Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Human Resource Management in Public Sector of Bahrain

The study's major goal will be understanding the importance of artificial intelligence (AI) in the human resources management, particularly in Bahrain. Also, to look into the issues that have arisen as a result of training programs launched in Bahrain's public sector. The training and development of employees are two of the most important topics tackled. The importance of training in delivering the required knowledge and capabilities should not be overlooked. As a result, the training must be successful enough to meet these goals. This study examines a number of contextual elements that have been discovered to have an impact on vocational training in different combinations with other previously examined effective factors. It will enquire into their relationship. It also identifies the various types of relationships that exist between the efficiency of training and environmental elements. The Kirkpatrick training model is used to build the study's framework. This paradigm has four stages that measure the success of training: reaction, learning, behavior, and result. This modest study project offers a glimpse into the future of employing AI to better understand HR practitioners’ attitudes and perspectives across a variety of systems. According to the study, Bahrain's public sector has been a tremendous potential in order to keep pace with the digital revolution by implementing its vision (2030 vision). As a result, the staff mix of commercial organizations has changed. It gives men and women the opportunity to compete in a variety of occupations. As a result of the requirement for gender equality, human resource management will be burdened in new ways. This also allows for a nice feminist component to be included. It is suggested that current artificial intelligence be used as a foundational method for firms that operate in a volatile environment.
Understanding the Use of Artificial Intelligence (AI) for Human Resources in the Dubai Government

The use of technology to enhance the quality of life and facilitate everyday activities is growing. Similarly, organizations are also enjoying technology implementation and its perceived benefits. This analysis also shows that AI can help address a wide range of regional economic and social challenges. The researcher particularly focused on the human resources departments to apply artificial intelligence in government institutions in the United Arab Emirates (Dubai government). To achieve this, the study followed the quantitative approach by interviewing n = 27 employees of the Dubai government at the different administrative levels, upper, middle, and executive, in several institutions in the UAE. The data was gathered specifically about the impact of artificial intelligence applications on human resources departments in Dubai. Important results and recommendations were reached, the most important of which was the establishment of public policies and ethical laws for using artificial intelligence applications in human resources in Dubai. Also. The obligation for all employees of government institutions to take training and development courses provided to them through distancing learning. Finally, deepening the leading role of the UAE in leading the applications of artificial intelligence in all fields will bring positive, constructive changes in the relevant institutions leading to greater national progress and growth.
Age Prediction of Social Media Users: Case Study on Robots in Hospitality

Social media has gained popularity and we witness a vast volume of publicly available social media posts where people are commenting on different topics. This discussion contains a lot of valuable information deeply hidden inside the data and its metadata, which can be valuable for different stockholders. To extract this information different methods have been proposed in the literature and methods relied on different aspects of data and were based on diverse techniques such as text mining, machine and deep learning, predictive analytics, and natural language processing. This work proposes a method that relies on transformer-based architectures and it is based on supervised machine learning that predicts the age indirectly hidden in the description users provided in their profiles. To test the accuracy of the proposed method the case study of robots acceptance in hospitality has been considered. Relevant posts from social media Twitter have been collected and the proposed model tested. Results from extensive experimental evaluation demonstrate the suitability of the proposed method achieving high accuracy of age prediction, to the extent of 82% on test data. To demonstrate the usability and value of predicting the age of social media users we calculate the emotions as well as sentiment in posts and investigate the acceptance of robots in hospitality for different age groups.
Path Following Algorithm with Small Error for Guide Robot

This paper proposes a path-following method, the Robot Side method, for controlling the guide robot. It assumes that we prepare waypoints in the environment, and the robot moves along the waypoints. The conventional path-following methods, such as the pure pursuit and the parallelogram, overshoot when the waypoint is not straight. The proposed method has three states depending on the robot’s position, angle, and path for reducing the trajectory deviation from the path. First, when the robot is far from the path, it controls the robot to approach the path rapidly, adjusts its direction to the path’s direction, and finally controls the robot to move along the path. We carried out the comparison experiments, and the results showed that the proposed method showed small trajectory errors and quick following behavior.
The Effectiveness of Applying Artificial Intelligence in Recruitment in Private Sectors

