Reviewerly/exHarmony huggingface dataset:
Advanced Spam Detection Using NLP and Deep Learning

Rapidly, advancing technology is a double-edged sword as both friend and foe get access to said technology. Spam has become more prevalent than ever with malicious actors using advanced technology to create extremely convincing spam that can lead to major cybersecurity breaches. It has become imperative that we use advanced techniques to combat the proliferation of spam. The objective of our work is to present a systematic overview of the effectiveness of different machine learning and deep learning models integrated with natural language processing concepts. The paper analyzes the different approaches that can be used to identify spam accurately and identifies the most efficient techniques to achieve it. The discussion utilizes a wide range of datasets from email and SMSs to tweets and implements different algorithms like Naïve Bayes, XGBoost, random forest, and convolutional neural networks among others to perform a comprehensive analysis of the best suited methods to achieve high efficiency in identifying spam. It was observed that deep learning models displayed the highest accuracies for spam detection in SMS and emails, while random forest was the most accurate for detecting spam in tweets.
Data Mining Solutions for Fraud Detection in Credit Card Payments

We describe an experimental approach to design a Fraud Detection system using supervised Machine Learning (ML) methods such as decision trees and random forest. We believe that such an approach allows financial institutions to investigate fraudulent cases efficiently in terms of accuracy and time. When ML methods are applied to imbalance problems such as Fraud Detection, the outcomes of decision models must be accurately calibrated in terms of predicted fraud probabilities. The use of different ML models allows practitioners to minimize the risks and cost of the solutions. We discuss the main results obtained in our experiments on the benchmark problems.
Movement Control Methods for Mobile Devices: An Empirical Study of Displacement Interfaces

We conducted a user study to compare four displacement interfaces on mobile devices. An Android application was developed with four displacement methods: Soft Joystick, up-down-right-left Buttons, Tilt Sensors, and Dual Soft Joystick. The study involved 12 participants using the four interfaces to control an object from a starting point to a goal point in five trials having different paths. The time (s), number of wall hits, efficiency (%), and out-of-path movement (%) per trial were logged to compare the methods. There was a significant effect of Displacement Method and Path on each dependent variable $$(p < .005)$$ . The Button method had 95.5%, 90.4%, and 84.2% less out-of-path movement than the Soft Joystick, Tilt Sensors, and Dual-Soft-Joystick methods, respectively. Also, the number of times the object missed the path using Buttons represented 10% of the total, followed by Dual-Soft-Joystick with 20% of the total. Tilt Sensors was 2.8% faster than Buttons (second fastest) in time per trial. Button reported the best numbers for efficiency with 58.8% on average per trial being 12% more efficient than Soft Joystick as the second best. In addition, there were statistical differences in post hoc tests $$(p < .005)$$ between Button, the best overall method, and the remain interfaces on all dependent variables results. The participants indicated Buttons as the most comfortable method overall and Tilt Sensor as the best and most promising method.
Metaverse Applications for Location-based Virtual Reality

Recently, the metaverse has considerably dominated tech headlines for researchers in the fields of human-computer interaction and human factors and ergonomics around the world, due to the advancement of its software and hardware. With its ability to immerse humans in a virtual, inexpensive, and safe world, it has greatly become popular in recent years, particularly virtual reality. Nevertheless, despite the extremely increasing attention to virtual reality, there is still very limited research work for summarizing and analyzing location-based virtual reality in which virtual reality tools are utilized for assisting humans to experience virtual reality by physically interacting with the environment. This paper proposes a new research study for investigating recent works of location-based virtual reality applications. After that, the evolution of virtual reality using location-based features is explored. Finally, we conduct a comparative analysis of virtual reality and augmented reality works, including location-based works in the past five years.
Anomalies and Breakpoint Detection for a Dataset of Firefighters’ Operations During the COVID-19 Period in France

Firefighters are exposed to many hazards. The main objective of this study is to apply machine learning techniques to tailor the need for firemen operations to their demands. This strategy enables fire departments to organize their resources, which leads to a reduction of human, material and financial requirements. This work focuses on predicting the number of firefighters’ interventions during the sensitive period of the global pandemic COVID-19. Experiments applied to a dataset from 2016 to 2021 provided by the Fire and Rescue Department, SDIS 25, in the region Doubs-France have shown an accurate prediction and revealed the existence of a turning point in August 2020 due to an increase in coronavirus cases in France.
Design of Image Retrieval Algorithm Based on Fine-Grained Digital Devices

