Reviewerly/exHarmony huggingface dataset:
Improving Information Extraction from Pathology Reports using Named Entity Recognition

Abstract Pathology reports are considered the gold standard in medical research due to their comprehensive and accurate diagnostic information. Natural language processing (NLP) techniques have been developed to automate information extraction from pathology reports. However, existing studies suffer from two significant limitations. First, they typically frame their tasks as report classification, which restricts the granularity of extracted information. Second, they often fail to generalize to unseen reports due to variations in language, negation, and human error. To overcome these challenges, we propose a BERT (bidirectional encoder representations from transformers) named entity recognition (NER) system to extract key diagnostic elements from pathology reports. We also introduce four data augmentation methods to improve the robustness of our model. Trained and evaluated on 1438 annotated breast pathology reports, acquired from a large medical center in the United States, our BERT model trained with data augmentation achieves an entity F1-score of 0.916 on an internal test set, surpassing the BERT baseline (0.843). We further assessed the model's generalizability using an external validation dataset from the United Arab Emirates, where our model maintained satisfactory performance (F1-score 0.860). Our findings demonstrate that our NER systems can effectively extract fine-grained information from widely diverse medical reports, offering the potential for large-scale information extraction in a wide range of medical and AI research. We publish our code at https://github.com/nyukat/pathology_extraction.
Effect of Word2Vec Weighting with CNN-BiLSTM Model on Emotion Classification

Emotion is an element that can influence human behavior, which in turn influences a decision. Human emotion detection is useful in many areas, including the social environment and product quality. To evaluate and categorize emotions derived from text, a method is required. As a result, the CNN-BiLSTM model, a classification method, aids in the analysis of the text's emotional content. A word weighting technique employing word2vec as a word weighting will help the model. The CNN-BiLSTM model with Word2vec as a pre-trained model is being used in this study to find the findings with the highest accuracy. The information is split into two groups: training and testing, and it is categorized into six categories according to how each emotion manifests itself: surprise, sadness, rage, fear, love, and joy. The best outcome from the CNN-BiLSTM model's accuracy of emotion classification is 92.85%.
Extending F1 Metric, Probabilistic Approach

This article explores the extension of well-known F1 score used for assessing the performance of binary classifiers. We propose the new metric using probabilistic interpretation of precision, recall, specificity, and negative predictive value. We describe its properties and compare it to common metrics. Then we demonstrate its behavior in edge cases of the confusion matrix. Finally, the properties of the metric are tested on binary classifier trained on the real dataset.
Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Maps

Autonomous Ground Vehicles (AGVs) are essential tools for a wide range of applications stemming from their ability to operate in hazardous environments with minimal human operator input. Effective motion planning is paramount for successful operation of AGVs. Conventional motion planning algorithms are dependent on prior knowledge of environment characteristics and offer limited utility in information poor, dynamically altering environments such as areas where emergency hazards like fire and earthquake occur, and unexplored subterranean environments such as tunnels and lava tubes on Mars. We propose a Deep Reinforcement Learning (DRL) framework for intelligent AGV exploration without a-priori maps utilizing Actor-Critic DRL algorithms to learn policies in continuous and high-dimensional action spaces directly from raw sensor data. The DRL architecture comprises feedforward neural networks for the critic and actor representations in which the actor network strategizes linear and angular velocity control actions given current state inputs, that are evaluated by the critic network which learns and estimates Q-values to maximize an accumulated reward. Three off-policy DRL algorithms, DDPG, TD3 and SAC, are trained and compared in two environments of varying complexity, and further evaluated in a third with no prior training or knowledge of map characteristics. The agent is shown to learn optimal policies at the end of each training period to chart quick, collision-free exploration trajectories, and is extensible, capable of adapting to an unknown environment without changes to network architecture or hyperparameters. The best algorithm is further evaluated in a realistic 3D environment.
Learning to Predict Navigational Patterns From Partial Observations

Human beings cooperatively navigate ruleconstrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes.Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations.However, algorithmically defining these navigational patterns is nontrivial.This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in realworld environments from partial observations only.We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data.We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset.We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.
Collaborative approach of AI in Internet of Things to incorporate M2M Interaction

