Reviewerly/exHarmony huggingface dataset:
Deep Learning Classification to Improve Diagnosis of Cervical Cancer Through Swarm Intelligence-Based Feature Selection Approach

Cervical cancer is one of the predominant cancers that cause death in women globally. This disease progresses slowly and is a curable cancer, if detected well in advance. Various studies on different data mining models used for diagnosing this disease have been carried out. Different approaches including support vector machine and recursive feature elimination (SVM-RFE) and support vector machine and principal component analysis (SVM-PCA) had been designed for cervical cancer diagnosis. However, these pose various challenges including low accuracy and high processing time for classification. The proposed system addresses these issues by designing a long short-term memory with artificial bee colony (LSTM-ABC) algorithm for cervical cancer detection. The study takes the cervical cancer dataset as an input and uses synthetic minority oversampling technique (SMOTE) for solving the class imbalance issue. From the preprocessed data, the feature selection is performed using artificial bee colony (ABC) algorithm. Long short-term memory (LSTM) scheme is then employed for classifying cervical cancer based on the selected features. Experimental outcomes demonstrate that the proposed system delivers superior results than the previous works with respect to sensitivity, accuracy, and specificity.
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning

Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). One of the approaches to encourage more systematic and efficient exploration relies on surprise as an intrinsic reward for the agent. We introduce a new definition of surprise and its RL implementation named variational assorted surprise exploration (VASE). VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent's model and policy. Our experiments show that in continuous control sparse reward environments, VASE outperforms other surprise-based exploration techniques.
Fano discrete-continuum interactions in broadband parametric downconversion

We identify broadband parametric downconversion (PDC) in the few-pump-photon regime as a Fano-type discrete-continuum interaction. We derive analytic expressions for PDC dynamics, revealing quantum phenomena including complete pump depletion and bound states in the continuum.
The ISO/IEC 27001 standard provides a systematic approach to information security management

The company's information security is not only compliance with a set of IT security measures, but also the correct choice of the appropriate standard. Let's look at what standards are aimed at ensuring the information security of the company.
Data-Driven Modeling of a Track-based Stair-Climbing Wheelchair

A stair-climbing wheelchair can notably enhance the autonomy in terms of mobility for the aged and disabled. This paper presents a data-driven system identification approach and a vision-based heading control algorithm for reliable operation of a stair-climbing wheelchair. We develop a track-based wheelchair model and propose a methodology for autonomous stair traversal. Modeling the dynamics of track-based systems is a challenging task. Hence, a data-driven approach based on the Observer Kalman filter Identification/Eigensystem Realization Algorithm is employed for modeling the complex dynamics of the system. For developing an affordable system, low-cost sensors such as Microsoft Kinect and IMU are utilized. We employ a computationally efficient image processing algorithm, and the heading angle is controlled using Linear Quadratic Regulator (LQR). The effectiveness of the proposed methodology for a safe stair traversal is verified in the ROS-Gazebo environment.
Method of Functional-Value Calculations of Complex Systems with Mixed Subsystems Connections

Researched method of functional-value calculations of complex systems with mixed subsystems connections in polynomial approximation of the dependence by their value on the level of functional perfection. The research results allow determining the rational management of the functional perfection of a complex system under the condition of its minimum value and given perfection. A rule has been formulated according to which the value of rationalization of a complex system can be assessed. The order of structural functional-value calculations of a complex system with a mixed subsystems connections based on the method of Lagrange factors and the involvement of an iterative approach in solving equations above the third order. Studies show that the achievement of the set value of the level of perfection should begin by improving the subsystems with a lower value coefficient. Are clarified the approaches to calculations according to the developed algorithms of functional-value modelling of a complex system with mixed subsystems connection. Graphical calculations are useful when you need to visualize the calculation process. The application of the advanced method helps to solve direct and inverse problems of rationalizing the structure of complex systems with mixed subsystems connection. It is aimed at finding the cheapest option for structural and parametric restructuring of the system in order to increase its functional perfection. The use of this method is appropriate for the study of weakly formalized and unformulated complex systems, which is why qualitative analysis of a complex system can be translated into quantitative.
Construction of Human Resource Ontology Model for Knowledge Graph

