Reviewerly/exHarmony huggingface dataset:
End-to-end Image Captioning via Visual Region Aggregation and Dual-level Collaboration

PDF HTML XML Export Cite reminder End-to-end Image Captioning via Visual Region Aggregation and Dual-level Collaboration DOI: 10.21655/ijsi.1673-7288.00316 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:To date, Transformer-based pre-trained models have demonstrated powerful capabilities of modality representation, leading to a shift towards a fully end-to-end paradigm for multimodal downstream tasks such as image captioning, and enabling better performance and faster inference. However, the grid features extracted with the pre-trained model lack regional visual information, which leads to inaccurate descriptions of the object content by the model. Thus, the applicability of using pre-trained models for image captioning remains largely unexplored. Toward this goal, this paper proposes a novel end-to-end image captioning method based on Visual Region Aggregation and Dual-level Collaboration (VRADC). Specifically, to learn regional visual information, this paper designs a visual region aggregation that aggregates grid features with similar semantics to obtain a compact visual region representation. Next, dual-level collaboration uses the cross-attention mechanism to learn more representative semantic information from the two visual features, which in turn generates more fine-grained descriptions. Experimental results on the MSCOCO and Flickr30k datasets show that the proposed method, VRADC, can significantly improve the quality of image captioning, and achieves state-of-the-art performance. Reference Related Cited by
Wireless positioning method for transmission pipe gallery inspection robot based on the TDOA algorithm

Abstract A wireless sensor network for positioning an inspection robot is developed for a transmission pipe gallery environment. The time difference of the arrival algorithm optimized by the least squares method is utilized to realize the wireless positioning function of the robot in the inspection process. The developed system adopts a spiral configuration to install the sensor nodes at equal intervals on the sidewall of the transmission pipe gallery, and only 10 sensor nodes closest to the wireless signal transmission source are activated for each measurement, which avoids the poor long-distance measurement effect of the sensor, ensures sufficient accuracy, and reduces calculation costs. A wireless positioning simulation test was conducted in an experimental transmission corridor environment. The error of the simulation results is equivalent to the measurement error of a single sensor, and the error is not accumulated in the calculation of the algorithm, indicating that the algorithm has achieved good results.
Ensemble Classifiers for Classification of Imbalanced Dataset of Malicious and Benign Websites

Today, we have millions of websites on the internet and it is important for users to be able to distinguish between potentially hazardous websites and genuine websites in order to safeguard one’s personal information. In this paper, the collected dataset is imbalanced because in real-life benign websites are more as compared to Malicious websites. Therefore, balancing has done using Synthetic Minority Oversampling Technique(SMOTE). A dataset is analyzed using various factors such as the URL of the website, IP address and other factors, and applied to Naive Bays, KNN, Logistic regression, Decision Tree and ensemble machine learning models. We performed experiments on both unbalanced and balanced data sets of malicious and benign websites. Experimental studies using a number of benign and malicious websites from real Internet resources have shown better prediction performance on balanced data-sets compared to unbalanced data-sets. We have achieved an accuracy of about 99.85% using ensemble based classifiers.
Exploring Cloud Computing for Education in Rural Nepal: Possibilities, Challenges, and Prospect

Rural education is one of the most significant subjects to talk about and a matter to resolve soon. As rural education in Nepal is very challenging, effective solutions can be brought into action, one of which is the use of technology. This study’s main goal is to investigate and evaluate the value and applicability of cloud computing for rural sector education in underdeveloped nations like Nepal. Through the infrastructure it offers, the usage of cloud computing in rural locations significantly increases the accessibility of essential educational computing services and applications to students and instructors. With applications in many different fields, cloud computing has experienced remarkable growth, and one of its most intriguing uses nowadays is in the educational sector. Due to the lack of schools, colleges, and universities in rural areas, the cloud computing system might be extremely helpful in increasing the education of students there. The primary goal of this study is to explore how formal, non-formal, and informal education are mixed for capital formation, empowerment, and self-sufficiency in rural communities. Its secondary goal is to chronicle the potential contributions of community learning centers in the process of rural transformation.
Problems Of Learning Local Content In The Bugis Language Learning Process In Sidrap District

