Reviewerly/exHarmony huggingface dataset:
Characterization of Self-Efficacy Model Using Social Cognitive Theory for Students’ Learning Abilities and Decision-Making Based on IoT

Aiming at the problem that the career decision-making self-efficacy model cannot accurately mine the implicit relationship between students and career decision-making, resulting in the low structural validity and reliability of the model, a career decision-making self-efficacy model of flight attendant students under social cognitive theory is proposed. This paper analyzes the characteristics of self-efficacy under the social cognitive theory, reduces the dimension of student career decision-making score matrix into two matrices: student implicit feature and implicit feature career decision-making, and excavates the implicit relationship between flight attendant students and career decision-making. Students are described as a multidimensional decision vector to determine the professional cognitive level of flight attendant students. The self-efficacy model of students’ career decision-making is established from five aspects: self-evaluation, collecting information, selecting goals, making plans, and solving problems, which is solved by the particle swarm optimization algorithm. The experimental results show that the structural validity and reliability of the model constructed in this paper are higher than those based on cluster analysis and user portrait. Therefore, it can effectively evaluate and measure the career decision-making self-efficacy of flight attendant students.
Data Hiding in Binary Images for Secret and Secure Communication Using Decision Tree

In today’s digital era, secret communication through digital media has become difficult due to advances in technology. Many methods are used to maintain secrecy like password protection, cryptography, etc. The drawback of applying these techniques is that it alerts about the presence of secret information within media. So information hiding in the multimedia medium is the most powerful mean for secret communication. Binary images are frequently transferred and so can be utilized as a carrier for hiding secret information. But hiding in binary images is challenging as changes caused due to hiding are very easily noticeable and hence limit hiding capacity. Image distortion, information hiding capacity, security, and imperceptibility are the common factors to evaluate the performance of any data hiding techniques. In existing techniques, either the data hiding capacity or visual distortion has been taken care of. This paper proposes a novel approach for hiding data in binary images which focuses on minimization in visual distortion providing good data hiding capacity and security to hidden data. To achieve this, the original image pattern is utilized in the best possible way and the message is hidden distributing over the image in the encrypted form. In this technique, the image and secret message are divided into parts where image partitions are further divided into 3 × 3 pixel blocks. These image partitions and secret message parts are provided to the decision tree for deciding the best suitable image partition for hiding secret message parts. The decision tree takes decisions based on the type of match of LRUD pattern of 3 × 3 pixel block and the message part pattern to be hidden. The receiver can retrieve the data without the original image with the help of indicator blocks which are prepared at the time of embedding. The results show that the proposed approach provides good hiding capacity minimizing distortion caused due to data hiding.
PEINet: Joint Prompt and Evidence Inference Network via Language Family Policy for Zero-Shot Multilingual Fact Checking

Zero-shot multilingual fact-checking, which aims to discover and infer subtle clues from the retrieved relevant evidence to verify the given claim in cross-language and cross-domain scenarios, is crucial for optimizing a free, trusted, wholesome global network environment. Previous works have made enlightening and practical explorations in claim verification, while the zero-shot multilingual task faces new challenging gap issues: neglecting authenticity-dependent learning between multilingual claims, lacking heuristic checking, and a bottleneck of insufficient evidence. To alleviate these gaps, a novel Joint Prompt and Evidence Inference Network (PEINet) is proposed to verify the multilingual claim according to the human fact-checking cognitive paradigm. In detail, firstly, we leverage the language family encoding mechanism to strengthen knowledge transfer among multi-language claims. Then, the prompt turning module is designed to infer the falsity of the fact, and further, sufficient fine-grained evidence is extracted and aggregated based on a recursive graph attention network to verify the claim again. Finally, we build a unified inference framework via multi-task learning for final fact verification. The newly achieved state-of-the-art performance on the released challenging benchmark dataset that includes not only an out-of-domain test, but also a zero-shot test, proves the effectiveness of our framework, and further analysis demonstrates the superiority of our PEINet in multilingual claim verification and inference, especially in the zero-shot scenario.
The Lossless Adaptive Binomial Data Compression Method

