Reviewerly/exHarmony huggingface dataset:
Path Planning of Robot Based on Improved Ant Colony Algorithm in Computer Technology

Abstract Mobile robot is a complex control system which integrates environment perception, path planning, dynamic decision making and behavior execution. After decades of development, ant colony algorithm has achieved good results in solving global optimization problems and is widely used in various fields. Although it has the ability to find the optimal solution, it has some defects: slow convergence speed and easy to fall into the local optimal solution. An improved ant colony algorithm is proposed to search through the improved it, select the path independently according to the number of pheromones, and improve by adjusting the heuristic factor function. The reason for this analysis is improved and optimized [1] .
Comparison of MySQL and MongoDB with focus on performance

Almost every web application uses a database for storing data. Storing and retrieving data causes a delay between request to the system and its response. This has an impact on user-experience. The market offers many types of databases and each is more suitable for different types of applications. The main difference is in data-structures which are used for data storing. Data-structures directly influence the performance of the database system and all dependent systems. In this paper, an overview and brief comparison of four types of NoSql databases has been made. Next, benchmark comparing performance of MongoDb and MySQL in different situation are discussed. Benchmarks are focused on operations such as inserting, updating, and deleting data with transactions and without transactions. The added value of the benchmarks is evaluated in terms of cost of indexed fields in the abovementioned situations.
Decision Making Analysis for Free Internet Quota Assistance Online Learning during the Covid-19 Pandemic

Abstract One of the Indonesian government’s efforts to reduce the impact of the crisis due to the Covid-19 pandemic is through the distribution of social assistance through the distribution of free internet data for students to support online learning during the implementation of government policies regarding large-scale social restrictions. This social assistance involves 350 students as an alternative as a target for decision making. In an effort to support this policy, this study offers a multi-criteria decision-making analysis approach using the Moora and Vikor methods. The results of this study present the optimal performance of the accuracy, precision and error rate of the two methods.
Perfect swap and transfer of arbitrary quantum states

A resonator–qubit system consisting of three resonators and two qubits is shown to enable a perfect swap and a faithful transfer of arbitrary unknown quantum states of photons, providing a much-needed building block for quantum information processing and quantum communication.
Decoding the Cauzin Softstrip: a case study in extracting information from old media

Having content in an archive is of limited value if it cannot be read and used. As a case study of extricating information from obsolete media, making it readable once again through deep learning techniques, we examine the Cauzin Softstrip: one of the first two-dimensional bar codes, released in 1985 by Cauzin Systems, which could be used for encoding all manner of digital data. Softstrips occupy a curious middle ground, as they were both physical and digital. The bar codes were printed on paper, and in that sense are no different in an archival way than any printed material. Softstrips can be found in old computer magazines, computer books, and booklets of software Cauzin produced. However, managing the digital nature of these physical artifacts falls within the scope of digital curation. To make the information on them readable and useful, the digital information needs to be extracted, which originally would have occurred using a physical Cauzin Softstrip reader. Obtaining a working Softstrip reader is already extremely difficult and will most likely be impossible in the coming years. In order to extract the encoded data, we created a digital Softstrip reader, making Softstrip data accessible without needing a physical reader. Our decoding strategy is able to decode over 91% of the 1229 Softstrips in our Softstrip corpus; this rises to 99% if we only consider Softstrip images produced under controlled conditions. Furthermore, we later acquired another set of 117 Softstrips and we were able to decode nearly 95% of them with no adjustments to the decoder. These excellent results underscore the fact that technology like deep learning is readily accessible to non-experts; we obtained these results using a convolutional neural network, even though neither of the authors are expert in the area.
On The Field: Examining Differences in Video Format in Sports Media Viewing

With virtual reality’s emerging popularity and the subsequent push for more sports media experiences, there is a need to evaluate virtual reality’s use into more video watching experiences. This research explores differences in experiences between Monitor (2D) video and HMD (360-Degree) video footage by measuring user perceptions of presence, suspense, and enjoyment. Furthermore, this study examines the relationship between presence, game attractiveness, suspense, and enjoyment as explored by Kim, Cheong, and Kim (2016). Differences were assessed via a MANOVA examining specifically presence, suspense, and enjoyment while the relationships were explored via a confirmatory factor analysis. Results suggest that there was a difference between Monitor (2D) video and HMD (360-Degree) in regard to spatial presence, engagement, suspense, and enjoyment, but the previous model from Kim et al. (2016) was not a good fit to this study’s data.
Secure and Privacy-preserving Energy Trading Scheme based on Blockchain

