Reviewerly/exHarmony huggingface dataset:
Gaining Insight from Semi-Variograms into Machine Learning Performance of Rock Domains at a Copper Mine

Machine learning (ML) is increasingly being leveraged by the mining industry to understand how rock properties vary at a mine site. In previously published work, the rock type, granodiorite, was predicted with high accuracy by the random forest (RF) ML method at the Erdenet copper mine in Mongolia. As a result of the optimistic results (86% overall success rate), this paper extended the research to determine if ML would be successful in modeling rock domains. Rock domains are groups of rocks that occur together. There were two additional goals. One was to determine if the variograms could predict or help understand how ML methods would perform on the data. The second was to determine if 2D modeling would perform well given the disseminated nature of the deposit. ML methods, multilayer perceptron (MLP), k-nearest neighborhood (KNN) and RF, were applied to model six rock domains, D0–D5, in 2D and 3D. Modeling performance was poor in 2D. Prediction performance accuracy was high in 3D for the domains D1 (92–94%), D2 (94–96%) and D4 (85–98%). Note that the domains D1 and D2 together constituted about 80% of the samples. Conclusions drawn in this paper are based on the results of 3D modeling since 2D modeling performance was poor. Prediction performance appeared to depend on two factors. It was better for a domain when the domain was not a minuscule proportion of the sample. It was also better for domains whose indicator semi-variogram (ISV) range was high. For example, though D4 only contributed 15% of the samples, the range was high. MLP did not perform as well as KNN and RF, with RF performing the best. The hyperparameters of KNN and RF suggested that performance was best when only a small number of samples were used to make a prediction. One overall summary conclusion is that the two most important domains, D1 and D2, could be predicted with high accuracy using ML. The second summary conclusion is that semi-variograms can provide insight into ML performance.
Robot Path Planning Based on Adaptive Parameter Ant Colony Algorithm

Aiming at the problems encountered by the basic ant colony algorithm in the research of mobile robot path planning, an improved adaptive parameter ant colony algorithm is proposed. The conical pheromone initialization distribution strategy is adopted to strengthen the initial pheromone concentration in the guidance area, and solve the problem of lack of pheromone guidance at the beginning of the algorithm. Introduce discrete coefficients, calculate the discrete coefficients of all ant paths in each iteration, and automatically adjust the heuristic factor parameters to improve the state transition equation and improve the overall effect of the algorithm. Compared with other algorithms in this paper, the algorithm in this paper has stronger optimization ability, and still has good optimization ability in complex environment. It can be seen from the experimental results that the self-adaptive parameter ant colony algorithm proposed in this paper can establish an efficient guidance area, improve the overall optimization ability of the algorithm, and has great practical value.
Intelligent Substation Online Monitoring System Based on Block Chain Technology

Intelligent substation as the basis of the smart grid and the key link, has the essential role, need its support for automatic control, intelligent control, advanced features such as onhne analysis and decision, the realization of the function of these need to be intelligent on-line monitoring system for substation is more real-time data centralized monitoring and comprehensive analysis abiliy, and the existing intelligent substation information data integration degree is low, Data security is difficult to guarantee. Based on block chain unforgeable, difficult to tamper with, can be traced to the characteristics of the distributed data storage, encryption technology, combined with block chain P2P network, intelligent technologies such as contract, put forward the intelligent on-line monitoring system for substation based on block chain, safely and efficiently information collection, storage, processing each equipment state inspection and operation condition information, Improve the operation efficiency of smart substation and information interaction between power grids.
Improved Genetic Algorithm for Multi-agent Task Allocation with Time Windows

Task allocation is a very important part of multi-agent systems. When assigning tasks to one of the agents in multi-agent systems, many constraints need to be considered to achieve optimal allocation results. In this paper, an improved genetic algorithm (GA) is proposed to solve the multi-agent task allocation with time window constraints. Firstly, the mathematical model of task allocation is established, and the constraint problem of time window is analyzed. The penalty function method is used to deal with the constraint condition. Secondly, the improved Large Neighborhood Search (LNS) is added to the local search to increase the diversity of population, which can make the algorithm easier to jump out of local optimum. Then genetic algorithm is used to solve the multi-agent task allocation problem with time window constraints. Finally, the simulation verifies the optimization performance of the improved algorithm.
Novel Data-Based Model for Future Epidemiology

