Reviewerly/exHarmony huggingface dataset:
Hierarchical Output Model of CNN Learning Using Multi Label Datasets

In this paper, we propose a learning method for a deep learning model that becomes more complex as multi label datasets increase. The existing deep learning models are mainly models that classify each class in the final output layer. In other words, the results are output by ensuring the diversity of the learned models using the ensemble method. However, as the number of classification classes increase the number of network connections increase in the fully connected layer, resulting in frequent interference between nodes. As a result, it leads to overfitting that is impossible to learn. In this paper, to solve the problem above, a multi output model that learns the output layer independently and a hierarchical output model that learns hierarchically and outputs the results are proposed. Accuracy, f1-score, and ROC curve are used to compare the performance. Through this, the multi output model and the hierarchical output model show a performance improvement of about 8% and 10% compared to the existing single output model and about 3% and 5% compared to the ensemble model.
Automating Transfer Credit Assessment-A Natural Language Processing-Based Approach

Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate this process. Given the unique structure, domain specificity, and complexity of learning outcomes (LOs), a need for designing a tailor-made model arises. The proposed model uses a clustering-inspired methodology based on knowledge-based semantic similarity measures to assess the taxonomic similarity of LOs and a transformer-based semantic similarity model to assess the semantic similarity of the LOs. The similarity between LOs is further aggregated to form course to course similarity. Due to the lack of quality benchmark datasets, a new benchmark dataset containing seven course-to-course similarity measures is proposed. Understanding the inherent need for flexibility in the decision-making process the aggregation part of the model offers tunable parameters to accommodate different levels of leniency. While providing an efficient model to assess the similarity between courses with existing resources, this research work also steers future research attempts to apply NLP in the field of articulation in an ideal direction by highlighting the persisting research gaps.
Linear Regression for Insolation Prediction

Solar energy is abundantly available in Jaipur, and one can harvest dividends by building smart energy storage systems based on solar energy. Government and energy utility authorities encourage the citizens as well as organizations for harnessing of solar energy through multiple initiatives and incentives. However, gaining competitive advantage in the domain of energy storage market can be done only when the delivery of energy services is reliable. Commercialization of solar energy cannot be realized to its optimum potential unless the reliability can be matched with the competing energy technologies. Hence, dependable machine learning models are very useful for the prognoses associated with the availability of solar radiation so that the associated energy storage management system can take appropriate corrective actions in wake of insolation variation. The prediction of available solar radiation provides resiliency to the energy storage system. In this work, weather data has been exploratorily correlated with solar radiation, and linear regression has been used as an instance of the multiple prognosis algorithms available, owing to its simplicity for this particular use case. The linear regression tool gives the coefficient of various stochastic weather factors, and with the currently available dataset and tool, mean absolute error of approximately 18% has been reported.
PCCP: A Private Container Cloud Platform Supporting Domestic Hardware and Software

Abstract With the widespread use of container cloud, the security issue is becoming more and more critical. While dealing with common security threats in cloud platforms and traditional data centres, there are some new security issues and challenges in the container cloud platform. For example, there are significant challenges in network isolation and resource management. This paper proposes a private container cloud platform PCCP based on Docker supporting domestic software and hardware to solve these security problems. This paper introduces the system architecture and functional architecture of the platform. The system has been tested and confirmed to have high availability and high reliability. The platform gives full play to the value of domestic software and hardware and is better able to serve the information construction of our country.
Combining Visual and Linguistic Models for a Robust Recipient Line Recognition in Historical Documents

Automatically extracting targeted information from historical documents is an important task in the field of document analysis and eases the work of historians when dealing with huge corpora. In this work, we investigate the idea of retrieving the recipient transcriptions from the Nuremberg letterbooks of the 15th century. This task can be solved with fundamentally different ways of approaching it. First, detecting recipient lines solely based on visual features and without any explicit linguistic feedback. Here, we use a vanilla U-Net and an attention-based U-Net as representatives. Second, linguistic feedback can be used to classify each line accordingly. This is done on the one hand with handwritten text recognition (HTR) for predicting the transcriptions and on top of it a light-wight natural language processing (NLP) model distinguishing whether the line is a recipient line or not. On the other hand, we adapt a named entity recognition transformer model. The system jointly performs the line transcription and the recipient line recognition. For improving the performance, we investigated all the possible combinations with the different methods. In most cases the combined output probabilities outperformed the single approaches. The best combination achieved on the hard test set an F1 score of 80% and recipient line recognition accuracy of about 96% while the best single approach only reached about 74% and 94%, respectively.
Nonlinear sociodynamics in the digital age

