Reviewerly/exHarmony huggingface dataset:
The Winner Takes It All: Choosing the “best” Binarization Algorithm for Photographed Documents

The recent Time-Quality Binarization Competitions have shown that no single binarization algorithm is good for all kinds of document images and that the time elapsed in binarization varies widely between algorithms and also depends on the document features. On the other hand, document applications for portable devices have space and processing limitations that allow to implement only the "best" algorithm. This paper presents the methodology and assesses the time-quality performance of 61 binarization algorithms to choose the most time-quality efficient one, under two criteria.
Application of Convolutional Neural Network in Image Processing

With the continuous development of computer technology, the wave of machine learning has become popular in various fields. Convolutional neural networks are based on the development of artificial neural networks and are widely used. How to optimize the algorithms of convolutional neural networks in image processing has become a research hotspot. There are many optimization algorithms for convolutional neural networks, but the results have not been ideal. In order to overcome the over-fitting phenomenon of convolutional neural network in image processing, and improve the accuracy of image classification and description conformity in complex scenes, this paper makes improvements based on the VGG model.First of all, this study chooses the VGG16 convolutional neural network model as the basic model, and introduces the Batch Normalization method to standardize the data. Then the Relu activation function with faster convergence speed is used to activate the neurons. Then use the improved dropout regularization method to optimize the model parameters. Finally, the improved model is tested by simulation experiments.The experimental results show that the error rate of the algorithm is as low as about 7% when iterates 300 times.Keywords: Convolutional Neural Network, Image Processing, Deep Learning, Image Classification, Dropout Regularization Method.
Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data

Fuzzy Cognitive Maps (FCMs) are computational models that represent how factors (nodes) change over discrete steps based on causal impacts (weighted directed edges) from other factors. This approach has traditionally been used as an aggregate, similarly to System Dynamics, to depict the functioning of a system. There has been a growing interest in taking this aggregate approach at the individual-level, for example by equipping each agent of an Agent-Based Model with its own FCM to express its behavior. Although frameworks and studies have already taken this approach, an ongoing limitation has been the difficulty of creating as many FCMs as there are individuals. Indeed, current studies have been able to create agents whose traits are different, but whose decision-making modules are often identical, thus limiting the behavioral heterogeneity of the simulated population. In this paper, we address this limitation by using Genetic Algorithms to create one FCM for each agent, thus providing the means to automatically create a virtual population with heterogeneous behaviors. Our algorithm builds on prior work from Stach and colleagues by introducing additional constraints into the process and applying it over longitudinal, individual-level data. A case study from a real-world intervention on nutrition confirms that our approach can generate heterogeneous agents that closely follow the trajectories of their real-world human counterparts. Future works include technical improvements such as lowering the computational time of the approach, or case studies in computational intelligence that use our virtual populations to test new behavior change interventions.
Study on the Influencing Mechanism and Path of Rural Talent Revitalization under the Rural Revitalization Strategy —Based on the In-Depth Investigation of Zhejiang Villages

Implementing the rural revitalization strategy is an important decision made at the 19th CPC National Congress, Rural talent development is an important part of rural revitalization.Through a comprehensive survey of the current situation of People returning rural areas in Zhejiang Province, it also points out the achievements in guiding talents to return home to start their own business and realizing rural revitalization, at the same time, there are still some phenomena of rural talent revitalization in Zhejiang Province, such as serious brain drain and low quality of rural talents, which greatly prevent the effective development of rural revitalization.On this basis, the paper puts forward the practice path of Zhejiang talent revitalization.
Topological Graph Convolutional Network Based on Complex Network Characteristics

Graph convolutional neural networks have received a lot of attention in various tasks dealing with graph data by aggregating information from neighboring nodes and passing node information.Many recent studies have looked at the impact of topological features on node classification tasks by altering aggregation based on degree values or incorporating topological data analysis into graph convolutional neural networks; however, graph data itself has many topological characteristics in complex networks.In most circumstances, the graph's topological characteristics reveal the nodes' similarity and facilitate the node classification task.This paper proposes a topological structure feature extraction method based on the concept of complex topological characteristics, which can obtain deeper topological features in the graph structure and use node features to obtain feature information that is more important for classification from both feature spaces.Evidence from experimental studies has established that the topological structure obtained by the method in this paper can be used as input to the GCN,and good results can be achieved on the classification task even without any external information on the nodes.In the graph dataset of connected topologies, the method exhibits a very large increase in accuracy and macro F1-score when compared to the state-of-the-art baseline model after mixing the node features.
A Succinct Analytical Study of the Usability of Encryption Methods in Healthcare Data Security

