Reviewerly/exHarmony huggingface dataset:
Upcoming Threats in Cyber-Security

As technology advances rapidly, the cyber-security field is constantly facing new threats. All the world is running over networks. Cyber-security is adapting new changes and grows exponentially with real-time. Cybercriminals are constantly adapting and finding innovative ways to exploit vulnerabilities in networks, systems, and devices. From artificial intelligence (AI)-powered attacks to breaches of supply chains, cyber-security threats are evolving at an unprecedented pace. To protect sensitive data, ensure business continuity, and protect against potential financial and reputational damage as organizations increasingly rely on digital technology to conduct their business. It is important for us to stay vigilant and proactively address these emerging threats. This introduction examines some of the upcoming cyber-security threats that are expected to pose significant challenges in the near future and highlights the need for robust cyber-security measures to effectively mitigate these risks.
Using Multiple Monolingual Models for Efficiently Embedding Korean and English Conversational Sentences

This paper presents a novel approach for finding the most semantically similar conversational sentences in Korean and English. Our method involves training separate embedding models for each language and using a hybrid algorithm that selects the appropriate model based on the language of the query. For the Korean model, we fine-tuned the KLUE-RoBERTa-small model using publicly available semantic textual similarity datasets and used Principal Component Analysis (PCA) to reduce the resulting embedding vectors. We also selected a highly-performing English embedding model from available SBERT models. We compared our approach to existing multilingual models using both human-generated and large language model-generated conversational datasets. Our experimental results demonstrate that our hybrid approach outperforms state-of-the-art multilingual models in terms of accuracy, elapsed time for sentence embedding, and elapsed time for finding the nearest neighbor, regardless of whether a GPU is used. These findings highlight the potential benefits of training separate embedding models for different languages, particularly for tasks involving finding the most semantically similar conversational sentences. We expect that our approach will be used for diverse natural language processing-related fields, including machine learning education.
Gesture Recognition Algorithm Based on Computer Vision

Abstract With the advent of the era of artificial intelligence, computer vision and machine learning are developing rapidly, and human-computer interaction technology has become one of the important research directions. This paper mainly combines computer vision and other related technologies to analyze and judge the gestures, and predict the corresponding models according to the different matching of gestures, so as to recognize the corresponding gesture information. By converting the image into HSV space, HSV channel separation is realized, and then filtering and skin color segmentation are carried out to carry out morphological operation. Finally, shape matching is carried out according to the contour information of the image to judge the information of gesture. The experimental results show that the gesture recognition algorithm proposed in this paper can effectively judge the gesture information contained in the image, has good operation effect and strong practicability.
A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

Abstract In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying only on images as observations. To process these images, convolutional neural networks (CNNs) are used to perform semantic segmentation and identify hazards corresponding to three different layers, namely feature detection, shadow detection, and slope estimation. The absence of active sensors such as light detection and ranging (LiDAR) makes it challenging to assess the surface geometry of a celestial body, and the training of the neural networks in this work is oriented towards coping with that drawback. The image data set for the training is generated using blender, and different body shape models (also referred to as meshes) are included, onto which stochastic feature populations and illumination conditions are imposed to produce a more diverse database. The CNNs are trained following a transfer learning approach to reduce the training effort and take advantage of previously trained networks. The results accurately predict the hazards in the images belonging to the data set, but struggle to yield successful predictions for the slope estimation, when images external to the data set are used, indicating that including the geometry of the target body in the training phase makes an impact on the quality of these predictions. The obtained predictions are composed to create safety maps, which are meant to be given as input to the guidance block of the spacecraft to evaluate the need for a manoeuvre to avoid hazardous areas. Additionally, preliminary hardware-in-the-loop (HIL) test results are included, in which the algorithms developed are confronted against images taken using real hardware.
Customers Satisfaction on Robots, Artificial Intelligence and Service Automation (RAISA) in the Hotel Industry: A Comprehensive Review

