Reviewerly/exHarmony huggingface dataset:
RDFAdaptor: Efficient ETL Plugins for RDF Data Process

Abstract Purpose The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency. Design/methodology/approach The RDFAdaptor is designed based on the prominent ETL tool—Pentaho Data Integration—which provides a user-friendly and intuitive interface and allows connect to various data sources and formats, and reuses the Java framework RDF4J as middleware that realizes access to data repositories, SPARQL endpoints and all leading RDF database solutions with SPARQL 1.1 support. It can support effortless services with various configuration templates in multi-scenario applications, and help extend data process tasks in other services or tools to complement missing functions. Findings The proposed comprehensive RDF ETL solution—RDFAdaptor—provides an easy-to-use and intuitive interface, supports data integration and federation over multi-source heterogeneous repositories or endpoints, as well as manage linked data in hybrid storage mode. Research limitations The plugin set can support several application scenarios of RDF data process, but error detection/check and interaction with other graph repositories remain to be improved. Practical implications The plugin set can provide user interface and configuration templates which enable its usability in various applications of RDF data generation, multi-format data conversion, remote RDF data migration, and RDF graph update in semantic query process. Originality/value This is the first attempt to develop components instead of systems that can include extract, consolidate, and store RDF data on the basis of an ecologically mature data warehousing environment.
Hybrid AI: The Way Forward in AI by Developing Four Dimensions

In recent years, AI based on deep learning has achieved tremendous success in specialized tasks such as speech recognition, machine translation, and the detection of tumors in medical images. Despite these successes there are also some clear signs of the limitations of the current state-of-the-art in AI. For example, biases in AI-enabled face recognition and predictive policing have shown that prejudice in AI systems is a real problem that must be solved. In this position paper, we argue that current AI needs to be developed along four dimensions to become more generally applicable and trustworthy: environment, purpose, collaboration, and governance. Hybrid AI offers the potential for advancements along these four dimensions by combining two different paradigms in AI: knowledge-based reasoning and optimization, and data-driven machine learning.
Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets

The ability to detect outliers is crucial in data mining, with widespread usage in many fields, including fraud detection, malicious behavior monitoring, health diagnosis, etc. With the tremendous volume of data becoming more distributed than ever, global outlier detection for a group of distributed datasets is particularly desirable. In this work, we propose PIF (Privacy-preserving Isolation Forest), which can detect outliers for multiple distributed data providers with high efficiency and accuracy while giving certain security guarantees. To achieve the goal, PIF makes an innovative improvement to the traditional iForest algorithm, enabling it in distributed environments. With a series of carefully-designed algorithms, each participating party collaborates to build an ensemble of isolation trees efficiently without disclosing sensitive information of data. Besides, to deal with complicated real-world scenarios where different kinds of partitioned data are involved, we propose a comprehensive schema that can work for both horizontally and vertically partitioned data models. We have implemented our method and evaluated it with extensive experiments. It is demonstrated that PIF can achieve comparable AUC to existing iForest on average and maintains a linear time complexity without privacy violation.
FOFO: Fused Oversampling Framework by addressing Outliers

The challenges regarding data distribution are proved to be fatal for any intelligent model. The imbalanced distribution among data is acknowledged to be a popular problem in real-life datasets. The disproportion in data distribution is often associated with the presence of outliers in the dataset. However, Machine Learning models have not been tested properly with synthetic sampling in presence of irregular data. In our paper, we propose a data pre-processing framework that follows a selective approach. A 2-step oversampling is done on the outliers and the entire dataset is done by effectively utilizing the Synthetic Minority Oversampling Technique (SMOTE) to mitigate data biases and the effects of outliers. Our model was tested with 6 robust machine learning models and on 3 real-life datasets. We further compared our framework with existing oversampling techniques. It is found that the proposed approach is has improved the performance of each of the classifiers.
Exploring IoT-Enabled Multi-Hazard Warning System for Disaster-Prone Areas