The article is devoted to understanding the phenomenon of complexity and historicity of the development of social systems in the modern digital era. The competition of two basic sociotypes of molecular and cosmic man is considered as the basis of their dynamic behavior. A characteristic feature of the applied methodology is a holistic approach, which is based on the thesis of the unity of sociogenesis and cosmogenesis. Social dynamics is considered as part of the evolution of the solar system. The emphasis is on the unity of the nonlinear dynamic laws governing these processes. The article provides a number of images of maps of the distribution of stability and instability zones of a non-linear discrete display that describes the interaction of the sociotype of a molecular and cosmic person in a social system. The mapping itself was described earlier, in previous works. The article focuses on the visual range and the intuitively fixed complexity of non-linear social dynamics. As a means of vectorization of natural non-linear competitive social dynamics, the role of the so-called residual or cosmic culture is considered. The idea is expressed and substantiated that it is the accumulation of residual true culture that represents the path of transformation of social dynamics into an upward progressive spiral of development.
EMPIRICAL STUDY ON MALARIA DETECTION USING MACHINE LEARNING

Malaria is the third-deadliest infection on the world. Malaria is cause 14 million more cases and 69,000 more deaths in 2020 than it did in 2019. Between 2019 and 2020, India was the only high-burden country to show progress by sustaining a reduction in malaria burden. In 2020, 29 of the 85 malaria-endemic countries accounted for 96 percent of malaria cases. India was responsible for 1.7 percent of malaria cases and 1.2 percent of deaths worldwide. The gold standard for malaria diagnosis is currently microscopic examination of blood films. It is, however, subjective, error-prone, and time-consuming. To address such issues, computational microscopic imaging methods have recently received a lot of attention in the field of digital pathology. As a result, over the last decade, researchers have focused on digital image analysis and computer vision methods for malaria diagnosis. Various contrast enhancement and segmentation methods for microscopic imaging have been discussed in this study for accurate malaria diagnosis.
Does Large Pretrained Dataset Always Help? On the Effect of Dataset Size on Big Transfer Model

Transfer learning often refers to an approach concerning machine learning in which the programmers relocate an initially developed model as the starting point for a model on a consequent task. It is evident that deep neural networks entail massive datasets to build and train feasible models. To gain massive and qualified datasets, nonetheless, seems expensive and time-consuming. This paper firstly examines the scale of a dataset so as to train an effective model as well as pertinent reactions corresponding to partial changes made to the scale of the dataset. In the practical experiments via training deep neural networks, we simplify hyperparameter tuning via transfer of pre-trained representations, hence promoting sample efficiency. To verify the usefulness of pre-trained models in datasets of different sizes, we have done relevant experiments on two benchmark datasets, cifar10 and cifar100. The results demonstrate that the larger the size of the pre-trained model, the better the fine-tuning effect of the network. With detailed analysis of primary elements contributing to high transfer performance, we aim to utilize pre-trained models with more efficient performance on dataset named as ImageNet-21k to benefit the computer vision research, in contrast to traditional models pre-trained on the smaller dataset, ILSVRC-2012.
Distributed Genetic Algorithm in Energy and Power Blockchain System Design

The traditional energy and power system needs to solve the information security problem brought by the network. Once the system is attacked, the information of users in the power market will be leaked. The blockchain technology combines energy and power to improve the system and make the information in the system database public. It can be applied to the distributed power transaction of the distribution network. The distributed genetic algorithm (DGA) can be used to improve the transaction efficiency in the power data transaction, optimize the performance of the energy and power (EAP) blockchain system to detect the failure of power grid equipment, and build a reliable local area. The electricity market and ensuring the security of electricity data transactions are of great significance.
Assessment of the Presented Models

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Smart QR-based Restaurant Dine-in System with Sales Analysis