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. This goal is often realized using closed form expressions that significantly utilize systems of differential equations. These systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non–active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. This is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required. The discussion in this research proposes a suitable solution in this regard. The proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework. In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact. Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. The research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet.
Image Quality Assessment for Interdependent Image Parameters Using a Score-Based Technique for Endoscopy Applications

It is an arduous task to perform an Image Quality Assessment (IQA) for all real-time imaging systems like endoscopy systems which have no reference images for comparison. That being a challenge in itself, the images must be assessed subjectively rather than objectively so that the assessments comply with human perception. ImageLab, a complete IQA solution developed in-house has image capture and illumination capabilities integrated with custom test charts and algorithms to provide a detailed analysis for individual image parameters. Though individual parameter analysis is the primary focus of the IQA tool, a technique to provide a score for image quality based on the interdependency of the image parameters is also integrated in the tool. Specifically, the dependency of visual noise and dynamic range on sharpness and resolution has been highlighted in this paper. Eight levels of sharpness were chosen and for every level, image quality in terms of visual noise and dynamic range were computed. The perceived visual noise levels which is part of subjective analysis were factored in the dynamic range calculations and the final image quality score was derived. These detailed measurements would aid in the development of endoscopy systems and the much simpler score-based technique would aid the technicians during maintenance and calibration check before endoscopy examination.
A High-Efficiency FPGA-based BLAKE-256 Accelerator for Securing Blockchain Networks

Developing hardware-efficient and high-speed BLAKE256 hardware has recently been a research trend because BLAKE256 is today an important hash function for maintaining the security of blockchain networks, such as Decred and HyperCash. However, existing BLA KE-256 circuits still have low performance and hardware efficiency. Therefore, this paper proposes the BLAKE256 accelerator to achieve high performance and hardware efficiency for securing the blockchain networks. To achieve those goals, the proposed BLAKE-256 accelerator has three novel optimization techniques. First, a fully unrolled datapath architecture is proposed to generate one hash per clock cycle, thus improwing the performance. Second, a pipelined arithmetic-logic unit (ALL) is proposed to shorten the critical path. Third, nonce generating and checking block mechanisms (NGB and NCB) are developed to reduce the data transfer time between the CPU and the accelerator, which can improve the total processing rate Based on our experiments on a Xilinx Zynq UltraScale+ MPSoC ZCU102 FPGA at the system-onchip level, the impact of proposed optimization techniques is clearly proven. Moreover, experimental results on several FPGAs show that our proposed accelerator has significantly better throughput and area efficiency than previous BLAKE-256 architectures.
Conversion of Clefia Algorithm to Decrease Memory Restrictions Encountered on IoT by Applying CMA Method

Most companies use the Internet of Things (IoT) since, it is a recent technology that promises to deliver faster services. IoT users view data safety and memory limitations as the most problematic issues. IoT devices require robust ciphers to secure confidential data. The majority of solid ciphers require a large amount of memory allocation during installation, which is impossible for certain IoT devices with limited memory. Regardless of encryption level, most ciphers are able to be installed on IoT gadgets due to memory limitations. Clefia is one of the various ciphers required on IoT devices. In this study, Clefia was converted to invent a new cipher called Conv_Clefia, with fewer memory limitations and roughly the exact encryption strength as an original Clefia. The conversion method examines all processes used on Clefia that have no count of significance regarding memory limitations, and encryption strength are mathematically evaluated. The entire Clefia processes with no added value are mathematically and statistically converted or removed from the Clefia algorithm. Converting Clefia to generate Conv_Clefia is considered as a novel approach since no one has ever explored or transformed the original Clefia to develop an algorithm that operates the same way as Conv_Clefia. Because the methodology used in this study is novel, new terminologies have been coined. Because Clefia is converted to generate a new algorithm, this revolutionary approach is dubbed “Conversion Method of Algorithm (CMA).” The new converted Clefia algorithm is dubbed Conv_Clefia. On Clefia, this approach was practically successful. The CMA successfully reduced Clefia memory by 10% to 30% while maintaining encryption strength that was nearly invariant (± < 1%). Additionally, encryption and decryption time is reduced by more than 50%. Then, the CMA delivered good results.
A Fast and Efficient FPGA-based Pose Estimation Solution for IoT Applications

