Reviewerly/exHarmony huggingface dataset:
ARDUINO BASED AUTONOMOUS FIRE FIGHTING ROBOT

A fire outburst is a dangerous act that causes various implications.Identifying the fire at the earliest stage and to quench it and can assist in avoidance of numerous accidents.To protect human beings and to reduce the risks taken by the firemen, we proposed a robot model.The goal of the robot is to automate the fire rescue operations.This proposed model can be used to rescue the life of humans, reduce damages in household environments, labs and small scale industries.The robot model is constructed to carry out a predefined path to execute different tasks.With the advanced development in the robotic field, interference of humans has been reduced and they are generally used for secure purposes.In daily life, fire incidents have become more general and it becomes the responsibility of fire fighters to take risks and extinguish fires protecting human life and other possible losses.In such cases, a robot is proposed to guard living beings and other possible losses from the fire accidents.There are different sensors used in the robot for sensing and detecting the fire and the accurate information about fire is passed on to microcontroller board.Then the microcontroller passes the signal to the motors and the motor driver controls the movements of the robot in the fire accident place for quenching the fire breakout by utilizing a pump.This technology is more secure because it reduces human efforts and risks.
A fast grey wolf optimization algorithm with exponential iterative search

Aiming at the problems of traditional Grey Wolf Optimization algorithm (GWO), such as easy to fall into local optimization, low precision of optimization, and slow convergence speed, a fast grey wolf optimization algorithm with exponential iterative search is proposed. The tent chaotic mapping method is introduced to realize the diversification of the initial grey wolf population. To balance the global search scope and search speed, an iterative search method for exponential nonlinear control parameters is designed to avoid the algorithm falling into local optimization and accelerate the convergence speed. Finally, an information-sharing method based on the inertia weight coefficient is proposed for the current optimal position of individuals, which dynamically updates the individual step size of grey wolves and improves the speed and accuracy of optimization. Through a variety of standard test functions, compared with the traditional GWO algorithm and three improved algorithms, the performance of the proposed algorithm with fast convergence and strong optimization ability is verified.
Research on video image stabilization decision algorithm based on ROI block matching method

Aiming at the problem that the feasibility and accuracy of image measurement and analysis are reduced due to video stabilization in automobile collision experiment, a video image stabilization decision algorithm based on block matching method based on region of interest (ROI) is proposed in this paper. Firstly, in the first frame, the user selects the ROI region containing the marker identification, uses the block matching method to automatically match the ROI region features frame by frame, and estimates the motion of the video frame. And then determines whether the feature matching fails according to the motion of the video frame. When the feature matching fails, the search range can be reduced and the ROI features can be re matched to improve the matching accuracy and speed, Finally, the video frame sequence after image stabilization is output by frame reverse motion compensation according to the amount of motion. The experimental results show that the proposed algorithm can effectively reduce the video stabilization in collision experiments in the subjective evaluation and the objective evaluation of local peak signal-to-noise ratio.
Research on path planning of unmanned vessel based on ant colony algorithm

Aiming at the problems of Ant Colony System (ACS) algorithm in unmanned vessel path planning, such as slow convergence speed and local optimization, an improved ACS algorithm is studied. The method of uneven distribution of initial pheromones, pheromone incremental segmental update and automatic adjustment of volatilization factor are adopted for improvement. The simulation results show that the method uses fewer iterations to obtain the optimal path planning effect, and the algorithm improvement research is reliable and effective.
Information Professionals of the Future and their Prospects in the Era of Fourth Industrial Revolution: The Need for Transformative Potential in Nigeria

This study examined information professionals of the future and their prospects in the Fourth Industrial Revolution. The study employed a mixed method, where quantitative data were analysed using a percentage and frequency table, while qualitative data were analysed thematically. Alumni from all Nigerian library schools constituted the target population. The study gathered data through a semi-structured questionnaire distributed via Google Forms to several platforms, including WhatsApp, Telegram, and Facebook. A total of 150 filled questionnaires were received from participants in Nigeria’s six geopolitical zones. The findings show that participants were fully aware of the emergence of the 4IR. They indicated the impact of the 4IR era on the future job market as driverless cars, the Internet of Things (IoT), Artificial Intelligence (AI), robot technology, metaverse, blockchain and soft skills. It is evident from the findings that these technologies have changed the landscape of all industries. Nigerian information professionals partially possess some 4IR skills, including active learning and learning strategies, and people management, and have good knowledge of skills required for current and future job markets. Challenges associated with the preparation of future information professionals are lack of motivation for trainers; lack of synergy between training schools and the industry; inadequate funding for training schools; lack of qualified personnel; and poor attitude by students to acquire new skills. The study concludes that poor preparation of future information professionals could result in job losses, economic disparities, unemployment, and underperformance in the workplace. The study recommends that training schools should develop a core curriculum for future information professionals that covers characteristics of the 4IR.
Feedback Attention-Augmented Bilateral Network for Amodal Instance Segmentation