Human resource is regarded as the most precious strategic resources, and human resource has become the core competitiveness. This paper uses a top-down method to construct the ontology model of the human resource knowledge graph. It is a better choice to first establish the ontology of the human resource knowledge graph, model the entity classes and relationships, and then construct the knowledge graph. Based on the above considerations, this chapter uses OWL language to construct relevant ontology of human resource domain knowledge, and realize the establishment of human resource knowledge graph model.
Efficient maliciously secure two-party mixed-protocol framework for data-driven computation tasks

In the artificial intelligence era, data-driven computation tasks, such as machine learning, have been playing an essential role as the decision-maker to unlock the value of big data in many fields. Moreover, the ultimate goal of pursuing accuracy and efficiency improvement to better promote the application of data-driven computation has never changed. Since the main method to improve the accuracy of these tasks (e.g., the model training of machine learning) is to increase the diversity of datasets, this requires multiple data providers to share their data. However, data providers, e.g., private companies, are reluctant to share their datasets directly, considering the privacy protection of user information and the leakage prevention of their business secrets. Therefore, how to securely and efficiently perform joint datasets based data-driven computation tasks has become the main problem. In this work, without getting the aid of any trusted-third party (e.g., the cloud server), we construct an efficient maliciously secure two-party mixed-protocol framework for data-driven computation tasks. In particular, we construct a new cryptography gadget called committed oblivious linear evaluation (C-OLE) based on the homomorphic commitments in the malicious model. Then we construct two types of share conversion protocols in the malicious model with the above C-OLE gadget to construct the two-party mixed-protocol framework for data-driven computation tasks. Without utilizing the random oracle, our framework can provide a stronger security guarantee than the other two-party mixed-protocol frameworks in the literature. Furthermore, we conscientiously evaluate the theoretical efficiency of the two shares conversion protocols and provide the result as an important reference for future developers who intend to securely and efficiently instantiate the data-driven computation tasks (e.g., privacy-preserving machine learning applications) in the malicious model.
Application of VR Technology in the Construction of MOOC Resources

Abstract In the context of the epoch-making development of science and the continuous improvement of the social system, VR technology has quietly emerged. With the rapid development of the Internet industry, various industries are also adapting to receive the collisions brought by new fields. VR is one example. In recent years, MOOCs have been continuously expanded, and the development of MOOCs has also attracted more and more attention. Through the analysis and discussion of the application status of VR technology, VR technology and MOOC resource construction are combined to discuss the effect of VR technology on MOOC resources. This article discusses and analyzes the effect and influence of VR technology on the cognitive psychology and thinking optimization of the Solfeggio and ear training courses in the MOOC. This paper uses two conservatories as experimental objects to analyze and compare the difference between the effect of using VR technology in the construction of MOOC resources and the original traditional teaching. The results of the experiment show that the average score of group B is 30.8 (standard deviation=4.2 ), is higher than the average of group A by 27.4 (standard deviation=2.5). Therefore, the introduction of VR technology can more effectively improve the cognitive psychology of learners, and it is also helpful to optimize learners’ musical thinking.
Psychological Insights In Anuradha Roy’s The Folded Earth

The present paper deals with the various psychological undercurrents present in Anuradha Roy’s novel The Folded Earth. It endeavors to study the emotional bearings of the characters by exploring the relevant psychological aspects of the novel. Here attempts have been made to gauge the untold facts of Roy’s novel The Folded Earth written on various social and psychological themes. The present paper attempts to discover the reality behind the state of mind of the characters, general human tendencies, compulsive behaviors, psychological blockages and their possible reasons. The study also aims to understand how far the readers may be able to connect themselves with the emotions and feelings of the characters of the novel, so that it can be a helpful catharsis to comprehend some contemporary burning problems of human lives. Effort has also been made to suggest solutions to the problems so that it can be a step towards contributing to society with an aim to lessen human struggles for emotional wellbeing.
Reliable Event Routing in the Cloud and on the Edge

