Reviewerly/exHarmony huggingface dataset:
A SCALE AND ROTATION INVARIANT FAST IMAGE MINING FOR SHAPES

Abstract In recent times, fast content – based image retrieval is required in image mining for shapes, as image database is rising exponentially in size with time. In this paper, a sample, fast and efficient process through Distance Mapping for CBIR is proposed. In distance mapped domain, zero valued points represent the boundary of the object and the rest are assigned the value with the shortest distance to the boundary of the object. All the regional points within the object are defined with positive distance while the background points are given negative distances. The shape signature proposed is obtained from the statistical properties of the distance mapped functional. The features used are the number of grid points within pre-specified narrow band region across the object boundary and their algebraic relationship. It is found that, these features are invariant to sealing and rotations. T his increases the retrieval rate and also the speed of retrieval as no pre-scaling and rotation are required to register the shape. Conventional distance mapping is a time consuming process. This process can be used as coarse level in hierarchical CBIR that shrinks the database size from large set to a small one. This tiny database can further the database can be scrutinized meticulously using the above said methods like wavelets, curve lets etc. For feature extraction to improve the retriveval rate or even in the present work using accurate distance mapping.
Design of Eco-Tourism Service Quality Evaluation Model based on Artificial Neural Network

In order to better improve the quality of eco-tourism services, this paper designs an eco-tourism service quality evaluation model based on artificial neural networks. This paper discusses the environment, housing, diet, characteristics, tour guide and other aspects of the tourism system, lists a number of index systems to analyze the tourism evaluation model, and then uses the artificial neural network to design the evaluation model of ecotourism service quality by combining qualitative and quantitative methods, and constructs the evaluation scale of ecotourism service quality combined with the actual characteristics In order to achieve the research goal of the evaluation model of ecotourism service quality, the importance of tourists to the five dimensions of service is sorted.
Global Starvation and Coronavirus

In this chapter, we study the vulnerability of countries to climate change. We also examine the effects of climate change and the coronavirus on countries with respect to global hunger. We note that some models of the coronavirus can be placed in the setting of intuitionistic fuzzy mathematics.
Application of Computer Network Based on Data Communication Technology in Production and Life

Abstract With the development of modern data communication, it can effectively realize the long-distance information transmission and processing. At present, when there are many advanced devices, the processing functions can be enriched to the greatest extent, and data communication can be widely used in various industries. In daily production and life, computer network has been widely used, which can realize the large-scale transmission and sharing of information. After long-term development and improvement, the application of computer network in people’s daily production and life can bring more convenient services, improve people’s work efficiency and improve their quality of life. At present, computer network has been widely popularized and promoted, which makes the application of computer network technology constantly increase, brings convenient services to people’s lives, and makes network technology more scientific and standardized. In the future, computer network technology will be widely used, so we should pay attention to the present application.
Tag based Product Recommendation System Using Rating Variance

Information overload have become a major problem due to sudden rise in the Internet data and users visiting websites. The recommendation system is a technique of information filtering to predict the preferences that users may like. The main target of recommendation system is to provide useful information to users and solve the large-scale problem of information overload where users are not able to get correct results. Different products have different ratings given by many users. On most websites, each product has some labels or phrases, which are used to identify the content of a product called as tags. In practice, users generally look at the ratings and tags attached to the item to decide whether they should buy it or not. The proposed approach generates recommendations using collaborative filtering by utilizing users’ tags and ratings information. The proposed approach predicts rating of products to the target user based on target user’s profile and other similar users. The proposed approach adjusts the predicted ratings of recommended items using standard deviation of items’ ratings.
CAE-based classification method of electric power business

Abstract Aiming at the problem that abnormal data in the business classification of power data communication networks will reduce the business classification accuracy, a classification method based on convolutional autoencoder combined with lightGBM was proposed. First, the flow features of the electric power business were extracted through the convolutional autoencoder. Then, anomaly detection was realized according to the relationship between the reconstruction loss of the convolutional autoencoder and the set threshold. Finally, the electric power business was classified through the LightGBM classifier. The Moore data set used for simulation verification. The results show that the proposed method can effectively detect abnormalities, thereby improving the accuracy of the electric power business classification.
Securely Identity Based PHR Sharing In Cloud Computing

