Miracle-dz/UniFAR-FTUs huggingface dataset:
Tangle the Blockchain:Towards Connecting Blockchain and DAG

Blockchain brings many added values to modern business systems. However, Blockchain-based applications with massive IoT devices experience some limitations. The limitations are due to the linear structure and the consensus algorithms used in Blockchain that consume the participating nodes' considerable resources. In addition, IoT devices are generally with limited resources and have limited bandwidth connections. IOTA, based on Directed Acyclic Graph (DAG), is a new distributed ledger technology (DLT) for IoT devices. It proves its high scalability by providing parallel data processing. However, DAG is still not mature enough to fully replace Blockchain. In previous work, we proposed combining Blockchain and IOTA technologies to allow scalable transactions where Blockchain is employed in the backend, and Tangle is used in the frontend. This paper considers the proposed solution, focusing mainly on the connector part that intermediates both DLT technologies. The connector is a decentralized software component that supports the interaction between the DLT implicitly. The experiments' results show the flexibility to merge both DLTs using a message queuing protocol that enables smart contracts to run on the Tangle nodes and enriches the new platform with reliability and working offline features.
Transient Temperature Rise Calculation for Power Cable in Ducts under Periodical Current

Transient temperature rise calculation is necessary for electrical engineers to obtain the accurate cable core temperature under cyclic current condition and manage power cable load. A lumped parameter transient thermal circuit model has been developed to evaluate the real-time transient temperature rise of the power cable in ducts. The temperature rise of each cable is divided into self-heating temperature rise caused by the heat in this cable and mutual-heating temperature rise caused by nearby other cables. Because it is difficult to calculate the value of thermal parameters in the lumped parameter model, all parameters are obtained with generic algorithm. The fitness function is the difference between transient thermal circuit analysis results and CYMCAP calculation results. The results obtained form the model agree with the accurate results obtained from CYMCAP well. It proved that the model can be used as a straightforward tool to calculate the transient temperature rise of power cable in ducts under periodical current.
Impression Allocation and Policy Search in Display Advertising

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.
Health Monitoring in Internet of Medical Things (IoMT) using Machine Learning (ML) Approaches

In today’s world, the Internet of Medical Things (IoMT) is a term that is catching the attention of many researchers. IoMT is used to exchange data between IoT enabled medical sensors and other smart devices for the purpose of healthcare. In this paper, existing machine learning (ML) techniques and models are used to monitor and predict the health condition of a patient by applying these techniques on the sensor data. Apart from collecting some data from the medical sensors used in a single person, IoT traffic of the medical sensors is generated using the IoT-Flock traffic generator. Extraction and selection of the features from the IoMT traffic is done using pcap-processor, WEKA and python script and then some machine learning algorithms like Naive Bayes, Linear Regression, K-Nearest Neighbour and K-Means Clustering are applied to obtain results for the IoMT sensor traffic received from those medical sensors taken into consideration. The processed data thus obtained is used with a time series prediction model such as ARIMA/GARCH to obtain predicted patterns from the selected features of the traffic. The accuracy obtained using this approach is deduced in terms of Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error(RMSE) of the selected features. In this paper, we have applied existing ML techniques to process the IoMT traffic and make predictions based on it which can help in identifying and predicting the health condition of a patient using IoMT sensors.
Sensor less Finite Control Set Model Predictive Control for Stabilizing DC-Grid Voltage in Islanded DC-Microgrids

Using a linear Kalman filter approach(LKFA), this study proposes sensor-less finite-control-set model predictive control for stabilizing DC-grid voltage estimation in direct current microgrids (DCMs). In controlling many parallel power converters in DCMs, the proposed control method eliminates the need for an inductance current sensor. The second prediction is derived from the state-space model so that an efficient algorithm may be performed. The dynamic model of DCMs in the second prediction is converted into a stationary linear stochastic discrete time-invariant system to standardize the state estimation design. The DC-grid voltage reference is then computed adopting LKFA using a state feedback control law based on the dynamic algebraic Riccati equation with integral action to guarantee zero steady-state error during transient responses. Under load demand variation, the efficacy of the proposed approach is shown using a sensor-less control algorithm. Robustness against parametric uncertainty, DC-grid voltage estimation, and equally estimated power-sharing amongst DERs have all been achieved, as have the fast convergence.
Multivariable Sequential Model Predictive Control of LCL-type Grid Connected Inverter

