Reviewerly/exHarmony huggingface dataset:
Application of Financial Information Big Data in Agricultural Machinery Information Issuing System

It is estimated that by 2025, the comprehensive mechanization level of my country’s main crops will exceed 70%, and the demand for socialized agricultural machinery services will also increase. As the number of agricultural machineries continues to increase, improper operations by agricultural machinery managers will cause serious waste of agricultural machinery resources, thereby reducing the efficiency of agricultural machinery operations. In the context of the era of big data, with the development of computer and cloud platform technology, financial information systems have also been designed. The synergy principle of financial information systems can be applied in many aspects, which is an important reference for the design of agricultural machinery information release systems, value. To this end, the framework principle of the financial information system is used in the design of the agricultural machinery information release system. Based on the principle of collaboration, the key research is carried out on the agricultural machinery scheduling management of the agricultural machinery information release system, and relevant conclusions are obtained. For the agricultural machinery information technology. The research is of great significance.
Soil Pollution Prediction Based on KPCA and Improved Deep Sparse Extreme Learning Machine

A soil prediction model combining kernel principal component analysis (KPCA) features extraction and optimized deep extreme learning machine(DELM) is proposed to solve the problems of traditional methods for predicting heavy metal content in soil are of low accuracy and soil testing is expensive. First of all, KPCA method is used to extract the effective parameters of soil heavy metals to realize data dimension reduction. Secondly, the input weights and biases of deep extreme learning machine (DELM) with kernel mapping theory are optimized by an improved Mayfly algorithm (MA) based on adaptive weights. Finally, MA-DKELM model is constructed; the data after dimension reduction are used as input to predict the heavy metal content in soil. The results show that the MA-DKELM model has strong prediction stability and can be applied to the prediction of heavy metal content in soil. Compared with PSO-DELM and DELM prediction models, MA-DKELM model has the highest accuracy and the best comprehensive performance in predicting soil heavy metals. MA-DKELM model is practical in predicting heavy metal content in soil.
Ticketing System Using AES Encryption Based QR Code

Current ticket reservation systerm is based on use of QR Code, which contains the details of the ticket records such as train timings, its arrival timings, departure timings and passenger reservations details. The printed ticket consists of information which includes all train details with QR Code Information. The Ticket reservation system involves three main factors the database, online passenger and dataset. In the proposed system GUI is developed for the users through by which users book their tickets and the ticket generated will be in the form of QR code which is generated after booking confirmation. The QR Code will be generated on the basis of encrypted data entered by the user. For encryption, using AES encryption algorithm QR Code will be generated as per passenger/user's and train details. The required data is then generated through patterns that are present in both horizontal and vertical components of the QR code. Hence, the QR generated will be secured and end to end encrypted which ensures data security while reserving ticket. Apache kafka is used in the system for messaging purpose. A mobile application is designed to scan the encrypted QR Code. On decrypting, the information about the passenger can be viewed.
An Improved R-L Fractional Differential Image Enhancement Algorithm

In order to expand the range of fractional differential orders, overcome the shortcomings of traditional fractional differentials that assume that surrounding pixels have the same effect on the center pixel during image enhancement, an improved fractional differential image enhancement algorithm is proposed in this paper. First, the similarity factor is calculated according to the distance between the center pixel and the surrounding pixels, and then an improved image fractional differentiation enhancement template is obtained according to the R-L fractional differentiation. Experiments show that, compared with the traditional fractional differentiation method, this method extends the order of differentiation and can better enhance the information of the edge and texture of the image without causing excessive enhancement of the image.
Mission of the school as an element of the socio-ecological model of developing students' sense of school belonging

While the benefits of school belonging are well documented, the issue of the possibility of strengthening and developing students' identification with this place by the school itself is less well known. The aim of this paper is to present a socio-ecological model of developing school belonging inspired by Urie Bronfenbrenner's theory of ecological systems. This article focuses on the mission of the school as an element of this model. The mission defines the school's priorities, defines values and goals, indicates the state towards which the organization is heading. From this point of view, the school mission can be an important tool in the process of strengthening students' sense of identification with the school.
Who’s Waldo? Linking People Across Text and Images

We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to encourage methods trained on such image–caption pairs to focus on contextual cues, such as the rich interactions between multiple people, rather than learning associations between names and appearances. To facilitate this task, we introduce a new dataset, Who’s Waldo, mined automatically from image–caption data on Wikimedia Commons. We propose a Transformer-based method that outperforms several strong baselines on this task, and release our data to the research community to spur work on contextual models that consider both vision and language. Code and data are available at: https://whoswaldo.github.io
Adaptive SRCKF Based on m Estimation