Natural disaster in India has become a great challenge in the recent years. Each year the rates have been escalating affecting both the social as well as economic progress of the country. India’s topographic/climatic and socio-economic features make the country most vulnerable to the devastating effects of such calamities. Hence, it is the need of the hour to come with a system capable of long term as well as quick prediction of disaster. This can be useful for early preparedness and developing well-planned mitigation/relief system which can reduce the effects of such disaster and can be also useful in channelizing the finding in a right way during calamities. The proposed system consists of modules for prediction of: Weather pattern, flood, earthquake, landslide, fire and gas leakage. The sensor node deployed at various disaster-prone areas transmits sensor data to a local aggregator that pre-process the data and relays to remote monitoring servers. The remote monitoring platform has algorithms for prediction of disaster as well as suggestions for quick response. Hence, the possibility of disaster can be predicted prior to the onset of these calamities.
Cross-issue correlation based opinion prediction in cyber argumentation

One of the challenging problems in large scale cyber-argumentation platforms is that users often engage and focus only on a few issues and leave other issues under-discussed and under-acknowledged. This kind of non-uniform participation obstructs the argumentation analysis models to retrieve collective intelligence from the underlying discussion. To resolve this problem, we developed an innovative opinion prediction model for a multi-issue cyber-argumentation environment. Our model predicts users’ opinions on the non-participated issues from similar users’ opinions on related issues using intelligent argumentation techniques and a collaborative filtering method. Based on our detailed experimental results on an empirical dataset collected using our cyber-argumentation platform, our model is 21.7% more accurate, handles data sparsity better than other popular opinion prediction methods. Our model can also predict opinions on multiple issues simultaneously with reasonable accuracy. Contrary to existing opinion prediction models, which only predict whether a user agrees on an issue, our model predicts how much a user agrees on the issue. To our knowledge, this is the first research to attempt multi-issue opinion prediction with the partial agreement in the cyber-argumentation platform. With additional data on non-participated issues, our opinion prediction model can help the collective intelligence analysis models to analyze social phenomena more effectively and accurately in the cyber argumentation platform.
ToP: Time-dependent Zone-enhanced Points-of-interest Embedding-based Explainable Recommender system

Points-of-interest (POIs) recommendation plays a vital role by introducing unexplored POIs to consumers and has drawn extensive attention from both academia and industry. Existing POI recommender systems usually learn latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatiotemporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers complicated check-in actions. In this paper, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe POIs dynamic function. Besides, check-in actions to a POI is also affected by the zone it belongs to. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance the accuracy of POI recommendations. Along this line, we propose a Time-dependent Zone-enhanced POI embedding model (ToP), a recommender system that integrates knowledge graph and topic model to introduce the spatiotemporal effects into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in different time to capture its dynamic functions. Jointly combining these vectors with zones representations, ToP enhances the spatiotemporal interpretability of POI recommendations. With this hybrid architecture, some existing POI recommender systems can be treated as special cases of ToP. Extensive experiments on real-world Changchun city datasets demonstrate that ToP not only achieves state-of-the-art performance in terms of common metrics, but also provides more insights for consumers POI check-in actions.
Agile Latency Estimation for a Real-time Service-oriented Software Architecture

This paper presents our testbed and software pipeline for automatic latency estimation for a service-oriented software architecture (SOA). This type of architecture consists of modular services that are dynamically combined at runtime to form a functioning system. As different service combinations become possible at runtime, agile approaches for testing the resulting systems become necessary. Besides other factors, latencies are of particular interest for the implementation of control systems. Our agile approach automatically generates dummy services, including interfaces and tasks for internal processing, based on a service description in a human-readable format. Services are then automatically distributed to the computers of our testbed, which are connected through Ethernet. We empirically obtain latency estimates for processing and communication steps for a given composition of services. In this paper we describe our data format, abstractions about internal run-time behavior of services and the code generation pipeline. The evaluation presents latency estimates that we are able to obtain through our testbed that resembles the sense-plan-act paradigm.
Research on Text Classification and Data Analysis of Complaints by Shanghai Gas Company

