Reviewerly/exHarmony huggingface dataset:
On Fuzzy Derivation of Ideals of BF-Algebras

The concept of fuzzy derivation of ideals of BF algebra is introduced. In addition, fuzzy left derivation of ideals of BF-algebras and fuzzy right derivation of ideals of BF- algebra are discussed. We also investigated fuzzy left (right) derivation of Cartesian product of ideals of BF-algebra. We initiated the idea of level cut in fuzzy derivation of ideals of BF-algebra and showed that the intersection of any set of left (right) fuzzy derivations of ideals of BF-algebra is also fuzzy left (right) derivation of ideals of BF-algebra.
Cryptanalysis of an optical cryptosystem with uncertainty quantification in a probabilistic model

In this paper, a modified probabilistic deep learning method is proposed to attack the double random phase encryption by modeling the conditional distribution of plaintext. The well-trained probabilistic model gives both predictions of plaintext and uncertainty quantification, the latter of which is first introduced to optical cryptanalysis. Predictions of the model are close to real plaintexts, showing the success of the proposed model. Uncertainty quantification reveals the level of reliability of each pixel in the prediction of plaintext without ground truth. Subsequent simulation experiments demonstrate that uncertainty quantification can effectively identify poor-quality predictions to avoid the risk of unreliability from deep learning models.
A Framework of Prompting Intelligent System for Academic Advising Using Recommendation System Based on Association Rules

A recommendation or a suggestion system is a branch of information filtering systems that aims to anticipate a user's liking of a specific product. The recommendation system mainly suggests a list of recommendations in an industry, using one of two methods: collaborative filtering or content-based filtering. In this paper, we propose an intelligent recommender system that aims to facilitate the process of academic advising. Our framework is built on a combination of association rules mining and a content-based filtering approach. Apriori algorithm (a well-known algorithm for mining frequent product sets and relevant association rule) is used to extract association rules, depending on the assumption that the space is (student x course), where number of students is greater than 100 and the number courses is less than 55 courses. The extracted rules are used as inference rules to present semantic knowledge to the results, where accuracy is improved. A list of recommended courses is offered after using Jaccard similarity coefficient to measure the similarity between courses.
Edge computing empowered anomaly detection framework with dynamic insertion and deletion schemes on data streams

Abstract Anomaly detection plays a crucial role in many Internet of Things (IoT) applications such as traffic anomaly detection for smart transportation and medical diagnosis for smart healthcare. With the explosion of IoT data, anomaly detection on data streams raises higher requirements for real-time response and strong robustness on large-scale data arriving at the same time and various application fields. However, existing methods are either slow or application-specific. Inspired by the edge computing and generic anomaly detection technique, we propose an isolation forest based framework with dynamic Insertion and Deletion schemes (IDForest), which can incrementally update the forest to detect anomalies on data streams. Besides, IDForest is deployed on edge servers in parallel through packing each tree into a subtask, which facilitates the fast anomaly detection on data streams. Extensive experiments on both synthetic and real-life datasets demonstrate the efficiency and robustness of our framework for anomaly detection.
Classification of Low Quality Underwater Objects Using Convolutional Neural Networks and Transfer Learning

Classification of low quality, degraded underwater images using deep learning based architectures are proposed in this paper. Deep learning based algorithms outperform traditional algorithms due to the vast availability of data. Performance evaluation of the proposed models indicates the transfer learning approach has better accuracy of 86% compared to CNN. Xception is the base model used for transfer learning. Data augmentation and Early stopping mechanisms are also incorporated to get the best performance from the proposed models.
Analysis of Heavy Vehicle Research Status about Knowledge Structure and Research Hotspots Based on CiteSpace

Abstract Heavy vehicle is an important carrier of cargo transportation and is always the research focus of the automotive and environment field. In this paper, the scientific metrological analysis method is used to systematically summarize the research topics and current situation of heavy vehicles. From 2000 to 2019, 1047 articles and 7259 references were investigated. The subject and subject of heavy vehicle research are determined. This paper makes an in-depth study on the phenomenon of citation burst. The research results of this paper not only provide practitioners with a deeper understanding of heavy vehicles, but also help researchers carry out relevant in-depth research.
A Nearest-Neighbour Gaussian Process Spatial Factor Model for Censored, Multi-Depth Geochemical Data

