Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-element quantitative extraction of mining area of laterite nickel mine combined deep learning with object-oriented method

Remote sensing technology has great advantages in timely and rapid monitoring of open-pit mining conditions. Based on Worldview-2 multi-spectral satellite remote sensing images, we took a typical lateritic nickel deposit in Indonesia as an example, and 9 types elements which are mining area, dump, collection sump, tailings pond, buildings, roads, smelter, vegetation and bare soil in mining active areas were extracted. Firstly, a deep learning method based on TensorFlow framework was used to extract the main roads and mining areas from the pre-processed images to obtain vector data. Secondly, according to the vector data, our study area can be divided into two areas, center and outskirts, by FNEA coarse segmentation, and the local variance change rates of the two areas are calculated, so as to select appropriate segmentation scales for each factor type and establish a bottom-up multi-scale segmentation hierarchy. Thirdly, the spectral difference index (SDI) and PCA-based GLCM texture features were proposed to expand the feature base. The FSO algorithm and SEaTH algorithm were combined to select the optimal features and separation thresholds. At last, the multi-element extraction of laterite nickel ore area was completed hierarchically. The overall accuracy reached 90.12%. Our results indicated that the proposed method takes into account the spatial differences of various elements, ensuring the accuracy of segmentation and element extraction. Furthermore, method of selecting scales and thresholds avoids multiple experiments and reduces the time and labor cost of trial and error, which ensures objectivity and improves the selection efficiency. In addition, the PCA-based texture features can shorten the feature calculation time from 6.25 min to 14 s, reducing the operation time of the algorithm and greatly saving the operation time while ensuring the correlation effectively.
Design and Comparison Analysis of Ge-source Hetero-stacked L-shape TFET with Homo-stacked L-shape TFET

In this paper, we propose and investigate a novel Hetero-stacked L-shape TFET using Silvaco TCAD simulation tool. Due to hetero-stacking of Ge material and Si material, higher Ion/Ioff ratio is obtained in the proposed Ge source based hetero-stacked L-shape TFET in comparison to Homo-stacked L-shape TFET. Further, the proposed structure not only provides higher Ion/Ioff ratio but also provides very low SS in comparison to Homo-stacked L-shape TFET. Further, this structure is vertically stacked so this non-planar TFET structure would require small area on the wafer as compared to the planar conventional TFET structures.
Computational and Adaptive Offloading in Edge/Fog based IoT environments

Despite the substantial efficiency advancements, today's cloud services and related paradigms need vast amounts of energy and network, and this demand is only expected to grow. One of the main research questions is whether and how transitioning to edge computing may help reverse this trend. Simulations play a vital role in research since real-world deployments of edge or fog infrastructure are infrequent. In this research, we look at how to use a range of simulation tools to help with computation-intensive offloading in edge networks. This paper aims to determine the average energy consumption of compute nodes, as well as network traffic and applications, over experimental edge-based networks using an adaptive offloading approach and a blend of analytical and precisely defined modeling tools.
Thick Cloud Removal and Reconstruction for Remote Sensing Images Using Attention-based Deep Neural Networks

Thick cloud removal for remote sensing images is an important and challenging task for researchers. Existed clouds removal methods always have some limitations with a large area of clouds or a long period between the cloudy image and the supplementary cloud-free image. In this paper, we proposed a deep-learning based framework for thick clouds removal. The method added prior spectral information into the model inputs and used deep convolutional neural networks (CNN) with dense connection and channel attention to reconstruct the cloudy areas. The loss function considered both spectral and structure similarity. We designed artificial and observed data experiments to show the performance of the network. Our method achieved the coefficient of determination (R2) of 0.976, structural similarity (SSIM) of 0.937 and root mean squared error (RMSE) of 0.016 in the artificial dataset and can generate reconstruction results with consistent spectral information and clear texture details, indicating that the proposed method is effective for cloud removal and data reconstruction.
Automatic Question Extractor and Answer Detector in a Recorded Webinar

Online learning becomes the primary method of education due to the novel coronavirus (COVID 19). This research paper describes the automatic extraction of domain-related questions in a lecture video, identifies the answers given by the lecturer for both voice-based questions and chat questions. The paper also presents a method to identify whether a lecturer gave a valid answer for the chat-based questions in the later section of the video. Additionally, this paper describes the approach to identify the most accurate solutions from the custom search engine-based responses.
Gamifying Learning in High Schools: Perceptions of Students in Selected African Countries