In order to solve the problems of model performance error and abnormal measurement value, the square-root Cubature Kalman filter (SRCKF) algorithm has problems such as filter performance degradation or filter effect divergence, this paper proposes a self-estimation based on M. Adaptive Square-root Cubature Kalman Filter based on M estimation (M-ASRCKF) algorithm, this filter algorithm uses an improved Huber weight function to improve the robustness of the SRCKF algorithm; and then adds the Sage-Husa order Excellent unbiased maximum a posteriori (MAP) estimator, real-time monitoring and estimation of unknown or inaccurate noise statistics, improving the tracking accuracy of the traditional SRCKF algorithm. MATLAB simulation results show that the proposed M-estimation-based adaptive SRCKF (M-ASRCKF) algorithm has better anti-non-linearity and anti-outlier interference capabilities, and this algorithm has strong robustness and high accuracy of results.
Human-Interface Approach Towards Multi-Agent System Field Optimization

There were still some issues regarding human-interface occurred in multi-agent field or artificial intelligence. In general, due to the rapid advancement of technology into more interactive and intelligent, better human-interface improved the needs of multi-agent systems and artificial intelligence for more use of technologies that can help solve human problems. Vice versa, the human-interface could be improved and eased by using multi-agent or artificial intelligence. In the future aspect, there is also a prospect from the perspective that the human-interface itself could be used to improve the multi-agent or artificial intelligence’s internal process. It needs a learning process as a part of their life cycle to achieve the end goal. It could be a more efficient way to give the agent or the AI some knowledge to choose what is best or meaningful for its next generation of learning. This improvement concept hopes can reduce the duration of the learning process and cut the computation cost of the processing. In this paper, we would explain about the related issues of human-interface aspect in the multi-agent or artificial intelligence field that we got into a study as the specific works of this research into the discussion.
Online platforms for research data: A requirements and cost analysis

Abstract Online platforms serving the research community offer the potential to accelerate research efforts to solve societal challenges. Based on Semantic Web technologies, domain-specific research platforms can pave the way for a ‘digital transformation’ in science. Researchers will be able to share and analyse research data. A prerequisite for the success of such platforms is the identification and consideration of stakeholders’ requirements. However, meeting all requirements is cost-intensive. Existing funding models cannot be easily transferred to such new online platforms. With this paper, we contribute to the discussion in science and politics on how the provision of research data can be organized via online platforms in the future. As a basis, this study empirically investigated funder and user requirements of an online platform for materials research data and conducted a cost analysis to assess possible funding models that might ensure the long-term operation of these platforms.
Multi-target Dynamic Group Segmentation Algorithm Based on Trajectory Probability Hypothesis Density

Aiming at the problem that the traditional algorithms is difficult to accurately segment the unknown multi-group target moving at close distance, a multi-target dynamic group segmentation algorithm based on trajectory probability hypothesis density is proposed. Considering that the manifold distance can be used to solve the problem that some targets in the group are relatively dispersed, the movement direction and velocity affecting the segmentation of adjacent dynamic multi-group targets. Firstly, the number and trajectory of the unknown multi-targets is obtained by trajectory probability hypothesis density. Secondly, according to the trajectory information, the direction and velocity between the targets are obtained according to the trajectory. Finally, the group segmentation of the close-range dynamic multi-group targets is completed by combining the manifold distance between the targets, the Angle between the directions and the velocity ratio. Simulation results show that the segmentation accuracy of this algorithm can reach 99.185% and 99.38% in two scenes, and the accuracy is stable with the increase of clutter, the sentence moment error is within 2 seconds. The proposed algorithm is well adapted to complex scenes such as close crossing, separation and merging of group objects, and has certain advantages over traditional algorithms in the areas of segmentation accuracy, response speed and clutter suppression.
A Multi-task Framework for Named Entity Recognition

Existing models usually treat named entity recognition (NER) as a single-target learning task. However, the NER task contains two prediction targets: boundary and type, the single-target learning can limit the performance and efficiency of the model. To solve this problem, we examine the NER task from a new perspective, and proposes a multi-task learning framework. The proposed framework converts the NER task into two subtasks: boundary labeling and type labeling, and then joint learning of the subtasks. Finally, the learning results of the subtasks are combined to establish the learning model of complex tasks. Extensive experiments were conducted on two publicly available datasets, the results show that our proposed multi-task learning model can be flexibly combined with various excellent semantic coding models to transform the single-target task into a multi-task learning mode, and our work can greatly decrease the learning time and bring huge performance improvement.
A progressive genetic-based neural architecture search