In the aid, the sector has resulted in the price-effective and convenient exchange of non-public Health Records (PHRs) among many collaborating entities of the e-Health systems. still, storing the confidential health info to cloud servers is prone to revelation or stealing and necessitate the event of methodologies that make sure the privacy of the PHRs. Therefore, we tend to propose a technique referred to as with efficiency sharing Personal Health Record between users and doctors in the cloud. The PHR theme ensures patient-centric management on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and by selection grant access to different kinds of users on totally different parts of the PHRs by booking appointments by symptoms, additionally read the nearest location of hospitals and direction of the hospitals. Moreover, the methodology is secure against business executive threats and additionally enforces forward and backward access management. what is more, we tend to formally analyze and verify the operating of PHR methodology through the High-Level Petri Nets (HLPN). Performance analysis concerning time consumption indicates that the PHR methodology has the potential to use for firmly sharing the PHRs within the cloud.
TideFC: Learning Temporal Interaction for Dynamic Embedding via Feature Crossing

Recommendation system is becoming more and more important. Specially in social network, such as Twitter, Facebook, and YouTube. In the beginning, a number of studies learn the relationship of users and items from a bipartite graph. They embed each user and item in an embedding space. However, they ignore temporal properties. They regard users and items as static embeddings. As time passed, user preferences and item concepts can change, and node embeddings should be dynamically adjusted in the embedding space. Nevertheless, most of existing models update embeddings only when user takes an interaction with an item. In this paper, we propose TideFC, a novel model based on the state-of-the-art dynamic embedding model JODIE. TideFC can predict the future trajectories of users' and items' embeddings. Besides, we take advantage of t-batch that creates time-consistent batches to make the training stage more efficient. More importantly, we incorporate feature crossing to generate high-order feature interactions in our TideFC. Experiments conducted on multiple real datasets demonstrate the promising performance of TideFC, compared with the state-of-the-art JODIE.
Investigation and Improvement of VGG based Encoder-Decoder Architecture for Background Subtraction

Object detection in motion pictures is always a challenging task due to the presence of dynamic background. Deep learning architectures especially encoder-decoder type has shown promising performance in segmenting foreground objects against the background in video sequences. Thus, in this work, a VGG-16 based encoder-decoder architecture is investigated and several modifications are proposed to improve the efficiency the model. The modified models are evaluated on two different standard databases- CDNet 2014 and SBI2015 with various scenes and achieved the highest precision of 0.99 which is competitive in nature with the current schemes in the state-of-the-art.
Application of CART algorithm and parametric modeling method in the design of sit-skis

2020 Winter Paralympic Games is about to be held in Beijing, and cross-country sit skiing account for a large portion of the Winter Paralympic Games. It is very important to improve the athletic ability of Chinese athletes. Sit-skis are the key equipment of cross-country sitting skiing competitions, and there are many existing problems in China on sit-skis. This study used the CART decision tree algorithm to design personalized sit-skis for athletes' physical characteristics. Combined with parametric modeling method to generate 3D model in real time and performed finite element analysis. The sit-skis produced by this research method were put into application and received good feedback. The whole process greatly improved the scientificity and rationality in the design of sit-skis, and reduced the time and labour cost of sit-skis design. It provided a possible solution to meet the huge demand for sit-skis in China.
Features Extraction of Mamographic Image using Zoning Method

Mammographic images are images that can be used as a tool to detect the presence of breast cancer. The presence of the disease is shown in the characteristics of the breast tumor object that appears in mammographic images. Visually, the structure of mammographic images has abnormalities so it is difficult to recognize the characteristics of these images. Therefore, in this paper an algorithm is used to extract the features of the breast tumor shape that appears in mammographic images using of zoning method. The zoning method divides the image of mammographic characters into several parts, then special features in each part are extracted for producing an eigen value that corresponds to the largest eigen value. Then, the eigen values are used to form the main characteristic space of the mammographic image. The results show that the algorithm is able to localize the suspected area as a form of breast tumor and can produce the characteristics of the images. Furthermore, the features produced in this paper can be used in the process of classifying breast tumor disease.
Hierarchical Attention-based BiLSTM Network for Document Similarity Calculation

Neural network model is a momentous method to calculate semantic similarity. Taking into account the complexity of document structure, introducing hierarchical structure and attention mechanism into neural network can calculate document semantic representation more precisely. In order to verify the validity of the model, LP50 dataset was tested. The experimental results reveal that accurate document representation can be obtained by using the attention mechanism at two levels of words and sentences. Since this method has taken both the influence of context information and the contribution of components to the document into consideration. Compared with several conventional methods, there is a significant improvement of performance in our model.
On the Similarity Search With Hamming Space Sketches