The aim of this paper was to present the findings from an empirically grounded investigation into the perception of Students on gamifying learning in high schools within African countries, informing the current literature on gamification in learning within African contexts and providing a new approach on how gamification can be incorporated into learning within African countries namely Namibia, Nigeria, Ghana and Kenya. The study was conducted in four countries in Africa. 800 quantitative survey questions were distributed among high school students in these countries. However, 525 questionnaires were completed. Simple random sampling technique was used. Descriptive Statistics was used to analyse the findings using the Statistical Package for Social Sciences (SPSS). The findings revealed that students are already actively involved in gamified learning activities in class and found them useful for their learning. However, there is a need to look at the distractive tendencies it poses in class. Recommendations on how to incorporate gamification in learning within African countries are proposed.
Research on Music Transition Using Neo- Riemannian Theory and Bayesian Network

In this study, two theoretical studies were combined. On the music theory side, we used Neo-Riemannian theory to analyze the music, and Bayesian networks on the mathematics side. R was used to construct the Bayesian network. Using these two methods, we have developed a mathematical model of musical transitions, and have also attempted to create music based on the probability of chord progressions.
Forecasting Electricity Consumption with Intelligent Hybrid Model

In this paper, an Extreme Learning Distributed Adaptive Neuro-Fuzzy Architecture (ELDANFA) model has been presented. It has been tested with real data for predicting energy consumption at an electrical substation in the South-Central region, near Plovdiv, Bulgaria. The main goal of this hybrid intelligent structure is to reduce the computational burdens of neuro-fuzzy models and to keep prediction error to a minimum. The obtained results prove that the proposed model predicts accurately electricity consumption. It is also suitable for real-time applications due to the reduced number of fuzzy rules and the small number of parameters updated during the learning procedure.
Liujiaxia Area Potential Landslides Identification and Analysis Based on InSAR Technology

The widespread loess landform, rainfall, reservoir construction and farmland irrigation in the Liujiaxia region cause frequent occurrence of landslides in the region. Therefore, it is of great significance to study the early identification of landslides for the region. Based on the SBAS-InSAR technology and by utilizing Sentinel-1A satellite image of 59 scenes, the recent three-year lineof-sight (LOS) surface deformation rate of the Liujiaxia region was extracted for this paper. Multiple potential landslide masses in the region were identified, and a time series analysis was performed on typical potential landslide masses. The results are as follows: (1) The surface deformation rate of the Liujiaxia region was $- 38 \sim 20$ mm/a during the study period, and a total of 24 potential landslide masses were identified according to the surface deformation rate and Google Earth images; (2) A detailed analysis was performed on the potential landslide masses that might pose serious threats to Wangjia Village and Bashigeyuan Village, showing that the surface deformation rate was significantly accelerated during farmland irrigation and concentrated rainfall. This study can provide data and theoretical support for the early identification of potential landslide masses in the Liujiaxia region as well as for disaster prevention and mitigation.
A User-based Collaborative Filtering Method to deal with Sparsity in Recommendation Systems by an unsupervised learning of Users’ Hidden Preferences

This paper focusses on sparsity in User-based Collaborative Filtering (UCF) type of Recommendation Systems. UCF mainly depends on Users’ Similarity Calculation (USC). The idea of the proposed method to overcome the sparsity problem of UCF is, overcoming the sparsity effect on USC through imputation of missing ratings. In the proposed method imputation is carried out by finding the hid-den preferences of users through an unsupervised learning using K-Means, basing on given ratings and item features. The proposed method aims to provide adequate information to the similarity metric used for USC, even with sparse rating data. The proposed method is compared with some other approaches in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error (MSE) values with varying levels of Sparsity and the RMSE, MAE and MSE are found to be the least for the proposed method. Also the RMSE of the proposed method for all the levels is close to 1 with the rating range of the dataset used being [1], [5] and the fact that the error-rate being almost constant across the sparsity levels shows that the proposed method is not greatly affected by sparsity.
Trans-Inf-Net: COVID-19 Lung Infection Segmentation Based on Transformer