The traditional single variable indirect model predictive control (SIMPC) of LCL-type three-level grid connected inverter (GCI) indirectly controls grid current by controlling the output current of the inverter, which usually produces large errors and reduces the anti-disturbance performance of the system. Although the existing multivariable weighted model predictive control (MWMPC) method can realize the direct control of multiple variables such as grid current and filter capacitor voltage, it needs to set the weight coefficient of variables, and the calculation of prediction and evaluation is large. In view of the above problems, a multivariable sequential model predictive control (MSMPC) method for LCL-type three-level GCI is presented in this paper. According to the sequence of variable control in the mathematical model of LCL-type GCI, inverter output current, filter capacitor voltage and grid current are predicted and evaluated one by one. Under the premise of realizing the direct control of the above variables, this method omits the setting of weight coefficient, and gradually shrinks the solution space to reduce the calculation amount of multivariable control. In this paper, the LCL-type three-level GCI system is built in the simulation environment to verify the effectiveness of the proposed algorithm.
Solving Solid Geometric Calculation Problems in Text

Solving solid geometric calculation problems is a hard branch problem in problem solving because it usually requires both qualitative reasoning and quantitative calculation to produce the solutions. This paper proposes a solver to solve the solid geometric calculation problems in text. First, it extracts a group of geometric relations from the problem text as the outcome of problem understanding by adopting syntactic-semantic method. Then, it finds out that the given problem should calculate on which solid geometric subject so that it constructs the subject in 3D Cartesian coordinate system. Finally, it calculates the answer using coordinate system, and generates readable solution including the solution text and 3D diagram. The proposed solver is tested on a dataset including 140 solid geometric calculation problems. The test result shows that the solver proposed in this paper achieves an accuracy of 76.43%.
Reliability Modeling and Analysis of Hospital Information System Based on Microservices

In recent years, modern hospital has a large scale, complex relationship, and hospital information system (HIS) due to rapid development of computer networks. But there is still a big gap for reliable use of clinical information and management system, especially in terms of fault prevention. The microservice architecture has great advantages for development and delivery of complex system. This paper proposes a novel microservice reliability model (MSRM) for HIS based on the formalism of Predicate Petri net (PrT net). First, microservice reliability requirement design is given and PrT net is used to model the reliability of microservice, and the corresponding syntax and semantics are also presented. Then the redundancy and circuit breaker is designed by using PrT net, a composition strategy is proposed and the reliability of microservices is analyzed qualitatively and quantitatively. Based on the constructed MSRM, the correctness of PrT net modeling and effectiveness of the strategies have been proven theoretically. Finally, a public healthcare case is used to explain modeling process, and verify the effectiveness of proposed method. Experimental results show that the strategy for HIS microservice reliability is effective.
Economic Dispatch of Model Predictive Controlled Distributed Power Generation

Conventionally, for model predictive power control (MPPC) of voltage source inverter (VSI) in a grid-connected microgrid, active power reference is often associated with dc power source while reactive power reference is often given as a constant zero. There is rare literature completely investigating the changing rationale of dynamic change of power references when the dc power source is invariable. In this paper, an economic dispatch method optimizing power flows based on evolutionary algorithms is proposed to dynamically determine the power references. From the microgrid’s hierarchical view, the MPPC at the device level is combined with the economic dispatch at the system level, via determining the common power references. Then, the system-level optimized power flow with the minimized cost is directly sent to the device-level power converters, enhancing the two levels' connection. The validity and effectiveness of the proposed strategy have been demonstrated by simulation results.
An Improved Circulating Current Suppression Strategy Based on Quasi-PR Controller for MMC-HVDC System