This chapter focuses on data searching, which is nowadays mostly based on similarity. The similarity search is challenging due to its computational complexity, and also the fact that similarity is subjective and context dependent. The authors assume the metric space model of similarity, defined by the domain of objects and the metric function that measures the dissimilarity of object pairs. The volume of contemporary data is large, and the time efficiency of similarity query executions is essential. This chapter investigates transformations of metric space to Hamming space to decrease the memory and computational complexity of the search. Various challenges of the similarity search with sketches in the Hamming space are addressed, including the definition of sketching transformation and efficient search algorithms that exploit sketches to speed-up searching. The indexing of Hamming space and a heuristic to facilitate the selection of a suitable sketching technique for any given application are also considered.
A New Remote Fuzzy User Password Authentication Scheme Using Sub-tree for Cloud Computing

Recent advancements in internet technology and the infrastructure have attracted more people and organizations to do everything online. Internet technologies have provided amazing and smooth ease for electronic sales and purchases. However, many people have refused to use these internet technologies in electronic purchases because of unstable and insecure forms. New hacking techniques and new types of attacks have been tackled to make these internet technologies better and safer. Smartcard-based password authentication schemes have been the mainstream in recent years, featuring their highly lightweight, easy-to-use equipment and lowcost apps. Various secure and faster authentication schemes have been proposed in the literature. However, most of the existing authentication schemes have found vulnerable to recent attacks and have security flaws. This paper provides and efficient way for authentication using the partial discrete logarithm and sub-tree structure. The proposed scheme has seen effective and more useful in cloud computing environment. The analysis based on the security and the computational cost shows that the proposed authentication scheme proves to be more secure and efficient compared to other protocols that serve the same purposes.
Defining a Core Ontology for Medical Devices in Germany to Ensure Semantic Interoperability

To manage the data of medical devices, systems have to fulfill high demands. These high demands are due to the legal requirements for medical devices and due to specific needs of users of these information. Furthermore, the lack of semantic interoperability prevents a centralized provision of high-quality data sets. The goal of our project was to develop an ontology for medical devices, and to derive a UML data model from our ontology. For this purpose, the approach “Ontology Development 101” was used. Based on the German law for medical devices and expert interviews, we could identify relevant domain topics and terms. Subsequently, we used the language OWL to define classes, properties and constraints for the ontology. Afterwards, the ontology was translated into a UML data model using an already defined set of rules. This led to the final hypothesis that semantic interoperability between hospital information systems can be achieved when using the ontology.
Visual Saliency Detection for Water Surface Contaminants

Visual saliency detection is an intuitive and effective means for water environment monitoring. Conventional saliency detection algorithms usually have high computational complexity, and the detection results are usually unsatisfactory for the complex and diversified water scene images. In this paper, we propose a novel method which is simple and effective for the detection of water surface contaminants. In our method, the input image is reconstructed according to block-based compressed sensing for reducing the computational complexity. Then, a local contrast principle is used to obtain the primary saliency map. Based on this, a linear combination of color coefficients is designed to construct final saliency map. The experimental results show that the proposed method has good detection performance in terms of accuracy and running time.
Using Indigenous Languages for Enhanced Global Marketing in Tanzania

As the sector of commercialization is expanding, within Tanzania and globally, finding wider markets has become a great challenge. The authors opine that businesses will be enhanced by using local languages. The study maintains that language links with the culture that has a great influence on global marketing such as product design, branding, and distribution process. The study collected data through surveys, interviews, and observation in three regions, namely Iringa, Dodoma, and Dar es Salaam. The results were later analyzed qualitatively using descriptions and tables. Results revealed that out of 60 respondents that took part, 23% of the customers are from the western countries, 20% are from East Africa, 16% from other African countries, and 51% are within Tanzania. The majority, 80%, stated that even though they market their products outside Tanzania, the only languages used are English and Kiswahili. It is anticipated that companies that use indigenous languages will bring their customers individualistically or collectively much closer, widening the products' markets in Tanzania.
PROMO for Interpretable Personalized Social Emotion Mining

