Reviewerly/exHarmony huggingface dataset:
Distributed Hybrid Recommendation Algorithm in News Dissemination Innovation System

With the rapid development of Internet technology, network information is becoming more and more abundant, and people have more demands for news dissemination. However, the traditional stand-alone recommendation system cannot meet the diverse requirements of users in a short time, so the distributed recommendation algorithm is gradually evolving. At the same time, there are a lot of repetitiveness, redundancy, and low retrieval efficiency in traditional search engines. How to use these massive available resources to obtain more accurate and accurate information has become a current research hotspot. Therefore, in this case, a hybrid recommendation system came into being. The distributed hybrid recommendation algorithm provides new ideas for the development of the news dissemination innovation system. This paper adopts experimental analysis and data analysis methods, and is intended to combine distributed hybrid recommendation algorithms to explore the development of news dissemination innovation systems, so as to achieve innovations in news dissemination methods and paths. According to the experimental results, the transaction success rate was 99%, 99%, 98%, 96%, and 93%. It can be seen that the transaction success rate of the system is relatively high. When the number of visitors is less than 200, the transaction success rate is close to 100%, which can better meet the needs of users.
Blockchain Application for a Sustainable Supply Chain Management

Today, complex and challenging industrial requirements face challenges related to distribution and production necessities. This changing environment also creates opportunities, which with the introduction of a disruptive technology such as blockchainBlockchain will enhance the emergence of the connected supply chainSupply chain as an essential element in the industrial world. By using blockchain, firms will have faster access to data, such as sales patterns on newly commercialized products or interruptions in the upstream supply, with the guarantee that the data is verifiable, transparent and perfectly traceable. This paper offers an analysis of blockchainBlockchain technology and smart contractsSmart contracts exploring the capabilities and advantages of this technology in making traditional supply chainSupply chain management a new and sustainable digital process.
Using Machine Learning to Model Older Adult Inpatient Trajectories From Electronic Health Records Data

Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients’ hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualisation differentiated patients progressing towards stable ‘discharge-like’ states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM;12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUCROC: MVTS data=0.85; HMM states=0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation.
Image Encryption Algorithm Using 2-Order Bit Compass Coding and Chaotic Mapping

This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. In recent years, various chaotic maps have been widely used for image encryption. If the floating-point number generated by chaotic mapping is applied to image encryption algorithm, it will slow encryption and increase the difficulty of hardware implementation. An innovative pseudo-random integer sequence generator is proposed.In chaotic system, the result of one-iteration is used as the shift value of two binary sequences, and then XOR operation is performed to generate a new binary sequence. Multiple iterations can generate pseudo-random integer sequences. Here integer sequences have been used in scrambling of pixel positions. Meanwhile, this paper demonstrates that there is an inverse operation in the XOR operation of two binary sequences. A new pixel diffusion technique based on bit compass coding is proposed. The key vector of the algorithm comes from the original image and is hidden by image encryption. The efficiency of our proposed method in encrypting a large number of images is evaluated using security analysis and time complexity. The security is tested in entropy, histogram differential attacks, gray value distribution(GDV), correlation coefficient, PSNR, key space, and sensitivity. Furthermore, the achieved results are compared to existing state-of-the-art methods. The
Learning Image Captioning as a Structured Transduction Task

Image captioning is a task typically approached by deep encoder-decoder architectures, where the encoder component works on a flat representation of the image while the decoder considers a sequential representation of natural language sentences. As such, these encoder-decoder architectures implement a simple and very specific form of structured transduction, that is a generalization of a predictive problem where the input data and output predictions might have substantially different structures and topologies. In this paper, we explore a generalization of such an approach by addressing the problem as a general structured transduction problem. In particular, we provide a framework that allows considering input and output information with a tree-structured representation. This allows taking into account the hierarchical nature underlying both images and sentences. To this end, we introduce an approach to generate tree-structured representations from images along with an autoencoder working with this kind of data. We empirically assess our approach on both synthetic and realistic tasks.
Laboratory Information Security Management System Based on K-means Algorithm