Purpose Recently, more and more attention has been put forth on the application and deep learning, due to the widespread practicability of neural network computation. The purpose of this paper is developing an effective algorithm to automatically discover the optimal neural network architecture for several real applications. Design/methodology/approach The author proposes a novel algorithm, namely, progressive genetic-based neural architecture search (PG-NAS), as a solution to efficiently find the optimal neural network structure for given data. PG-NAS also employs several operations to effectively shrink the search space to reduce the computation cost and improve the accuracy validation. Findings The proposed PG-NAS could be utilized on several tasks for discovering the optimal network structure. The author reduces the demand of manual settings when implementing artificial intelligence (AI) models; hence, PG-NAS requires less human intervention than traditional machine learning. The average and top-1 metrics, such as error, loss and accuracy, are used to measure the discovered neural architectures of the proposed model over all baselines. The experimental results show that, with several real datasets, the proposed PG-NAS model consistently outperforms the state-of-the-art models in all metrics. Originality/value Generally, the size and the complexity of the search space for the neural network dominates the performance of computation time and resources. In this study, PG-NAS utilizes genetic operations to effectively generate the compact candidate set, i.e. fewer combinations need to be generated when constructing the candidate set. Moreover, by the proposed selector in PG-NAS, the non-promising network structure could be significantly pruned off. In addition, the accuracy derivation of each combination in the candidate set is also a performance bottleneck. The author develops a predictor network to efficiently estimate the accuracy to avoid the time-consuming derivation. The learning of the prediction process is also adjusted dynamically; this adaptive learning of the predictor could capture the pattern of training data effectively and efficiently. Furthermore, the proposed PG-NAS algorithm is applied on several real datasets to show its practicability and scalability.
Color Weighted Rank Transform and Improved Dynamic Programming for Fast and Accurate Stereo Matching

Real-time stereo matching with high accuracy is a dynamic research topic, it is attractivein diverse computer vision applications. This paper presents a stereo matchingalgorithm that produces high quality disparity map while maintaining real-time performance.The proposed stereo matching method is based on three per-pixel differencemeasurements with adjustment element. The absolute differences and the gradientmatching are combined with a color weighted extension of complete rank transformto reduce the effect of radiometric distortion. An improved dynamic programming isused for disparity optimization and calculation. It allows to solve the inter-scanline inconsistencyproblem and increase the matching accuracy. The proposed algorithm isimplemented on parallel high-performance graphics hardware using the Compute UnifiedDevice Architecture to reach over 240 million disparity evaluations per second.The processing speed of our algorithm is 98 frames per second on 320 × 240 pixelsimages and 32 disparity levels. Up to the submitting, our method ranks fourth in termsof accuracy and runtime on Middlebury stereo benchmark.
Real-Time Facial Expression Driving based on 3D Facial Feature Point

Facial expression driving can make the expression of virtual characters more real and natural. It is widely used in movies, games and some social software. The existing real-time facial expression driving algorithms have the limitation of physical hardware, prefabricated model or long-time training. In order to get rid of the limitation of existing algorithms, this paper proposes a real-time expression driving algorithm based on 3D facial feature points with RGBD data as input. In the face capture, we use ICP algorithm to get the rigid data of the face and deformation transmission algorithm to capture the non-rigid data of the face. Moreover, the whole face process only takes 0.4ms to complete. Because the calculation of the algorithm is based on 3D feature points of the face, there is no need to prefabricate a specific face model and a lot of time training. Our algorithm can not only use real faces to drive virtual faces, but also use virtual characters to drive virtual faces.
KTM-POP: Transliteration of K-POP Lyrics to Marathi

Considering the popularity of the K-pop music in India, automatic transliteration of K-POP (Korean POP songs) to Marathi (KTM-POP: Korean to Marathi POP) music is the idea proposed in this paper. Native speakers of Maharashtra could not able to speak Korean lyrics. Though the Romanization of the Korean script is available, pronunciation of those scripts is difficult because of multiple phonetical sounds for the Romanization script. For the correct pronunciations of the K-POP lyrics, this paper proposed transliteration of the Korean lyrics in Marathi script. Bilingual script dictionary and hand-crafted rules are used for the transliteration. Pilot study shows the successful transliteration of K-POP lyrics to Marathi with word accuracy of 82.5%.
Comparison of MLP-BPNN and MLP-PSO for Automatic Essay Grading System for Japanese Language Exam