For the global health crisis COVID-19, the radio-logical imaging techniques CT have demonstrated effectiveness in both current diagnosis and evaluation of disease evolution. However, the manual delineation of lung infections is tedious and time-consuming work, and infection annotation by radiologists is a highly subjective task, often influenced by individual bias and clinical experiences. To address these challenges, we proposed a transformer learning method (Trans-Inf-Net) to automatically identify infected regions from chest CT slices. In our Trans-Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a segmentation framework based on a randomly selected propagation strategy and transformer, which only requires a few labeled images and leverages primarily unlabeled data. We apply attention in conjunction with convolutional networks, while keeping their overall structure in place. a pure transformer applied directly to sequences of image patches can perform very well on image segmentation tasks. Our framework can improve the learning ability and achieve a higher performance. Extensive experiments on COVID-SemiSeg and real CT volumes demonstrate that the proposed Trans-Inf-Net outperforms most cutting-edge segmen-tation models and advances the state-of-the-art performance.
Digital Implementation of P-Q Control in Grid-Tied Inverters for Educational Purposes

This paper presents the development of an educational tool, aiming at the in-depth study of P-Q control concept in grid-tied inverters. The proposed tool is based on the development of the digital control model in MATLAB® Simulink and its conversion into executable code for the TMS320F28379D microcontroller unit. Hence, students are encouraged to enhance their programming and digital control skills, along with the in-depth analysis and comprehension of power electronics systems. Finally, the overall educational tool is experimentally validated through an H-bridge grid-tied inverter.
Research on Traffic Intrusion Detection Method Based on Deep Learning

With the rapid development of computer technology and the expansion of the Internet, intrusions on the Internet have become more frequent. With the development of machine learning technology, people apply machine learning technology to the anomaly detection of network traffic. However, traditional traffic classification not only relies on complex features, but also extracts users' private content, which has a negative impact on users. It is already difficult to meet the current increasingly large-scale network. Due to the rapid development of deep learning recently, it has very good applications in many fields. In this article, on the basis of it, we use Convolutional Neural Networks (CNN) and long- and short-term memory networks, and comprehensively put forward corresponding intrusion detection models based on the actual classification characteristics of the data set and model, optimization is performed.
Personality Prediction Based on Users' Tweets

The extensive use of social media has a tremendous impact on culture, business and politics in the world at large with potentially positive and negative effects. Personality traits are generally referred to as relatively stable patterns based on one's past experiences, upbringing, environment, all the opinions that one forms over time. It's closely bonded to how one feels, thinks and behaves, which has been associated with a wide range of important life outcomes and choices. Hence, in recent years, there has been a massive increase in the interest to develop models which use online data on human behaviour and preferences to automatically predict an individual's personality traits. In our research, to compute the accuracy of the personality predicted, it is first ensured that the dataset of stream of Tweets is almost equally divided into 16 different personality types, and be cleaned properly. We propose an advanced framework to identify and scrape tweets and retweets of Twitter user accounts and eventually applying Machine Learning algorithms for Personality Prediction.
A Wearable Biometric Performance measurement System for Boxing - A SURVEY

The performance measurement during the training period or competition is of major interest in the sports research domain. The requirements for an ideal sports performance measuring sensors are less costly, miniature size, and non-intrusive types. It was made possible by the recent developments in MEMS (Micro-Electro Mechanical Sensors) technology of inertial measurement units. The merging of technology with boxing during training and competition has become more common in today’s sports scenario, which is a motivation for proposing this survey paper. The Inertial sensors are being used more commonly for action recognition and classification, automatic scoring of real-time bouts, head impact and performance monitoring of boxing during training and competition. For combat sports, it has become a trend to use inertial sensor technology. Totally 18 records were considered for survey to display the concept that inertial sensor measurements were primarily used for the measurement of boxing striking/hitting quality, boxing strike classification, automatic scoring of bouts, head impact, machine learning technique used, IMU placement on the body, and validation technique employed in boxing. The methods followed to select and implement the inertial sensor technology appear under-researched, and no appropriate protocols for study the results. Our survey helps in understanding of Inertial Measurement Unit (IMU) into application technology to boxing sport.
Design of Ultrasonic Distance Measuring System for Auto Palletizer in Earthquake Rescue Warehouse