The laboratory information management system is a system platform that combines Internet technology, computer technology and database technology for system development and design to form a set of laboratory information management system platforms that conform to the actual work. With the vigorous application and popularization, this type of system has a wide range of applications and has reached a new height. The purpose of this paper is to research laboratory information security management system based on K-means algorithm. For the research laboratory information management system, a new direction is put forward, which is not only the management of laboratory information, but also the analysis of laboratory data. The system test results show that the K-means algorithm is very good for the cluster analysis of the data in the laboratory information system. Among all the clusters, the highest member is cluster 2 with 302 instances. The law of the existence of unqualified sample factors in the laboratory information was recorded as a reference to provide a reliable basis for future management.
Dialogo: A Controlled Portuguese for Developing Agroecological Information Systems

Information systems can be useful tools to understand complex agroecological systems. Farmers and extensionists in the Global South may not have access to such information systems due limited resources, skills and opportunities. End-user development (EUD) has the potential to best suit local needs and conditions. This article summarises and furthers a research and development effort targeting communities as well as agroecological extension professionals and organisations in the Serra da Capivara Territory, Piauí, Brazil. In a retrospective ethnographic study we observed information abundance, top-down IS bias, informational competence and digital infrastructure limitations. A set of requirements was identified, with Portuguese syntax and semantics being crucial for an EUD solution. Based on these requirements a multi-paradigm controlled natural language is specified and described as well as a prototype implementation and evaluation method. This language should provide a language that enables the end-user to develop IS suitable to their needs and conditions.
Methodology

Abstract This chapter introduces the book’s empirical investigation of the role that agricultural growth has played in aggregate economic growth in Ethiopia in the twenty-first century. This chapter describes the main method and data used for empirical investigation. It describes both the data contained in the three Social Accounting Matrices (SAMs) that form the book’s main empirical data source, as well as the methodological proceedings of the Semi-Input-Output multiplier model that the book applies.
THE CONCEPT OF THE SOCIAL FIELD AND ITS HEURISTIC POTENTIAL FOR EXPLAINING THE TRANSFORMATIONS OF HIGHER EDUCATION DURING THE PANDEMIC

The article raises the problem of structural changes in higher education through an appeal to the theoretical framework of the field (P. Bourdieu). Using the example of the practice of class attendance, it is shown how, under the influence of the mass transition to distance learning, the contours of a new field began to form, in which the dominant practices that regulate attendance at classes are faced with the reproduction of alternative practices. It is shown that it was in the distance learning format that this opposition manifested itself most clearly.
Reducing False Detections in Aerial Images by Exploiting the Context Information and Centroid Relationship

Object detection in aerial images is critical for military and civilian applications. Due to its broad imaging range, an aerial image contains more complex scenes than natural images, which may result in increased confusion between targets and the background. To address this issue, we propose a context-aware and centroid-prior-based object detector for the object detection task in aerial images. Unlike existing object detectors, the proposed model investigates the feasibility of reducing false detections from confusing backgrounds by exploiting both semantic and neighbor contexts. Additionally, a new label assignment algorithm is developed based on the centroid prior assumption to mitigate the localization errors introduced by context information. The experimental results demonstrate that the proposed model outperforms six advanced object detectors on the optical and synthetic aperture radar (SAR) image datasets, indicating the proposed model&#x2019;s effectiveness.
Blockchain for intrusion detection systems

Blockchain is a decentralized core architecture that uses cryptographic validation, also known as hashing function, to link blocks with one another. This linking plays a major role in ensuring the security, completeness, reliability, and accuracy of the data being stored and transparency in processing. Blockchain also provides a cryptographically safe and secure technique to obtain verified and immutable records in a chain that is chronologically ordered by discrete timestamps. These days, with almost everything becoming digital, the increasing rates of cyber threats, incidents, and policy violations have become a huge concern for organizations. It is only reasonable to use the vast variety of features that are offered by blockchain to assist intrusion detection systems (IDSs). IDSs are mainly used to monitor networks for malicious activities and report such activities. In this chapter, we will discuss the different IDSs, the current situation of IDSs, and how we can enhance IDSs using blockchain.
Research on the construction of the referral networks of city hospitals and invulnerability in response to major public health emergency