Amodal instance segmentation is a recently novel research in the field of computer vision, which includes the prediction of the visible and occluded parts of each object instance. Many modern computer vision methods demonstrate excellent performance by using the mechanism of looking and thinking twice and the attention mechanism. In this paper, we propose a feedback attention-augmented bilateral network. Specifically, after the convolutional network extracts the first round features of the input image, a feedback connection for relearning is introduced to extract the second round features. Meanwhile, a spatial detail preservation network with multi-scale attention feature module is designed to preserve and capture the rich spatial details information. Finally, a simple but practical feature fusion module is used to effectively combine both shallow and deep features from the backbone branch and the spatial branch. We achieve better results on COCOA dataset and D2SA dataset. For COCOA dataset, our method outperforms different state-of-the-art models by 1.2% and 1.6% mAP under “All regions” and “Things only”, and brings 9.4% improvement on D2SA dataset.
Automated payroll system

An automated payroll system is a computerized system designed to process employee compensation and related information.It includes various features such as employee information management, time tracking, tax management, and reporting.The system offers several benefits, including increased efficiency, accuracy, and compliance, while also improving employee satisfaction.However, the implementation of such a system can also present challenges related to cost, technical expertise, and employee resistance to change.Additionally, security and privacy risks associated with automated payroll systems need to be addressed through advanced security measures such as encryption and access controls.Overall, the literature suggests that the benefits of implementing an automated payroll system outweigh the challenges, making it an important tool for organizations of all sizes to improve their payroll processing.
Automatic Modulation Classification Under AWGN and Fading Channels Using Convolutional Neural Network

The advancement of 5G technology raises the need for wireless communication, increasing the number of customers who use it. The wireless communication band will become too crowded to support the increasing number of users in the future, resulting in a crowded spectrum. Furthermore, existing wireless technology has security flaws, limited capacity, and poor service quality. The implementation of cognitive radio (CR) technology is intended to improve the efficiency of the existing system and meet the bandwidth requirements of expanding users. Automatic modulation classification (AMC) is critical in CR technology. The objective of this research paper is to develop a modulation classifier by using a convolutional neural network classifier (CNN). The proposed classifier’s performance is evaluated at various SNRs and training rates in AWGN and fading channels. The study revealed that the model is more efficient than existing models in identifying 11 digital and analog modulation classes with less computational complexity and higher accuracy.
Charity Blockchain: The Practice of the Public Trust by China's No. 1 Public Welfare Digital Platform

The “Internet plus public welfare 3.0 era” featuring “universal participation” and “joint creation” has arrived. However, the issue of trust in public welfare has become increasingly prominent. How to build a trust mechanism has become an urgent and important issue, so as to stimulate the good energy of the Internet plus public welfare and promote the iteration and upgrading of the public welfare model. On September 5, 2019, Alibaba Public Welfare and Ant Financial blockchain team launched the “Charity blockchain"(CBC) for the first time. Its purpose is to provide public welfare organizations and public welfare projects with open and transparent systematic solutions. It is currently the first domestic full link that can trace the whereabouts of donations to innovative technology platforms. CBC is committed to promoting public transparency and public trust, and has become the number one public welfare digital platform on the Internet. So, how does CBC proceed from the realistic demands of the development of a harmonious society in contemporary China, build a sound and scientific public welfare process and standard scheme based on blockchain technology and platform, practice the original aspiration of public welfare, take responsibility for trust, and build a sound public welfare system?
Multidimensional Subset-based Systems for Bias Elimination Within Binary Classification Datasets

