Reviewerly/exHarmony huggingface dataset:
Close Examination Camera with Automatic Quality Assessment Capability for Telemedicine

Telemedicine is the medical technology that enables patients from villages and rural hospitals to get expert advice from specialist doctors located anywhere in the world. These systems work primarily by sharing audio-, visual-, and text-based information over suitable communication medium such as the internet. Close examination camera is a handheld device used to examine body parts such as skin, eye, etc. It is usually operated by a nurse or a doctor. It is seen that due to lack of good focusing system and/or image stabilization techniques, the output from these handheld devices may not always be of acceptable quality. Motion blur and out-of-focus blur are commonly seen issues, particularly if the nurse operating the device is older in age. This can result in doctor being unable to make diagnosis or even worse—make wrong diagnosis. Due to this reason, quality detection of the transmitted images is quite important. For selecting the best image, we need to rank all the images that are taken by the camera in a given duration based on a Quality Metric. This paper describes a system that will automatically analyze the quality of the acquired images and then transmit and present the visually good images to the remote specialist’s side. Proposed method provides high correlation with human judgment when compared on the same set of images. Real and simulated databases were used to verify the correlation.
Mobile Learning Based on the Frame Model and Applied to the Solution of Differential Calculus Optimization Problems

The use of mobile devices in teaching and learning processes promote changes in the design of pedagogical and didactic strategies. The following is the design and implementation of an educational scenario supported by the problem-based learning (PBL) methodology and the characteristics of mobile learning in solving differential calculus optimization problems. To strengthen the instructional design process, a study of mobile applications is previously carried out in the area of differential calculus and the scenario is verified against the Frame model [5] for the design of mobile learning methodologies. The study, through a quantitative analysis and experimental design, tends to analyze the perception and academic performance of students at the University of La Salle, making a comparison with the traditional learning method.
Stack performance improvement of stacked U-Net

Stacked U-Net is a multistage model of U-Net used for image-to-image conversion, which is known to improve accuracy by increasing the number of stacks. This model is useful because the accuracy is improved by simply increasing the number of stacks. Alternatively, the problem of deteriorating accuracy is encountered if the number of stacks is excessively increased. We propose herein a structure where the global skip connection of the U-Net transmits only high-frequency components. Then, we confirmed by experiments that the accuracy does not degrade with an increase in the number of stacks compared with the existing methods.
An improved parallel matrix factorization method for data imputation of multivariable time series data with high level noises

Large number of multivariable time series data with high level noises are generated during the industrial production process. However, due to various uncontrollable reasons in the production process, the data missing problem often occurs. Considering that there may be potential correlation between the variables of multivariable time series, and the single variable will not fluctuate dramatically over time, an improved matrix factorization method was proposed in this paper to impute the missing data of multivariable time series. Due to the efficiency requirement of data imputing in industrial production, a dynamic updating method of parameters was proposed. In addition, filter matrix and improving correlation judgment was proposed to increase the imputing accuracy. Experiments on real datasets and self-made datasets show that the proposed method can effectively improve the imputing accuracy and efficiency.
Harmonic adaptive speech synthesis

This chapter presents a novel approach to speech synthesis by adaptively constructing and combining the harmonic components. Harmonic adaptive speech synthesis foundations are based on the fusion of Fourier series and adaptive filtering. Speech is a well-known biometric signal composed of a linear combination of sine and cosine components, which under stationary condition, can be effectively presented as a Fourier series. Theoretically the Fourier series coefficients for each stationary speech segment can be obtained by corresponding integration equations over the segment’s length. This chapter presents a practical online adjustment of the coefficients by an adaptive harmonic filter (AHF) that tracks the real speech and adaptively forms the Fourier series coefficients thereby synthesizing the corresponding speech. The experiments are carried out on speech synthesis from real speech records using an AHF with a least means square learning algorithm. For a number of real speech segments, the synthesized speeches are generated by AHF. The results are numerically evaluated by the correlation values between the real segments and synthetic ones. The correlation-based confusion matrix indicates a high level of similarity of each synthetic speech segment to its corresponding real segment.
Exploring the Integrated Application of Aesthetic QR Code in Video