The AgeTech domain poses both opportunities and challenges for the architects of Internet of Things (IoT) solutions. Key requirements concern data privacy and cost-efficiency, as well as guaranteed, interoperable delivery of event messages from resource-constrained edge devices to the cloud and consumer applications; public cloud deployments and asynchronous, queue-based messaging are common building blocks of such solutions. This experience report features the conceptual, pattern-oriented design and open source implementation of a custom-built embeddable event router that natively supports the CloudEvents specification from the Cloud Native Computing Foundation. Our CloudEvents Router introduces standardized event routing targeting the software landscape of the AgeTech startup CARU; this landscape reaches from an embedded microcontroller to cloud-scale services running on Amazon Web Services. The report further analyses the message delivery guarantees given in messaging protocol specifications and their implementations (for instance, MQTT libraries) and presents an alternative delivery management approach, implemented and validated in the open sourced Rust microkernel of the CloudEvents router. The report concludes with lessons learned about messaging edge cases and protocol particularities, in MQTT in particular.
Decision Support System For Computer Recommendations Using Web-Based Fuzzy Tahani Logic Method

Nowadays people are familiar with computers and many use computers to support their needs, but many people do not understand the specifications of computers that suit their needs, so a web-based application is made that provides recommendations for computer specifications using fuzzy logic. This research method uses observation to obtain information about computer components. The results of this study indicate the recommendations for computer components, namely computer, monitors, keyboard, and mouse according to the user's wishes. The conclusion of this study, this website makes it easier for users to choose computer components.
An adaptive spark-based framework for querying large-scale NoSQL and relational databases

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.
Optimal Dispatch of Consumer-Side Distributed Resources Based on IoT and Energy Blockchain

With the popularization and rapid development of distributed generation and energy storage technology, many end users have changed from simple energy consumers to prosumers. However, consumer-side distributed resources, like PV generation and energy storage devices, have limited capacity and are not directly regulated by the electric power network dispatch system. In modern energy internet, these distributed resources are designed to receive dispatch instructions from new operation entities like load aggregators and virtual power plants. In this paper, an optimal dispatch platform of consumer-side distributed resources based on Internet of Things (IoT) and blockchain technology is proposed. Firstly, the operation framework considering participants, optimal operation scheme, data structure, value settlement is designed. Afterward, an energy blockchain structure for consumer-side distributed resources regulation is proposed. Then, the implementation of energy blockchain technology for distributed resources to participate optimal operation of distribution system is introduced. Finally, the availability of the proposed technologies is verified by simulation.
An Proficient Quad Verification Strategy in Cloud Storage

In this investigation, distributed storage plan is recommended to have a four-factor information security assurance instrument with factor revocability. This framework enables a sender to send an encoded message to a collector by means of a distributed storage server. The sender just needs to understand the personality of the collector, however no other data, (for example, the key or authentication of their state). The collector must have two stuff to unscramble the ciphertext. The primary thing put away in the PC is its mystery key. The subsequent thing is a particular person. With no piece, it is difficult to decode the ciphertext. All the more critically, this gadget will be pulled back once the wellbeing gadget is looted or lost. No ciphertext can be decoded. The cloud server can do this, which will quickly play out certain calculations to modify the current ciphertext with the goal that this machine can un-decode it. This strategy is absolutely straightforward to the transmitter. What's more, no ciphertext can be decoded by the cloud server whenever. The appraisal of wellbeing and viability demonstrates that not exclusively is our framework safe yet in addition useful.
Innovation and Practice of Computer Applied Talents Training Mode under School Enterprise Cooperation Education Mode based on Big Data