Circulating current flowing in the internal electrical system of Modular Multilevel Converter (MMC) bridge arm is a common phenomenon when system operates normally. This current will lead to active power loss and cost increase. To solve the problem, an improved control strategy is proposed in this paper to reduce the current circulation in MMC system based on quasi-PR controller. This control strategy neither requires additional controller nor complicated control parameters. For the purpose of showing the validity of the controller, this paper employs PSCAD/EMTDC simulation platform establishing the model of electrical circuit and controller and analyses the simulation result. From the simulation results, it is obvious that the improved strategy has the capacitance of significantly inhibiting the bridge arm current circulation and has well performance.
A Study on Objective Dimensionality Reduction of Unbalanced Distribution System Operation

The penetration of distributed generators (DGs) causes voltage unbalance issues in distribution systems. The issues increases the number of objectives in distribution system operation, making it more challenging to achieve optimal operation. Reducing the dimensionality of the objectives by revealing the correlations between objective functions is a practical approach to such challenges. This paper reports reduction of objective functions in an optimal operation problem of unbalanced power distribution systems. Four typical objectives, including voltage unbalance, are considered, and correlations are analyzed by principal component analysis (PCA). In addition, comparisons are made under different constraints. Despite the results revealing two significant conflicts between the objective functions, performing objective reduction using PCA is challenging in many cases. However, the Pareto fronts of some objective functions indicate the existence of reasonable knee points, suggesting that further objective dimensionality reduction may be achieved by integrating some objective functions based on the knee point.
A new local image descriptor based on local and global color features for medicinal plant images classification

Machine learning and computer vision methods are frequently applied for identifying different plant species from images in recent years. The local and global features are extracted from the image by hand-crafted algorithms such as LBP, SIFT, SURF, HOG and Hu which have demonstrated strong performance on datasets of leaves. This article propose a new method that combines local features using SIFT and bag of visual words with HOG and Hu on the two plant image datasets: Folio contains 637 images from 32 species of plants and VNPlant-255 with 255,500 samples that has a complex background. All images will be put through an image processing step, which uses image segmentation techniques. In order to evaluate the proposed approach, we conduct experiments to find a good solution. The results have shown that the SIFT + BoW - HOG - Hu techniques extracted better features than the CNN technique on a relatively small dataset and it gives 99.22% accuracy. Moreover, it also works well on medicinal plant images taken in a natural environment.
Dictionary Pair-based Data-Free Fast Deep Neural Network Compression

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based Data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.
Dynamically Adapting Floating-Point Precision to Accelerate Deep Neural Network Training

Mixed-precision (MP) arithmetic combining both single- and half-precision operands has been successfully applied to train deep neural networks. Despite its advantages in terms of reducing the need for key resources like memory bandwidth or register file size, it has a limited capacity for diminishing further computing costs, as it requires 32-bits to represent its output. On the other hand, full half-precision arithmetic fails to deliver state-of-the-art training accuracy. We design a binary tool SERP based on Intel Pin which allows us to characterize and analyze computer arithmetic usage in machine learning frameworks (Pytorch, Caffe, Tensorflow) and to emulate different floating point formats. Based on empirical observations about precision needs on representative deep neural networks, this paper proposes a seamless approach to dynamically adapt floating point arithmetic. Our dynamically adaptive methodology enables the use of full half-precision arithmetic for up to 96.4% of the computations when training state-of-the-art neural networks; while delivering comparable accuracy to 32-bit floating point arithmetic. Microarchitectural simulations indicate that our Dynamic approach accelerates training deep convolutional and recurrent networks with respect to FP32 by 1.39 × and 1.26 ×, respectively.
Nine-Level Active Neutral Point Clamped Converter with Cascading H-Bridges Fed by Flying Capacitors