Companies seek for improving quality in order to survive in the competitive market. To survive, you must search for the right candidates and hire them in the best-fit position at the right time. Due to the rapid development in the world, several methods (traditional methods) are no longer efficient. People and companies are looking forward deploying more efficient methods in recruiting candidates. Currently, the trend in recruitment is moving forward applying artificial intelligence. This paper focus on impact of Artificial intelligence (AI) on private sectors. Since this industry is introduced recently, various companies are interested in applying AI. But the dilemma is that the advantages and disadvantages of applying AI in HR is still not commonly known for all of them. Also, the long-term effects for this industry still not revealed.
Factors that Impact a Company’s Digitalization and Employee Skills: The Case of Saudi Aramco

With the effect of rapid advances in communication and technology, it has become a necessity for businesses to keep up with the times. In this process, it is necessary to carry out digitalization-based transformation activities such as revising the ways of doing business in businesses and designing new business models. In addition to the feeling of having to comply with the transformation, the main goal should be for the managers who manage the transformation to combine digitalization with creativity and innovation, and to try to create and present the technology of the future in advance. In the digital world, which is developing faster than ever with the development of the digital age, businesses are trying to transform rapidly, while the adoption of digital technologies continues to progress rapidly. The objective of this research is to study the impact of company size, Six Sigma, and online staff training of Saudi Aramco on its digitalization (i.e., digital transformation), which in turn influences its employee skills. The author supported the argument from the literature review and asked seventy female and male Aramco employees about their opinions on the impact of their company size, Six Sigma, and online training on the company's digitalization and their skills. The author posits three propositions based on the argument and respondents’ opinions.
Harmony Gradient Boosting Random Forest Machine Learning Algorithms for Sentiment Classification

The exchange of ideas and opinions on social media is increasingly becoming essential. A vast amount of data is produced by the intense analysis of online social media. The harmony random forest machine learning algorithm is suggested in this study as a method for sentiment analysis on social media. Additionally, social media data sentiment analysis employs the gradient boosting harmony algorithm. Compared to other methods, the harmony gradient boosting random forest machine learning technique yields better outcomes. In contrast to the gradient boosting approach, which creates ensembles of weak and shallow succeeding trees, random forest develops ensembles of independent trees. In comparison to the harmonic random forest, the suggested approach has a classification accuracy of 5.68%. Thus, the suggested harmony gradient boosting random forest machine learning is more effective than.
Understanding People’s awareness towards social engineering with survey

The world is recovering from Covid-19, and along with that it has brought the zeal to use the digital media, concepts like work from home, connecting the whole world using applications and social media. However, with good things follow bad and we observe a lot of people being affected by social engineering attack via multiple means be it as elementary as an unfamiliar person calling us to ask us about our day or complicated and puzzling as someone acting like the victim’s senior. In some cases, people are aware of the process but are unaware of the terms they are victimized with others do not know many kinds of social engineering attacks. Therefore, it is imperative for an organization and an individual that they are aware of how Social Engineering is carried out. In this paper, we represent the survey filled by more than 100 people from diverse age groups and work profiles seeking their views on the attack and knowledge about social engineering.
Video Anomaly Detection Using Variational Autoencoder

Video anomaly detection in the surveillance video is one of the essential components of the intelligent video surveillance system. However, anomaly detection remains an ill-defined problem, despite the diverse applications due to its rareness and equivocal nature. A Long Short Term Memory - Variational Autoencoder (LSTM-VAE) model is proposed to detect video anomalies. The model consists of a spatial encoder comprised of convolutional layers, a temporal encoder as well as a decoder comprised of Convolutional LSTM (ConvLSTM), and a spatial decoder consisting of transposed convolution layers. The generative model is trained only on normal video clips with the objective of minimizing the reconstruction error. Then, the trained model is tested on the test video sequences comprised of both normal and abnormal incidents. The reconstruction error corresponding to the test frame sequences having video anomalies will be very high as the model is not trained to reconstruct them. Subsequently, the corresponding frames will have a low regularity score. An appropriate threshold regularity score is set to segregate the anomaly frames from the normal ones. Frames having a regularity score less than the set threshold value are considered as anomalous frames. The model is developed by using one of the publicly available bench-marked video anomaly datasets, i.e., UCSD Ped2. The performance metrics of the proposed model are promising.
Real-time Powered Wheelchair Assistive Navigation System Based on Intelligent Semantic Segmentation for Visually Impaired Users

