Reviewerly/exHarmony huggingface dataset:
Analysis and application research of mobile robot navigation related technologies

With social progress and technological development, mobile robots have played an important role in industries, medical care, safety, home and other fields due to their advantages of saving labor costs, reducing personnel work intensity, and avoiding potential job hazards. Especially in indoor environments, such as industrial indoor operations, warehousing and logistics distribution, indoor safety patrols, and other application scenarios, mobile robots have highlighted inestimable application value. Intelligent mobile robot is an important tool in the field of service and automation, and robot navigation technology is an important technology for intelligent mobile robot to achieve self-positioning, robot mapping and path planning. This paper expounds three technologies of mobile robot navigation mapping: visual mapping and positioning, lidar mapping and positioning, and sensor fusion mapping and positioning. After that, the method of path planning of the mobile robot is analyzed. Finally, the application of intelligent mobile robots in factory automation and supermarket guidance is pointed out, and the development trend of intelligent mobile robots is prospected.
Improving the artificial potential field by A-star to solve the local minima problem

This paper discusses the method for artificial potential field in traditional sense. It is based on the algorithm with the planning of physics that guides a robot along a gradient toward a target endpoint by applying forces with gravitation and repulse within a robot environment. The article proposes to improve the potential field method by incorporating the A-star algorithm for the improvement of path planning in accuracy and efficiency. The A-star algorithm uses a cost function to measure the goodness of a node and finds the optimal path totally and thoroughly through heuristic search. The proposed method uses the A-star algorithm to generate the optimal path, which takes potential field and its method as basis, thus repeating the process until the robot reaches the end point. By combining the A-star algorithm and the method based on artificial potential field, the robot's driving route is made more reasonable, and thus testing and verifying how the method works through an experiment with simulation.
OPTIMIZING THE IMPLEMENTATION OF THE YOLO AND DATA ALGORITHM AUGMENTATION IN HANACARAKA JAVANESE SCRIPT LANGUAGE CLASSIFICATION

The Javanese language and Javanese script are one of the rich cultural heritages in Indonesia, but there are still many people who do not have sufficient understanding of the Javanese language and culture, including the Javanese script. This can have an impact on the loss of language and cultural diversity, as well as the loss of the local identity of the Javanese people. The lack of knowledge about Javanese script language culture can be caused by various factors, such as the lack of access to adequate learning resources, the minimum use of Javanese language and script in daily life, and the lack of attention from the government and society towards the preservation of Javanese language and culture. This study aims to optimize the application of the YOLO (You Only Look Once) algorithm and data augmentation techniques in the Javanese language classification Hanacaraka script. The method used in this study was collecting data on Javanese Hanacaraka script images, data labeling, data augmentation, and model training using the YOLO algorithm. The results showed that the Javanese script pattern recognition method used the YOLO algorithm which had gone through the data augmentation process, showing good results with an accuracy of 96.4% using 3021 image data sources for the hanacaraka letters.
A Novel Approach for Triggering the Serverless Function in Serverless Environment

Serverless computing has gained significant popularity in recent years due to its scalability, cost efficiency, and simplified development process. In a serverless environment, functions are the basic units of computation that are executed on-demand, without the need for provisioning and managing servers. However, efficiently triggering serverless functions remains a challenge, as traditional methodologies often suffer from latency, Time limit and scalability issues and the efficient execution and management of serverless functions heavily rely on effective triggering mechanisms. This research paper explores various design considerations and proposes a novel approach for designing efficient triggering mechanisms in serverless environments. By leveraging our proposed methodology, developers can efficiently trigger serverless functions in a variety of scenarios, including event-driven architectures, data processing pipelines, and web application backend.
Optimal Framework for Level Based Access Control for VM Auditing on Cloud