인공지능 기술이 발전함에 따라 인공지능 관련 공정성 문제가 주목받고 있다. 이에 따라 관련 연구가 많이 진행되었으나 모델 및 학습 방법 연구가 주를 이루고 있으며 근본적인 원인인 학습에 쓰이는 데이터 내에 존재하는 편향을 제거하기 위한 연구는 아직 미흡하다. 따라서 이 논문에서는 데이터 내에 존재하는 편향을 레이블 편향과 서브 그룹 편향으로 구분하고 편향을 제거함으로써 공정성이 향상된 데이터셋을 생성하는 시스템을 설계 및 구현한다. 제안하는 시스템은 서브셋 생성 단계와 편향 제거 단계로 구성된다. 먼저 서브셋 생성기는 기존 데이터 세트 내 각 값의 조합이 형성하는 고윳값들에 따라 단일 값만을 가지는 서브셋으로 나눈다. 이후 검증 데이터셋을 기반으로 기존 데이터셋을 검증하여 얻은 공정성 지표값을 기준으로 우세그룹과 약세그룹으로 서브셋을 구분한다. 다음으로 편향 제거기는 각 서브셋의 우세그룹을 대상으로 순차 추출 및 검증을 병행하여 약세그룹과의 차이를 줄이는 과정을 반복하여 서브셋에서 나타난 편향을 줄인다. 이후 편향이 제거된 서브셋들을 병합하며 공정한 데이터셋을 반환한다. 검증에 사용된 공정성 지표는 F1 score와 균등 확률(equalized odd)을 사용한다. 실제 Census income 데이터, COMPAS 데이터 및 Bank marketing 데이터를 검증 데이터로 사용한 포괄적인 실험을 통해 제안된 시스템이 더 나은 공정성 개선율을 산출하고 대부분의 기계 학습 알고리즘에서 더 높은 정확성을 제공함으로써 기존 기술을 능가한다는 것을 보여준다. As artificial intelligence technology develops, artificial intelligence-related fairness issues are drawing attention. As a result, many related studies have been conducted on this issue, but most of the research has focused on developing models and training methods. Research on removing bias existing in data used for learning, which is a fundamental cause, is still insufficient. Therefore, in this paper, we designed and implemented a system that divides the biases existing within the data into label biases and subgroup biases and removes the biases to generate datasets with improved fairness. The proposed system consists of two steps: (1) subset generation and (2) bias removal. First, the subset generator divides the existing data into subsets on formed by a combination of values in an datasets. Subsequently, the subset is divided into dominant and weak groups based on the fairness indicator values obtained by validating the existing datasets based on the validation datasets. Next, the bias remover reduces the bias shown in the subset by repeating the process of sequentially extracting and verifying the dominant group of each subset to reduce the difference from the weak group. Afterwards, the biased subsets are merged and a fair data set is returned. The fairness indicators used for the verification use the F1 score and the equalized odd. Comprehensive experiments with real-world Census incoming data, COMPAS data, and bank marketing data as verification data demonstrated that our proposed system outperformed the existing technique by yielding a better fairness improvement rate and providing more accuracy in most machine learning algorithms.
CEG: A joint model for causal commonsense events enhanced story ending generation

With the success of pre-trained language models, the performance of story ending generation has been dramatically improved while remaining challenging due to the lack of commonsense reasoning ability. Most previous works mainly focus on using commonsense knowledge to enhance the implicit correlations between words but ignore the hidden causality of sentences or events. In this paper, we propose Causal commonsense Enhanced joint model for story ending Generation (CEG), which incorporates causal commonsense events knowledge to generate a reasonable story ending. Specifically, we first develop a commonsense events inference model trained on GLUCOSE, which converts static knowledge into a dynamic generation model to discover unseen knowledge. It uses prompts to produce various commonsense events behind the stories as pseudo-labels of the dataset. Then, we propose a joint model for the causal events inference task and the story ending generation task to inject inference knowledge into the generation, which consists of a shared encoder, an inference decoder, and a generation decoder. In the causal events inference task, we use the shared encoder and the inference decoder to reason the causal events behind each sentence of the story context to help the model better understand the story and provide long-distance dependencies for the story ending generation. In story ending generation, we combine the hidden states of the causal events with the story context to generate the story ending by the shared encoder and the generation decoder. We jointly train the model on two tasks so that the generation decoder produces the story endings that better match the clues. Experimental results on the ROCStories dataset show that our model outperforms the previous works, demonstrating the effectiveness of the joint model and the generated causal events.
DEVELOPMENT OF AN E-COMMERCE STORE WITH INTEGRATED AUGMENTED REALITY

Augmented reality is a cutting-edge technology that is still in its infancy when it comes to being integrated into the e-commerce industry.However, by incorporating this technology, we can provide customers with a more immersive and personalized shopping experience.Currently, e-commerce lacks the capability to offer a virtual experience, but with augmented reality, this issue can be addressed and a new form of interaction between consumers and e-commerce platforms can be established.The future of e-commerce is expected to be heavily influenced by augmented reality technology as customers demand a more tailored shopping experience.In this study, we developed an e-commerce store by creating a web application that utilizes AR to overlay 3D models of products onto the real-world environment, thereby transforming the user experience.
Agile Methodologies based method for the UDA-ERP software implementation