Rapid digitization of medical records and administrative databases, combined with developments in statistics and computing power, promises to make epidemiological research for bettering healthcare more fruitful than ever before. The requirement for access to a copious amount of potentially sensitive patient health information is common in such circumstances (protected health information, such as names, record numbers, addresses, and medication). Such digitalization can also lead to a potential risk for patient confidentiality if a data leak occurs. Keeping the fact in mind, certain methods are being adopted by many organizations to curb such problems. One of the major techniques involved is encryption. In this chapter, we are going to reflect how protected health information (PHI) can be protected with the help of various encryption techniques. It also covers strategies for securing information and maintaining data integrity, as well as the relationship between patient data, measurement data, and diagnosis. This study ends by concluding how far this technique has been helpful in protecting healthcare data, its advantages over other techniques, and its current presence in the system.
ASPA-MOSN: An Efficient User Authentication Scheme for Phishing Attack Detection in Mobile Online Social Networks

Over the last few years, with the massive growth of smartphone technology and mobile Internet, the use of various online social networks (OSNs) have increased rapidly. This ever-growing use of social networks leverages cyber-attackers to exploit various phishing schemes, spoofed accounts, and other threats to steal users’ credentials. Phishing is an online crime that employs both technical subterfuge and social engineering to steal consumers’ personal identity, financial account credentials, and other sensitive information. In general, a phishing attack is carried out by the exercise of sending fraudulent communications (like a fake email with harmful uniform resource locators), that pretends to come from a reputable source. The problem of designing user authentication protocol for mitigating phishing attacks in OSNs is a challenging research problem. In this article, we propose a secure and lightweight cryptography-based authentication scheme, called authentication scheme for phishing attack (ASPA)-mobile online social network (mOSN), that provides resistance to phishing and other related attacks in OSNs. The security of the proposed scheme is explained using both informal security analysis and formal security analysis through the widely recognized real-or-random model and ProVerif simulation tool. Finally, we compare the security, functionality, computation, and communication costs of the proposed ASPA-mOSN with related schemes. The comparison results show that ASPA-mOSN outperforms other existing competing schemes.
Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

The field of data mining is progressing rapidly presenting researchers with many opportunities for research.Sequential pattern mining is a popular and long established technique in data mining which extracts data in the form of sequential patterns satisfying a threshold value such as utility, support, profit or a combination of these.The application of fuzzy theory in sequential pattern mining has also been favored leading to more natural liinguistic representation.Researchers have proposed various hybrid algorithms with fuzzification of any one parameter such as time or quantity.This paper proposes a hybrid fuzzy algorithm for mining of frequent and high utility sequential patterns with fuzzification of both purchase time and purchase quantity parameters, thereby giving more useful fuzzy sequential patterns.Experimental results also prove that the proposed algorithm is better than the existing algorithms.
Study of Combating Technology Induced Fraud Assault (TIFA) and Possible Solutions: The Way Forward

The study aims to identify modes of fraudulent payments and create awareness of such incidences to avoid decisive virtual activities. Disruptive developments such as contactless payments, mobile payments, and e-cloud payments witnessed a large-scale data breach. As a result, new fraud pathways have emerged and made traditional detection technologies less effective. Thus, there is a need to understand the contemporary fraud induced technology and corrective remedies—a relevant assessment of published approaches for detecting fraudulent card payment is critical to study. The researchers tried to identify fundamental issues using AI-ML-DL to detect fraud. An intellectual computing strategy is offered as a capable research avenue while boosting commercial data patronage. The paper discusses a standard method used by cybercriminals to mislead individuals and businesses. The study also focused on the methods utilized by cybercriminals and the economics of each confronted gadget. The paper talked about how systems can detect and block cybercriminals in three domains: card payment fraud, mobile payment fraud, and telephonic fraud.
Eye-in-Hand Visual Servoing Control of Robot Manipulators Based on an Input Mapping Method