People with movement disabilities may find powered wheelchair driving a challenging task due to their comorbidities. Certain visually impaired persons with mobility disabilities are not prescribed a powered wheelchair because of their sight condition. However, powered wheelchairs are essential to the majority of these disabled users for commuting and social interaction. It is vital for their independence and wellbeing. In this paper, we propose to use a semantic segmentation (SS) system based on deep learning algorithms to provide environmental cues and information to visually impaired wheelchair users to aid with the navigation process. The system classifies the objects of the indoor environment and presents the annotated output on a display customised to the user's condition. The user can select a target object, for which the system can display the estimated distance from the current position of the wheelchair. The system runs in real-time, using a depth camera installed on the wheelchair, and it displays the scene in front of the wheelchair with every pixel annotated with distinguishable colour to represent the different components of the environment along with the distance to the target object. Our system has been designed, implemented and deployed on a real powered wheelchair for practical evaluation. The proposed system helped the users to estimate more accurately the distance to the target objects with a relative error of 19.8% and 18.4% for the conditions of a) semi-neglect and b) short-sightedness, respectively, compared to errors of 47.8% and 5.6% without the SS system. In our experiments, healthy participants were put in simulated conditions representing the above visual impairments using instruments commonly used in medical research for this purpose. Finally, our system helps to visualise, on the display, hidden areas of the environment and blind spots that visually impaired users would not be able to see without it.
Computer Vision-Based Bengali Sign Language To Text Generation

In the whole world, around 7% of people have hearing and speech impairment problems. They use sign language as their communication method. As for our country, there are lots of people born with hearing and speech impairment problems. Therefore, our primary focus is to work for those people by converting Bangla sign language into text. There are already various projects on Bangla sign language done by other people. However, they focused more on the separate alphabets and numerical numbers. That is why, we want to concentrate on Bangla word signs since communication is done using words or phrases rather than alphabets. There is no proper database for Bangla word sign language, so we want to make a database for our work using BDSL. In recognition of sign language (SLR), there usually are two types of scenarios: isolated SLR, which takes words by word and completes recognize action, and the other one is continuous SLR, which completes action by translating the whole sentence at once. We are working on isolated SLR. We introduce a method where we are going to use PyTorch and YOLOv5 for a video classification model to convert Bangla sign language into the text from the video where each video has only one sign language word. Here, we have achieved an accuracy rate of 76.29% on the training dataset and 51.44% on the testing dataset. We are working to build a system that will make it easier for hearing and speech-disabled people to interact with the general public.
A Light Weight Approach for Real-time Background Subtraction in Camera Surveillance Systems

Real time processing in the context of image processing for topics like motion detection and suspicious object detection requires processing the background more times. In this field, background subtraction solutions can overcome the limitations caused by real time issues. Different methods of background subtraction have been investigated for this goal. Although more background subtraction methods provide the required efficiency, they do not make produce a real-time solution in a camera surveillance environment. In this paper, we propose a model for background subtraction using four different traditional algorithms; ViBe, Mixture of Gaussian V2 (MOG2), Two Points, and Pixel Based Adaptive Segmenter (PBAS). The presented model is a lightweight real time architecture for surveillance cameras. In this model, the dynamic programming logic is used during preprocessing of the frames. The CDnet 2014 data set is used to assess the model's accuracy, and the findings show that it is more accurate than the traditional methods whose combinations are suggested in the paper in terms of Frames per second (fps), F1 score, and Intersection over union (IoU) values by 61.31, 0.552, and 0.430 correspondingly.
What’s in my Fridge?

The question of what to prepare today affects all of us. Most of the time, when asked what we would like to eat, we are unable to make a decision since we don’t have a variety of options. The goal of this project is creating an application called “What’s in My Fridge." This application will scan the ingredients in the fridge and suggest a meal that can be cooked based on the ingredients available in the fridge. The application will shorten the time and effort of cooking the meal. Additionally, our technique consists of 7 phases that will assist in developing our application. Identifying the objects that the user scans might be one of our limitations. In the future, we’ll enhance our application by adding new features like alerting users when an item is about to expire.
Automatic Attendance Management System Using AI and Deep Convolutional Neural Network