This research aims toknowing the learning problems that occur in the process of teaching and learning Bugis language in learning Bugis language local content in Maritengngae District, Sidrap Regency. This type of research uses a qualitative descriptive approach in the form of a case study with data collection methods, observation, interviews, and distributing questionnaires. The results of the study show that there are 4 learning problems in the teaching and learning process, namely teacher problems, learning device problems, curriculum problems, and student problems. Teacher problems, namely teachers who teach not in accordance with their educational background, curriculum problems, namely the absence of regional language curricula, learning device problems, namely there are no adequate learning tools
“Happy” College Students

“The children are desperately studying when they should be playing; the young people are desperately playing games when they should be studying.” On June 21st, 2018, Minister of Education Chen Baosheng said at the National Conference on Undergraduate Education in the New Era that the phenomenon of “hard-working high schools and happy universities” in Chinese education should be reversed, and that “the burden should be effectively reduced for primary and middle school students and reasonably increased for college students”, so as to enhance the raise challenges of college students. Compared with the busy high school life, after entering university, the time and form of classes have been greatly adjusted, and students have the right to choose between study and leisure to a large extent, so they have significantly more time at their disposal. College students are mainly self-management; how to make good use of study time efficiently and how to make good use of after-school time are important questions that contemporary college students need to think deeply about.
Now decision theory

The spirit of now in nowcasting suggests expanding the current to include the near future. Decision theory is then developed by incorporating the consequences of actions into the present. With the future falling into the present discounting it is no longer permitted. Value functions are then observed to be determinate only up to scale and shift that are then locked down by fixing values arbitrarily in two selected states, much like declaring water to freeze and boil at zero and a hundred degrees celsius. The locked down value functions associated policy functions are seen to exist in decision contexts in where the only time is now. Examples are studied in univariate and multivariate dimensions for the decision state space and the dimension of shocks delivering state transitions. The policy functions are expanded from realisitic training sets to the full state space using Gaussian Process Regression. They are implemented on real data with reported performances.
An Improved Approach on Breast Cancer Detection Using Machine Learning

Cancer begins when changes called mutations take place in genes that regulate cell growth. The cells can expand and divide uncontrollably thanks to the mutations. The type of cancer that arises in breast cells is called breast cancer. Generally, breast ducts or lobules are where breast cancer first appears. The ducts that convey the milk from the glands to the nipple are where the milk is created by lobules. Moreover, cancer can develop in the breast's fatty tissue or fibrous connective tissue. Unchecked cancer cells can travel to the lymph nodes under the arms and frequently invade nearby healthy breast tissue. After the cancer has reached the lymph nodes, it has a pathway to spread to other organs, parts of the body. As per a 2013 WHO study, "i t is projected that more than 508,000 ladies passed away all around the world in 2011 because of bosom disease". Early breast cancer development may be treated and prevented. Nonetheless, a lot of women receive a malignant tumor diagnosis after it has advanced past the point of no return. The objective of this paper is to present several approaches to investigate the application of multiple algorithms based on Machine Learning for early breast cancer detection.
Investigating the Correlation Between Presence and Reaction Time in Mixed Reality

Measuring presence is critical to improving user involvement and performance in Mixed Reality (MR). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Presence</i> , a crucial aspect of MR, is traditionally gauged using subjective questionnaires, leading to a lack of time-varying responses and susceptibility to user bias. Inspired by the existing literature on the relationship between presence and human performance, the proposed methodology systematically measures a user's reaction time to a visual stimulus as they interact within a manipulated MR environment. We explore the user reaction time as a quantity that can be easily measured using the systemic tools available in modern MR devices. We conducted an exploratory study (N=40) with two experiments designed to alter the users' sense of presence by manipulating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">place illusion</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">plausibility illusion</i> . We found a significant correlation between presence scores and reaction times with a correlation coefficient -0.65, suggesting that users with a higher sense of presence responded more swiftly to stimuli. We develop a model that estimates a user's presence level using the reaction time values with high accuracy of up to 80%. While our study suggests that reaction time can be used as a measure of presence, further investigation is needed to improve the accuracy of the model.
Survey on Estimation of Body Mass Index Using Facial Images