In face of major public health emergencies, how to ensure the orderly and stable operation of city hospitals? Based on the theory and method of complex network, in this paper, we put forward the construction method of city hospital referral networks, analyze the static characteristics of the constructed network, discuss the invulnerability of networks with four attacking modes, and propose two ways (or integrated) to optimize the invulnerability of networks, which are: (i) Identifying and protecting key hospitals that can increase network invulnerability, (ii) Adding hospitals to the network. Taking hospitals of Wuhan as an example and using the proposed construction method for networks, in this paper, a directed-referral network I with 219 major hospitals in Wuhan is constructed. On the basis of network I, 16 mobile cabin hospitals, Huoshenshan hospital and Leishenshan hospital have been added, the referral-hospital network II of Wuhan is achieved. Compared with network I, network II has better referral ability and invulnerability.
Automated Generation of Control Concepts Annotation Rules Using Inductive Logic Programming

Capturing domain knowledge is a time-consuming procedure that usually requires the collaboration of a Subject Matter Expert (SME) and a modeling expert to encode the knowledge. This situation is further exacerbated in some domains and applications. The SME may find it challenging to articulate the domain knowledge as a procedure or a set of rules but may find it easier to classify instance data. In the cyber-physical domain, inferring the implemented mathematical concepts in the source code or a different form of representation, such as the Resource Description Framework (RDF), is difficult for the SME, requiring particular expertise in low-level programming or knowledge in Semantic Web technologies. To facilitate this knowledge elicitation from SMEs, we developed a system that automatically generates classification and annotation rules for control concepts in cyber-physical systems (CPS). Our proposed approach leverages the RDF representation of CPS source code and generates the rules using Inductive Logic Programming and semantic technologies. The resulting rules require a small set of labeled instance data that is provided interactively by the SME through a user interface within our system. The generated rules can be inspected, iterated and manually refined.
A Lazy Desugaring System for Evaluating Programs with Sugars

Extending a programming language with syntactic sugars is common practice in language design. Given a core language, one can define a surface language on top of it with sugars. We propose a lazy desugaring system, which can generate the evaluation sequences of sugar programs in the syntax of the surface language. Specifically, we define an evaluation strategy on a mixed language which combines syntactic sugars with the core language. We formulate two properties, emulation and laziness, and prove that the evaluation strategy produces correct evaluation sequences. Besides, we have implemented a system based on this novel method and demonstrate its usefulness with several examples.
Honvis+: An Exploration and Visual Comparison Tool for Dynamic Higher-Order Networks

Unlike the conventional first-order network (FON), the higher-order network (HON) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for HONs. The existing FON visualization is limited for applications such as developing strategies to control species invasion through global shipping, which is known to exhibit higher-order dependencies.In this paper, we present HONVis+, a visual analytics framework for exploring and comparing dynamic higher-order networks. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual node levels). Users can quickly identify nodes of interest at the global level and specify a first-order node to investigate its higher-order nodes at the individual node level. Finally, a larger-scale impact can be further investigated by exploring HON at the local level. To understand the dynamic behavior of multiple HONs, we enable comparative visualization for each view to compare the HONs at the corresponding level. We demonstrate the effectiveness of our approach using two real-world applications: evaluating maritime control policy using a global shipping network and identifying traffic anomalies using the New York City taxi trips. Domain experts are involved in producing use cases for each application.
Sugeno Integral Extended to Undefined Inputs

In this contribution, we introduce an extension of the Sugeno integral to undefined inputs and outputs. Undefined inputs are encoded by a single dummy value out of the domain as well as the range of the Sugeno integral; moreover, all internal operations of the integral are extended to deal with this encoding.
Toward an Enhanced Learning Through Existing Massive Open Online Courses Platforms

Massive Open Online Courses (MOOCs) attract many actors all over the world, especially those of higher education. So, there is a need to enhance and to tailor the instruction of MOOCs to meet the learner’s needs. This chapter investigates the MOOCs system by reviewing the available literatures and suggesting a framework based on Analytic Hierarchy Process (AHP) algorithm for an adaptive learning through existing MOOCs. In this suggested framework, a variety of aspects including choice of learning path, learner satisfaction, and achievement are considered to present an adequate list of existing MOOCs to a learner. The main aim of this framework is to improve learning effectiveness and ensure learning quality.
A Corpus-Based Semantic Analysis of the Pattern ‘bǎ+NP+VP’ in Mandarin Chinese

