ED for correlated signals. In this paper, taking the advantages of the two algorithms, a combined method of the maximum eigenvalue and energy of the signal is proposed to achieve detection performance improvements. The proposed method is a generalization of the ED and MED methods, which takes the ED and MED algorithms as special cases. The theoretical analysis on detection probability, false alarm probability and threshold are given, and the optimum parameter selection of the proposed method is discussed. Finally, simulation results show the effectiveness of the proposed algorithm.
A Linear Programming Approach to Design Online Triggering Mechanisms for Robust MPC

In this paper, an event-triggering approach is proposed for a robust model predictive control method. The approach is applicable to constrained, linear time-invariant systems with bounded, additive disturbances. At each triggering instant, the triggering mechanism is designed online using a linear programming approach. Intuitively, the mechanism is a sequence of hyper-rectangles that surround the optimal state trajectory, over the prediction horizon. Standard analyses of robust feasibility and robust stability of the closed-loop, event-triggered control system are conducted. A numerical example is presented to show benefits of the proposed approach. In particular and under the assumption that the disturbance has a uniform distribution, we further study some statistical properties of the generated triggering instants.
Tile Arrays for Space Based Solar Power Satellites

This paper investigates the use of thin phased array tiles for the microwave transmitter portion of a space based solar power satellite. Tile phased arrays have been developed for avionic radar, and for 5G telecommunications. This paper attempts to answer the question: "Will it be possible to use these tile arrays for space based solar power systems? "
Stability Analysis of a Parallel-Converter System with Master/Slave Configuration

Parallel operation of power converters provides a promising solution for the integration of distributed generations. To properly share the load power among different converters, the master-slave control architecture has been widely adopted in the literature. In this paper, it is revealed that the interactions among the master converter and the slave converters will cause instability under certain conditions. To properly explain the mechanism and address this problem, an explicit frequency-domain stability analysis of the parallel-converter system is presented in this paper, which would provide valuable guidance for the converter controller design. Finally, simulation results of a parallel-converter system with master-slave configuration are presented for verification.
An Accurate Online Prediction Model for Kiln Head Temperature Chaotic Time Series

The kiln head temperature (KHT) is a key thermal variable to condition monitoring and stable control of the rotary kiln. In this paper, a hybrid dynamic prediction framework (HDPF) is proposed to realize the accurate online prediction of the KHT chaotic time series of the rotary kiln. Where, in order to response time-delay and time-varying behaviors of the rotary kiln, the phase space reconstruction (PSR) method is used to achieve the high-dimensional dynamic trajectory of the sintering system of the rotary kiln and the local Volterra filter based on the sliding window technique is developed to construct the dynamic relationship from the high-dimensional dynamic trajectory. Furthermore, considering the practical industrial data contains unreliable risk sometimes, each predicted value is corrected by the offset compensation technique as the final output. Based on well-known Mackey-Glass chaotic time series and real-world KHT chaotic time series from the Inner Mongolia branch of Datang International Power Generation Corporation Limited, comparative experiments show that the HDPF has the highest prediction accuracy which means the HDPF can capture the dynamic evolution of the KHT over time and has the strongest robustness for four kinds of noise which means it can be applied in practical industries with noise. Meanwhile, it has low computational complexity which can meet the real-time requirements of the monitor and control. Further, those good performances imply the HDPF is effective not only for the rotary kiln but also for other complex nonlinear systems with the time-delay and time-varying behaviors.
Optimal Task Assignment With Delay Constraint for Parked Vehicle Assisted Edge Computing: A Stackelberg Game Approach

Parked vehicle assisted edge computing is a paradigm to employ parked vehicles for processing workloads in vehicular networks. They collaborate with edge servers for joint task execution within task deadline and parking durations. We study task assignment problem with Stackelberg game, where the overall cost of a task publisher in terms of monetary cost and subjective dissatisfaction caused by un-offloaded workloads is minimized. To reach Stackelberg equilibrium, a dedicated algorithm is performed among players in a distributed manner for privacy preservation. Numerical results indicate that the cost is decreased about 25% in our scheme compared to that in existing work.
A Port-Hamiltonian Approach to Plug-and-Play Voltage and Frequency Control in Islanded Inverter-Based AC Microgrids

In this paper, we propose a decentralized scalable, plug-and-play control of voltage-source inverters (VSIs) in islanded, inverter-based AC micro-grids at primary level. Particularly in islanded mode without inertia from conventional generators in the main grid, voltage and frequency stabilization must be performed exclusively by these VSIs. In contrast to existing approaches, we propose a systematic procedure that does not require the proposition of a Lyapunov function as well as avoids computationally expensive and possibly infeasible numerical optimization. It follows passivity techniques, namely interconnection and damping assignment passivity-based control (IDA-PBC) on the basis of port-Hamiltonian systems (PHSs) theory. By employing the Hamiltonian naturally obtained from the PHS approach as Lyapunov function and analyzing load dynamics, we prove microgrid-wide asymptotic voltage and frequency stability. A simulation validating our theoretical results concludes our work.