One of the most concerning public health issues the world is currently dealing with is obesity. Body mass index (BMI) is used to measure one’s obesity and is useful for evaluating an individual’s health. BMI is calculated by dividing a person’s weight by the square of their height. Calculating BMI from just a facial image has been a challenge in the field of computer vision, and researchers have used various methodologies. In this paper, various methods used by different researchers have been described and summarized according to the research objectives. Researchers have used two major methods for extracting features from facial images, i.e., extracting geometric features and deep features. According to our research, region-aware global average pooling together with deep features were recommended, and training of dataset based on gender and race was recommended which improves accuracy and gives the best results for estimation of BMI from facial images.
Self-adaptive Bald Eagle Search for Energy Efficient Load Balancing in Cloud System

By distributing incoming traffic among several servers, networks, or other resources, load balancing helps organizations manage workload demands while enhancing performance and preventing service interruptions. This research work follows 3 modules for load balancing including: workload submission, central manager (CM), as well as migration. Cloud users submit the tasks during the submission of workloads. Migration as well as allocation of resources takes place in the second stage. Employing Pearson Correlation Coefficient (PCC) technique, a unique safety constraint is used. A new Self Adaptive Bald Eagle Search (SA-BES) optimization algorithm is introduced for choosing the VM and for allocating the resources. Moreover, Global Agent (GA) as well as correlation-based VM placement applies during the load balancing. Lastly, actual migration is performed by the VM from the schedule gathered from the GA.
EDAMOD: Edge Detection and Moving Object Detection Algorithm to Control Traffic Lights Dynamically

Recently, one of the biggest problems in most of the large cities throughout the entire world has been traffic congestion. The fast exponential growth of vehicles and the scarcity of appropriate roads to manage a large number of vehicles are the vital reasons for traffic congestion. Many researchers use frame differencing, edge detection (ED), and moving object detection (MOD) techniques individually to determine and predict traffic density; however, the ED method only detects edges for the input static image dataset, while the MOD paradigm determines the density of the traffic when vehicles are in motion. In reality, when a traffic intersection's red light is on and the cars are stationary, it is preferable to use the ED technique to estimate the traffic density lively. Conversely, when the green traffic signal comes on and the cars instantly begin to move, it is optimal to use the MOD approach. This study demonstrates a novel strategy known as the Edge Detection and Moving Object Detection (EDAMOD) approach, which partitions the Region of Interest (ROI) into ROI-1 and ROI-2 and then employs ED, MOD paradigms to identify the live area wide vehicle capacity at the traffic signal junction. In ROI-1, it employs ED, and in ROI-2, MOD.
1932

On 9 March, Aisin Gioro Puyi (爱新觉罗·溥仪) (1906–1967), Emperor of the puppet Manchurian Regime.
Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad (RGPBIT), a new investment ranking model for power grid construction projects (PGCPs) is proposed. Given the uncertainty of each index value under the market environment, fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones. Taking into account decision-maker’s subjective risk attitudes, a multi-criteria decision-making (MCDM) method based on improved prospect theory is proposed. First, the [−1, 1] RGPBIT operator is proposed to normalize the original data, to obtain the best and worst schemes of PGCPs. Furthermore, the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function, and the positive and negative prospect value matrices of the project are obtained. Then, the optimization model with the maximum comprehensive prospect value is constructed, the optimal attribute weight is determined, and the PGCPs are ranked accordingly. Taking four PGCPs of the IEEERTS-79 node system as examples, an illustration of the feasibility and effectiveness of the proposed method is provided.
Analysis and Evaluation of Text Comparison Based on Intelligent Optimization Algorithm