Due to the 4th Industrial Revolution, Artificial Intelligence and other internet technologies such as RAISA (Robots, Artificial Intelligence and Service Automation) have been adopted in various industries, including the hospitality and tourism industry.As the industry traditionally offers services through manual labor force, more companies adopt RAISA through a commercial service in the form of chatbots, delivery robots, robot concierge, conveyor restaurants, self-service information/check-in/check-out kiosks, and many others.The current academic resources mainly focus on customer perception or evaluation of robots themselves, and few studies link robot services with customers' overall experience of hospitality services.This study aims to evaluate the current status of RAISA studies in the hotel industry and propose directions for future research.Thirty-seven articles were identified from Google Scholar.These articles were reviewed from four perspectives, namely, journal/year distribution, methodology, research context, theoretical foundation.Findings reveal that most studies on RAISA in the hotel industry focus on RAISA's impacts on the hotel industry, RAISA service quality, customer acceptance of RAISA service, customer satisfaction and factors influencing Customers' Satisfaction on RAISA Service and quantitative approach is the dominant research method.To this end, five research directions cover outstanding themes as follows: 1) strategic assessment of RAISA performance, 2) hotel financial impacts on RAISA employment, 3) customers' satisfaction and RAISA service quality, 4) macro environment impacts on RAISA in hotels, and 5) premium determinants on customers' acceptance on RAISA.
A Weight-Generating Approach of a Deep Neural Network for the Parameter Identification of Dynamic Systems

The general learning process of deep learning is extremely time-consuming. Unlike the traditional learning process, a weight-generating approach to quickly generate the weight vectors of a deep neural network model is proposed, which can be used for parameter identification of a dynamic system. Based on the analysis of three trained deep neural network models, which are used to identify the parameters of three different dynamic systems, the statistical relationships between the weight vectors of each hidden layer and its inputs are revealed. Then, the statistical patterns of the weight vectors are imitated by exploiting the statistical patterns of the inputs and these relationships. Then, a weight-generating approach is designed to quickly generate the weight vectors of a deep neural network model. The effectiveness of the weight-generating approach is tested on the tasks of parameter identification for the three dynamic systems. The numerical results are provided to demonstrate the validity and high efficiency of the proposed weight-generating approach.
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery

Abstract In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDialog curated by ourselves. The current models indeed suffer from spurious correlations and have a tendency to generate irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined ConSTrain, to overcome data sparsity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.
RECOGNITION OF DYNAMIC LITHUANIAN LANGUAGE GESTURES

This paper proposes a method for automated Lithuanian hands gestures data collection and for Lithuanian hands gestures classification. The dataset of 1100 samples was collected for 10 different classes of Lithuanian hands gesture. The features of hands gestures were extracted with CNN network. The classification was made with LSTM network. The trained LSTM network classified the Lithuanian hands gestures with 85% accuracy.
Geology of the Copper Mineralization in Proterozoic Ajabgarh Meta-Sediments of, Dokan-Dariba Belt, Sikar District Rajasthan Northwestern India

The Khetri copper belt is a well-known metallotect in northern part of Delhi fold belt in Rajasthan.On the eastern margin of the Khetri sub basin of North Delhi basin separated by a basement high, another sub basin Alwar-Ajabgarh sub basin exposes that a thick sequence of Ajabgarh group of rocks overlies a thick arenaceous sequence of Alwar group of Delhi Super Group of rocks.The Ajabgarh meta sediments here in the Neem Ka thana area are characterized by presence of Bornite dominated copper mineralization with silver association and minor presence of Pb.The mineralization has been described by various workers as strata-bound, hypogene and IOCG.But these inferences are based on part information and the inference drawn is sectorial in nature.The current study includes a holistic study based on exploration over a period of more than two decades and the data generated suggest thereof, that this syngenetic sulphide mineralization associated with the sedimentation of marl and carbonate rocks.Subsequently it has been relocated during 2 nd deformation accompanied by epigenetic component of mineralization depicted in terms of vein filled coarse grained aggregates of bornite and chalcopyrite disposed across the general disposition of litho-package.The EPMA and fluid inclusion data generated from the area indicate association of typical hydrothermal environment minerals like, Perkrite, Wittchenite, Aguilarite, Molybdnite etc.The mineralizing fluids have been trapped between the temperature ranges of 130˚C to 375˚C with average being 250˚C to 300˚C.The fluid salinity also varies from near pure hot water to moderately saline fluid indicative of multi-episodic mineralization of syngenetic nature coupled with epigenetic component.The ore textures indicate 500˚C temperature range; certain intergrowths of minerals like specular hematite and bornite suggest How to cite this paper:
Optimal Choices of Reconstruction Parameters United Improved C-C Algorithm and Improved G-P Algorithm

