Reviewerly/exHarmony huggingface dataset:
Twajood: Two-Factor Authentication Based on Distance and Face Recognition for Secure and Efficient Employee Attendance Monitoring

In this paper, we aim to solve critical issues organizations face during attendance monitoring. Conventional log-in systems fail to effectively ensure successful attendance monitoring, and challenges such as user manipulation, social distancing making biometric devices obsolete, and other issues arise. To address these challenges, we propose a two-factor authentication system based on distance and face recognition. The system incorporates advanced geo-tracking tools and technologies with web3 features and double-factor authentication using face recognition technologies and accompanying distance monitoring devices and tools. Our system provides secure, adaptive, and advanced log-ins for employees and attendance monitoring for employers. The proposed system is scalable by simply accompanying more distance-tracking devices with no additional support systems required. It is a smart, user-friendly, and effective log-in system designed to optimize resource and time allocation for any organization. Compared to other two-factor authentication systems, our system is faster, more secure, and does not require central devices. It is also more friendly and flexible, offering a viable solution for maintaining a safe environment and easing procedures for employees and managers.
Saw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images

The detection of objects in aerial images is impor-tant for many real world problems related to military defense, transportation, and etc. However, this is a challenging task as a result of the presence of various scales of objects in the same image, the large variety of contexts across aerial images, various brightness levels due to image acquisition at different times of the day and so on. To address these challenges, this paper introduces Saw-YOLOv5 for object detection in aerial images. Saw-YOLOv5 is a deep network based on YOLOv5, which was proposed for object detection in natural images. Saw-YOLOv5 extends YOLOv5 with the addition of several attention modules in its design. The results of our experiments, conducted on the aerial dataset delivered by the Turkey Technology Team for the Artificial Intelligence in Transportation Competition, showed that Saw-YOLOv5 outperforms previous methods, particularly for pedestrian detection, by yielding a mean mAP of 80.23% over all objects.
Drug Target Interaction Prediction Using Support Vector Machine (SVM)

Support vector machine (SVM), a classifier based on machine learning, has also been utilized. The training and evaluation of machine learning was conducted using data from a drug bank. The absence of negative DTI to train on is the greatest obstacle in using machine learning for this purpose. Despite the vast disparity in computing power, the support vector machine (SVM) obtained a superior area under the ROC curve (AUC) of 0.753 0.006 to the most advanced network-based method's 0.886 0.010. After extensive testing, we determined that SVM provided the maximum level of accuracy, 93.76 percent. This was unexpected and may indicate the existence of previously unknown DDI varieties or the maturation of scientific methodologies for studying DDIs. It could be used to characterize several DDI types that were not discovered until advanced processing methods or instruments, such as high-throughput screening, were developed.
A Comparative Assessment of Various Embeddings for Keyword Extraction

Automatic keyword extraction from a text document is the problem of identifying in-text words or phrases that best describe the content of the text document. Recently, word embeddings found application in keyword extraction as they improve the performance by incorporating semantic information. In this study, we focus various embeddings and and compare their performance in keyword extraction. To this aim, firstly, we modified a keyword extraction system called KeyBERT to work with different embeddings. Then, we run the modfied application using ten models on seven benchmark datasets. The experimental findings show that all-mpnet-base-v2 achieved statistically better results over the other models in precision, recall, and F1 score. Moreover, all-mpnet-base-v2 achieved highest scores for MAP and MRR and also retrieved the most number of relevant keywords on the average.
Detection and Analyzing Phishing Emails Using NLP Techniques

The most common detrimental technique used by attackers to deceive victims into disclosing personal information is phishing, in which they pose as trustworthy individuals or organizations often via email. Although fake email attacks are a common tactic used by cybercriminals, their use has recently increased as attacker's profit from victims' anxiety. As a result, further study is required to determine how to recognize bogus emails. This paper proposed a new model to extract the Arabic email content and compare it using three determinants based on neural language programming (NLP) for the purpose of discovering whether it is a legitimate email or a phishing email. The first is a black list of Arabic common phishing words, the roots of a black list of Arabic common phishing words, and a list of Arabic common phishing sentences, the best two results for applying the above conditions were (99% Legal and 96% Phishing) when using the three conditions together and (99% Legal and 94% Phishing) when using a blacklist of common words of phishing, and then will present and discuss the results obtained.
Improved Malaria Cells Detection Using Deep Convolutional Neural Network