In image-based visual servoing (IBVS), parametric uncertainties tend to cause the model inaccuracy and limit the control performance. Considering these uncertainties can be embodied by the output–input data from the visual servoing system, this brief proposes an eye-in-hand visual servoing control (VSC) scheme based on the input mapping method, which directly utilizes the past output–input data to enhance the original feedback control law rather than identifying the model. The system with the input mapping method is proven to not only maintain the stability of the original VSC but also accelerate the convergent rate. The results of the experiments on a manipulator with an eye-in-hand camera demonstrate the superiority of our proposed method.
The Need for Universal Design of eXtended Reality (XR) Technology in Primary and Secondary Education

eXtended Reality (XR) provides new opportunities for immersive and engaging ways of learning in primary and secondary education. Educators and educational decision-makers, however, lack knowledge about the opportunities of this emerging technology. At the same time, XR technology lacks a fundamental universal design framework targeted at improving accessibility and usability for all, specifically for pupils with disabilities. In the following paper, we present the results from two selective and weighed literature reviews identifying: (1) opportunities, positive outcomes and challenges of using XR technology in primary and secondary education; (2) general advantages and limitations in terms of universal design, including barriers and possible solutions of XR technology for pupils with disabilities. The results show that utilization of XR technology in primary and secondary education is versatile, may positively influence learning outcomes in pupils, contributes to increase motivation, engagement, and interest. The challenges in implementing XR technology were mainly related to economic cost, health-related limitations, pedagogy, editorial limitations, and lack of universal design. Pupils with disabilities and varying degrees of abilities face challenges because there is lack of multimodality, practical issues when setting up or using devices, lack of interoperability and compatibility with assistive technology, financial costs, health-related issues, overreliance on gamification, and ethical considerations. In addition, most devices are designed for adults and not recommended to be used by children younger than 13 years of age. Future research needs to address development of guidelines including best-practice examples for increased accessibility and usability of XR technology, as well as advance the standardization and solidification of said guidelines into standards, regulations, and laws.
Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion

Many knowledge graphs (KG) contain spatial and temporal information. Most KG embedding models follow triple-based representation and often neglect the simultaneous consideration of the spatial and temporal aspects. Encoding such higher dimensional knowledge necessitates the consideration of true algebraic and geometric aspects. Hypercomplex algebra provides the foundation of a well defined mathematical system among which the Dihedron algebra with its rich framework is suitable to handle multidimensional knowledge. In this paper, we propose an embedding model that uses Dihedron algebra for learning such spatial and temporal aspects. The evaluation results show that our model performs significantly better than other adapted models.
High Performance Algorithm for Content-Based Video Retrieval Using Multiple Features

With the growth of technology, there has been a revolution in multimedia. The current text-based video retrieval method necessitates a large amount of manual intervention and provides very poor retrieval performance when the database size is large. Given the limitations of current algorithms, obtaining videos based on their own content outperforms traditional methods. There exist many algorithms to recoup a video from a large database. None of them could reduce the time consumption, which does not fulfill the user requirements. But only, the propounded system integrates the spatiotemporal features by exploring the whole information of the video, and it improves the efficiency of video retrieval. The proposed algorithm is an advanced spatiotemporal scale invariant feature transform (ST-SIFT). This system considers the color and motion feature of the video to attain spatiotemporal feature. Here, in this algorithm, we implement HSV-CH technique for taking out of color feature and motion histogram technique to take out video motion feature. The CBVR algorithm's performance was examined using the TRECVID and YouTube datasets, and the retrieved videos demonstrate the proposed algorithm's high performance.
A Comprehensive Study of Open-Source Libraries for Named Entity Recognition on Handwritten Historical Documents

In this paper, we propose an evaluation of several state-of-the-art open-source natural language processing (NLP) libraries for named entity recognition (NER) on handwritten historical documents: spaCy, Stanza and Flair. The comparison is carried out on three low-resource multilingual datasets of handwritten historical documents: HOME (a multilingual corpus of medieval charters), Balsac (a corpus of parish records from Quebec), and Esposalles (a corpus of marriage records in Catalan). We study the impact of the document recognition processes (text line detection and handwriting recognition) on the performance of the NER. We show that current off-the-shelf NER libraries yield state-of-the-art results, even on low-resource languages or multilingual documents using multilingual models. We show, in an end-to-end evaluation, that text line detection errors have a greater impact than handwriting recognition errors. Finally, we also report state-of-the-art results on the public Esposalles dataset.
Multisource Weighted Domain Adaptation With Evidential Reasoning for Activity Recognition