The growth in the cloud computing have motivated and enable lot of application developer to deploy the applications on cloud. The major challenge of hosting on cloud is the service provider or the application provider must comply to a good number of rules. These compliance reports are time to time validated and checked by external auditors. The auditing process for the cloud services are critical and the access controls must be enabled. Due to the higher complexity and less flexibility of the virtual machines, most of the cases this access control mechanism is compromised. This work proposes four algorithms to identify and enhance the LBAC mechanism for cloud services with access updates based on time variant characteristics analysis and predictive analysis with selective cryptographic methods. The proposed model produces significantly improved results to overcome three major issues in the cloud service management as selective LBAC, static privileges and open access control for the auditors.
Opinion Mining on Ukraine–Russian War Using VADER

The purpose of our research paper is to discover the best method to implement opinion mining on Ukraine–Russian war based on the tweets generated by people all over the world on Twitter. We have performed VADER, Naive Bayes, and DistilBERT and have done some relative comparative studies finalizing that VADER is the best approach to perform opinion mining for a vast data like Ukraine–Russian war because VADER gives high accuracy based on emotions just like a human, who understands contexts where machines cannot. We choose DistilBERT and Naive Bayes as they are some of the popular models which give the best output based on sentiment score hence, we figured out that the best model which is optimal to find the sentiment accuracy based on the emotion is VADER when compared with DistilBERT and Naive Bayes. The two main aspects which highly differentiate VADER from all other methods are Polarity and Intensity. VADER being a library has a powerful set of modifiers making it more unique which gives higher accuracy to perform opinion mining on Ukraine–Russian war tweets.
Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python

The objective of AI-based resume screening is to automate the screening process, and text, keyword, and named entity recognition extraction are critical. This paper discusses segmenting resumes in order to extract data and perform text analysis. The raw CV file has been imported, and the resume data cleaned to remove extra spaces, punctuation and stop words. To extract names from resumes, regular expressions are used. We have also used the spaCy library which is considered the most accurate natural language processing library. It includes already-trained models for entity recognition, parsing, and tagging. The experimental method is used with resume data sourced from Kaggle, and external Source (MTIS).
Studying the Online Deepfake Community

Deepfakes have become a dual-use technology with applications in the domains of art, science, and industry. However, the technology can also be leveraged maliciously in areas such as disinformation, identity fraud, and harassment. In response to the technology's dangerous potential many deepfake creation communities have been deplatformed, including the technology's originating community – r/deepfakes. Opening in February 2018, just eight days after the removal of r/deepfakes, MrDeepFakes (MDF) went online as a privately owned platform to fulfill the role of community hub, and has since grown into the largest dedicated deepfake creation and discussion platform currently online. This position of community hub is balanced against the site's other main purpose, which is the hosting of deepfake pornography depicting public figures- produced without consent. In this paper we explore the two largest deepfake communities that have existed via a mixed methods approach utilizing quantitative and qualitative analysis. We seek to identify how these platforms were and are used by their members, what opinions these deepfakers hold about the technology and how it is seen by society at large, and identify how deepfakes-as-disinformation is viewed by the community. We find that there is a large emphasis on technical discussion on these platforms, intermixed with potentially malicious content. Additionally, we find the deplatforming of deepfake communities early in the technology's life has significantly impacted trust regarding alternative community platforms.
Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Objective: The objective of this work is to detect video shot boundary which is an important phase in the domain of video processing. The applications such as indexing, retrieval and browsing of video are majorly depends on accurate detection of shot boundary so the detection is highly necessary in the area of video processing. Methods: Many researchers have contributed to the field of Shot Boundary Detection (SBD) to improve video processing throughout the previous decade. Some of the techniques have detected abrupt as well as gradual transitions with remarkable performance metrics values, but they compromised with the execution time and operating speed. Some of the techniques proposed and implemented the simpler approaches, but failed to detect the gradual transitions. Additionally, the effects like illuminations and camera motion lead to produce false results. Thus, taking these aspects into consideration, the novel approach for SBD has to be implemented. Findings: In this paper, fusion of transform and filtering at an early stage has been applied for reducing the false hits that occurs due to illumination noise. The fusion of transform with appropriate classifier enhances the detection performance. Another deep learning based technique with Rider bald eagle search optimization given the solution for the SBD. Novelty and applications: The proposed two approaches give the detection of abrupt and gradual transitions. It has been noticed that the said approaches found effective against the illuminations. To evaluate the performance it uses precision, recall and F1 score as performance metrics. The experiments are performed on latest TRECVID datasets and few open source video datasets. Keywords: Shot Boundary Detection; Walsh Hadamard Transform (WHT); Dual Tree Complex Wavelet Transform; Deep Belief Network; Deep Convolutional Neural Network
Asking Questions about Scientific Articles—Identifying Large N Studies with LLMs