In recent years, the way of content communication has gradually shifted from analogy to digital. QR Code is an interface that connects the virtual and real world, but its aesthetics still needed to be improved. Therefore, this research beautifies and integrates QR Code into the video. In this study, the QR Code information was hidden in the corner areas of the three videos by the error diffusion algorithm, and the digital files and display-and-captured files were read and identified by error analysis. The results show that the aesthetic QR Code can be successfully applied to the video and content for value-added applications. It is also known that the QR Code recognition rate of darker image content will be poor, and it should be avoided in future applications. The decoding error trend of the digital files and display-and-captured files is consistent. The results of this research can make the integration and application of QR Code in videos faster and more convenient in the future, and it has many potential industrial applications.
Integration and allocation method of fire support operational resources for cloud cooperative operation

With the emergence of cloud cooperative operation mode, a integration and allocation method of fire support operational resources for cloud cooperative operation mode is proposed to solve the problem of integration and allocation of operational resources in this paper. First, the operational knowledge graph is established to integrate and dynamically manage the operational resources spread across the battlefield. Then a optimization matching method of sub service and operational resources is established for selecting the operational resources that can meet the task requirements. Finally, the simulation results show that this method can solve the problem of integration and allocation of fire support resources in the cloud cooperative operation environment.
Analysis Of The Accuracy of The Level Of Rainfall For Time Series Data Using Neural Network Method Based On Particle Swarm Optimized

Data Mining and economic studies show that the method is suitable for use given by the Neural Network (NN ) with a fairly low error rate, the ability of Neural Networks in Universal Approximation has been studied by various researchers for forecasting time series data on various types of data, so that performance from Neural Network which is satisfactory in time series data forecasting . To determine the best model from each previous period, we need to optimize the weights of each relevant training data variable, so that the Neural Network method based on Particle Swarm Optimization (PSO) can be applied even though the maximum feasibility level is not certain. The biggest cost in the response to natural disasters such as floods, droughts, late planting patterns, help farmers avoid crop failures that can be caused to one's determining the types of crops grown in those days, in need right way to predict the amount of rainfall in the coming months, in order to anticipate unwanted events . Cost suppression can also be done by analyzing the changing time series patterns of rainfall data sets taken from the Romang Polong Station in Makassar from 2005-2015 , which facilitates the process of analyzing the prediction of accuracy generated from the time series data sets, if implemented by combining the Particle Swarm Optimization (PSO) based on Neural Network (NN) method to find out the patterns generated from the data set. Thus saving time and operating power.
The Website-based Information Literacy system and Application in Education Facing the Age of Industrial Revolution 4.0”

Abstract This article discusses about the importance of web-based information literacy systems in the era of the industrial revolution 4.0. This era was built on the integration of intellectual (artificial intelligence) with the physical world by opening unlimited space and opportunities for humans to access information and interact virtually fast and easily and very low cost. Therefore, the industrial revolution 4.0 is perceived as a new age that will open opportunities as well as challenges for human life, including for education. In facing this era, high intelligence is needed to explore educational resources and to apply technology that is relevant to the evolving virtual reality. This web-based education information literacy system is one solution to facilitate students in obtaining, designing and processing knowledge and skills needed in their future lives. Education managers must innovate and utilize this system by continuously updating the latest so that the outputs and outcomes of education will be ready to face the rapidly advancing technological developments. Education is not only maintaining and preserving the tradition (culture) of society about learning but it is carving and engineering the future of the young generation in the face of global world competition that continues to roll without any time and space limits.
Human Interaction Recognition Based on Joint Sequence

Human interaction recognition based on skeleton data has attracted widespread attention due to its fast speed and robustness. Aiming at the current problem that the skeleton data is imaged and combined with the convolutional neural network for recognition, which cannot effectively model the video time-series relationship. An interaction recognition method for joint sequence images is proposed. First calculate the joint-joint distance features of a single frame, and then quantize them into a grayscale image every three frames. Then each grayscale image is sent to the convolutional neural network to extract the deep features, and finally send these features to the Long Short-Term Memory network for time series modeling to achieve the human interaction recognition. Experiments on the internationally published SBU Kinect interaction database have achieved a recognition rate of 96%, which verifies the effectiveness of the proposed algorithm.
Analysis of Learning Influence of Training Data Selected by Distribution Consistency