Abstract The traditional training mode of computer applied talents is not perfect. The course content of the school almost only includes theoretical study. The lack of practical teaching leads to many computer graduates do not meet the requirements of social enterprises [1]. Many school graduates of computer science will not flexibly use the knowledge they have learned. In order to effectively solve this problem, the Ministry of Education decided to adopt the mode of school and enterprise joint education to cultivate computer professional application-oriented talents. The combination of industrial enterprises and education is a key opportunity for school talent cultivation. This paper briefly introduces the traditional university enterprise cooperation of computer personnel training mode and puts forward its main shortcomings. On this basis, this paper puts forward the innovation strategy of computer application-oriented talents training mode under the school enterprise cooperation education mode.
Promoting Cardiovascular Health Using a Recommendation System

Lifestyle habits have a direct influence on people’s health. Regular physical activity, combined with good nutrition, helps to prevent the early onset of diseases such as cardiovascular disease. In fact, a significant number of patients diagnosed with cardiovascular disease is associated with a poor diet and a sedentary routine. However, in today’s busy life, it is not always easy to find the motivation for adopting healthy lifestyle habits. Therefore, the existence of a system for recommending healthy meals and workouts can provide the necessary incentive for a healthier life. In this paper, we describe the implementation of a recommendation system following a case-based reasoning approach supported by specific relational databases and ontologies in the field of nutrition and physical activity. The system creates a plan for daily recommendations adapted to the preferences and restrictions of its users, and evaluates the outcome of the recommendations using indexes that quantify cardiovascular health. The success of the recommendations thus depends on a positive evolution of the index after the end of the proposed plan. This system thus presents a new perspective using case-based reasoning and ontologies to propose a diet and exercise plan, evaluating the success of the recommendations objectively through cardiac indexes.
Wavelet Feature-Based Microcalcification Detection in Mammogram

Breast cancer is one of the most prevalent cancers among women aged above 40 years. Early detection and appropriate treatment extend the survival period of the individual. The mammogram is one of the best-suited and worldwide accepted screening modalities for breast cancer. However, the interpretations of mammogram by radiologists fail at certain times, so this leads to false-positive results. To facilitate detection of microcalcifications of malignant and benign nature and to describe the characteristics of medical image in different scales and solve the problem of automatic image category annotation, wavelet-based multiscale feature extraction and medical image classification using random forest and J48 is discussed. The feature vector is constructed with a set of statistical image features extracted at various scales. The experimental results show that, the Random Forest classification approach with multiscale features achieved higher accuracy than J48 decision tree based medical image classification.
Toward Best Practices for Unstructured Descriptions of Research Data

Abstract Achieving the potential of widespread sharing of open research data requires that sharing data is straightforward, supported, and well‐understood; and that data is discoverable by researchers. Our literature review and environment scan suggest that while substantial effort is dedicated to structured descriptions of research data, unstructured fields are commonly available (title, description) yet poorly understood. There is no clear description of what information should be included, in what level of detail, and in what order. These human‐readable fields, routinely used in indexing and search features and reliably federated, are essential to the research data user experience. We propose a set of high‐level best practices for unstructured description of datasets, to serve as the essential starting point for more granular, discipline‐specific guidance. We based these practices on extensive review of literature on research article abstracts; archival practice; experience in supporting research data management; and grey literature on data documentation. They were iteratively refined based on comments received in a webinar series with researchers, data curators, data repository managers, and librarians in Canada. We demonstrate the need for information research to more closely examine these unstructured fields and provide a foundation for a more detailed conversation.
Disruption and Fusion Learning for Fine-grained Image Recognition

In this paper, we propose a novel "Disruption and Fusion Learning" (DFL) method to improve the accuracy of fine-grained image recognition and acquire "sharp eyes" by training the classification model. As the first step of our method, a shuffled image is acquired by controllable shuffling, that is, replacing the block matrix of the original image in a controllable range. This step destructs global structure information in the image but retain local details, forcing the model network to focus on the distinguishing local areas to get useful local feature information for recognition. At the same time, global feature information is extracted by the original image with the same structure network. Those two steam of feature later are computed by knowledge distillation part, which distills and concentrates the features both from the original image and the shuffled image to obtain effective features that help improve the recognition rate of the model. By conducting experiments on three widely used fine-grained image recognition datasets (CUB-200-2011, Stanford Cars, Stanford Dogs), our method achieves state-of-the-art performance.