A deep feature embedding model framework baseline for feature extraction is proposed to extract the global convolution features of digital devices. At the same time, the strategy of joint training of loss function and other trick techniques are proposed to improve the feature extraction ability of the model.For the model reasoning part, a fine-grained digital device model reasoning method combining post-processing optimization and re-ranking is proposed. For images with more semantic information, post-processing optimization is introduced to complete semantic segmentation through attention map extraction of attention mechanism. K-recursive algorithm is used to optimize the search results.
Design and Research of Virtual Simulation Experiment of Optimal Fire Escape Strategy Based on Internet of Things

With the urgent demand for Internet of Things professionals, colleges and universities are faced with the challenge of training integrated talents of Internet of Things. Combined with the social background of high incidence of fire and difficulty in escape of high-rise buildings, we designed the simulation experiment of optimal escape route selection for fire distribution prediction based on the intelligent fire virtual simulation teaching platform. The simulation experiment takes Unity3D as the tool, comprehensively utilizes computer simulation technology and big data technology of Internet of Things to establish mathematical models of fire data in various situations, and dynamically simulates the scope, intensity and spread trend of different fires. In addition, according to different fire situations, combined with the overall structure of the building and multi-sensor data, based on the optimal path planning principle, the system plans the optimal escape route for personnel in case of fire, so as to guide students to conduct fire escape drills based on different real scene simulation. The teaching system takes the three-layer structure of the Internet of Things as the main line and adopts the "bottom-up" teaching concept to enable students to master the theoretical knowledge and key technologies of the Internet of Things. By completing the simulation experiment designed by us, students should be able to solve practical problems by comprehensively applying Internet of Things engineering technology. The simulation experiment has been put into practical teaching, which has received positive response from students and achieved good teaching effect.
A Metadata-Driven Tool for FAIR Data Production in Citizen Science Platforms

Citizen Science (CS) platforms include a large number of projects that manage data from citizen observations. However, data and metadata are not easily available and do not generally comply with standards. This makes it difficult to share data through the mechanisms commonly used in the scientific community, affecting the reuse of data outside the context of CS platforms. The adoption of Web standards could improve the FAIR (Findable, Accessible, Interoperable and Reusable) quality of shared data. Adopting standards is not enough; it is also important to provide the technologies that make it possible to find the data, access it, share it and be able to interoperate with the data. For this purpose, this paper presents a tool for the production of FAIR data from PPSR (Public Participation in Scientific Research) Core metadata model based platforms. The tool allows (i) transforming metadata from CS platforms to the DCAT (Data Catalogue Vocabulary) standard, (ii) generating Web APIs from the available data, and (iii) building a DCAT-validated data catalogue. This approach improves the FAIR compliance of CS data, empowering data consumers and developers.
Self-adaptive Eyes off Objects Detection for Distracted Driving

Advanced driver assistance systems (ADAS) have been the standard configurations for modern vehicles. The distracted driving monitor is an important part of ADAS. The current study ignores a common phenomenon that the driver takes his eyes off the object (EOO) on the road. This study proposes a vision-based method for EOO detection. Our method employs the deep network called PFLD for facial landmark detection and uses YOLOv4 to detect the objects on the road. Then, we propose a head pose estimation method based on the regression model. After that, our method introduces an off-line calibration process to associate the detected objects and gaze area together, and EOO can be detected by a conversion process. Experimental results demonstrate the effectiveness of our presented work.
Developing the system of open access to research data. FAIR principles

The evolution and typology of open access system, as well as Ten Plan S Principles are discussed. The focus is put on the open access systems and FAIR principles (findability, accessibility, interoperability and reuse of digital assets). The article is prepared within the framework of the State Order No. 075-00642-21-00 “Design and development of the system of Open Archive of Integrated Information Resources of Russian National Public Library for Science and Technology as the modern system of knowledge in digital environment: On the way to open science” for the years 2020–2022.
Multi-Modal Sentiment Analysis Based on Multiple Kernel Learning with Margin Dimension Constraint

Most of the sentiment analysis studies focus on the sentimental classification of pictures in the video, ignoring the spatio-temporal information of the sequence of picture frames as well as text and audio information. The multiple kernel learning is a new hotspot in the field of nuclear machine learning, capable of handling multiple modalities. For multiple kernel learning, it is easy to ignore the basic features that are not discriminative, and cannot make full use of the base features of different modes. This paper puts forward a novel multi-modal fusion model for sentiment analysis, in which a multiple kernel learning algorithm based on convolution margin dimension constraint is proposed for feature fusion. Moreover, the 3D convolutional neural network is used to extract the features of visual information, and the multiple kernel learning algorithm based on margin dimension constraint is used to fuse visual, text and audio sentiment features. Experiments conducted on the MOUD and IEMOCAP sentiment databases show that the proposed model outperforms existing models in the field of multi-modal sentiment analysis research.
Multimodal Human Systems Exploration with Tangible XR for the Internet of Production: An expert survey