Enterprise resource planning could be discussed for an organization in the SME sector due to the processes required to adopt it.On the other side, agile deployment methods contribute to reaching the development and implementation objectives based on the user experience through incremental deliverable software components.In this paper, we researched the application of the Lean Thinking (Kanban) methodology and Agile Deployment Methods to propose a method that collects a set of activities for its implementation.Two scenarios were considered for the UDA -ERP software validation: i) deployment of a software module without an implementation method and ii) deployment of a software module applying the proposed method.Consequently, the Servqual and User Experience (SUS) models were considered to apply satisfaction surveys to validate the results.The research results led to generating a five stages method that involves: i) initial contact with the client, ii) requirement definition, iii) Implementation in a test environment, iv) production and v) closure of the implementation project.
Creating a Teaching Platform for Course Design under the SCRUM Framework

The year 2019 was a turning point for the University of Monterrey (UDEM).The most important changes were the strategic initiatives obtained by an international consulting firm, from which several projects were derived in different areas of the university.One of the most relevant academic initiatives was the introduction of Active Learning within the university's Pedagogical Model.In order to carry this out, it was decided to create agile teams which started functioning under the SCRUM framework.Following the functionality structure of this system -planning, sprints, refinement, review, retrospective, among others -and through various design thinking methods -crazy eights, insights, empathy processes -innovative ideas were generated to provide a real solution to customer needs.Through different experiments, user testing and empathies, as well as a clear definition of the Objective Key Results -engagement, collaboration and enthusiasm -Shape Your Class was developed.In 2020, due to the pandemic, Shape Your Class migrated from a physical prototype to a digital technology platform.In a way, this allowed for an acceleration to online teacher training and enabled a greater utilization and diversification of resources that physically would not have been accessible.Shape Your Class is a virtual space for class design based on Active Learning that provides an authentic combination of functionality and innovation.Through a set of digital cards, various design options are presented, identifying: strategic elements, essential moments (class opening, learning tactics and class closure) and complementary moments (evaluation and reflection).It provides teachers with a ludic, playful and pedagogical way for the creative process of designing their sessions.In this way, Shape your Class was able to position itself within a highly innovative course design model, supported by 24 experiments, 95 courses, 119 teachers, and 969 students, obtaining above 80% of satisfaction rates in all Objective Key Results, as well as testimonials from both teachers and students.The most outstanding products that have already been delivered include the digital platform, course design model, user and technical manual, video tutorial, training model, communication strategy, among others.It is important to mention that Shape Your Class holds a patent and is registered in the Copyright National Institute in Mexico.The next steps for Shape Your Class include the delivery to the operational area of the Academic Development Center, with the objective of increasing the use of the tool to a greater volume of professors.Currently, training workshops are being offered and at the same time a greater diffusion of the implementation of Shape Your Class in the classroom is being created, always with the objective of growing academically as one of the best universities in Monterrey.
Integrated Model Knowledge Management System and E-Government to Improve Community Service

One of the government's functions for society is to provide services for administrative needs for business licensing, citizens' rights to education, health, welfare and other citizens' rights.The number of services that must be provided by the government to society where these services are increasingly complex requires the government through its staff to provide the best service.The ability of the government to provide the best service must be accompanied by high knowledge of government staff while currently the government service and knowledge of government staff have not been integrated so that it hampers government services to the community.The purpose of this research is to help the government improve the quality of services for society by integrating knowledge management systems with egovernment.The result of this research is the integration model between e-government and KMS.The research method uses a qualitative approach through observation and literature review to identify problems.Literature review is also used to find alternative solutions with information technology.
Navigating with Sharks: How the Persuasive Usability Framework Help to Create Successful Phishing Emails

A phishing email is a crime where a scammer sends an email to get sensitive data.Everyday phishing email attacks impact billions of people worldwide.Understanding the factors that lead to phishing email clicks is essential to combat this threat.Another area that targets email clicks is email marketing, a commercial message sent to people by email.This work considers that scammers can be using marketing techniques in phishing emails.However, little or no research was developed to investigate if the factors that impact email marketing clicks can also impact phishing email clicks.In this research, we developed the Persuasive Usability Framework (PUF), designed with the best practices of marketing and phishing.Now, we want to determine which factors included in PUF have a significant effect on phishing email CR.To address the research problem, we will survey 400 participants in a role-play job function in which they review emails and process sensitive information.We expect this research to contribute to understanding the factors that significantly affect phishing email CR, which is relevant for training people to identify phishing emails better.