Abstract We investigate the relationships between local environmental variables and the geochemical composition of the Earth in a region spanning over 26,000 km2 in the lower South Island of New Zealand. Part of the Southland–South Otago geochemical baseline survey—a pilot study pre-empting roll-out across the country—the data comprise the measurements of 59 chemical trace elements, each at two depth prescriptions, at several hundred spatial sites. We demonstrate construction of a hierarchical spatial factor model that captures inter-depth dependency; handles imputation of left-censored readings in a statistically principled manner; and exploits sparse approximations to Gaussian processes to deliver inference. The voluminous results provide a novel impression of the underlying processes and are presented graphically via simple web-based applications. These both confirm existing knowledge and provide a basis from which new research hypotheses in geochemistry might be formed.
Design and Implementation of a Data Sharing Model for Improving Blockchain Technology

With the continuous development of technology, the advancement of various data sharing technologies has gradually penetrated into various fields. Thus, data sharing and security are vital to realizing the value of the data. Nevertheless, the original data sharing model is not easily monitored for traces of electronic data use. In addition, the reluctance of data providers to share their data is also a problem. In order to address the issues of traditional centralized data sharing and management in terms of security and control, this research proposes the data sharing model based on blockchain technology, thereby enabling secure access to data. Furthermore, this study evaluates the model in terms of functionality and security. The results show that the blockchain-based data sharing model designed in this paper has great feasibility, security, controllability, and efficiency.
Novel 3D Contextual Interactive Games on a Gamified Virtual Environment Support Cultural Learning Through Collaboration Among Intercultural Students

This study aims to help international students learn the language and cultural knowledge of their future study destination by collaborating with local students through coplaying games in online virtual rooms. Therefore, this study explores whether the 3D interactive game with specific contexts on a virtual platform can support intercultural collaboration and improve the students’ language and cultural learning. This study created novel 3D contextual interactive games (3DCIGs) in a gamified virtual environment (GVE), established on a unique virtual platform named Terf ® . Terf ® enables the observing and recording of data related to the conversations and behaviors of users. To investigate the effects of 3DCIGs on students, a focus group consisting of newly arrived Chinese students and Irish students from an Irish university participated in this study. The study adopted mixed methods of qualitative and quantitative analysis to examine whether 3DCIGs effectively motivate the collaborative learning of intercultural students compared with text-based assignments set in the Game Play Rooms. The findings reveal that the novel 3DCIGs developed in this study have a positive potential to motivate intercultural students to engage in team collaboration and help their cultural and language knowledge exchange.
Information Retrieval from Alternative Data using Zero-Shot Self-Supervised Learning

Traditionally, in the financial services industry, a large amount of financial analysts’ time is spent on knowledge discovery and extraction from different unstructured data sources, such as reports, research notes, SEC filings, earnings call transcripts, news etc. In addition to inefficiency, this manual information retrieval process can be prone to human error, subjectivity, and inconsistency. Recent advances in representation learning provide a reliable platform for mapping a large volume of unstructured data to a high dimensional vector space where similarities and differences between data points can be quantified and used for featurization, pattern recognition and information retrieval. In this work we demonstrate that by properly representing terms, documents and companies in the same informative vector space and applying a simple self-supervised learning framework, relevant companies and documents can be retrieved with a good level of accuracy given the topics of interest, even with no prior labeled data.
Exhibition Area Digitalization Using IoT Sensorios and Application to Smart Tourism Services

Mobile devices in Internet of Things (IoT) can accompany human (or robot) when moving on the area. Many e-tourism platforms exist to collect data on points of interests located in some exhibition area. Artificial Intelligence (Al) provides methods to derive knowledge on recognize human activity and to recognize person’s interests in exhibition area space. In this paper, we propose to apply Al methods to IoT- enabled sensed data in respect to the observed human activity. The research goal is to elaborate possible smart extends to e-tourism platforms. Our case study is the Cycle-Advisor system. We introduce a concept model of a smart tourism recommender system that uses human-accompanying IoT-devices to sense the human activity. From the sensed data person’s interests are recognized and then used for constructing recommendations.
A Classification Model with Cognitive Reasoning Ability