The large-scale integration of distributed energy resources has resulted in surgical changes in energy trading systems. Traditional centralized trading systems suffer from high management cost and low efficiency. The recent advance of blockchain technology has enabled the invention of distributed energy trading systems, which can overcome the limitations of centralized trading systems. However, the distributed energy trading systems also bring new security and privacy challenges. For instance, transactions on blockchain are publicly visible which can lead to privacy leakage of trading information. Moreover, user privacy can also be leaked during verification of the aggregated energy trading result. In this paper, we propose a privacy-preserving energy trading scheme based on blockchain to meet the security requirements for distributed energy trading. We adopt a stealth transmission approach based on blockchain to ensure data privacy and break the linkage between consumers and providers in the energy trading process. We also use the non-interactive zero-knowledge proof technology to achieve privacy-preserving and trustworthy trading result verification. Security analysis and evaluation results have demonstrated that the proposed scheme can effectively protect the data privacy for distributed energy trading systems.
Watermarking and Coefficient Scanning for Light Field Images in 4D-DCT Domain

Light field images have emerged as an important advance in the representation of visual data; at the same time, dealing with such images has brought new challenges related to multimedia transmission and security. In this article, we introduce a robust and blind scheme to watermark lenslet light field images. The method, which is based on quantization index modulation, allows to recover the watermark even after the image is submitted to compression in the 4D-DCT (four-dimensional discrete cosine transform) domain or other attacks. To achieve this, an empirical strategy for scanning the coefficients in the referred domain is also proposed; we demonstrate that, in general, such a strategy outperforms state-of-the-art methods whose purpose is to exploit the energy compacting property of the 4D-DCT. The introduced watermarking scheme has been evaluated using 30 light field images and several embedding / extracting parameters. The obtained results indicate that the watermark is satisfactorily imperceptible and confirm its robustness against various attacks.
An Algorithm of Occlusion Detection for the Surveillance Camera

As more and more surveillance cameras are deployed in the Internet of Things, it takes more and more work to ensure the cameras are not occluded. An algorithm of detecting whether the surveillance camera is occluded is proposed by comparing the similarity of the images in this paper. Firstly, the background modeling method based on frame difference is improved. The combination method of the background difference and frame difference is proposed, and the experimental results showed that the combination algorithm can extract the background image of the video more quickly and accurately. Secondly, the LBP (Local Binary Patterns) algorithm is used to compare the similarity between the background image and the reference image. By changing the window size of the LBP algorithm and setting an appropriate threshold, the actual demands can be satisfied. So, the algorithms proposed in this paper have high application value and practical significance.
Attentive-Feature Transfer based on Mapping for Cross-domain Recommendation

Recommendation systems have been widely developed for numerous applications. Existing systems may still suffer from negative transfer or cold starts. These drawbacks are essentially due to overlooking domain-specific users' personal preferences or cross-domain user-item interactions. To address these problems, we propose a cross-domain recommendation algorithm built on a mapping-based attentive feature transfer (MAFT) model. Our MAFT model utilizes matrix factorization and an attention mechanism for fine-grained modeling of user preferences. Then, overlapping cross-domain user features are combined through feature fusion. Moreover, a multilayer perceptron (MLP) is built to map the obtained user features to target-domain user features. Finally, the user-item ratings can be predicted in the target domain. We carried out experiments on the large-scale MovieLens dataset as well as the real Douban Book and Douban Movie datasets. The results show that the precision of the MAFT-based method is clearly higher than those of other cross-domain recommendation methods, especially for cold-start users with few item interactions.
A Persian part of speech tagging system using the long short-term memory neural network

Identification of syntactic categories of speech that known as POS tagging are among the most fundamental operations in natural language processing. POS taggers are one of the important tools that should be considered in this regard because of their widespread use in natural language processing tasks. The first problem for designing Persian POS taggers systems is the extraction of probabilistic and statistical information as well as practical features from the Persian text corpus files. In this study, the probabilistic and statistical features and information obtained were used with a long short-term memory (LSTM) neural network with longer-term memory and were trained and tested. In the present study, to increase the accuracy, measures such as increasing the length of dependency on the previous words and bidirectional word assessment for the LSTM neural network were taken. To assess, the results were compared with those of other methods, with the best accuracy obtained with the two-word dependency with the bidirectional LSTM network. The accuracy rates of 98.1% was received using previous 5 words. Comparing the state-of-the-art results, the proposed scheme has better accuracy in all condition of POS tagging.
Multi-task Deep Reinforcement Learning: a Combination of Rainbow and DisTraL

