Reviewerly/exHarmony huggingface dataset:
Feature Identification based Automatic Sign and Photo Detection in a Document

This naner proposes a method for automatically detecting images and signatures in uploaded documents. The prime objective is to assist the organization in reducing the number of faulty admit cards issued to trainees who have limited computer knowledge by providing an automated software service that can rearrange the unmatched uploads made by trainees on the portal during their exam registration process. This will be helpful not only for trainees but also for other applications that require the unloading of documents containing a signature and a photograph. In addition to a trained neural network for face and signature classification, Haar cascades are utilized to improve facial feature identification. For signature detection, three distinct edge detection models are utilized to improve signature feature identification. The face detection model uses two datasets. namely LFW and FER-2013, while the signature detection model uses three datasets of signatures in three different Ianguages. The accuracy of the face detection model is 99.89 percent, while the accuracy of the signature analysis model is 98.56 percent. Finally, a weighted average of both models is calculated to produce an output with improved validity.
Enhancing MANET communication services through blockchain technology

Blockchain offering various impactful data communication services across the network platform, the measurable characteristics of Blockchain are it’s impressive features like immutability, consensus, proof of work and smart contracting technology bring Blockchain as a one of the strong technology of present and future network, if one talk about the network like MANET then the lack of communication resources makes wireless network very poor in which low capacity sensors are facing very serious problem of attacks, since outside attackers can access data, which makes MANET network very poor and low featured network for present as well as future of mobile ad hoc network technology, in this case Blockchain can be the best solution for lacking technology, Blockchain brings a trustworthy environment for the storage of data and also the management of intermediates like other participates since Blockchain ensure the security of data as well as the validity of authorized participation makes Blockchain technology beneficial for the deployment MANET through a series of blocks. This paper we put a latest research idea of deploying a trustworthy communication system in traditional MANET using Blockchain based data exchanging algorithm called BSLS (Blockchain based secured lightweight shield) and also concentrate on the challenging part of Blockchain in MANET like selection of valid and authorized node to involve in the process of communication along with reducing the complexity issues occurred during the management and maintenance of Blockchain deployment.
Needs analysis of Android based science learning material development focused on tropical rainforests environment subject

Tropical rainforests of East Kalimantan famous for its biodiversity. Sustainable development must be a commitment to save the environment. Education is the main foundation for sustainable development. Environment-oriented learning needs to be supported by the use of appropriate learning materials. This research was aimed to describe needs for Android-based learning material focused on tropical rainforest subject. The method used in this research is survey method. The research participants were 117 students from primary teacher training department of Mulawarman University. Data collection was carried out by means of a online questionnaire. Based on the results of the data obtained, shows that : 1) There are no learning materials specifically developed for elementary science basic concept courses, especially regarding tropical rainforest and environment, 2) digitalization of learning materials is needed to support online learning activities during the Covid-19 pandemic, 77.8% of students feel it is not enough if only listening to the lecturer’s explanation so that they have difficulty understanding the science lecture material and 94.9% students think that interactive teaching materials in the learning process are necessary, 3) learning material often used by lecturers, such as power point slide with a percentage of 75.2%, while android based learning media is still very rarely used. 4) students do not play an active role when the lecturer only explains the material and gives examples of questions because the teaching materials mostly used by students are only PPT, textbooks, and learning video, then 94.9% students stated the need of learning materials on Android.
Android based game and blended learning in social science: Effect on student achievement

The purpose of this study was to analyze and prove the effect of android based educational game media with blended learning in social science on the student achievement of fifth grade students at SD Negeri Muntilan. The research method used is an experiment in the form of a quasi-experimental type of non-equivalent control group design. The data collection technique used is a multiple choice test to measure student achievement. Results of analysis this study took a subject of 58 students of class V. The results of the study were obtained from pre-test and post-test data in the form of multiple choice test. Data were analyzed using t-test to determine the effect of using android based educational game media on the student achievement. The results showed that there was an influence of android based educational game media with blended learning in student achievement. The average value of the experimental class was 75.43. After the t-test obtained tcount (5.712) > ttable (1.673) then Ho is accepted. This shows that the android based educational game with blended learning has an influence on student achievement in social science for class V SD Negeri Muntilan.
Implementation of e-learning course and the impact for technology literacy of teacher in 3T area