Unearthing a set of users' collective emotional reactions to news or posts in social media has many useful applications and business implications. For instance, when one reads a piece of news on Facebook with dominating "angry" reactions, or another with dominating "love" reactions, she may have a general sense on how social users react to the particular piece. However, such a collective view of emotion is unable to answer the subtle differences that may exist among users. To answer the question "which emotion who feels about what" better, therefore, we formulate the Personalized Social Emotion Mining (PSEM) problem. Solving the PSEM problem is non-trivial in that: (1) the emotional reaction data is in the form of ternary relationship among user-emotion-post, and (2) the results need to be interpretable. Addressing the two challenges, in this paper, we develop an expressive probabilistic generative model, PROMO, and demonstrate its validity through empirical studies.
To Explore the Application of Computer Technology in Reservoir Completion Development from the Perspective of Dynamic Analysis

Abstract In recent years, reservoir cooperation and exchange between countries and regions with a Shared economy based on dynamic analysis has made some progress, but on the whole, there are still some problems in reservoir completion opening up. China should actively integrate into the construction of sharing economy, seek reservoir cooperation in a wider range, further optimize reservoir completion trade structure, innovate reservoir investment pattern, expand reservoir technology exchange space, increase policy support, and strive to build a new pattern of reservoir completion opening to the outside world.
Study and Simulation of Autonomous Quadrotor Tracking a Pre-Defined Trajectory Using Matlab

Abstract The automated airborne vehicle (UAV) is an airplane that is worked without a human pilot. It was controlled either by the controller of a pilot or self-governingly by locally available PCs on the ground or in another vehicle. Modern Quadrotors are evolving than the old designs into a small agile vehicle. The quadrotor is increasingly becoming more and more popular, there is an increasing need for making this vehicle autonomous. The Methods for developing autonomous unmanned aerial vehicles has been an active area of research under the mobile platform. There are many commercially available drone platforms but all of them are required user interaction through a controller. In this work, we have created a control algorithm and trajectory generation for a smooth minimum snap trajectory through the given waypoints. Each waypoint specifies the desired position velocity, yaw, pitch, and roll angles. Such waypoints generated by our algorithm generate a smooth minimum snap trajectory between these waypoints. A PID controller is used to control the motions of the quadrotor in all planes.
Navigation Lamp Model on Maritime Vocational Education Integrated With STEM Approach: Analysis and Design

Abstract The application of knowledge to practical in engineering maritime of vocational education for professional practice. Engineer faced the problems are intimately tied up with design. The aim of this research is develop navigation lamp model on the ship with STEM approach by engineer cadets. The research method was used analysis study and design for cadets applied in practice before on shipboard. Navigation lamp model built consist of anchor light, masthead light, towing light, and side light. Analysis of navigation light base on rules of COLREGs about light and shapes. Navigation lamp built as miniature model with source of energy from solar cell 50 WP and the electric usage controller added for charge and load of the lamp. Both charge and load are measured with digital volt/ampere meter. We obtained for the conclusion that engineering cadets understand of rules of COLOREGs about light and shapes which can built from miniature models with PV solar cell as source energy.
A path optimization algorithm based on multi-objective monitoring scenario analysis for UAV

Abstract The path planning of UAV monitoring task was used to a case study, a path optimization algorithm based on multi-objective monitoring scenario analysis for UAV was proposed. In the proposed algorithm, the UAV multi-objective task monitoring scenario was analyzed and modeled, Markov decision-making process model was established by model checking tool prism, two different sub-algorithms for MPOA_LRTP and MPOA_RR were provided to verify and analyze the model of UAV multi-objective task monitoring scenario. Compared MPOA_LRTP and MPOA_RR, the results of formal verification show that MPOA_LRTP with eight direction selecting was an effective way to complete the monitoring task for UAV, it spent shorter time and had higher efficiency, and the probability of completing the task in a certain time was greater. Besides, the smaller the workload and the less fatigue of the operator, the better the system performance and the higher the efficiency of completing the task.
Rancangan Aplikasi Administrasi Logistik Pada PT Container Maritime Activities Palembang

Nowdays, the use of technology has been highly developed and has been used in almost every activity, including to complete office work, one of which is at PT. Container Maritime Activities. However, the use of technology in this company is not optimal because it only relies on excel data that is copied and moved manually. Dispatching of containers is also done manually by sorting containers by date of entry. This can cause data to be difficult to process and not updated. Using data that is not updated causes errors in container delivery. For this reason, a program design was made to minimize container delivery errors, and to make arrangements for containers to be delivered to the shipper easier, because they had to be issued using the FIFO (First In First Out) system, then sorting by date with the system was needed. Application development using prototype models, designed using Photoshop applications and using the Mysql database. The design of this application is expected to help employees make it easier to do logistics administration activities.Keywords: Technology, Prototype Model, Logistics Administration