This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting.
Leveraging User Experience for Enhancing Product Design: A Study of Data Collection and Evaluation

User feedback refers to the valuable information, opinions, and reactions provided by individuals who have used a particular product or service. It is a vital source of insight for businesses and developers to understand how users perceive and interact with their offerings. And since the feedback can come in various forms there are numerous methods and approaches when evaluating it. This paper aims to categorize and examine the different strategies for gathering insight and knowledge about the user experience.
Utilizing GloVe Embeddings for Deep Learning-Based Analysis of Research Paper Abstracts

Researchers are finding it harder and harder to locate relevant articles as the body of scientific literature expands at an exponential rate. Due to the sheer volume of publications, manual classification and categorization of these articles is no longer possible. By addressing the task of accurately classifying research papers based on their abstracts, the purpose of this paper is to address the task of improving the proposal and search procedures for efficient academic information retrieval. While ordering papers in computer science, mathematics, physics, and statistics, the models accomplish high precision, accuracy, recall, and F1-score by using profound learning calculations (LSTM, GRU, Bi-LSTM, and Bi-GRU) and GloVe word embeddings to catch semantic data. The LSTM, Bi-LSTM, GRU and Bi-GRU models were used to accurately classify the abstracts of research papers into computer science, mathematics, physics, and statistics. These models performed well concerning accuracy, recall, and F1-score, as well as accomplishing high precision. Automatic categorization of research papers was made possible by combining GloVe word embeddings with deep learning algorithms, which sped up information search and knowledge discovery. These models can help academic researchers and practitioners streamline the process of categorizing research papers and boost their research efforts.
Variable Grasp Pose and Commitment for Trajectory Optimization

We propose enhancing trajectory optimization methods through the incorporation of two key ideas: variable-grasp pose sampling and trajectory commitment. Our iterative approach samples multiple grasp poses, increasing the likelihood of finding a solution while gradually narrowing the optimization horizon towards the goal region for improved computational efficiency. We conduct experiments comparing our approach with sampling-based planning and fixed-goal optimization. In simulated experiments featuring 4 different task scenes, our approach consistently outperforms baselines by generating lower-cost trajectories and achieving higher success rates in challenging constrained and cluttered environments, at the trade-off of longer computation times. Real-world experiments further validate the superiority of our approach in generating lower-cost trajectories and exhibiting enhanced robustness. While we acknowledge the limitations of our experimental design, our proposed approach holds significant potential for enhancing trajectory optimization methods and offers a promising solution for achieving consistent and reliable robotic manipulation.
A medical history card utilizing the Blockchain technology

An authoritative healthcare system guarantees access to high-quality medical care to the population to boost their comprehensive health condition in the most effective means. The Blockchain technology is anticipated to make animminent change in the entire healthcare entity by securing the electronic medical records. The paper intends to put emphasison a web application especially, a medical history card with a vision of overcoming the deficiencies of the existing healthcare system of Bangladesh. Notwithstanding that, this researchlikewise delineates the ability of the decentralized database of the Blockchain technology to improvise the respective system. In particular, the aforementioned card highlights a patient's particulars, diagnoses, prescriptions, vaccinations, drug history, investigation profile, family history, blood transfusion history, and many more since birth. These records are the key to having a successful treatment whatsoever. With this in mind, the history card would securely upgrade the current healthcare world.
Object Detection System in Self-Driving Cars by Calibrated Camera and LiDAR Sensor data-Implementation of the Hybrid PointPillar Method

The object detection system in a self-driving car is a challenging part in the product development as all the decision making is depended on the objects detected by the LiDAR Sensor and the Camera, which then need to be fused by using sensor fusion techniques to process the data for object detection. A deep neural network is required for object detection which takes the camera data, LiDAR data, and calibrated data as the input and produces the object detection results bounded by 3D bounding boxes in camera data, LiDAR data as output. Using a neural network, the point cloud data (PCD) is converted into a 2D grid format in PointPillars method. KITTI training dataset, COCO training dataset is used to train and test the model which provides us a pytorch model, KITTI testing dataset is used to test the outputs of the trained model.
An extensive review of applications, methods and recent advances in deep reinforcement learning