The development of science, technology and art, especially in education sector, encourages universities to keep pace with the rate of development. STKIP Weetebula as one of the tertiary institutions in underdeveloped, outermost and frontier (3T) areas to responds the challenges of science and technology development by providing e-learning courses in curriculum changes. The aims of this study is to describe the implementation of e-learning courses in STKIP Weetebula Primary School Teacher Education (PGSD) study program and its impact on the technological literacy of teacher. This research uses survey research methods with quantitative approaches. The data collection technique used is documentation study and a survey technique using the google form application. The results of the study show that the e-learning course in the PGSD study program aims to equip students in developing electronic learning using various platforms such as google meet, google classroom, zoom, youtube, blog, google form and zoho form. The data from the questionnaire shows that the e-learning courses that are studied during lectures are good provisions and very useful for teachers. During the Covid-19 pandemic, learning was carried out by blended learning that is combine face-to-face and online learning. Even though they are in 3T area, teachers can carry out online learning with the knowledge and skills they have acquired during lectures. The results showed that the teachers technology literacy skills were in a good category.
Challenges and Strategies for Managing Electronic Theses and Dissertations in Public University Libraries of Bangladesh

In the recent years due to the advent of ICT Electronic Theses and Dissertations (ETDs) have playing a significant impact on academic community through teaching, research, and learning. This study aims to identify the challenges and present some strategies for the maintenance of ETDs in public university libraries of Bangladesh. In this connection for ensuring representativeness, researcher purposively selected 04 public university libraries out of 49 under the investigation. Data were collected from the selected university libraries and analyzed using SPSS v-20. The result highlighted that ETDs leading towards dynamism among the universities. Some of the major challenges for smooth development of ETDs were common metadata format, standard preservation and dissemination policy, networked central database, ICT infrastructure and adequate skilled manpower.
Integrating Real-Time and Non-Real-Time Collaborative Programming

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes.
Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects

With the advent of the Industry 4.0 era, information technology has been widely developed and applied in the construction engineering field. Text mining techniques can extract interesting and important data hidden in plain text, potentially allowing problems in the construction field to be addressed. Although text mining techniques have been used in the construction field for many years, there is a lack of recent reviews focused on their development and application from a literature analysis perspective; therefore, we conducted a review with the aim of filling this gap. We use a combination of bibliometric and manual literature analyses to systematically review the text mining-based literature related to the construction field from 1997 to 2022. Specifically, publication analysis, collaboration analysis, co-citation analysis, and keyword analysis were conducted on 185 articles collected from the SCOPUS database. Based on a read-through of the 185 papers, the current research topics in text mining were manually determined and sorted, including tasks and methods, application areas, and core methods and algorithms. The presented results provide a comprehensive understanding of the current state of TM techniques, thereby contributing to the further development of TM techniques in the construction industry.
Proposed association rule hiding based privacy preservation model with block chain technology for IoT healthcare sector

The purpose of this study is to improve healthcare system performance by utilizing cutting-edge computing technologies like blockchain and the Internet of Things. Blockchain-based data transfer, Association Rule hiding, and ideal key generation are the three primary aspects of the proposed work. Initially, data are altered using blockchain, then the data enter the Proposed Association Rule concealing stage. In this research a novel association rule concealment phase is implemented, which has three crucial processes: (1) data pattern mining using the improved apiori algorithm, (2) detection of sensitive data based on the improved apiori algorithm, and (3) a method for cleaning and restoring data. Using the generated optimal key, the sanitized sensitive data are recovered. Keys are critical to both the data sanitization and restoration procedures. Hence, a multi-objective hybrid optimization model is known as the Rock Hyraxes Updated Marriage in Honey Bee Optimization (RHUMBO) is employed. Then, the confidentiality of the suggested model's performance has been validated. From the experimental analysis the proposed model achieved 97% for Cleveland dataset at 90th learning percentage which is the best score. And the cost function of the suggested model is minimum (∼0.08 at 100th iteration).
Adaptive Spatial Regularization Target Tracking Algorithm Based on Multifeature Fusion