In this paper, we study the classification problem of large data with many features and strong feature dependencies. This type of problem has shortcomings when handled by machine learning models. Therefore, a classification model with cognitive reasoning ability is proposed. The core idea is to use cognitive reasoning mechanism proposed in this paper to solve the classification problem of large structured data with multiple features and strong correlation between features, and then implements cognitive reasoning for features. The model has three parts. The first part proposes a Feature-to-Image algorithm for converting structured data into image data. The algorithm quantifies the dependencies between features, so as to take into account the impact of individual independent features and correlations between features on the prediction results. The second part designs and implements low-level feature extraction of the quantified features using convolutional neural networks. With the relative symmetry of the capsule network, the third part proposes a cognitive reasoning mechanism to implement high-level feature extraction, feature cognitive reasoning, and classification tasks of the data. At the same time, this paper provides the derivation process and algorithm description of cognitive reasoning mechanism. Experiments show that our model is efficient and outperforms comparable models on the category prediction experiment of ADMET properties of five compounds.This work will provide a new way for cognitive computing of intelligent data analysis.
Table2Graph: Transforming Tabular Data to Unified Weighted Graph

Learning useful interactions between input features is crucial for tabular data modeling. Recent efforts start to explicitly model the feature interactions with graph, where each feature is treated as an individual node. However, the existing graph construction methods either heuristically formulate a fixed feature-interaction graph based on specific domain knowledge, or simply apply attention function to compute the pairwise feature similarities for each sample. While the fixed graph may be sub-optimal to downstream tasks, the sample-wise graph construction is time-consuming during model training and inference. To tackle these issues, we propose a framework named Table2Graph to transform the feature interaction modeling to learning a unified graph. Represented as a probability adjacency matrix, the unified graph learns to model the key feature interactions shared by the diverse samples in the tabular data. To well optimize the unified graph, we employ the reinforcement learning policy to capture the key feature interactions stably. A sparsity constraint is also proposed to regularize the learned graph from being overly-sparse/smooth. The experimental results in a variety of real-world applications demonstrate the effectiveness and efficiency of our Table2Graph, in terms of the prediction accuracy and feature interaction detection.
Efficient and provably secure intelligent geometrical method of secret key generation for cryptographic applications

Keys are the essential elements needed in cryptography systems. Both hardware and software solutions are known to produce cryptographic keys. This paper reports a fast software-based approach to generate a private key. The proposed method considers three question–answer pairs decided by the user and email-Id, which is unique for a particular user, as the input. In the first step, a seed is generated, combining a triplet and a unique parameter. Triplet is constructed by representing question–answer pairs as three distinct points and using a harmonic set construction concept. A unique parameter is defined using the triangle formed with this construction and the user’s email-Id. In the next step, taking seed as input, a unique key is generated with the help of cyclic idiosyncratic architecture. This private key generation concept is tested for correctness, randomness, dissimilarity, information entropy, reliability, and resilience against several security threats.
Improving the performance of semantic graph-based keyword extraction and text summarization using fuzzy relations in Hindi Wordnet

In this study, a Fuzzy Semantic Graph-based approach is proposed to extract keywords and generate extractive text summaries from Hindi text documents. Hindi Wordnet is used as a knowledge source to construct the semantic graph. As the semantic relations defined in Hindi Wordnet are crisp, they do not capture the semantic relationship as a matter of degree. Due to that, many terms are represented as not being related, while these can share some meaningful relationship as per real-life scenarios. To overcome this curb of Hindi Wordnet, the paper presents several fuzzy semantic associations between such terms by assigning a value ranging from 0 to 1 to such relations. While constructing the semantic graph to represent documents using Hindi Wordnet semantic relations, the terms sharing fuzzy semantic relations are also added to enhance the quality of the graph. The experiments are done to extract potential keywords and to generate a good content summary. It is observed that such semantics generate a more accurate summary and produce prospective keywords for the document. The performance of the proposed approach fuzzy-based semantic graph is compared to semantic graph-based approach for keyword extraction and text summarization. The keywords extracted and the summary generated by the proposed approach is match up to human extracted keywords and human-generated text summary. The proposed approach results are evaluated using precision, recall, and f-measure. Different outcomes of generated text summaries are evaluated using the ROUGE matrix. The results of the proposed approach are pretty encouraging.
Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events