The modern-day customer has new priorities when it comes to dine-out and the top of them is safety. Most of the restaurant’s meal orders rely on the interaction with waiters to place orders into the kitchen. Due to the pandemic situation, there happens to be discomfort interacting with waiters and other staff members of the restaurant. To solve such problems this system is developed. This system covers the whole order process of a restaurant including the interaction between the customer, the waiter, the kitchen, and the cashier through a web application. Additionally, restaurant owners will be able to enhance sales by using this system to analyze data gathered. This system will attempt to replace the traditional manual ordering process.A better user experience that includes food recommendations can indirectly boost customer loyalty to the restaurant. It is a complete product for managing restaurant services with minimal human interaction while providing maximum contactless service.
SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning

We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer.It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods.We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization.In other cases, it generates spans that are valid but differ from the benchmark.When evaluated with metrics based on overlap, we find that SlotGAN performs within 95% of the precision of a supervised method, and 84% of its recall.Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.
Neural machine translation model combining dependency syntax and LSTM

For the problem of the lack of linguistic knowledge in the neural machine translation model, which is called Transformer, and the insufficient flexibility of positional encoding, this paper introduces the dependency syntax analysis and the long short-term memory network. The source language syntactic structure information is constructed in the neural machine translation system, and the more accurate position information is obtained by using the memory characteristics of LSTM. Experiments show that using the improved model improves by 1.23 BLEU points in the translation task of the IWSLT14 Chinese-English language pair.
Distributed Consensus for Permissioned Blockchain

Consensus protocols are used to manage large databases in a distributed environment without trust among participating nodes in the network. The choice of a specific protocol relies on the characteristics and purpose of the system itself. This chapter aims to identify the practical advantages and disadvantages of consensus protocols. Seven consensus protocols for permissioned environment are covered in this chapter. All the consensus algorithms solve either crash faults or byzantine faults. PAXOS and RAFT, falling under crash fault tolerance, are intended for systems with no apprehension of unreliable users. The notion of byzantine fault was developed from the Three General Byzantine Problems and Lamport Shostak Pease Algorithm. Practical Byzantine Fault Tolerance (DBFT) is intended for systems with fewer nodes. Federated Byzantine Fault Tolerance (FBFT) shows better scalability and is more appropriate for large-scale systems but can resist fewer malicious nodes. Proof of Authority (PoA) and Proof of Elapsed Time (PoET), even though used in both permissionless and permissioned environments, its characteristics make it more suitable for the permissioned environment. PoA can resist the most significant number of malicious nodes without hindering the system’s functioning. Various companies have developed applications that are based on consensus algorithms. While working with extensive transactions and a large chain of blocks, scalability, efficiency, and malicious attacks are significant issues. This chapter also present a comparative analysis of all the consensus algorithms based on such issues.
Mining Procedure in Distributed Consensus

The rapid growth of blockchain technology over the preceding decade has attracted the interest of both scholars and businesses. Bitcoin and cryptocurrency are the foundations of blockchain technology, widely recognized as a decentralized, immutable ledger system for transaction data ordering and an open network structure. Anyone may join the network because it is an accessible network framework. It might also be a legal or illegal entity. This involves the implementation of a blockchain that is both secure and tamper-proof. Mining is a core of a distributed consensus mechanism that maintains transaction consistency, making the network secure and efficient. Every miner must establish their identity in an access network to join the blockchain network. The miner must complete numerous tasks and allocate their time and resources to become a member of the network and earn a reward. This process has been designed in such a manner that it requires a large number of processing resources and power. With the help of an example, this chapter explains the entire process of mining a block. This mining process creates an incentive for miners to contribute more toward the network. Difficulty requirements are an important factor to consider during the mining procedure of a block. As a result, the connection between mining and hash cash is thoroughly examined. Moreover, the growing demand for blockchain technology highlights the need for mining pools and their many sorts, as well as comparative research. In addition, this chapter explores the permissionless and permissioned consensus algorithm and its working process. This chapter explores the types of sources, cryptocurrency, and required energy to execute any specific consensus algorithm.
BUSCAR NA MOTIVAÇÃO UMA FORMA DE INSTITUIR A ATIVIDADE FÍSICA NA ROTINA DAS PESSOAS POR MEIO DE GRUPOS DE APOIO