The accuracy and robustness of object-tracking algorithms are challenging tasks in the field of artificial intelligence. The discriminative correlation filter has fast tracking speed and target discrimination ability in visual target tracking, but it will be affected by unnecessary boundary effects. Although spatially regularized discriminative correlation filters (SRDCF) effectively solve this problem, it faces a slow tracking speed and cannot meet the real-time requirement. Moreover, the constraints added by this algorithm are fixed during the tracking process, which needs to reflect better the characteristics and appearance changes of a particular object. Therefore, we propose an adaptive spatial regularization target tracking model based on multifeature fusion and learn the spatial regularization weights by adaptive spatial regularization terms, so that the filter can adapt to the changes of the target, which can highlight the target area and suppress the background area more accurately. At the same time, in order to cope with the problem of target loss when the target is obscured by a large area or rotated to a large extent, texture features are introduced to improve the deficiencies of HOG features and CN features, so as to achieve the complementary advantages among different features. In addition, the benchmark algorithm SRDCF uses Gaussian–Seidel iterative method to solve the filter, which makes the tracking speed very slow and cannot be tracked in real time. Therefore, this paper proposes an ADMM (alternating direction method of multiplier) algorithm to optimize the solution of the filter and achieve the tracking real-time requirement. The experimental results on the OTB-2015 dataset show that the accuracy and success rate of the improved algorithm reach 86% and 81.9%, respectively. At the same time, in the OTB-2013 dataset, it has increased by 3.4% and 6.3%, respectively, on the basis of the benchmark algorithm. It is verified that the improved model not only improves the robustness of the algorithm but also can better adapt to the changes of the target, while effectively reducing the computational overhead and meeting the tracking real-time requirements.
Detection and azimuth information display of infrared moving targets

This paper proposes a method of infrared moving target detection and its azimuth information display based on background difference. First of all, in view of the shortcomings of the current VIBE (Visual Background Extractor) algorithm, we propose a moving target detection algorithm based on the improved visual background extraction. This improved algorithm makes full use of the temporal and spatial domain information of the image, which can not only adapt to different detection environment, but also effectively eliminate ghosting to have better detection results for small targets. Then, we design an infrared video moving target detection system based on FPGA, including three modules, namely target detection module, GPS (Global Position System) information extraction module and character superimposing module. Furthermore, the system has the function of target extraction and azimuth information display. Compared to the traditional two-dimensional(2D) infrared moving target detection, this method superimposes GPS data onto 2D image information to facilitate real-time detection so that the moving state of the target can be understood more comprehensively, which has wide application value in the sea, land and air moving target detection and location.
The Main Trends in Media and Information Literacy in the Era of the COVID-19 Pandemic

and USA.Main areas of research during the pandemic are identified.Thematic focuses of articles directly related to COVID-19 are analyzed.The main results of research on the impact of the pandemic on the educational system, social relations, business, politics and a number of other areas are summarized.Particular attention is paid to the recommendations formulated by the authors to combat the manifestations and consequences of coronavirus in the field of pedagogy, interpersonal relations, management and working activity.It is concluded that, in general, many authors of articles reacted promptly and adequately to the changed realities.An assumption is made about a possible change in attitude to the complex of knowledge, skills and abilities associated with media and information literacy and expansion of the structure and scope of these types of literacy in future.
Countering the Hate Speech: An Analysis of Muslim Countries Response to Macron’s Speech Against Muslims After the Charlie Hebdo Incident