In this paper, we propose a new method for the binomial adaptive compression of binary sequences of finite length without loss of information. The advantage of the proposed binomial adaptive compression method compared with the binomial compression method previously developed by the authors is an increase in the compression rate. This speed is accompanied in the method by the appearance of a new quality—noise immunity of compression. The novelty of the proposed method, which makes it possible to achieve these positive results, is manifested in the adaptation of the compression ratio of compressible sequences to the required time, which is carried out by dividing the initial set of binary sequences into compressible and incompressible sequences. The method is based on the theorem proved by the authors on the decomposition of a stationary Bernoulli source of information into the combinatorial and probabilistic source. The last of them is the source of the number of units. It acquires an entropy close to zero and practically does not affect the compression ratio at considerable lengths of binary sequences. Therefore, for the proposed compression method, a combinatorial source generating equiprobable sequences is paramount since it does not require a set of statistical data and is implemented by numerical coding methods. As one of these methods, we choose a technique that uses binomial numbers based on the developed binomial number system. The corresponding compression procedure consists of three steps. The first is the transformation of the compressible sequence into an equilibrium combination, the second is its transformation into a binomial number, and the third is the transformation of a binomial number into a binary number. The restoration of the compressed sequence occurs in reverse order. In terms of the degree of compression and universalization, the method is similar to statistical methods of compression. The proposed method is convenient for hardware implementation using noise-immune binomial circuits. It also enables a potential opportunity to build effective systems for protecting information from unauthorized access.
Computational Linguistics Based Emotion Detection and Classification Model on Social Networking Data

Computational linguistics (CL) is the application of computer science for analysing and comprehending written and spoken languages. Recently, emotion classification and sentiment analysis (SA) are the two techniques that are mostly utilized in the Natural Language Processing (NLP) field. Emotion analysis refers to the task of recognizing the attitude against a topic or target. The attitude may be polarity (negative or positive) or an emotional state such as sadness, joy, or anger. Therefore, classifying posts and opinion mining manually is a difficult task. Data subjectivity has made this issue an open problem in the domain. Therefore, this article develops a computational linguistics-based emotion detection and a classification model on social networking data (CLBEDC-SND) technique. The presented CLBEDC-SND technique investigates the recognition and classification of emotions in social networking data. To attain this, the presented CLBEDC-SND model performs different stages of data pre-processing to make it compatible for further processing. In addition, the CLBEDC-SND model undergoes vectorization and sentiment scoring process using fuzzy approach. For emotion classification, the presented CLBEDC-SND model employs extreme learning machine (ELM). Finally, the parameters of the ELM model are optimally modified by the use of the shuffled frog leaping optimization (SFLO) algorithm. The performance validation of the CLBEDC-SND model is tested using benchmark datasets. The experimental results demonstrate the better performance of the CLBEDC-SND model over other models.
Emotion Analysis Based on Neural Network under the Big Data Environment

Aiming at the problems of poor emotional tendency prediction effect and low utilization of syntactic information, this study proposes a big data sentiment analysis method based on neural network. First, the BERT model is used to vectorize the input data to reduce the semantic loss when the data is vectorized; then the word vector is input into the bidirectional LSTM encoder to obtain data features. Finally, the representation of the attention layer is used as the final feature vector for sentiment classification, reducing the influence of irrelevant data. The experimental results show that the method has high accuracy, recall, and F1 value and can effectively improve the accuracy of fine-grained sentiment classification of ambiguous texts.
Composable partial multiparty session types for open systems

Abstract Session types are a well-established framework for the specification of interactions between components of a distributed systems. An important issue is how to determine the type for an open system, i.e., obtained by assembling subcomponents, some of which could be missing. To this end, we introduce partial sessions and partial (multiparty) session types . Partial sessions can be composed , and the type of the resulting system is derived from those of its components without knowing any suitable global type nor the types of missing parts. To deal with this incomplete information, partial session types represent the subjective views of the interactions from participants’ perspectives; when sessions are composed, different partial views can be merged if compatible, yielding a unified view of the session. Incompatible types, due to, e.g., miscommunications or deadlocks, are detected at the merging phase. In fact, in this theory the distinction between global and local types vanishes. We apply these types to a process calculus for which we prove subject reduction and progress , so that well-typed systems never violate the prescribed constraints. In particular, we introduce a generalization of the progress property, in order to accommodate the case when a partial session cannot progress not due to a deadlock, but because some participants are still missing. Therefore, partial session types support the development of systems by incremental assembling of components.
Computational Intelligence and Blockchain-Based Security for Wireless Sensor Networks