Reinforcement learning is a learning problem where an actor must behave optimally in its given environment. A subtype of representation learning, deep learning approaches concentrate on extracting the required features for the problem. Deep Reinforcement Learning (DRL) is an extremely dynamic area that combines the two. It is also the most popular kind of machine learning since it can tackle a variety of complex decision-making issues that were previously beyond the capacity of a computer to think like a human. This paper first analyses the various case studies related to the applications of DRL. DRL applications involve robotics, video data analysis, transportation, industrial applications, Natural Language Processing (NLP) and so much more. DRL finds major success in applications involving finding out the best policy for an action to maximize an objective given a rich context in time. The second part of the paper will discuss different methods that are used to implement DRL concepts. These involve Deep Q Learning (DQN), Markov Decision Processes (MDP), and Deep Deterministic Policy Gradient (DDPG). Additionally, it discusses the two widely categorized types of methods: policy-based and value-iteration based. Lastly, this paper provides a survey of the recent advances made toward improving the performance of DRL-based implementations.
Dynamic Programming vs Q-learning for Feedback Motion Planning of Manipulators

Reinforcement Learning (RL) based methods have became popular for control and motion planning of robots, recently. Unlike sampling based motion planners, optimal policies computed by them provide feedback motion plans which eliminates the need for re-computing (optimal) trajectories when a robot starts from a different initial configuration each time. In related studies, an optimal policy (actor) and the associated value function (critic) are usually calculated preforming training in a simulation environment. During training, RL allows learning by interactions with the environment in a physically realistic manner. However, in a simulation system, it is possible to make physically unimplementable moves. Thus, instead of RL, one can make use of Dynamic Programming approaches such as Value Iteration for computing optimal policies, which does not require an exploration component and known to have better convergence properties. In addition, dimension of a value function is smaller than that of a Q-fuction, thereby lessening the severity of the curse of dimensionality. Motivated by these facts, the aim of this paper is to employ Value Iteration algorithm for motion planning of robot manipulators and elaborate its effectiveness compared to a popular RL method, Q-learning.
Sentiment-enhanced Neural Collaborative Filtering Models Using Explicit User Preferences

The integration of recommender systems contributes to the tourism industry as it provides tailored recommendations to users, assisting them in discovering and selecting the most suitable accommodation options based on their particular needs and preferences. By providing personalized recommendations that are tailored to each user's preferences and needs, hotel rec-ommendation systems could assist in reducing the time and effort required to find the best hotel options. In addition, users could discover new and relevant accommodation alternatives that they might not have previously considered. Despite the importance of the reasons underlying user preferences, existing review-based recommendation systems often neglect the importance of sentiment words linked to related item aspects. To address this need, this study presents a sentiment-enhanced hotel recommender system using neural collaborative filtering that incorporates information derived from both textual reviews and user-hotel relationships. This study employs a neural collaborative filtering approach to learn the relationship between user-hotel interactions and a sentiment-enhanced recommendation system. In regards to the experiment conducted in this study, our method enhances the model's ability to capture user preferences and item features through information from sentiment-enhanced text reviews in comparison to sub-ratings generated by users. Aspect-based sentiment analysis improves personalized hotel recommendations by taking into account the sentiment toward specific aspects of the hotel, such as cleanliness, service, or location.
BilinBot: A Bilingual Chatbot using Deep Learning

Chatbots are widely famous all over the world. Businesses of all scales, web services, customer supports and various others are integrating chatbots. However, most of them work in the English language. Our research aims to create a bilingual chatbot that can be used by people from different regions and converse in both Bangla and English. It will not just enable seamless communication in both languages, but enhance user experience for people of all fields and regions in Bangladesh. We named our chatbot “BilinBot” which comes from the idea of creating a “Bilingual Chatbot”, Our BilinBot can take questions as user input in English and Bangla language, predict the answer and respond accordingly as it has been trained. Natural language processing approaches are used to process the data. We performed ROUGE-1, ROUGE-2 and ROUGE-L evaluations to assess BilinBot. Our experimentation indicated that Google BERT is the best feature engineering approach for processing natural language, and for the training phase, LSTM and GRU performed best. We compared BilinBot with the state-of-art works and achieved better performances.
Optimizing Variable Selection and $k$ in the $k$-NN Classifier with Precision Objective