The exponential growth of scientific publications increases the effort required to identify relevant articles. Moreover, the scale of studies is a frequent barrier to research as the majority of studies are low or medium-scaled and do not generalize well while lacking statistical power. As such, we introduce an automated method that supports the identification of large-scale studies in terms of population. First, we introduce a training corpus of 1229 manually annotated paragraphs extracted from 20 articles with different structures and considered populations. Our method considers prompting a FLAN-T5 language model with targeted questions and paragraphs from the previous corpus so that the model returns the number of participants from the study. We adopt a dialogic extensible approach in which the model is asked a sequence of questions that are gradual in terms of focus. Second, we use a validation corpus with 200 articles labeled for having N larger than 1000 to assess the performance of our language model. Our model, without any preliminary filtering with heuristics, achieves an F1 score of 0.52, surpassing previous analyses performed that obtained an F1 score of 0.51. Moreover, we achieved an F1 score of 0.69 when combined with previous extraction heuristics, thus arguing for the robustness and extensibility of our approach. Finally, we apply our model to a newly introduced dataset of ERIC publications to observe trends across the years in the Education domain. A spike was observed in 2019, followed by a decrease in 2020 and, afterward, a positive trend; nevertheless, the overall percentage is lower than 3%, suggesting a major problem in terms of scale and the need for a change in perspective.
Block Chain based Contribute Sequence Tracking System for Secure Data Ascription Management

Block chain technique is developed from a distributed, dependable application development platform to an irreversible record of crypto currency transaction history. Block chain technology's emergence has sparked a number of possible changes in how corporate operations are managed across numerous industries. However, to the best of research experience, no prior effort has concentrated on integrating block chain to establish a secure and unchangeable data and evidence attribution maintenance architecture that autonomously checks the origination records. Therefore, the research work proficiently introduces the novel Block Chain based Contribute Sequence Tracking (BCCST) system with amalgamation of Trail based Smart Indenture (TSI) and Release Attribution Model (RAM). The study begins by outlining a practical application of the proposed TSI for the disintermediation of commercial activities utilising a notional, shared information ledger. This ledger not only makes monitoring data exchange easier, but it also encourages supply chain participants to cooperate together on a multilateral network. The novel RAM to create immutable data trails that make it easier to gather, maintain, and verify reliable data provenance.
Atmospheric Turbulence Correction via Variational Deep Diffusion

Atmospheric Turbulence (AT) correction is a challenging restoration task as it consists of two distortions: geometric distortion and spatially variant blur. If left unaddressed, these distortions can significantly impact downstream vision tasks. Diffusion models have shown impressive accomplishments in photo-realistic image synthesis and beyond. In this paper, we propose a novel deep conditional diffusion model under a variational inference framework to solve the AT correction problem. We use this framework to improve performance by learning latent prior information from the input and degradation processes. We use the learned information to further condition the diffusion model. Experiments are conducted in a comprehensive synthetic AT dataset. We show that the proposed framework achieves good quantitative and qualitative results.
A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP.
Echo State Networks for Surface Current Forecasting in a Port Access Channel