Text transcription is crucial in Chinese information processing. Text transcription has always existed since ancient times, but no matter whether it is manual transcription in ancient times or modern transcription using communication and storage devices, random errors cannot be avoided when a message has been forwarded and transcribed many times. In this paper, we study how to measure the size of differences between different versions of texts, how to estimate the number of transmissions experienced between two texts, and how to design an effective and fast algorithm for the calculation of the first two types of problems in the study of text transcription, with respect to the characteristics of text transcription. This paper proposes the concept of text similarity, constructs the TF-IDF similarity evaluation model of text, the text transmission evaluation model based on Gaussian process (i.e., GFCT Model), and the model based on the immune frog jumping algorithm to analyze the comparative processing of text, so as to achieve accurate and effective information processing, with a view to providing a new method for text data processing, and improving the accuracy and effectiveness of text data processing.
Deep Vision Guided Mobile Robot Dual Axis PID Dynamic Real Time Adjustment Following

The real-time tracking of moving objects by social robots is crucial for ensuring object-oriented services. This paper presents a real-time following algorithm for mobile robots based on deep vision object detection. Firstly, using a binocular depth vision camera, real-time target information from a firstperson perspective is collected, and a lightweight deep learning model is employed to detect the relative position of the human body posture with respect to the social robot. Subsequently, to address the issue of unsmooth trajectory during the tracking process, a dual-axis PID algorithm is developed for cooperative control of the robot’s speed and heading angle. Given the inherent randomness in human body movement, target loss can occur during tracking. To enhance the algorithm’s robustness, a dynamic tracking mechanism is introduced. Finally, we implement a vision-driven two-wheel differential social robot following algorithm on the ROS system and validate it through three real-world experimental scenarios. The mean values of the target acquisition rate, tracked target position, and angle smoothness are found to be 0.81, 0.46, and 1.02, respectively.
Research on Cloud Data Storage Security Privacy Protection System under Digital Campus

This paper proposes a digital campus cloud data storage security and privacy protection strategy under the Internet of Things environment. Campus users first apply for the attribute key from the trust center and then outsource the encrypted private data to the cloud server. The authorized user generates keyword traps through attribute keys. Only when the attributes of the authorized user meet the specified access control tree, the authorized user is allowed to search the cloud encrypted data through this trap door. A homomorphic encryption algorithm based on ECC is proposed based on the advantages of high security performance and low computational complexity. The algorithm is applied to cloud storage ciphertext processing and cloud computing data aggregation privacy protection. The results show that the scheme cannot only ensure the security of data storage but also protect the identity privacy of campus users.
Review of the International Scientific and Practical Conference “Modern Ontology – XI: Ontology and Religion” (Saint Petersburg, Volgograd, June 26–30, 2023)

The review of the international scientific and practical conference "Modern Ontology – XI: Ontology and Religion", which took place from June 26 to June 30 at the Saint Petersburg State University of Aerospace Instrumentation and Volgograd State University, is presented. The speeches of the conference participants were devoted to several significant topics on the stated issues. The first thematic block consisted of theoretical issues of the relationship between ontology and religion. Within the framework of this topic, the plenary report of P.M. Kolychev's "Ontology and religion", in which theology was considered as part of ontology became the key one. The second thematic block included the problems of human existence in the context of religion. Within the framework of this topic, S.B. Tokareva in the plenary report "Ontological consequences of human and angelic freedom: a modal approach" examined the interpretations of the real, possible and necessary, analyzed the ontological consequences of human and angelic freedom. Within the framework of the third thematic block, in which the problems of the relationship between ontology and religion were discussed in a historical and philosophical retrospective, the most interesting was the plenary report of I.I. Evlampiev "The problem of creation and the status of nothing in the metaphysics of mystical Pantheism", which presented an original view on the development of the concepts of creation in the history of Christian thought. The fourth thematic block covered the problems of theory and practice of ontology. Within the framework of the conference, a fruitful discussion was held on the topic "What kind of philosophy do we need?", dedicated to the problems of teaching philosophy for non-philosophical specialties. Most of the participants supported the idea of developing a unified program of a general philosophy course based on the domestic (including Soviet period) experience of teaching this discipline. The following structure of the philosophy course is proposed: 1) ontology; 2) philosophy of natural science; 3) philosophy of socio-anthropological reality; 4) philosophy of religion.
Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning

Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.