Traditionally, the attendance of scholars has been a significant concern for colleges, and schools mainly need to pay valuable of your time, and it may be tedious to mark group actions manually. The present biometric group action system isn't automatic; it wastes heaps of your time, is tough to take care of, and needs a queue for scanning fingerprints to mark their group action. Within the epoch, everybody encompasses a smartphone and is connected via the web on every occasion. During this paper, group action watching will be done through smartphones offered to most school members. Some in-style object detection algorithms are back propagation neural network, region-based convolution network (RCNN), quicker RCNN, and single-shot detector. Our unified structure relies on the YOLO V3 (You solely look once) formula for face detection and Microsoft Azure victimization face API for face recognition (face database). The distinctive half maybe a camera put in within the room can take footage doubly one at the beginning and one at the tip to confirm students have attended complete category. YOLO V3 can first count the scholars in a picture, followed by distinguishing faces as well-known and unknown, generating spreadsheets individually, and distributing an email at the tip of the month to students, parents, and college. The designed system performs expeditiously in real-time implementation for enumeration and detection. Our entire system has tried to assemble high accuracy in face detection and performance.
Exact PPS sampling with bounded sample size

Probability proportional to size (PPS) sampling schemes with a target sample size aim to produce a sample comprising a specified number n of items while ensuring that each item in the population appears in the sample with a probability proportional to its specified "weight" (also called its "size"). These two objectives, however, cannot always be achieved simultaneously. Existing PPS schemes prioritize control of the sample size, violating the PPS property if necessary. We provide a new PPS scheme, called EB-PPS, that allows a different trade-off: EB-PPS enforces the PPS property at all times while ensuring that the sample size never exceeds the target value n. The sample size is exactly equal to n if possible, and otherwise has maximal expected value and minimal variance. Thus we bound the sample size, thereby avoiding storage overflows and helping to control the time required for analytics over the sample, while allowing the user complete control over the sample contents. In the context of training classifiers at scale under imbalanced loss functions, we show that such control yields superior classifiers. The method is both simple to implement and efficient, being a one-pass streaming algorithm with an amortized processing time of O(1) per item, which makes it computationally preferable even in cases where both EB-PPS and prior algorithms can ensure the PPS property and a target sample size simultaneously.
Solar Power Estimation Methods Using ANN and CA-ANN Models for Hydrogen Production Potential in Mediterranean Region

Among renewable energy sources, investments in solar and hydrogen energy as eco-friendly energy sources are continuously expanding. The energy generated by solar photovoltaic (PV) panels, varies during the day. In order to ensure energy continuity, hybrid systems are being developed by integrating solar energy systems with different energy systems. Solar-assisted hydrogen production energy systems have a number of advantages, such as having zero carbon emissions, being able to be used as fuel in fuel cells, and having a high energy density of hydrogen. The sustainable energy obtained from solar energy is used to meet the energy demand in numerous applications due to the high solar energy potential in Mediterranean Region. In this article, solar PV panel output power prediction based on Artificial Neural Network (ANN) and Cultural Algorithm-Artificial Neural Network (CA-ANN) for a solar-assisted hydrogen production energy system have been performed. In this study, the real time data of a 4 MW solar PV power plant is used to estimate solar PV output power. Predicting the output power of PV power plants with high accuracy is of great importance in the realization of energy management, hydrogen production and storage strategies. The numerical results obtained at the end of the study are presented. The results obtained with the ANN and CA-ANN methods are given in detail. In addition, the hydrogen production amount in the Mediterranean region is calculated using MATLAB/Simulink software. The analysis results show that the Mediterranean region has high efficiency hydrogen production potential.
Attribute-based keyword search encryption for power data protection

To protect the privacy of power data, we usually encrypt data before outsourcing it to the cloud servers. However, it is challenging to search over the encrypted data. In addition, we need to ensure that only authorized users can retrieve the power data. The attribute-based searchable encryption is an advanced technology to solve these problems. However, many existing schemes do not support large universe, expressive access policies, and hidden access policies. In this paper, we propose an attribute-based keyword search encryption scheme for power data protection. Firstly, our proposed scheme can support encrypted data retrieval and achieve fine-grained access control. Only authorized users whose attributes satisfy the access policies can search and decrypt the encrypted data. Secondly, to satisfy the requirement in the power grid environment, the proposed scheme can support large attribute universe and hidden access policies. The access policy in this scheme does not leak private information about users. Thirdly, the security analysis and performance analysis indicate that our scheme is efficient and practical. Furthermore, the comparisons with other schemes demonstrate the advantages of our proposed scheme.