Abstract Large collections of electronic clinical data today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. It is particularly challenging for extracting useful disease progression patterns from such data because it is longitudinal, incomplete, irregular, and heterogeneous of the patient conditions. In this article, we propose an integrated clinical event prediction model medical concept integrated residual short‐long temporal convolutional networks (SL‐TCN) to address these challenges. Compared to existing models, our model has three‐fold advantages: (1) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence. (2) it learns a continuous‐time progression model from discrete‐time observations with nonequal intervals and high‐dimensional features. (3) We fuse the temporal convolutional network, the long short‐term memory network, and the residual connector so as to capture the local and global dependency of the sequence and make the clinical event predictions more robust. Through extensive experiments on the MIMIC III dataset, we demonstrate that our SL‐TCN achieves higher precision in clinical event prediction and derives some interesting clinical insights.
Multi-Class 3D Object Detection with Single-Class Supervision

While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An alternative approach is to have targeted single-class labels on disjoint data samples. In this paper, we are interested in training a multi-class 3D object detection model, while using these single-class labeled data. We begin by detailing the unique stance of our “Single-Class Supervision” (SCS) setting with respect to related concepts such as partial supervision and semi supervision. Then, based on the case study of training the multi-class version of Range Sparse Net (RSN), we adapt a spectrum of algorithms - from supervised learning to pseudo-labeling - to fully exploit the properties of our SCS setting, and perform extensive ablation studies to identify the most effective algorithm and practice. Empirical experiments on the Waymo Open Dataset show that proper training under SCS can approach or match full supervision training while saving labeling costs.
Optimized Bandwidth Allocation for MEC Server in Blockchain-Enabled IoT Networks

Powered by the development of the fifth-generation mobile communication technology (5G), the Internet of things (IoT) has been widely applied in people’s life. Due to the limitation of storage and computing power, the data transmission of IoT devices faces challenges in terms of security and privacy. Therefore, many researchers provide the conjunction of blockchain and mobile edge computing (MEC) to make up for the lack of computing and security. MEC can meet the storage and computing requirements for IoT devices. Blockchain can provide a decentralized, antitamper solution that can help devices overcome security deficiencies, whereas the speed of blockchain communication is not fast enough because of the consensus mechanism. In this article, we focus on the permissioned blockchain and propose an optimized bandwidth allocation algorithm to promote the performance of consensus communication. The algorithm contains an In-Network control ideology and supports deployment on MEC servers. Deep reinforcement learning (DRL) is employed to perform the computation of available bandwidth in our scheme. We implement a prototype system in the testbed and perform a simulation, and the results show the advantages compared with the current widely used algorithm. By applying our method, the Internet of things devices can transmit data safely and efficiently.
A supervised discriminant data representation: application to pattern classification

Abstract The performance of machine learning and pattern recognition algorithms generally depends on data representation. That is why, much of the current effort in performing machine learning algorithms goes into the design of preprocessing frameworks and data transformations able to support effective machine learning. The method proposed in this work consists of a hybrid linear feature extraction scheme to be used in supervised multi-class classification problems. Inspired by two recent linear discriminant methods: robust sparse linear discriminant analysis (RSLDA) and inter-class sparsity-based discriminative least square regression (ICS_DLSR), we propose a unifying criterion that is able to retain the advantages of these two powerful methods. The resulting transformation relies on sparsity-promoting techniques both to select the features that most accurately represent the data and to preserve the row-sparsity consistency property of samples from the same class. The linear transformation and the orthogonal matrix are estimated using an iterative alternating minimization scheme based on steepest descent gradient method and different initialization schemes. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. According to the experiments conducted on several datasets including faces, objects, and digits, the proposed method was able to outperform competing methods in most cases.
Fuzzy Intelligence in Physical Immersion Teaching System Based on Digital Simulation Technology

In order to improve the effect of physics teaching, this study combines digital simulation technology to construct a physical immersion teaching system to improve the effect of physics teaching in colleges and universities. Moreover, this study transforms abstract physical knowledge into recognizable digital physical images and realizes the idea of multifeature fusion through reasonable feature selection and the use of a classifier algorithm suitable for the subject of this paper. In addition, this study proposes a new algorithm based on the morphological features of geometric images, which combines the transformation detection method of cluster analysis to realize the intelligent processing of images. Finally, this study verifies the effectiveness of the physical immersion teaching system based on fuzzy intelligence and digital simulation technology through experimental research. The results show that the system can effectively improve the effect of physics teaching.