Abstract For the phase space reconstruction of chaotic time series, the precise selection of delay and embedding dimension is indispensable. Therefore, this paper makes an in-depth study on the selection of reconstruction parameters, and puts forward some suggestions for improving the theoretical defects of the original C-C algorithm and G-P algorithm. The modified C-C algorithm optimizes the intermediate determination parameters of delay time and embedded window of the original algorithm, and makes the selection of delay time and embedded window more accurate. The improved G-P algorithm optimizes the range of neighborhood radius and the selection of scale-free interval, and improves the calculation accuracy of embedding dimension. On the theoretical basis, Lorenz sequence and Rossler sequence are used to verify, and the simulation results can be obtained. The improved algorithm can select parameters more accurately. Finally, the parameters obtained by the two improved algorithms are compared, and it is considered that the phase space reconstruction can be better by using the improved C-C algorithm to solve the delay and the improved G-P algorithm to solve the embedding dimension.
A Robust Correlation Filtering Tracker Based on Joint Reliability Evaluation of Target Position Changes

Abstract Visual tracking is an important branch in computer vision. In complex scenarios, there exist various interference factors, e.g . background clutter, similar objects etc ., making robust tracking based on correlation filter algorithm still a challenging task. In this paper, a correlation filter algorithm based on a novel adaptive multi-cue fusion strategy was proposed. First, a unified response map evaluation strategy was presented to assess the tracking reliability by combing the average peak correlation energy and the response peak historical information. Second, according to the cue reliability, an adaptive multi-cue fusion strategy was proposed to adaptively fuse two tracking cues, correlation filter and color histogram. The experimental results on OTB-2013 and UAV123 demonstrated that the proposed algorithm achieved competitive performance to the state-of the-art trackers.
Measurement-Device-Independent Quantum Key Distribution Based on Decoherence-Free Subspaces with Logical Bell State Analyzer

Measurement-device-independent quantum key distribution (MDI-QKD) enables two legitimate users to generate shared information-theoretic secure keys with immunity to all detector side attacks. However, the original proposal using polarization encoding is sensitive to polarization rotations stemming from birefringence in fibers or misalignment. To overcome this problem, here we propose a robust QKD protocol without detector vulnerabilities based on decoherence-free subspaces using polarization-entangled photon pairs. A logical Bell state analyzer is designed specifically for such encoding. The protocol exploits common parametric down-conversion sources, for which we develop a MDI-decoy-state method, and requires neither complex measurements nor a shared reference frame. We have analyzed the practical security in detail and presented a numerical simulation under various parameter regimes, showing the feasibility of the logical Bell state analyzer along with the potential that double communication distance can be achieved without a shared reference frame.
Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question-answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.
Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms

Abstract Cybersickness (CS) affects a large proportion of virtual reality (VR) users causing a combination of nausea, headaches and dizziness which would create barriers to the users, VR designers/developers and the stakeholders in the production industry. Although design principles suggest methods to avoid CS, challenges remain as new demands and systems continue to penetrate the competitive market. The dilemma is whether to use VR technology by experiencing the ultimate virtual world using a head-mounted display (HMD) with possible CS triggers or to avoid the triggers by avoiding using VR. With the huge success and potential in the entertainment industry, it is very important to focus on the solutions to handling CS dilemmas. Therefore, the main observation for the developers is to have a guide around the set of established design principles aiming to broadly reduce CS. In this paper, we provide a method to apply artificial intelligence (AI) techniques and use machine learning (ML) algorithms including support vector machines (SVMs), decision trees (DTs) and K-nearest neighbours (KNNs) to predict CS outcomes. Based on our findings, we have observed that DT and SVM surpassed KNN in test accuracy. Additionally, DT exhibited better results than both SVM and KNN in train accuracy. By exploiting the power of ML, developers will be able to predict the potential occurrence of CS while developing VR projects to find ways to alleviate CS more effectively.
How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?

Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.
A Recommendation Model for Highway Horizontal Curve Indicators Based on Machine Learning

Abstract The horizontal alignment design is the result of the designer’s comprehensive consideration of smooth alignment and satisfying multiple design goals such as safety, environmental protection and economy.Through analyzing a large number of highway horizontal design cases,the statistical laws of intersection spacing, declination angle and the relationship between them are introduced. Based on this and the machine learning algorithms, a horizontal basic curve indicator recommendation model suitable for the single intersection design and the double intersection design process is built. Finally, the reliability of the model is verified and evaluated by using the calculation accuracy, test set error and average difference.
Spatial Relations between Geological Structures and Precious and Base Metal Deposits from Magnetic Investigation of the Pangar-Djérem Zone, Cameroon

The version three of Earth Magnetic Anomaly Grid (EMAG2-v3) used in this work is aimed at establishing the spatial relationships between tectonic structures and gold and associated mineralization in the Pangar-Djérem area, central and southern Cameroon. Various processing and transformation of image data such as the reduction to the equator, horizontal gradient, generalized derivative, and pseudogravity allowed the identification and delineation of numerous faults related to Syn-D1, Syn-D2, and Syn-D3 deformations. The main orientations, W-E, SW-NE, and SE-NW, obtained from these faults define the intensity of the Pan-African and Eburnian tectonics in the Cameroonian basement. Correlations between mapped faults and mineral occurrences define a structural control of gold mineralization in the study area. Furthermore, the spatial relationships between these faults and the igneous/metasedimentary rocks demonstrate that the Eburnian and Pan-African orogenies favoured the circulation of gold-enriched fluids along faults and shear zones. Gold mineralization in the Pangar-Djérem zone is of the vein type (primary gold), resulting from the intrusion of type I granites and the circulation of enriched fluids in a brittle to ductile shear context. The pseudogravity anomalies associated with the sedimentary deposits/rocks and the SW-NE structures of the Sanaga and Bétaré-Oya Faults define an environment of detrital sedimentary deposits, consisting of metavolcanic and metasedimentary rocks, and justify the existence of gold-bearing quartz veins.
Bibliometric Visualization of Research Regarding Performance Measurement System (PMS) During the Covid-19 Pandemic Using VOSviewer

The impact of the Corona Virus Disease (covid-19) pandemic has reduced the stability of the world economy.One of the most common phenomena is the decline in performance during the COVID-19 pandemic.This research is a bibliometric visualization by taking journals through the publish or perish application with the scope of data available on Google Scholar about the performance measurement system.During the covid-19 pandemic using the VOSviewer application.The purpose of the bibliometric analysis in this study is to find out; 1) publication development trends related to the performance measurement system during the covid-19 pandemic every year, 2) Journal Publication Trends, 3) collaboration between researchers with similar topics, 4) trend terms title and abstract and 5) trend terms author keywords.Data was collected from the Publish or Perish database published in 2019-2022 using the keyword "performance measurement system during covid-19".Then the author used VOSviewer software to analyze and visualize the database obtained.The results of the study show that 439 articles discuss the performance measurement system during the covid-19 pandemic.The year with the most research themes on performance measurement systems during COVID-19 was 2021, with 210 articles.The majority of research collaborations are carried out by researchers from China, including; Henry J. Liu, Jonathan Z. Li, Jiexiang Wang, etc.The analysis of the term title and abstract formed 5 clusters.The analysis based on keywords formed 6 clusters.The term in the title that was used the most in program coordination articles was "PMS" with a total of 185 events .