CNN-based pose estimation can be widely used in various edge computing applications. However, its complex computations severely bring about a high processing latency and a high demand in computing resources. In this paper, we present an FPGA-based heterogeneous SoC design for real-time pose estimation. First, on top of SimpleBaseline, a new CNN model – LightPose is proposed for an efficient and lightweight processing of human’s pose estimation. By using MobileNetV2 as its backbone for feature extraction and using depth-wise separable transpose convolutions for up-sampling, LightPose has successfully obtained an over $50 \times$ reduction in both computation complexity and network size. Second, by using a quantization-aware training method with an additional shortcut optimization, an 8-bit quantization is conducted on LightPose with an only 0.5% drop in average precision. Finally, with specially designed computing engines and pipelined modules, we build the LightPose model on a Xilinx xc7k325t FPGA together with a RISC-V CPU for system management and external communications. Experimental results show that our new LightPose model and its hardware-accelerated solution have successfully reached an outstanding performance of 411.6 FPS in speed and 0.546 in average precision, surpassing the existing peer works in both processing rate and power efficiency.
A Dynamic Gesture Recognition Algorithm based on Feature Fusion from RGB-D Sensor

The motion process of dynamic gestures is often very complicated, and extracting appropriate features from the sequence of gestures to characterize the gesture is a daunting task. The current dynamic gesture recognition algorithms generally have problems such as complicated computational process and poor robustness to the environment, which cannot meet the requirements of real-time and complex scenarios. How to improve the simplicity and robustness of the algorithm is a very challenging research topic. Since the common problem affecting system performance is descriptors, it is possible to improve the robustness of descriptors to complex environments and simplify feature extraction algorithms to meet the requirements of interactive systems. This paper proposes a new video descriptor (HOG2-9ULBP2), and utilizes the descriptor to complete dynamic gesture recognition. First, the RGB image sequence and the depth image sequence of the gesture are simultaneously captured by Kinect; then, the HOG2 feature of the gesture is extracted from the depth image sequence and the 9ULBP2 feature of the gesture is extracted from the RGB image sequence; next, the two features are fused to represent the motion feature of the dynamic gesture; finally, the fusion feature is fed into the SVM for gesture recognition. The proposed dynamic gesture recognition method is tested in the SKIG dataset. The experimental results show that compared with other gesture recognition methods, the method can achieve a recognition rate of up to 98.51%.
Enhanced Data Integrity Encryption Algorithm for Cloud Computing

Cloud computing storage services allow people to easily maintain and manage large amounts of data at a low cost. However, it cannot guarantee data integrity. Data is transferred to unsafe and untrustworthy remote cloud servers. Meaning that data could be manipulated by unauthorized users or the service provider unintentionally, resulting in data loss or modification. As a result, protecting data from hackers has become crucial in order to maintain its integrity. The Enhanced Data Integrity Encryption (EDIE) technique was proposed in this paper to enhance data integrity in cloud computing. The proposed algorithm is designed by integrating Moving Target Defense (MTD) and New Lightweight Cryptographic Algorithm (NLCA). The EDIE algorithm used MTD to dynamically change network configurations and frequently confuse attackers, therefore minimizing the level of attacks and leading to enhanced data integrity. Computer simulation results further show that, compared to other traditional data integrity algorithms, EDIE yields an energy consumption of 6%, encounters the lowest level of 0.2% of all initiated man-in-the-middle attacks, and reduces server computing time by 21%.
Motorized buoy path following based on improved LOS algorithm and Aquila optimizer algorithm

This paper improves the traditional LOS guidance algorithm and obtains a Los guidance algorithm based on time-varying virtual guidance distance. Through comparative simulation, it is proved that the method is better to solve the guidance angle when designing the path following controller of motorized buoy. On the other hand, in order to reduce the unnecessary energy loss of the motorized buoy, the rudder energy loss of the motorized buoy is taken into account, and a more energy-saving path following controller of the motorized buoy is designed. Aquila optimizer is introduced to complete the solution of the control quantity. Ultimately, through comparative simulation, it is proved that the improved controller can achieve more energy-saving control performance while maintaining superior track control effect.
A Toolkit for Text Extraction and Analysis for Natural Language Processing Tasks

Text extraction is an important part of natural language processing (NLP) tasks. Most NLP tasks like text classification, machine translation, text-to-speech, text-based language identification, text summarization, and named-entity recognition involve the use of textual data. Such data is limited for low-resourced languages making it difficult to experiment advanced NLP techniques on these languages. This paper presents a Python-based toolkit for text analysis and text extraction from different types of images, documents, and audio files. The toolkit is built as a library that has functions that can be imported and utilized for text extraction.
Ontology Reuse: Neural Network-Based Measurement of Concepts Representations and Similarities in Ontology Corpus