Agile product development makes it possible to react flexibly to changing customer requirements. In this context, user and expert assessments, explorations and tests are very important, since customer requirements can often be easier identified by testing a first product or a prototype. Due to complex and long development processes, the development of prototypes is often time-consuming and costly. To improve this, we are developing a method in which the use of mixed reality (XR) allows certain product functionalities to be explored at a very early stage by using multimodal virtual prototypes. Traditional virtual reality (VR) addresses only the visual and acoustic modalities and is limited in its immersion, which is especially impeding for products where physical interaction and haptic experience is an important part of the product functionalities. Based on the findings of the multiple resources theory, Tangible XR allows part of this haptic experience to be experienced and integrated with the virtual experience, so that change requests to the product based on user or expert assessments can be identified and implemented at an early stage of the development process of socio-technical systems. In addition to this retrospective user assessment, Tangible XR can also be used to prospectively explore and develop product functionalities in an exploratory manner together with stakeholders. This exploratory paradigm can be an essential component of agile processes. The explorative approach developed is presented by using a Tangible XR car door opening mechanism as a demonstrator. Based on an expert study, a first proof of concept is presented.
Single Valued Neutrosophic Sets with Optimal Support Vector Machine for Sentiment Analysis

Sentiment analysis (SA) is mainly employed to investigate the polarity of the sentiment existent in a content. It assist in understanding the opinions or feelings expressed by people. It find useful in several application areas such as e-commerce, education, etc. Natural language processing (NLP) and machine learning tools can be employed for SA. In this view, this study develops a Single Valued Neutrosophic Sets with Optimal Support Vector Machine (SVNS-OSVM) model for SA. The major intention of the SVNS-OSVM model is to identify the existence of sentiments exist in the data. The SVNS-OSVM model initially performs data pre-processing to transform the input data into a useful format. In addition, SVNS model is applied to derive word embeddings. Then, SVM model is applied for the detection and classification of sentiments. At the last level, the improved particle swarm optimization (IPSO) algorithm is used to fine tune the parameters involved in the SVM model. For ensuring the improved outcomes of the SVNS-OSVM model, a wide range of simulations were performed and the results are inspected under several aspects. The comparative study highlighted the betterment of the SVNS-OSVM model compared to recent approaches.
3D Dense &amp; Scaled Reconstruction Pipeline with Smartphone Acquisition

Computer vision tackles the hard problems of understanding images and videos. It is a challenge widely discussed in many recent works. Mobile devices such as smartphones or tablets are equipped with sensors more and more precise. They have powerful processing units to collect information about their environment, making them the perfect tool to understand surroundings. Humans understand their world in 3D and have no problems estimating the approximate scale of an object placed in front of them. We tackle this problematic by using a smartphone to acquire visual data and inertial data to reproduce human vision. In that sense, we propose a pipeline for 3D reconstruction that generates dense, scaled and textured 3D model from a series of images acquired with a monocular smartphone. We present a solution of high quality 3D scan within everyone’s reach thanks to data acquisition on a smartphone. We propose a new keyframe selection algorithm that includes an augmented reality object during the scan in order to improve both data quality and ease of use during a scan. Finally, we produce a high quality, dense, textured, scaled 3D model without artifacts thanks to our optimized 3D reconstruction pipeline.
On the Order-Compatibility of Fuzzy Logic Connectives on the Generated Clifford Poset

Fuzzy logic connectives have been order-theoretically explored in many recent works. Among them, Clifford’s relations, both the additive and multiplicative versions, are prominently employed for their generality as well as utility. While the algebraic properties of the original operation are preserved, its order-theoretic properties, viz., monotonicity, boundedness, etc., are not always preserved on the obtained Clifford poset. In this work, we characterize the necessary and sufficient conditions for these and examine the behaviour of certain fuzzy logic connectives on the induced Clifford posets.
Accurate and Lightweight MRI Super-Resolution Via Multi-Scale Bidirectional Fusion Attention Network