Among the previous studies on deep reinforcement learning (DRL) challenges, optimizing data usage efficiency in rich and complex environments has been discussed by many researchers. Recently many scientists have tried to enhance the use of data in reinforcement learning (RL) field. In this regard, data sharing in learning manifesting in transfer learning got high popularity among notable artificial intelligence Procedures. Recent successes in multi-task RL, in which transfer learning plays a major role to use common shared learning data between each separated task effectively, have been the main motivation for this research. On the one hand, DisTraL in multi-task learning has represented potential achievements in application, and on the other hand, by combining Q learning improvements in a hybrid architecture called rainbow, one would be able to benefit from all of the features used. In this study, to achieve higher performance in multi-task DRL scenarios, a rainbow agent in a DisTraL framework has been suggested. Moreover, in order to prove the superiority of the suggested approach over the suggested approach over the standard DisTraL methodology, the results and comparisons have been demonstrated.
Modeling and Research Multiphases Signal Transducers of Power Control Systems

Presented model and results of research of transforming processes, which flowing in converting elements and circuits of transducer of primary multiphases electrical current to secondary signal. Modeling and research of main characteristics of primary measure transducers of multiphase primary electrical currents of power energy to secondary signal based to graph model with high formalized, effectively and digital-based cloud computing technology.
A Context-free Grammar based Association Rule Mining Technique for Network Dataset

Abstract Among various data mining concepts like prediction, clustering, classification, association and outlier discovery, association is a useful technique to extract the interesting relations among data items effectively. Association technique is applied in a number of applications like marketing, education, chemical, bioinformatics, computational linguistics and etc. The important purpose of association is to provide useful information of buying preferences of customers in supermarket in order to increase the sales opportunity, which is called as market- basket analysis. Till now there are many algorithms were developed, but the usage of formal grammars in association rule mining (ARM) is a latest technique to mine required data by means of grammars. In this paper ARM is performed using Context –free Grammar (CFG) – (ARM – Grammar) and the experiments are conducted on MATLAB 2017 software using network dataset, KDDCUP’99. Experimental outcomes prove that the proposed ARM – Grammar is effective than the traditional ARM approach.
Application of Pruning Yolo-V4 with Center Loss in Mask Wearing Recognition for Gymnasiums and Sports Grounds of Colleges and Universities

Since the first case of COVID-19 was founded, the international organization began to fight against the epidemic. In addition to various research and findings from experts in medical and health care, the daily behavior of citizens has became the key of fighting against the epidemic. In China, thanks to the government's active measures and citizens' spontaneous compliance with policies such as quarantine and wearing masks when going out. As the first country be attacked, China becomes the country with the best epidemic-control in the world. However, it is not enough for the masses to wear masks consciously by citizens, and it is still necessary to supervise the wearing of masks in various public places by governments. In this process, this paper proposes to replace manual inspection with a deep learning method, using the most powerful object detection algorithm YoloV4, supplemented by CenterLoss and pruning algorithm, so that it can be better used in the actual environment, especially regarding the supervision of wearing masks in indoor stadiums and college physical education classes. The experimental results prove that the algorithm in this paper can effectively deal with multiple targets, occlusions, different sizes of people, and achieve effective supervision of personnel.
De-Speckling of Synthetic Aperture Radar Using Discrete Fourier Transform

The role of Synthetic Aperture Radar (SAR) images is inevitable in remote sensing applications. One of the major concern in SAR imagery is that basic textures are generally affected by multiplicative speckle noise. Speckle noise is a consequence of image formation under coherent radiation, but it often carries useful information about the scene being imaged. However, speckle noise is considered undesirable as it damages the resolution and affects the tasks of human interpretation. The proposed framework uses Boxcar filter for reduction of speckle-noise whilst retaining the point targets, edges and texture with the inclusion of Discrete Fourier Transform (DFT) in the speckle reduction framework. A novel technique is propounded, which aims at having a fine control on the high-frequency components by tuning the radius of the frequency rectangle.
Super-Resolution of Remote Sensing Images based on a Deep Plug-and-Play Framework

Single image super-resolution (SISR) based on deep neural network (DNN) has been widely studied in recent years as a crucial technique for remote sensing (RS) applications. However, owing to the complexity and diversity of ground objects, there remains fundamental challenges to reconstruct a high-resolution (HS) RS image from a low-resolution (LR) RS image, especially with blur. In this paper, I propose a deep plug-and-play residual network, namely DPSRResNet, which can reconstruct high-quality HR RS images from LR SR images with Gaussian blur kernels via a deep plug-and-play framework. Specifically, a degradation model from the DPSR framework is given to utilize matured deblurring methods. Moreover, I adopt a deep plug-and-play algorithm to optimize the energy function, which allows plugging any super-resolver with a prior term. The proposed DPSRResNet is used as the crucial super-resolver for the framework, and a series of experimental results are presented to demonstrate the effectiveness of the proposed method on RS images.
Development of Robot Navigation System