In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.
Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

Reinforcement Learning (RL) has shown promising performance in environments for both robotic control and strategic decision making. However, they are usually treated as separate problems with different objectives. In this work, we propose the use of Reinforcement Learning to solve both control and strategic problems as one, in a multi-agent robotic soccer environment. We use the IEEE Very Small Size Soccer (VSSS) challenge from the Latin American Robotics Competition (LARC) as a study case. In the VSSS, two autonomous teams of wheeled robots compete by pushing the ball around to score goals. To unify both control and strategy problems, our approach gives full control of the actuators&#x2019; speed to the RL algorithm whilst keeping the broader objective of winning the game. Our method achieves win rates as high as 93% against hand-coded heuristic strategies. In this work we contribute by developing an RL agent that can learn from self-play and generalize against new opponents. Our methodology uses multi-agent Reinforcement Learning with self-play in order to build up the knowledge for complex tasks. We also developed a simulated environment for the robotic soccer game.
Variable-Rate Deep Image Compression With Vision Transformers

Recently, vision transformers have been applied in many computer vision problems due to its long-range learning ability.However, it has not been throughly explored in image compression.We propose a patch-based learned image compression network by incorporating vision transformers.The input image is divided into patches before feeding to the encoder and the patches are reconstructed from the decoder to form a complete image.Different kinds of transformer blocks (TransBlocks) are applied to meet the various requirements in the subnetworks.We also propose a transformer-based context model (TransContext) to facilitate the coding based on previously decoded symbols.Since the computational complexity of the attention mechanism in transformers is a quadratic function of the sequence length, we partition the feature tensor into different segments and conduct the transformer in each segment to save computational cost.To alleviate the compression artifacts, we use overlapping patches and apply an existing deblocking network to further remove the artifacts.At last, the residual coding scheme is adopted to get the compression performance for variable bit rates.We show that our patch-based learned image compression with transformers obtain 0.75dB improvement in PSNR at 0.15bpp than the prior variable-rate compression work on the Kodak dataset.When using the residual coding strategy, our framework keeps good performance in PSNR and is comparable to BPG420.For MS-SSIM, we get higher results than BPG444 across a range of bit rates (0.021 at 0.21bpp) and other variable-rate learned image compression models at low bit rates.
Linked Data and Ontologies for Semantic Interoperability

Abstract The purpose of Linked Data and ontologies is to achieve semantic interoperability across different systems and agents. As a concrete implementation, Digital Construction Ontologies (DiCon) are meant to provide efficient information sharing and utilization between the tools in the BIM4EEB toolkit. This section explains the motivations, design principles, and organization of DiCon ontologies.
GP-MSJF: An Improved Load Balancing Generalized Priority-Based Modified SJF Scheduling in Cloud Computing

In today's research of multidisciplinary environment, heterogeneous devices are connected to meet the performance by solving the popular real-life applications like IoT for developing a cyber-physical system, smart healthcare and agriculture, etc. Such a heterogeneous system is risky to maintain its regularities in terms of scheduling the resources for better optimal throughputs. In this paper, we target to develop an optimal resource scheduling system in the cloud environment. More specifically, the improved load balancing algorithms are developed which give optimal allocation of the resources requested by the virtual machine. In the proposed algorithmic approach, the major efforts are to focus on developing an optimal load balancing system for allocating various tasks to virtual machines. The popular scheduling algorithm in a multitasking environment, the Round-Robin algorithm, allocates the task to VM on FCFS basis. In this paper, generalized priority-based modified shortest job first (MSJF), a premier load balancing algorithm is joint which resulted in a scheme that is effectively suitable for resource optimization and reduces the makespan at a large scale. We further presented the important objective of the load balancing technique via showing the difference between makespan and resources allocation by implementing RR, FCFS and MSJF. To optimize the allocation of available resources among the task with different requests, the proposed system satisfactorily reduces makespan and deactivates idea resources.