Since 2018, Shanghai Gas Company has entered customers' complaints by 12345 hotline into the company's platform system. This article uses customers' complaints from 2018 to 2020 for data analysis. Thee complaint text classification based on the improved naive Bayes algorithm to solve the three major points: in the process of manually classifying the complaint text, there are defects and errors caused by human negligence, this is accuracy problem. Due to the large amount of complaint data, manual classification of text data requires a lot of time and effort, this is efficiency problem. The text classification and analysis of the complaint text by human is not deep enough to accurately and quickly locate the hot issues in user complaints, this is depth problem. In view of the above three problems, an improved naive Bayesian algorithm is proposed, this algorithm combines n-gram algorithm and naive Bayes algorithm to realize the classification of complaint texts and solves the disadvantage of naive Bayes not considering word order.
Road Object Detection in Bangladesh using Faster R-CNN: A Deep Learning Approach

The importance of object detection in our lives is increasing day by day. The role of object detection is very important in autonomous cars, intelligent driving assistance, and advanced traffic analysis. In the case of traffic analysis and intelligent driving assistance in Bangladesh, it is very important to properly identify all the objects from real-time video. Because in both cases the main responsibility of the system is to give the driver or authority a clear idea about the road or the environment around the vehicle. And for this, we need to use modern algorithms and architecture based neural network models with much better object detection accuracy such as Faster R-CNN. There are currently a couple of algorithms that work faster than Faster R-CNN but cannot detect objects accurately, as is the case with small-to-medium objects. We used Faster R-CNN on our data to analyze the environment around the road and the environment around the car. We trained the network for 19 object classes and tested its ability to detect objects with real-time video analysis with an accuracy of 86.42%. Moreover, FPR(false positive rate) and FNR(false negative rate) is calculated to evaluate the proposed model from confusion matrices. In this study, the FPR of the Faster R-CNN model is 15.97% and the FNR of the Faster R-CNN model is 12.2%.
Assessment Study For E-Learning Using Bayesian Network

E-Learning for educational institutions has created a challenging situation due to the COVID-19 pandemic. The universities and institutions can impart knowledge. However, evaluation of the learner&#x2019;s learning and outcomes remained a challenge for them. The article aims to add its dimension towards the evaluation of outcomes especially for the learners of E-learning platforms. E-Learning delivers the training using the online mode of knowledge dissemination. E-learning has a wide range of resources that may improve the learning assessment. Stil,l assessment in education remains a challenge. E-Learning for learner&#x2019;s knowledge is extremely significant because the beginner does not realize learning assessment properly. To improve the learner performance of an evaluation system for classified learners, the Bayesian Network (BN) for a random process is used. A BN is a graphical representation of the probabilistic relationships of a complex system. BN is the most challenging task in the e-learning framework. The goal of this work is achieved by constructing a BN to make causal analysis and then provide personalized interventions for different learners to improve learning. This network was effectively implemented with the accuracy of 45&#x0025; of a slow learner, 15&#x0025; of the average learner, and 40&#x0025; of excellent learners from the required quantitative data set. Further the results of the experiments are promising.
Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction

Plant roots are the main conduit to its interaction with the physical and biological environment. A 3D root system architecture can provide fundamental and applied knowledge of a plant’s ability to thrive, but the construction of 3D structures for thin and complicated plant roots is challenging. Existing methods such as structure-from-motion and shape-from-silhouette require multiple images, as input, under a complicated optimization process, which is usually not convenient in fieldwork. Little effort has been put into investigating the applications of deep neural network methods to reconstruct thin objects, like plant root systems, from a single image. We propose an unsupervised learning scheme to estimate the root depth from only one image as input, which is further applied to reconstruct the complete root system. The boundaries of the reconstructed object usually contain large errors, which is a significant problem for roots with many thin branches. To reduce reconstruction errors, we integrate a cross-view GAN-based network into the reconstruction process, which predicts the root image from a different perspective. Based on the predicted view, we reconstruct the root system using stereo reconstruction, which helps to identify the accurately reconstructed points by enforcing their consistency. The results on both the real plant root dataset and the synthetic dataset demonstrate the effectiveness of the proposed algorithm compared with state-of-the-art single image 3D reconstruction models on plant roots.
Deep-Sea Biological Image Augmentation: A Generative Adversarial Networks-Based Application