A novel active neutral point clamped bidirectional multilevel inverter is proposed, consisting of flying capacitors with H-bridge interconnection circuit that allows for commutating any combination of flying capacitors and their polarity in the current path. The flying capacitor voltages are scaled by 1/2nfactor to maximize the number of output voltage levels. Each intermediate output voltage pole has multiple possible combinations, where each flying capacitor can be charged or discharged, thus ensuring the simple capacitor voltage balancing capability within the same voltage pole. The simulation analysis demonstrated good performance and feasibility of the proposed topology for high power converters.
Vehicle Number Plate Detection and Recognition using YOLO- V3 and OCR Method

Automatic Number Plate Recognition is one of the many information systems which is used data extraction from given vehicle image and apply the data for further usage in safe, secure and modernistic Transportation System. The Novelty of the project is that even if the image is blurred, our system can deblur the given image and apply it to the Machine Learning models further. In the proposed work, You Only Look Once [YOLO] V3 model for Region of Interest [ROI]; Convolution Neural Network [CNN] for optical character recognition was implemented. After the ROI is detected, it will be enhanced with pre-processing steps before it is fed to CNN model. Dataset of different Indian Number Plates’ Font was created, consisting of 6439 images of different alpha- numerical characters. Accuracy of 91.5 percent is obtained. The extracted and sorted characters of the number plate is cross- checked with the Indian RTO database and the information regarding which RTO the input vehicle image belongs to, is provided.
Preference-aware Group Task Assignment in Spatial Crowdsourcing: A Mutual Information-based Approach

With the popularity of GPS-enable smart devices and the development of wireless network, Spatial Crowdsourcing (SC), as a framework for assigning location-sensitive tasks to moving workers, has received wide attention in recent years. In real-world scenarios, some complex tasks exist that may not be completed by a single worker. In this case, the tasks are often assigned to multiple workers, which is called group task assignment. However, the assignment of tasks that satisfy all group members in an even way remains a challenge. To this end, we propose a novel preference-aware group task assignment framework that includes two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). Specifically, MIPM learns the preferences of worker groups by maximizing the mutual information among workers based on the worker-task interaction data and the group-task interaction data, where an attention mechanism is used. PGTA adopts an optimal task assignment algorithm based on tree decomposition to assign tasks to appropriate worker groups, which aims to maximize the overall number of assigned tasks while giving priority to the groups of workers that are more interested in the tasks. Finally, extensive experiments are conducted, verifying the effectiveness and practicality of the proposed solutions.
Analysing Student's Sentiment on Virtual Learning Experience during COVID-19 Crisis

The aim of the present study is to analyze the student's sentiments on virtual learning experience during COVID-19 Crisis. The study critically examines the students understanding towards crisis communication, students understanding on crisis communication experience and students understanding about digital platforms of learning during COVID-19 crisis. Through questionnaire method, the student's responses were gathered to find out their sentiments and perception on various aspects. Convenience random sampling method was used to gather the data. Various statistical techniques such as descriptive statistic, Correlation analysis, ANOVA and regression analysis were used to analyze the data, obtained from 105 respondents. The results showed that there is a significant influence of student's understanding on crisis communication, crisis communication experiences and understanding about digital platform on their virtual learning experience during Covid-19 Crisis.
Hybrid Genetic algorithm for Blocking Flowshop scheduling with due date windows

In production, considering the due date of a job as an interval of time is more practical than a point time. In this paper, the flowshop scheduling problem with blocking constraint is investigated where the objective function is the minimization of the total weighted earliness tardiness from the due date window. To the best of our knowledge, this problem has not considered in the literature on scheduling problems. A new benchmark of 110 instances is generated to test the proposed resolution method. Since the problem is NP-hard a new hybrid procedure that combines the genetic algorithm and the iterated greedy is introduced to solve the problem. Our approach is compared with a set of three competing methods well selected from the literature. All algorithms are adapted and well adjusted in order to reach maximum efficiency. The results of our developed method proven that it has the best results.