A wireless sensor network (WSN) provides the base architecture to all popular technologies like internet of things (IoT), unmanned arial vehicle (UAV), etc. Recently, a push came to make the information available to humans from the real-time environmental data collected through small sensing devices. WSN is self-organized wireless ad hoc networks to facilitate the interaction between the human and physical worlds. Rapid growth in sensing devices connected to the internet with intelligence and capabilities also opens the door because more devices connected devices means more chances of security vulnerabilities. Blockchain (BC) technology is introduced to address authentication and other security-related challanges by eliminating the role of central authority. This chapter starts with unique characteristics and security challenges in WSN and further identified different ways to apply blockchain with its potential benefits. The chapter presented the integration of blockchain in CI-enabled WSN with respect to focused sectors.
Expressing Hilbert and Riesz transforms in terms of wavelet transforms

An alternative wavelet inversion formula was considered by Lebedeva and Postnikov in 2014. The main aim of the paper is to give a multidimensional version of their formula; the Hilbert and Riesz transforms of functions are expressed in terms of the wavelet transforms.
An optimal bilevel optimization model for the generalized total variation and anisotropic tensor parameters selection

• This paper introduces a bilevel problem for identifying parameters in generalized total variation and anisotropic tensor. • The existence of the solution for the bilevel model has been proved. • A rigorous primal-dual algorithm is applied to the optimization problem to identify the desired parameters. • Representative experiments have been carried-out and many comparison have been checked to show the efficiency of the proposed model. This paper investigates a novel variational optimization model for image denoising. Within this work, a bilevel optimization technique with a suitable mathematical background is proposed to detect automatically three crucial parameters: α 0 , α 1 and θ . The parameters α 0 , α 1 control the Total Generalized Variation (TGV) regularization while the parameter θ is related to the anisotropic diffusive tensor. A proper selection of these parameters represents a challenging task. Since these parameters are always related to a better approximation of the image gradient and texture, their computation plays a major role in preserving the image features. Analytically, we include results on the approximation of these parameters as well as the resolution of the encountered bilevel problem in a suitable framework. In addition, to resolve the PDE-constrained minimization problem, a modified primal-dual algorithm is proposed. Finally, numerical results are provided to remove noise and simultaneously keep safe fine details and important features with numerous comparisons to show the performance of the proposed approach.
Self-supervised learning mechanism for identification of eyelid malignant melanoma in pathologic slides with limited annotation

The lack of finely annotated pathologic data has limited the application of deep learning systems (DLS) to the automated interpretation of pathologic slides. Therefore, this study develops a robust self-supervised learning (SSL) pathology diagnostic system to automatically detect malignant melanoma (MM) in the eyelid with limited annotation.Development of a self-supervised diagnosis pipeline based on a public dataset, then refined and tested on a private, real-world clinical dataset.A. Patchcamelyon (PCam)-a publicly accessible dataset for the classification task of patch-level histopathologic images. B. The Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) dataset - 524,307 patches (small sections cut from pathologic slide images) from 192 H&E-stained whole-slide-images (WSIs); only 72 WSIs were labeled by pathologists.Patchcamelyon was used to select a convolutional neural network (CNN) as the backbone for our SSL-based model. This model was further developed in the ZJU-2 dataset for patch-level classification with both labeled and unlabeled images to test its diagnosis ability. Then the algorithm retrieved information based on patch-level prediction to generate WSI-level classification results using random forest. A heatmap was computed for visualizing the decision-making process.The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the algorithm in identifying MM.ResNet50 was selected as the backbone of the SSL-based model using the PCam dataset. This algorithm then achieved an AUC of 0.981 with an accuracy, sensitivity, and specificity of 90.9, 85.2, and 96.3% for the patch-level classification of the ZJU-2 dataset. For WSI-level diagnosis, the AUC, accuracy, sensitivity, and specificity were 0.974, 93.8%, 75.0%, and 100%, separately. For every WSI, a heatmap was generated based on the malignancy probability.Our diagnostic system, which is based on SSL and trained with a dataset of limited annotation, can automatically identify MM in pathologic slides and highlight MM areas in WSIs by a probabilistic heatmap. In addition, this labor-saving and cost-efficient model has the potential to be refined to help diagnose other ophthalmic and non-ophthalmic malignancies.
Soft and hard hybrid balanced clustering with innovative qualitative balancing approach