This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings.
A Weighted Federated Averaging Framework to Reduce the Negative Influence from the Dishonest Users

Federated learning becomes popular for it can train an excellent performance global model without exposing clients’ privacy. However, most FL applications failed to consider there exists fake local trained models returned from attackers or dishonest users. Not only would the fake parameters be harmful to the convergence of the global model but also be wasting of other users’ computational resources. In this paper, we propose a framework to grade the users’ credit score based on the performances of the returned local models on the testing dataset. We also consider historical data using the exponential moving average to give a relatively higher weight for the most recent testing results. The experiments show that our system can efficiently and effectively find out the fake local models and then speed up the convergence of the global model.
Application of Bellman's Equation in Ant-Like Robotic Device Path Decisions

Swarm Intelligence is about emergency of collective intelligence from groups of homogeneous robotic devices deployed for a purpose. Ant Colony Systems, in particular, are inspiring. They commonly have drawn inspiration from the behaviors of real ants in nature in order to construct routes between the food sources and the nest. There are still gaps in alternative options for path decision in ant agents. Bellman's equation has been successfully used to solve path decision problems in machine learning. We proposed to investigate impact of a Bellman's equation inspired algorithm for path decision on stigmergic ant agent robotic devices. A design science research paradigm was used to design our research experiment in which a simulated environment was designed to simulate the behavior of ant agents when using a Bellman's equation inspired algorithm for path decision. We introduced a reward function to the orientation process of ant agents. Reward function rewards a decision made when an ant moves from one point to an adjacent cell. The Bellman's inspired algorithm for ant orientation led to convergence of ant agents even though there was reduced quality of convergence. Evaluation of results show that Bellman's equation can be used in path decision processes for ant agent robotic devices. Our results contributed to adding an alternative way of implementing path decision for ant agents. This will help in growing the knowledge around ant agents and finding better ways to implementing path decisions for ant agents.
Subgraph nomination: query by example subgraph retrieval in networks

This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs. This type of problem appears time and again in real world problems connected to, for example, user recommendation systems and structural retrieval tasks in social and biological/connectomic networks. We formally define the subgraph nomination framework with an emphasis on the notion of a user-in-the-loop in the subgraph nomination pipeline. In this setting, a user can provide additional post-nomination light supervision that can be incorporated into the retrieval task. After introducing and formalizing the retrieval task, we examine the nuanced effect that user-supervision can have on performance, both analytically and across real and simulated data examples.
An improved ant colony algorithm for path planning of manipulator

In order to solve the path randomness of delta manipulator in sorting corresponding objects, this paper proposes a path optimization algorithm ant colony algorithm based on ordinary path sorting. The random path of delta manipulator not only affects the sorting efficiency, but also increases the operation cost. After the object coordinates are recognized by computer vision, they will be converted with the coordinates of the manipulator itself, so as to get the object coordinates under the delta manipulator. The coordinates are optimized by path optimization algorithm in the system code, and the optimal path of the suction head of the delta manipulator can be obtained. Therefore, a good path optimization scheme directly determines the operation state of delta manipulator. In view of the fact that ant colony algorithm is easy to fall into the local optimal solution, this paper optimizes the ant colony algorithm, realizes the optimized algorithm with MATLAB language, and carries out simulation test. In MATLAB 2018 environment, the running time of different cases is compared, which shows the effectiveness of the algorithm.
Apply Quantum Search to the Safety Check for Mono Operational Attribute Based Protection Systems

Interrelated computing device’s system such as IoT, RFID, or edge device’s systems are pervasively equipped for today’s information application and service systems, protecting them from unauthorized access i.e. safety is critical, because a breach from the device may cause cascading effects resulting to data lost or even crash of the whole information system. However, to determine a protection system’s safety is proven to be undecidable unless the system has limited management capabilities. And even with such limitation, it is too expensive to perform a safety test in term of computation time when a device has more than hundreds of subjects which is not uncommon for interrelated computing devices. Nevertheless, the required exponential computing time for safety test can be significantly reduced to its square root if computed by quantum algorithm. In this paper we demonstrate an application of quantum search algorithm to reduce the computation time for safety test for limited (i.e. mono operational) protection systems which are based on attribute-based access control model. The improvement of the performance allows the safety test for interrelated computing device’s system to be much less expensive to compute.
Comparison of SVM and Random Forest Methods for Online Signature Verification