The number of obese people around the world is increasing alarmingly and a sedentary lifestyle is one of the causes. This work aims to plan a weekly group mentoring, which will be carried out through the Google Meet digital platform. The research justifies the fact that it was identified a difficulty for individuals to create the habit and institute physical activity in their daily routine.. The lack of time, incentive and motivation are the main barriers encountered. In view of this problem, a group intervention was proposed, in the mentoring format, lasting 04 (four) months, in which participants will be able to share their experiences and difficulties during the weekly meetings, as well as participate in lectures with a professional from the physical education area. At the end of the experiment, it is expected that everyone has understood that the motivational factor has a great relevance to keep them focused within the project of instituting physical activity as part of their routine. The application of this work will help people look for a way to stay motivated to exercise, as this new lifestyle significantly reduces the health risks related to obesity, also contributing to weight control and loss, decreased anxiety. and improved self-esteem.
Computer Control Using Vision-Based Hand Motion Recognition System

In our day-to-day communication and expression, gestures play a crucial role. As a result, using them to interact with technical equipment requires small cognitive data processing on our part. Because it creates a large barrier between the user and the machine, using a physical device for human-computer interaction, such as a mouse or keyboard, obstructs the natural interface. In this study, we created a sophisticated marker-free hand gesture detection structure that can monitor both dynamic and static hand gestures. Our system turns motion detection into actions such as opening web pages and launching programs. This system will bring a revolution in various industries, which has the potential to replace traditional devices and time-consuming computer handling methods.
Blockchain for Government Services

The government sector is suffering from bribe issues, third-party mafias, and many more nowadays, which hinder the overall functions of government services. Blockchain technology is a viable solution that efficiently manages government services to overcome these issues. It is an emerging technology that works on the distributed ledger principle. Elimination of third-party, data transparency, high security, and privacy are the significant characteristics of blockchain technology. Blockchain can be applied for various functionalities in the government sector, including tax payments, land registration, funds tracking, and many more. In tax payments, the primary focus is towards the traceability of taxes and blockchain’s immutable ledger stores all traces of historical data transactions, i.e., the record of each and everything. In the land registration scenario, the aim is to eliminate the third party and include transparency using cryptographic hash functions. Andhra Pradesh (a state of India) has taken the initiative and decided to incorporate blockchain technology in its land registration system. Further, this chapter includes various use case scenarios for an in-depth discussion with smart contracts, which explains the technicality of the blockchain-based system.
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing

The syntax-based AMR parsing approach assumes a close mapping between syntactic and semantic structures. However, syntax-semantic mapping is not evident in complex sentences, causing parsers to fail to build the correct core structure of a tree. In this paper, as an aid to AMR parsing, we propose a dependency matching system that first detects complex sentence structures in a dependency parse tree of a sentence and then returns a corresponding AMR skeleton structure. We manually designed a dictionary of dependency patterns and the corresponding AMR skeletons for the types of complex sentence constructions that appear in the AMR corpus. A disambiguation step is necessary for certain types of constructions with semantically ambiguous subordinators. We show that the disambiguation can be formulated as sentence-pair classification using the fine-tuning approach of a pretrained BERT model. The classification models were trained on data derived from AMR and Wikipedia corpora, establishing a novel baseline for future research.
Towards a Network of Dynamic Message Signs for Congestion Alerting

Traffic applications such as Google Traffic and Waze have been introduced to let users know about existing congestions on real time. However, this cannot help drivers who are not using these applications or not connected to Internet. Besides, it also suggests that drivers can interact with their smart phones while driving, which is illegal in most countries. The idea of this paper is to use dynamic road signs which can collect real-time data from traffic applications and alert drivers who are heading towards congestions. A proof-of-concept of the dynamic road sign has been developed.
Research on trust issues in cloud computing

Although cloud computing has some eminent advantages such as low cost, resource elasticity, yet the highly dynamic, distributed and opaque nature of cloud services makes it a meaningful challenge to establish and manage trust between cloud service providers and consumers. Based on this background, this paper first describes the common concepts and classifications of trust issues and trust models, then generalize trust issues in cloud computing based on the research of existing work, next enumerates typical trust evaluation models from several different perspectives in cloud computing. Finally, conclusions including several potential trends are proposed for the future work.