Ontologies are the heart of the semantic web. They are designed to be reused in web applications. This paper aims to discover a given concept's representation against existing ontologies in a corpus, and if the concept is represented, other similar concepts and terms to it are extracted. A corpus formed of several ontologies in the agricultural domain was constructed. SPARQL queries were used to extract the required data from existing ontologies. And a machine learning technique, the Word2Vec, was employed for ontology reuse process to measure concepts similarity against the existing ontologies. The experimental results showed that the proposed methodology successfully detected previously seen vocabularies during the training on the data in the ontology corpus, and retrieved other similar concepts from the ontologies as well as their degree of similarity (Cosin similarity). Furthermore, the proposed model could process over two million terms in around one minute, reflecting its effectiveness in this context. The proposed method would be useful to ontology and knowledge engineers to conduct a preliminary investigation about which existing ontologies are suitable for reuse in the process of developing new ontologies. Other applications of the proposed method may include ontology alignment to measure the degree of similarity between existing ontologies.
C+L-Band Dispersion Compensation through Optical Reservoir Scheme

The dispersion compensation is thoughtfully investigated in the nonlinear-optical loop mirror (NOLM)-based optical reservoir scheme. The BER of lower than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> for P AM-4 signals is achieved at the whole C+L band through optimizing the training length and the neuron's number.
Using Q-learning to Automatically Tune Quadcopter PID Controller Online for Fast Altitude Stabilization

Unmanned Arial Vehicles (UAVs), and more specifically, quadcopters need to be stable during their flights. Altitude stability is usually achieved by using a PID controller that is built into the flight controller software. Furthermore, the PID controller has gains that need to be tuned to reach optimal altitude stabilization during the quadcopter’s flight. For that, control system engineers need to tune those gains by using extensive modeling of the environment, which might change from one environment and condition to another. As quadcopters penetrate more sectors from the military to the consumer sectors, they have been put into complex and challenging environments more than ever before. Hence, intelligent self-stabilizing quadcopters are needed to maneuver through those complex environments and situations. Here we show that by using online reinforcement learning with minimal background knowledge, the altitude stability of the quadcopter can be achieved using a model-free approach. We found that by using background knowledge and an activation function like Sigmoid, altitude stabilization can be achieved faster with a small memory footprint. In addition, using this approach will accelerate development by avoiding extensive simulations before applying the PID gains to the real-world quadcopter. Our results demonstrate the possibility of using the trial and error approach of reinforcement learning combined with activation function and background knowledge to achieve faster quadcopter altitude stabilization in different environments and conditions.
Circle Area Detection Based on Convolutional Neural Networks

It is difficult for the traditional circle detection methods to locate the circle areas in an image. In this paper, we develop an end-to-end convolutional neural network (CNN) to detect circle areas for objects. In the CNN, an edge feature extraction module is designed to extract the edge feature. Then the edge and high-level features are fused in order to locate the circle areas accurately. A circle dataset with 4024 images is built for the CNN training. Several circles are embedded into the image, which will be used to train the CNN, in order to realize data augment simply and efficiently. The comparison experiments to the state-of-art CNN-based methods are well conducted. Our circle detector achieves the average precision 92.7% and recall rate 95.8% on datasets, and the time-cost is at the same level with others. The effectiveness of the proposed method is validated by a series of experiments.
Application of Artificial Neural Network on South African Sign Language Recognition System

Sign language assumes a significant part in filling the oral communication void for non-hearing or hard of hearing people. Despite the use of sign language, most people might not understand sign language clearly. Globally, research has been ongoing to explore and recommend various sign language recognition systems for different sign languages. On the other hand, researchers are still faced with challenges of practically implementing recognition systems. Though, machine learning has emerged as one of the advanced sign language recognition systems. Artificial neural network (ANN) models and techniques are machine learning models which are widely utilised to improve the accuracy level thus offering better results when recognising sign language gestures. However, there are few or no actively existing South African Sign Language (SASL) automatic recognition systems readily available to facilitate communication seamlessly and instantly between non-hearing and hearing people. This research concluded that application of finger tracking technique significantly improved feature extraction, training and prediction of hand gestures produced under different conditions, i.e. skin color and background. While the application of artificial neural network on SASL recognition system led to better performance with 98.77% and 97.5% accuracy scores during training and prediction, respectively. In this paper, the proposed ANN utilised finger tracking techniques and forward and backward algorithms to implement a system that recognizes fingerspelling in SASL.