This paper focuses on maximizing the precision in binary classification problems using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -Nearest Neighbour (k-NN) algorithm by simultaneously selecting the variables and neighbourhood size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\boldsymbol{k})$</tex> . The inputs to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN include a set of variables, the neighbourhood size and the distance metric usually selected based on data characteristics. The first two are typically decided sequentially in many studies. The current simultaneous optimization problem is formulated by a mixed-integer linear fractional program and solved by parametric algorithm. The squared Euclidean distance metric is used but the model can be adapted for other distance metrics. The methodology is tested on ten publicly available datasets. Results showed that using at least half to all variables with appropriate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{k}$</tex> value can achieve better or equally good precision. An effective set of variables jointly determined with neighbourhood size can facilitate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN to perform classification with high precision.
Implementation of Bangla Extractive Update Summarization Task on BUSUM-BNLP-Dataset: A Multi-Document Update Summarization Corpus

The research inspires automated multi-document update summarization to perform a more complete linguistic (semantic) assessment of Bangla newspaper articles, research articles, or multiple texts by selecting the most essential ideas. Automated update summarizer methods are critical in identifying relevant information and up-to-date material for the user, minimizing the amount of time and effort necessary to read an entire article or many articles on a similar topic. Many updated summary approaches have been developed and implemented in the English language, while updated summarization in Bengali remains restricted. Keeping this in mind, we want to devise a straightforward method for developing a Bengali automatic updated summary task. To collaborate on this research, we proposed BUSUM-BNLP, a multi-document updated summarization Bangla dataset that is divided into six categories: international, sports, politics, entertainment, Bangladesh, and business. Each article has been divided into two groups, A and B. Essentially, every pair of A and B has five news article files, each with a title, date-time, and pertinent news content. Similarly, for each set of documents, we developed three human-written summaries. To generate a quality dataset, we reviewed 1,000 Bangla news articles from major newspaper e-platforms (Prothom Alo, Jugantor, Kaler Kantho, BBC Bangla) and researched certain Bangla language and summarizing norms. Since there are two kinds of summarization methods: extractive and abstractive. In this paper, we created a TF-IDF-based model and used a pre-trained SentenceRank to obtain an updated Bengali summarization model. We tested these two models using our BUSUM-BNLP dataset, and the experienced TF-IDF model outperformed the pre-trained SenteneRank model.
Analysis of the results of using gesture recognition in software applications

The paper presents an analysis of the obtained results of control and interaction by means of a gesture of different software. Four different software applications have been developed for this purpose, using a specialized or web camera. The user performs the gesture using their hand, a special stick, a printed gesture or using a black glove. The data were processed using specialized software for working with statistical data. The importance of external factors on the final result is determined.
Contextual Bangla Next Word Prediction and Sentence Generation Using Bi-directional RNN With Attention

In recent years, there has been a demand for natural language processing techniques that accurately predict the following word in a phrase. Existing Bangla next word prediction systems have significant draw- backs, including the inability to predict outside of their library and lengthy training cycles. To address these limitations, we present a Bi-directional Long Short-Term Memory (Bi-LSTM) with a Self-Attention method for developing language models that can anticipate the next word from a given input. Our models beat existing state-of-the-art systems. Our models obtain high testing accuracy for several n-gram language models, including 97.98% for the 7-gram, 97.97% for the 6- gram, and 97.91% for the 5-gram. Furthermore, the training time and number of epochs required to reach the desired accuracy are greatly reduced by our suggested system. While other works utilized 1000s of epochs to reach desired precision, we employed the Self-Attention method to achieve the same degree of accuracy in only 200 epochs. Our goal is to contribute to the advancement of natural language processing technology for use in a variety of industries such as healthcare, education, and finance. We hope to assist industry in developing sustainable technology by delivering accurate and efficient language models
Enhancing Performance of Abstractive Multi-Document Update Summarization on TAC Dataset

In this paper, we investigate the efficacy of various cutting-edge models for update summarization on the TAC 2009 dataset. To construct abstractive and extractive summaries of news items, we use the T5 Transformer model and Textrank + Pegasus model. Our goal is to assess how well these models capture key information from updates and generate coherent and useful summaries. Here we use conventional assessment measures such as ROUGE to assess the performance of the models. We analyze the fluency, coherence, and informativeness of generated summaries from the T5 Transformer model, Textrank + Pegasus, and TensorFlow models against human-authored gold summaries.