Great significance has been ascribed to the pattern “bǎ+NP+VP” due to its high frequency and various usages. Based on a 2.2-million-word corpus, this study aims to answer the following questions: How are lexical items associated with this pattern? How should semantic associations within it be best described? What are the key semantic subcategories and typical usages of this pattern? The major innovation of this study lies in the adoption of the three-level semantic coding system of the New Synonym Dictionary, and the utilization of co-occurrence networks for better visualization and description of language data. The results indicate that the pattern exhibits a restriction on the choice of N(P) and V(P). Moreover, semantic categories of head nouns and verbs are associated with each other, and simultaneously confined by this pattern. Ab (person) and Id (action of upper limbs) constitute the most important semantic subcategories of noun and verb, respectively, in this pattern. Finally, four typical usages of this pattern are summarized through co-occurrence networks.
Natural Language Processing Using Database Context

The usage of natural language in the industry has become more prevalent in recent years. Nowadays it is much easier to operate with complex infrastructures using natural language. This subfield of artificial intelligence is becoming more widespread every year. Feature semantic parsing represents one of the tasks of converting natural language utterances into structured logical parts that can be used as queries to generate responses. This paper introduces an algorithm that transforms natural sentences to obtain structural results. Such a functionality is effective for Question and Answering (Q&A) and allows for spoken language understanding. Obtaining the input as natural language sentence allows many people, even those without technical skills to access information effectively. The generated queries are being executed on specific tables or databases and then the information is retrieved in a comprehensible way. The process of generating queries from natural language sentence implies different operational steps, such as recognition of different types of words, synonym detection, feature grammar parsing, etc.
Extreme Multi-label Classification with Hierarchical Multi-task for Product Attribute Identification

Identification of product attributes (product type, brand, color, gender, etc.) from a query is critically important for e-commerce search systems, especially the identification of brand intent. Recently, Named Entity Recognition (NER) method has been used to address this issue. However, the limitation of NER method is that it can only identify brand intent specified by terms of a query and cannot work appropriately if brand terms are not provided explicitly. To overcome this limitation, we propose a novel Extreme Multi-label based hierarchical Multi-tAsk (EMMA) framework, where we treat the brand identification as an issue of extreme multi-label classification; thereafter, a deep learning model is also developed to jointly learn query's product intent and brand intent in a coarse-to-fine approach. The results from both online A/B test and offline experiment on real industrial dataset demonstrate the effectiveness of our proposed framework. Additionally, this framework may be extended potentially from e-commerce system to other search scenarios.
Deep Learning in Solar Image Generation Tasks

It has been witnessed that deep learning has been applied to classification in previous chapters. In fact, deep learning also demonstrated great ability of image generation which is more challenging than classification. In this chapter, several applications of deep learning in solar image enhancement, reconstruction and processing are presented, including image deconvolution of solar radioheliograph, desaturation of solar imaging, generating magnetogram, image super-resolution. These tasks are all concerned with image generation, by employing generative neural networks. As a representative of generative networks, GAN was widely exploited in image generation tasks. It can generate high fidelity and photo-realistic content mainly owning to an adversarial loss.
A Multicriteria Decision Model for Optimizing Costs and Performances for a Cloud User

Cloud computing is a recent technology in the field of computing. This paradigm is gaining a great place in everyday life by providing services on demand for the benefit of clients. Optimizing the characteristics of cloud providers has become indispensable so combinatorial optimization is a necessary mechanism combining several techniques of discrete mathematics and computer science to solve real-life difficulties. Given the number of cloud providers that are very large and the variety of criteria for choosing them, decision making has become increasingly difficult and requires a robust multicriteria system. In this chapter, we propose a model of a multicriteria decision support system for the optimization of costs and performances in cloud computing. The proposed model uses a modified neural network with two hidden layers: the first one uses the ELECTRE technique and the second a modified Tabu search algorithm; this modification consists of using the weighted sum instead of the objective functions. The input for our modified neural network is cloud performance.