With the recent progress of convolutional neural networks in image processing and pattern recognition, deep learning has become the mainstream method of object recognition in images. Typically, A model is trained prior to inference and the training process requires many training samples with sufficient randomness. However, the detection and recognition of deep-sea objects for deep-sea video image data has always been limited by the size of the training samples in existing databases, current deep-sea image samples are not sufficient for supporting training deep neural networks for recognition purposes. With the current increasing usage of generating adversarial networks (GANs), this report introduces a GAN-based application to generate deep-sea image samples, and the improvements of training deep neural network models using augmented training samples.
Advantages of Multimedia Network Teaching in Ice and Snow Sports Education in Higher Vocational Colleges

Abstract With the continuous development of artificial intelligence technology, more and more fields in China gradually develop with new technology. The education industry is also constantly pursuing innovation, among which more and more colleges and universities gradually use multimedia network teaching. The purpose of this study is to analyze the advantages of multimedia network teaching in ice and snow sports teaching in higher vocational colleges. This study selects the ice and snow sports course teaching of some universities in Heilongjiang Province as the research object, and divides it into different experimental groups through different teaching methods. Finally, it makes statistics on students’ achievements and teaching feedback, and reflects the role and influence of multimedia network teaching combined with the standard of t test. The results show that the popularity rate of online teaching has also increased from 8.5% in 2012 to 42.1% in 2019. And 87.9% of teachers and students support and are satisfied with the content and method of multimedia teaching. It is concluded that the multimedia network teaching discussed in this research has played a good role in the teaching of ice and snow sports, and has high efficiency, which has contributed to the development of smart devices in the education field.
An Introduction to Blockchain Technology and Their Applications in the Actuality with a View of Its Security Aspects

Blockchain, in this innovative context of digital transformation, is nothing more than a chain of digital blocks, in which each block has a specific data and a code that connects to the previous block, and consequently, a chain is created that protects the information. The relationship between Blockchain and information security comes from the decentralization to store information with greater security, still considering the ability to enable the secure tracking of transactions, offering transparent access to information. Still evaluating the aspect of cybersecurity as one of the advantages of Blockchain, after all enabling the tracking of information, it also allows for a more secure decentralization of data storage than other known technologies. Still reflecting that in addition to authenticity, it is possible to optimize the signing of online contracts, thus preventing fraud in the registration of contracts from being made and generating losses. Guaranteeing the basic properties of Confidentiality, Integrity, and Availability (CIA) and concerning natural protection with blockchain distributed digital ledgers (once in the ledger, occurs after mining by users of the blockchain network validators) and consensus rules. Thus, Blockchain is a technology, and it is an efficient alternative to protect critical and sensitive information, capable of encrypting, tracking, and certifying any information, with potential in relation to cybersecurity that allows recording digital transactions. Therefore, this chapter aims to provide an updated overview of Blockchains, showing its relationship as well as approaching its success, with a concise bibliographic background, addressing its applications, and fundamental concepts, synthesizing the potential of technology.
A Comparative Study on the Perception Performance of Handwriting in Korean and English Using Machine Learning

The following topics are dealt with: organisational aspects; learning (artificial intelligence); production engineering computing; security of data; deep learning (artificial intelligence); innovation management; Internet of Things; small-to-medium enterprises; statistical analysis; supply chain management.
Waste Collection &amp; Segregation using Computer Vision and Convolutional Neural Network for Vessels

The global crisis of pollution has influenced our lives adversely. Preparing the most salutary in reducing the catastrophe has its dominant necessity. Due to the expanding population, plastics in our neighborhood water bodies have increased for the past ten years, progressing the obligation to clean it up. This work intends towards the analysis of an automatic garbage collection system for a vessel/boat with improved performance. The focus of this paper is the development of a sorting and classification mechanism/model for the collected garbage by the vessel/boat incorporating the application of convolutional neural network (CNN) and computer vision. Using CNN & computer vision, the garbage features can be extracted and can be classified further into biodegradable and non-biodegradable with a prediction accuracy of more than 90%, which can be further increased by increasing the data set quantity for the model.
Statistical Tests for Replacement Blocks Assessment

The basic operations of the round function of any block cipher are substitution and permutation. The article discusses approaches to the implementation of the replacement operation. The main cryptographic primitive, namely the block of substitutions or S-block, and its variations, such as, the R-block, are considered. Requirements for a high-quality substitution block are formulated. The approaches to the assessment of replacement blocks are analyzed. Methods to improve their efficiency are proposed. Forecasts for the development of replacement blocks are made.