K-means is a popular clustering method that has consistently failed to produce a balanced cluster structure. While changes in cardinality, variance, and density have arisen due to the importance of balancing in different fields, balancing has never been viewed from a qualitative viewpoint. The current paper takes a new look at cluster balancing by presenting a soft (balance-driven) and hard (balance-constrained) hybrid qualitative balanced clustering (SHHQBC). It starts by identifying and prioritizing key features using a random forest algorithm and the mean decrease in the Gini coefficient criterion. It then uses a weighted linear combination of features with the importance of above 25%, 50%, and 75% to construct a feature called value criterion. The developed clustering approach is then implemented to establish clusters with the highest value with the least cardinality or a value similar to other clusters. By implementing the SHHQBC on 14 different datasets, first soft clustering is implemented for all three cases and balanced conditions are checked. Hard clustering is then performed to make balanced conditions. Finally, the best-balanced case with the least objective function is selected. Formulating the value criterion facilitates the interpretation and labeling of clusters, and quantitative clustering criteria are improved.
Hypergraph Transformer Neural Networks

Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are often referred to as heterogeneous information networks (HINs). Previous GNNs have laboriously modeled heterogeneous graph networks with pairwise relations, in which the semantic information representation for learning is incomplete and severely hinders node embedded learning. Therefore, the conventional graph structure cannot satisfy the demand for information discovery in HINs. In this article, we propose an end-to-end hypergraph transformer neural network (HGTN) that exploits the communication abilities between different types of nodes and hyperedges to learn higher-order relations and discover semantic information. Specifically, attention mechanisms weigh the importance of semantic information hidden in original HINs to generate useful meta-paths. Meanwhile, our method develops a multi-scale attention module to aggregate node embeddings in higher-order neighborhoods. We evaluate the proposed model with node classification tasks on six datasets: DBLP, ACM, IBDM, Reuters, STUD-BJUT, and Citeseer. Experiments on a large number of benchmarks show the advantages of HGTN.
Wikidata and knowledge graphs in practice: Using semantic SEO to create discoverable, accessible, machine-readable definitions of the people, places, and services in Libraries and Archives

Libraries expand the access and visibility of data and research in support of an informed public. Search engines have limited knowledge of the dynamic nature of libraries - their people, their services, and their resources. The very definition of libraries in online environments is outdated and misleading. This article offers a solution to this metadata problem by redefining libraries for Machine Learning environments and search engines. Two ways to approach this problem include implementing local structured data in a knowledge graph model and “inside-out” definitions in Semantic Web endpoints. MSU Library has found that implementing a “Knowledge Graph” linked data model leads to improved discovery and interpretation by the bots and search engines that index and describe what libraries are, what they do, and their scholarly content. In contrast, LSE Library has found that contributing to Wikidata, a collaborative and global metadata source, can increase understanding of libraries and extend their reach and engagement. This article demonstrates that Wikidata can be used to push out data, the technical details of knowledge graph markup, and the practice of semantic Search Engine Optimization (SEO). It explores how metadata can represent an organization equitably and how this improves the reach of global information communities.
Cloud computing based deduplication using high-performance grade byte check and fuzzy search technique

Background: Data redundancy (DR) and data privacy (DP) is a critical issue that increases storage and security problems in cloud environments. Data de-duplication (DD) is one of the efficient backup storage techniques to reduce DR. The main problem with using cloud computing (CC) is more storage, the cost of deployment and maintenance. Objective: To minimize this problem, High-performance Grade Byte Check and Fuzzy search Techniques (HP-GBC-FST) based DD is proposed in this paper. Methods: The HP-GBC-FST is based on the pre-process of data by comparing their first byte and categorizing the byte based on the first byte. After DD, encryption has been processed on data to improve the data security in the cloud environment and then encrypted data is stored in the cloud. This HP-GBC-FST recognizes DR at the block level, reducing the redundancy of data more effectively. Then, HP-GBC-FST is created to detect and eliminate duplicates, improve security and storage efficiency (SE), reduce DD time and computation cost (CPC) in the DD verification and auditing phase. Result: The experiment has been conducted in an Intel I5 system and 500GB, 1Tb memory space and implemented in the Java programming environment. The results of the experiment reveal that the HP-GBC-FST improved the DD ratio and security by 3.7 and 97%, respectively, and reduced the DD time and CPC by 87% and 84.4%, respectively, over the existing technique. Conclusion: It concluded that the HP-GBC-FST has greater improvement over DD data in the cloud. Finally, the performance analysis of the HP-GBC-FST achieves higher storage, both privacy and security attributes, and incurs minimal CPC, DD time compared with the state he art research.