In order to ensure the safe use of smart devices such as auto palletizer, unmanned navigation robot, self-operated robot, etc. in modern warehouse, automatic obstacle avoidance and navigation technology have been widely used to measure the spacing between equipment, equipment and warehouse facilities or other obstacles. A design scheme of ultrasonic ranging system based on Tiva C series single chip microcomputer is proposed. Since the frequency of Tiva C series MCU is as high as 80 to 120 MHz, the capacity of time measurement will be enhanced which will improve the measurement accuracy. When the timer is started, the PWM channel is started to drive the ultrasonic transmitter and the capture channel will capture the echo signal. The ultrasonic velocity is adjusted real-time according to the actual environmental temperature. With the system, distance between stacker and the obstacle were calculated in order to determine following motion of the stacker, such as keeping moving or making a sound-light alarm or decelerating to stop, with the pre-design threshold value of distance. Through abundant tests, the system illustrated that it achieved predetermined function, the alarm and stop distance meet the requirements and could be used in the actual environment.
A Question Answering Method of Knowledge Graph Based on BiLSTM-CRF and Seq2Seq

In natural language processing, intelligent question answering based on knowledge graph has received great attention. In the previous knowledge base question answering, the traditional word vector is difficult to express the text semantic information, and the cyclic neural network is easy to cause gradient disappearance and gradient explosion. At the same time, it is lack of comprehensive consideration of text context information. This paper proposes an intelligent Q & A method based on knowledge graph, which uses BiLSTM-CRF model to realize entity recognition. The intelligent Q & A model is constructed based on Seq2Seq, and the above methods are verified by taking the intelligent Q & A as an example, which effectively improves the accuracy of intelligent Q & A.
Antenna Array Design for Higher Spectral Effeiciency for Hybrid Beamforming 5G MIMO Wireless Communication System

This paper presents the best suited antenna array for the hybrid beamforming in 5G MIMO wireless communication system. Investigation finds impact on the performance matric using different antenna array size. We design the transmitter antenna array of size 16×16, 8×8, 4×4, 2×2 for the 28 GHz frequency. Our intrigued of investigation and performance metric are spectral efficiency, energy efficiency and bit error rate. In this paper we shows, as the size of antenna array increase the spectral efficiency and energy efficiency will increase by great extent. We also find the bit error rate of wireless communication is reduced with the size of antenna array. Furthermore, we demonstrated our result using MATLAB as the larger size of antenna array increase beam is more steered in desired direction and reduced the side lobes in other direction.
Joint Usage of Neural Networks and Stochastic Referencing of Images While Estimating Defects in Bridge Structures

This paper deals with the research aimed at estimation of dynamics in growth of defects revealed in video images of metal bridge structures. A video image is formed by an UAV. Joint usage of the neural network and adaptive pseudogradient referencing of images was used to estimate the dynamics of changes in defects. The examples of the images and video image sequences are given herein.
Mixed Reality Tailored to the Visually-Impaired

The goal of the proposed device and software architecture is to apply the functionality of Mixed Reality (MR) in order to make a virtual environment that is more accessible to the visually-impaired. We propose a glove-based system for MR that will use finger and hand movement tracking along with tactile feedback so that the visually-impaired can interact with and obtain a more detailed sense of virtual objects and potentially even virtual environments. The software architecture we propose is making the current MR frameworks more accessible by augmenting the existing software and extensive 3D model libraries with both the interfacing of the glove-based system and the audibly navigable user interface (UI) of a virtual environment we have developed. We have implemented a circuit with flex sensor flexion angle tracking of a single hand and variable vibration intensities for the vibromotors on all 5 fingertips of a single hand. Alongside the circuit, we have developed a virtual environment that can be hosted on a Windows 10 application. The virtual hand and its fingers can be moved with the system’s different inputs and the virtual fingertips touching the virtual objects trigger vibration motors (vibromotors) to vibrate while the virtual objects are being touched. A rudimentary implementation of picking up and moving virtual objects inside the virtual environment has also been implemented. In addition to the vibromotor responses, text to speech (TTS) has also been implemented in the application for when virtual fingertips touch virtual objects and other relevant events in the virtual environment. A visual UI has also been designed and developed for the aiding of current and future development.
6D Pose Estimation Based on Edge-Enhanced Point Pair Features for Surgical Navigation

This paper proposes an efficient 6D pose estimation method for the automatic spinal surgical navigation system. Visual-based pose estimation is challenging in realistic surgical scenes due to the human spine's geometric complexity and symmetric ambiguity. Instead of considering the whole scene for pose estimation, we introduce a targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is then presented to resolve the symmetric ambiguity. Note that our method can also work on incomplete scenes since our sampling and validation do not require a whole model. We perform evaluations on two challenging datasets, which demonstrate the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We also implemented a prototype of a surgical navigation system based on the proposed method, which may provide a viable option for improving the spinal clinical surgery system.