Abstract This work employs Echo State Networks (ESN) to improve one day ahead forecasts of water currents in a port access channel entrance, with the aim to provide a more accurate method that can be used to alert the port authority about unsafe conditions for navigation in the near future. For this task Leaky integrator Echo State Networks (LiESNs) are applied to predict one day ahead currents in the Santos channel, Brazil; The method has been trained with data obtained from an Acoustic Doppler Current Profiler installed in the channel, with data available between the years of 2019–2020. Results have been compared with the Santos Operational Forecasting System (SOFS), a numerical model already in use for the same region. Experiments show that the LiESN outperforms the numerical model by the metrics of Index of Agreement (0.922 versus 0.812) and RMSE (0.152 ms−1 versus 0.220 ms−1).
A New Cloud Computing-Based Assessment of Issues in Online Teaching Management in the Post-Epidemic Era of COVID-19

The COVID-19 pandemic has accelerated the development of online teaching and learning, and educational institutions are now facing new challenges and opportunities. Online teaching management has become a crucial issue in ensuring the quality of education in the post-epidemic era. Online teaching management is a complex issue that requires schools to provide technical support, develop interactive teaching methods, ensure fair assessment, provide time management support, and offer training and support to teachers. By addressing these challenges, schools can ensure the effectiveness and quality of online teaching in the post-epidemic era. This paper examined the issues and challenges associated with Online Teaching Management in the Post-Epidemic era of CoVID -19. The analysis is conducted in a study of China. The GAN_OTM (Generative Adversarial Network for Online Teaching Management) model is proposed as a solution to address the challenges faced in online teaching management during the post-epidemic era of COVID-19. This model leverages the power of deep learning and generative adversarial networks to generate realistic virtual classrooms and learning environments, personalized learning experiences, and provide real-time feedback to students and teachers. With developed GAN_OTM model discussed its potential benefits and contributions to online teaching management, including improved student engagement, personalized learning experiences, and scalability. Furthermore, highlight the performance metrics used to evaluate the GAN_OTM model and the importance of analyzing its results. In the other hand, teachers are not comfortable with online teaching management to interact with students.
Introducing the Open Research Library to the Kenyan Academic Community

TCC Africa partnered with Knowledge Unlatched and the Kenya Libraries and Information Services Consortium (KLISC) to introduce Open Access Research Discovery Tool, The Open Research Library, to the Kenyan Academic Community.
Use of Generative AIs in the Digital Communication and Marketing Sector in Spain

The riseRise of synthetic contentSynthetic content generators has gained momentum over the past few years, with the increasing need for automation in content creation. These tools use artificial intelligence (AI)Artificial intelligence (AI) and machine learning (ML)Machine learning (ML) algorithmsAlgorithms to generate content, including text, images, videos, and audio. Synthetic contentSynthetic content generators have become popular, with several platforms offering them as service, allowing users to create content with minimal effort. Despite the growing popularity of these tools, we are still in the early stages of research on how people use them and their impact on human communicationHuman communication. The current study analyses how professionals from communicationCommunication and marketingMarketing agencies in Galicia (SpainSpain) are beginning to use synthetic contentSynthetic content generators and their motivations for doing so. The study will also examine the extent of use of these tools and their impact on human communicationHuman communication. To achieve this objective, an online form was developed and distributed among participants who have used generative AIsGenerative AIs or synthetic contentSynthetic content generators before in their work as advertising agencies. The online form collected both quantitative and qualitative data, allowing for a mixed-method analysis. The main results show that respondents are testing these tools, but still using them cautiously since this technological advance generates suspicions. The main uses are related to brainstorming—helping in creativity processes—and generating first drafts for texts. ChatGPTChatGPT is the most known application, acting as a key for the introduction of other AI applicationsAI applications and assistants.
Dimensional clinical phenotyping using <i>post‐mortem</i> brain donor medical records: <i>post‐mortem</i> RDoC profiling is associated with Alzheimer's disease neuropathology

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records.We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures.Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes.This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.