After the Charlie Hebdo incident and the brutal murder of a teacher, the French president Emmanuel Macron addressed against Muslims which made him a target of anger in the Muslim world.In response, the representatives of the Muslim majority nations issued statements to condemn his speech.This study is an attempt to analyze the discourse produced by the leaders of the Islamic countries in response to the French president's speech regarding the Charlie Hebdo incident analyzing the sentiments of Muslims globally.To examine this discourse, the research engaged with Critical Discourse Studies as its framework, with a focus upon Van Dijk's Ideological Square Model as well as NVivo for linguistic inquiry.The study incorporated responses collected from October to November 2020 from the leaders of Islamic countries and examined through macro and micro analysis by incorporating the clash of civilization and orientalism theories.The study concluded that the French President's speech generalized the Muslims around the world and tagged them with the attackers as if the whole Muslim community was responsible for it.It further apprehended that a quick narrative can counter Islamophobia just as Muslim leaders responded to Macron's hate speech to address Muslims' sentiments around the world.
µBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem

Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Challenge on Moj, an Indian video-sharing social networking service, powered by ShareChat and IIIT- Delhi for the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> Workshop on Emerging Advances in Multimodal AI (EAM) at IEEE BigMM, 2021. The challenge dealt with detecting abusive comments, in 13 regional Indic languages, on the videos on Moj platform. Our solution utilizes the novel µBoost, an ensemble of CatBoost classifier models and Multilingual Representations for Indian Languages (MURIL) model, to produce SOTA performance on Indic text classification tasks. We were able to achieve a mean F1-score of 89.286 on the test data, an improvement over baseline MURIL model with a F1-score of 87.48.
Traffic Big Data Intelligent Object Tracking Technology under Noise Conditions

Intelligent transportation systems make extensive use of image processing techniques such as object detection and tracking. Real-world traffic conditions require specialized processing of image noise, typically caused by weather conditions or occlusions. This paper proposes a contextual community attention graph neural network structure, which uses the attention mechanism representation of different community nodes to associate important recognition features. This structure can effectively track objects in noisy videos. Algorithm comparison experiments on the dataset show that the method performs better in rainy and night conditions.
Multi-target Tracking with Random Finite Set in Decentralized Large-scale Sensor Networks

This paper presents the multi-target tracking (MTT) problem in decentralized large-scale sensor networks. A novel random finite set (RFS)-based framework is designed, including an improved Probability Hypothesis Density filter with Gaussian Mixture (GM-PHD) representation, and an improved Generalized Covariance Intersection (GCI) fusion algorithm. For the local GM-PHD filter, the intensity of new-born targets is initialized by pre-segmenting the measurement set to address the uncertainty of measurement origin and the computational burden of large-scale measurements. Then, a sensor node-dependent clutter model is established to deal with the cardinality overestimation problem brought by the decentralized structure. For the proposed fusion algorithm, i.e., fusing the posterior PHDs generated by the proposed GM-PHD filters in a distributed manner, it is based on the idea of compensating the severe missed detection problem of GCI in the case of large-scale fusing resources by arithmetic average (AA) fusion. In this way, GCI and AA can be fully utilized to guarantee the estimation accuracy for common targets and, respectively, the robustness for exclusive targets. A simulation of a decentralized large-scale network demonstrates the effectiveness of the proposed framework with respect to estimation accuracy and computational cost.
Chatbot as a Persuasive Technology to Promote Responsible Recycling in the City of Lima

Persuasive technologies have gained popularity around the world. This type of technology aims to change the behavior of people in different circumstances using the persuasion method. Within this classification of technologies we have virtual assistants whose function is to interact in a personalized way with the user. This research aims to provide a solution to the low levels of recycling habits in citizens, which is caused by inefficient programs and/or activities applied by authorities, educational centers and households of socioeconomic levels. low fees by promoting recycling issues. We proposed the use of a virtual assistant to increase knowledge and show the benefits of recycling, in order to generate an improvement in the behavior of the population. To validate this research, two knowledge tests on recycling issues were administered to 69 university students at the beginning and end of the process. After using our virtual assistant, the participants appeared and increased their knowledge by 70.49%.