High-resolution magnetic resonance (MR) imaging has attracted much attention due to its contribution to clinical diagnoses and treatment. However, because of the interference of noise and the limitation of imaging equipment, it is expensive to generate a satisfactory image. Super-resolution (SR) is a technique that enhances an imaging systemâ€™s resolution, which is effective and cost-efficient for MR imaging. In recent years, deep learning-based SR methods have made remarkable progress on natural images but not on medical images. Most existing medical images SR algorithms focus on the spatial information of a single image but ignore the temporal correlation between medical images sequence. We proposed two novel architectures for single medical image and sequential medical images, respectively. The multi-scale back-projection network (MSBPN) is constructed of several different scale back-projection units which consist of iterative up- and down-sampling layers. The multi-scale machine extracts different scale spatial information and strengthens the information fusion for a single image. Based on MSBPN, we proposed an accurate and lightweight Multi-Scale Bidirectional Fusion Attention Network(MSBFAN) that combines temporal information iteratively. That supplementary temporal information is extracted from the adjacent image sequence of the target image. The MSBFAN can effectively learn both the spatio-temporal dependencies and the iterative refinement process with only a lightweight number of parameters. Experimental results demonstrate that our MSBPN and MSBFAN are outperforming current SR methods in terms of reconstruction accuracy and parameter quantity of the model.
AUTOMATIC SELECTION OF XRF SPECTRAL FEATURE VARIABLES FOR SOIL HEAVY METAL BASED ON FiPLS AND BiPLS, 52-59.

When using X-ray fluorescence (XRF) spectrometry to detect heavymetal content in soil, there are several factors that affect the range ofspectral variable resolutions such as characteristic peak broadeningof heavy metal, characteristic peak overlapping of heavy metal,instrumental noise, and potential absorption enhancement effect ofelements.
A Hybrid Learning Approach for Text Classification Using Natural Language Processing

Text classification and categorization is a hot topic that involves assigning tags or categories to a text based on its content. It is one of the important tasks of automatic natural language processing (NLP) in many applications such as topic tagging, sentiment analysis, intent detection, spam filtering, and email routing. Machine learning text classification can support businesses to automatically analyze and structure their textual documents promptly and inexpensively, to automate processes and improve data-driven decisions. In this article, we propose a new algorithm to classify textual documents using a hybrid approach that combines a set of given algorithms, using the best for each class. These documents can be classified into a set of possible class labels given a priori. Two machine learning algorithms are used to evaluate our proposed approach: Naive Bayesian (NB) and Logistic Regression (LR). The obtained results showed that the proposed hybrid algorithm is more efficient than NB and LR algorithms with an accuracy of 91.86%.
Ontologies for emergency management

In emergency management (EM), different domain vocabularies are used by distinct specialized actors involved in catastrophes’ response. Ontologies enables information sharing among them. This review of ontologies is an exploratory work aimed at collecting references of already proposed ontologies for the realm of EM; a first step for the proposal of a coherent and integrated architecture for EM ontologies. This work conforms to the PRISMA method and was performed by systematically searching several electronic databases for identifying proposed EM ontologies, published between the period of 1970 to 2021. From a total of 1885 articles identified, 104 articles met the full inclusion criteria for the systematic review. The articles found were classified according to (among other categories) the type of addressed disaster by the ontology, the main focus of the proposed approach, and the methods and techniques adopted. Despite the exploratory nature of this work, the review highlighted underexplored topics, and research gaps, due to the lack of integration of the ontological proposals, which hampers their semantic alignment in a modular architecture of ontologies, amenable of an infrastructure for distributed data sources of the Linked Open Data initiative.
A Backbone Whale Optimization Algorithm Based on Cross-stage Evolution

The swarm intelligent algorithms (SIs) are effective and widely used, while the balance between exploitation and exploration directly affects the accuracy and efficiency of algorithms. To cope with this issue, a backbone whale optimization algorithm based on cross-stage evolution (BWOACS) is proposed. BWOACS is mainly composed of three parts: (1) adopts the density peak clustering (DPC) method to actively divide the population into several sub-populations, generates the backbone representatives (BR) during backbone construction stage; (2) determines the deviation placement (DP) by constructing the co-evolution operators (CE), the search space expansion operators (SE) and the guided transfer operators (GT) during bionic evolution strategy stage; (3) realises the bionic optimisation through DP during backbone representatives guiding co-evolution stage. To verify the accuracy and performance of BWOACS, we compare BWOACS with other variants on 9 IEEE CEC 2017 benchmark problems. Experimental results indicate that BWOACS has better accuracy and convergence speed than other algorithms.
Applications to Inference

In this chapter, we apply some basic asymptotic concepts and results to gain insight into the large-sample behavior of some fundamental tests. Section 13.2 considers the robustness of some classical tests, like the t-test, when the underlying assumptions may not hold. For example, for testing the null hypothesis that the underlying mean is zero, it is seen that the t-test is pointwise asymptotically levelPointwise asymptotically level $$\alpha $$ for any distribution F with finite nonzero variance. However, such a robustness of validity result does not always extend to other parametric procedures, as will be seen.