In this paper, a study was conducted for a hybrid model for Multilayer Perceptron (MLP) with Particle Swarm optimization (PSO). The PSO was used to replace the Backpropagation method for the weight optimization. The comparison was conducted between MLP-BPNN and MLPPSO for an automated essay grading system for Japanese language exam. The MLP-PSO model achieved a more accurate but less stable result. The MLP-PSO model with 10 particles trained for 15 steps achieves the best result out of the two MLP-PSO models tested, with an average 8.48% error for the grade population. Compared to the MLP-PSO model, it was discovered that MLP-BPNN with Adam optimizer achieves better overall performance and results concerning both the accuracy and the stability of the model.
Using CNN to Explore the Dependence among Attributes and Instances for Multi-Instance Learning

Multi-instance learning (MIL) is a special weak supervised machine learning framework, where training samples are bags composed of instances, and the labels of bags are known while the labels of instances are unknown. To utilize the dependence existing among attributes of an instance and among instances within a bag at the same time, a multi-instance convolution neural network with attention mechanism (AM-MICNN) is proposed in this paper. Firstly, a multi-granularity instance embedding module based on dual attention CNN is designed in AM-MICNN to explore the interdependence among attributes of an instance and obtain more effective instance-level representations, which is consist of four layers, i.e., instance convolutional layer, instance channel attention layer, instance spatial attention layer, and multi-granularity fusion layer. Then, a bag embedding module based on single attention CNN is designed in AM-MICNN, the module takes the instance-level representations as input, includes preprocessing layer, bag convolution layer, bag attention layer, and aggregation layer. It mines the dependence among instances in a bag and gets effective bag-level representations. Finally, the bag-level representations are fed into a multiple-layer perceptron (MLP) for label prediction. Experimental results show that the proposed algorithm achieves the best results compared with other algorithms on MIL benchmark datasets.
Artificial Intelligence in the Delivery of Mobile Tourism Services

Tourists are more attached to mobile phones which oblige tourism businesses to give importance to the automated services in mobile phones for better services. This chapter discussed the artificial intelligence-integrated mobile technologies in tourism in terms of technological, marketing, and managerial perspectives. The integration of artificial intelligence-based open access application programming interfaces such as language translation, recommendation making, mapping, etc. to the mobile technologies created great changes in tourist behavior and business processes in tourism. Moreover, businesses can provide automated and contactless services by using mobile big data and analyze better the needs of the tourists, especially in the post-pandemic era. Finally, a framework has been proposed for artificial intelligence integration to mobile tourism services, and theoretical-managerial implications were developed.
Text-Based Point Cloud Generation Adversarial Network

Point clouds become the popular representation of 3D shapes. Meanwhile, deep generative adversarial networks (GANs) have been used to generate reasonable point clouds for specific categories, such as airplane, chair. But existing point clouds generation tasks are almost unconditional, which means the generative results cannot be controlled. In this work, we propose the text-based point cloud generative adversarial network short as TPC-GAN for generating point clouds from natural language. To this end, we first contribute a point clouds dataset with text description based on the chair category of ModelNet40. Then we learn the implicit cross-modal con-nections between texts and point clouds through category labels. Using a pretrained text encoder that had gained the mapping relations between texts and class labels to obtain text embedding of input text, which will concatenate with a noise vector and be feed into TPC-GAN. Moreover, the discriminators of TPC-GAN can be regarded as point clouds classifier. Finally, the TPC-GAN can generate point clouds that meet the input text description. To evaluate the performance of our approach, some experiments are conducted. To the best of our knowledge, our method is the first one to generate point clouds from texts.
Air Writing-Based Automated Room

One of the most commonly studied subjects in today’s world is air writing or gesture recognition in the air. Gesture recognition in the air requires the detection of the alphabet or number that is being written and correctly predicted. In this paper, if a person makes a specific gesture with a finger, the gesture is recognized, and all gestures correspond to a certain alphabet that triggers certain actions in the surrounding area, such as turning on the appliances. Here, we have built a cheap and powerful motion capture device at home to execute a desired operation with the flick of our finger. Using a standard night vision camera (a modified Raspberry Pi camera), Raspberry Pi, CNN, OpenCV computer vision library and machine learning, we have achieved a device that recognizes characters in a three-dimensional space with six degrees of freedom. The proposed system achieves 94.77% accuracy in the recognition rate when tested with English alphabets and numbers.