Abstract The development of a new indoor mobile robots navigation method is one of the emerging areas in the engineering field. The robot device consists of a tag sensor RFID, a laser range detector and a handheld platform and ultrasonic sensors. Mobile robot navigation systems are important for ensuring a more reliable and precise protection and stability of human robot activity. The device needs to be developed using the latest RFID technologies to reduce the expense and precision of current navigation systems containing more sensors to achieve the necessary characteristics. The labels have novel distinguishing pieces of proof inside the recollections, and the recollections might be perused from a few meters away. RFID labels are used as a landmark for the global route plans and as course guidance to a target, the topological relationship map showing the correlation of distributed tags to the environment is used. The robot runs automatically through range data for scanned to locate a mark, after returns to movement after next through charts of topological. A precise means of estimating the position depends on the points of interest. Windows, ventilators, walls are needed for landmarks for buildings. A topological chart with nodes that fit the moments issued for navigation. Navigation can be studied through nodes for charts of topological and ultrasonic sensor is often used where an obstruction is sensed where turning left or right.
An improved Hopfield Lagrange network with application on motor efficiency optimization

Abstract In this paper, an improved Hopfield Lagrange network (IHLN) is proposed and applied to solve motor efficiency optimization in an electric vehicle (EV) with a permanent magnet synchronous motor (PMSM). In contrast to the Hopfield Lagrange network (HLN) incorporating deterministic gradient descent, a stochastic searching method, simulated annealing (SA), is introduced in IHLN to prevent the network from falling into local minima and improve the optimality. Some important issues regarding the performance of the IHLN are thoroughly investigated such as whether the optimization problem is nonconvex with local minima, the stability of the network's equilibria, and the convergence condition of the SA. Simulations are conducted to validate the effectiveness of IHLN, and results demonstrate the improvement brought by IHLN concerning different indices.
Demonstration of Flexible and Scalable Quantum-resistant Encryption with Threshold Key Management in Optical Networks

We demonstrate a flexible and scalable quantum-resistant encryption framework applicable to existing optical networks. Computationally intensive key exchange processes are offloaded on a centralized crypto module while encryption keys are converted into n shares and distributed to key servers in such a way that those keys can be securely reconstructed by a threshold key management protocol.
Multi-Agent Pathfinding with Hierarchical Evolutionary Hueristic A

Multiagent pathfinding (MAPF) problem is an important topic to various domains including video games, robotics, logistics, and crowd simulation. The goal of a pathfinding algorithm for MAPF is to find the shortest possible path for each agent without collisions with other agents. Search is among the most fundamental techniques for problem solving, and A* is the best known heuristic search algorithm. While A* guarantees to find the shortest path using a heuristic function, it cannot handle the large scale and many uncertainties in MAPF. The main challenge of MAPF is the scalability. The problem complexity grows exponentially as both the size of environments and the number of autonomous agents increase, which becomes more difficult for A* to compute results in real time under the constraints of memory and computing resources. To overcome the challenges in MAPF, distributed approaches are introduced to reduce the computational time and complexity. Contrast to centralized approaches, which use a single controller to determine every move of all agents, distributed approaches allow each agent to search for its own solution. Distributed MAPF algorithms need to refine solutions for all agents that are collision-free. The algorithm should lead agents to take another path, or standby on the same node at the moment, to avoid conflicts between any two paths. Under the circumstances, an optimal solution is no longer simply finding the shortest path for each agent. Instead, it should contain a collision-free path for every agent, with the lowest makespan, which is the number of time steps required for all agents to reach their target. However, minimizing the makespan and the sum of cost for all agents is a NP-hard problem. Given MAPF problems often require to be solved in real time with limited resources, minimizing only the makespan is a more practical approach.To achieve accurate search and high scalability, a MAPF algorithm must fulfill the following requirements: 1) it is capable to compute collision-free paths for all agents; 2) it can provides an accurate priority decision mechanism to ensure solution optimality; and 3) it should maintain the successful rate to obtain a solution as the number of agents increases. In this paper, we proposed a novel hierarchical pathfinding technique named Multi-Agent Hierarchical Evolutionary Heuristics A* (MA-HEHA*). Our contributions in this paper are: 1) we propose MA-HEHA* that can identify bottleneck areas to reduce collisions in abstract search; 2) our algorithm evolves heuristic functions by itself to avoid potential conflicts during local search; 3) we prove that MA-HEHA* maintain high successful rate when the scalability is high; 4) we evaluate MA-HEHA* on different types of MAPF problems to show its effectiveness. Our experiment results show that our MA-HEHA* can efficiently solve large scale MAPF problems compared to traditional MAPF approaches.