Signatures are widely used for authentication purposes. Signature verification has many applications in banking, in crossing international borders, in boarding of planes etc. For verifying identity of a person, signature is legally and widely accepted biometric trait. This work presents two simple and efficient methods for online signature verification. The paper proposes Support Vector Machine (SVM) and Random Forest Method for verifying online signatures. To measure the performance of algorithm f1 score is used and experiments were performed on SUSIG dataset. In Method-1, data after preprocessing is taken as feature set and in Method-2 feature vector are made by concatenation of bins of different attributes of the signature. The attributes taken are kth derivative of x and y coordinates, and kth derivatives of pressure. The classification is performed on extracted features using SVM and Random Forest. The performances of proposed methods were evaluated by using confusion matrix on SUSIG dataset. Results show that the proposed methods are capable of verifying online signatures with acceptable level of accuracy.
Classification of Deformed Objects Using Advanced LR Parsers

An analysis of text and image data is today one of the core fields of artificial intelligence. Among the means that can be used to process this information are structural methods. However, input chain deformations can often occur when analyzing data using structural methods. These are caused, for example, by the inaccurate recording of scanning means. In order to process inaccurate input information, it is necessary to extend a grammar describing the input objects to deformation rules and a weighing system indicating the degree of deformation of the rule. However, the expanded grammar is usually ambiguous. Specially designed syntax analyzers are required to process it. These analyzers may be time consuming; for example, Early Parser calculates a new state dynamically during the analysis. To accelerate processing, a decision table can be used, where every new state is pre-defined. To process distorted inputs, it is possible to use the modified Tomita parser, which contains mechanisms for processing ambiguities, while using the LR table, which reduces dynamically computed tasks.
Implicit Trust Recommendation Algorithm based on Item Meaning

Incorporating user trust relationships can effectively alleviate the cold start and data sparse problems in traditional collaborative filtering algorithms. But in real life, the user trust relationships that can be collected also face data sparse problems. In addition, the existing recommendation algorithm based on user implicit trust relationship does not consider the negative and enthusiasm of certain products that have special significance to users and the common experience items between users when mining implicit trust relationships among users. In response to this type of problem, this article uses the historical scoring information of users to infer the meaning of the project to users, and the influence of the negative and enthusiasm of the user experience project on the implicit trust relationship between users, supplementing the existing trust statement between users to improve the accuracy of recommendation. Firstly, divide the community based on user trust relationships. Second, use the divided community user rating information to dig out the meaning of different items for different users, and infer the implicit direct trust value between users based on the positive and negative ratings between users, then the implicit direct trust between users is used to obtain the implicit indirect trust value according to the trust propagation. Finally, the implicit trust relationship between users was obtained according to the implicit direct and indirect trust between users, and the implicit trust between users was used to replace the similarity in traditional collaborative filtering, so as to find the recommended nearest neighbors of target users and predict the score of missing items. In the experimental results of the real data set FilmTrust and Epinions, compared with the comparison algorithm given in this article, it can effectively improve the accuracy of recommendation.
Class-unbalanced domain adaptation for object detection via dynamic weighting mechanism

The state-of-the-art object detection frameworks often suffer from a performance decline when the feature distribution differences between the source (training) domain and the target (testing) domain are existed. To alleviate this problem, recent works proposed various domain adaptation methods to improve object detection frameworks. Existing methods only consider the feature discrepancy between the source and target domains and ignores unbalanced in class space under cross-domain settings, which can lead to serious negative transfer problems. To address this issue, we propose a novel domain adaptation for object detection to reduce class space discrepancy between domains. Specifically, the weighted mechanism is used to increase the weight of public categories between domains to promote positive transfer and reduce the weight of non-public categories to retard the impact of negative transfer. Moreover, the model reduces domain feature distribution discrepancy by adding domain classifiers and employing adversarial training methods. The results of our experiments on several datasets demonstrate that our model can effectively solve the problem of performance degradation caused by the discrepancy in class space and significantly improve the detection accuracy in each domain.