A I is a branch of computer science which is a study of developing machines to perform tasks. On the other hand IoT is all about things (devices) that connect to internet to perform tasks. The primary criteria of both the techniques are to incorporate machine-machine intervention. AI provides automation to com- plete the tasks without any human intervention. The collaboration of both the techniques will definitely results into a better outcome. Internet of things has become very popular now. Artificial intelligence mainly uses machine learn- ing algorithms into IoT. Artificial intelligence is used to easily identify useful patterns and anamolies form the data collected from IoT sensors. Artificial intelligence is easily embedded into various programs and chips which are all connected to IoT networks. IoT is helping the business world to capture large amount of data from various sources. In general both the techniques are very powerful where IoT (nerve), AI (brain). Artificial intelligence along with Internet of Things provides a wide range of advantages for businesses and con- sumers, including proactive intervention, tailored experiences, and intelligent automation. Artificial intelligence is combined with IoT which explains how the data is analyzed and makes decision and acts on the data without any human intervention. The combination of both the technologies plays a vital role in all real-time applications. The combination of AI (Artificial Intelligence) with the IoT has the potential to design and develop industries, businesses, and economies to operate properly. IoT with AI generates good results that help in proper decision-making with little or no human intervention. The combination of both the technologies can help both the normal and the business people. IoT deals with devices connect- ing with each other over the internet and Artificial Intelligence is focused with devices learning from their data and experience. Prerequisites: IoT in automatic irrigation, IoT in automatic car, IoT in auto- matic liquid monitoring.
ABAC Scheme on Electronic Health Records Using Hyperledger Fabric

In this modern world, data is getting more significant with new emerging tech- nologies, and a transparent system must be made with security mechanisms.For example, an Electronic Health Record (EHR) is a system that records patient data in a digital format across different healthcare settings. The ulti- mate benefits of Blockchain technology are to safeguard patient records and pave a path for health centers, clinics, and medicals. A high-level invulnerable system is required to prevent the rising cybersecurity threats targeting these organizations. A Blockchain that fulfills the HIPAA regulation is a protective barrier and transparency required by many fields. Attribute-based access con- trol (ABAC) is a scheme that provides an effective authentication and autho- rization model that considers attributes from the user, instead of roles, to grant permission to the user to access. This system makes it easier for Certificate Authorities (CA) to manage user permissions by assigning specific rights and privileges based on the individual’s characteristics. Thus, writing policies will secure data transparency as the one requested can only view the data if they satisfy the policy’s criteria.
Synchronization of quantum communication over an optical classical communication channel

Precise synchronization between transmitter and receiver is crucial for quantum communication protocols, such as Quantum Key Distribution (QKD), to efficiently correlate the transmitted and received signals and increase the signal-to-noise ratio. In this work, we introduce a synchronization technique that exploits a co-propagating classical optical communication link and test its performance in a free-space QKD system. Previously, existing techniques required additional laser beams or relied on the capability of retrieving the synchronization from the quantum signal itself, though this is not applicable in high channel loss scenarios. On the contrary, our method exploits classical and quantum signals locked to the same master clock, allowing the receiver to synchronize both the classical and quantum communication links by performing a clock-data-recovery routine on the classical signal. In this way, by exploiting the same classical communication already required for post-processing and key generation, no additional hardware is required, and the synchronization can be reconstructed from a high-power signal. Our approach is suitable for both satellite and fiber infrastructures, where a classical and quantum channel can be transmitted through the same link.
Thrust Vectoring and its Effects on Vertical Take-off and Landing of Monocopters - A Review

Vertical Take-off and Landing (VTOL) vehicles have evolved and progressed through the ages and have come to the level of present-day advanced drone technology. Unmanned aerial vehicles (UAVs) are widely used in the modern world for numerous applications. These vehicles are basically VTOL systems that are miniature in structure and cost-effective when compared to their predecessors. This paper covers an idea on the conventional VTOL systems and modern drones which is then followed by focusing more on a lesser explored area of drones which is the monocopter, especially thrust vectoring monocopter. Monocopter has inherent advantages like small structure and lightweight. The paper also discusses different types of conventional monocopters along with their benefits and limitations. The thrust vectoring monocopter is given highlight because it has added advantage of compensating the inherent autorotation in the conventional monocopter with the help of control fins thereby building a stable cost-effective system. Hence this paper presents a comparative study on the VTOL systems, conventional monocopters and thrust vectoring monocopter.
Towards a New Job Offers Recommendation System Based on the Candidate Resume

The recruitment web platforms are a solution that has solved several problems, such as eliminating the filing of paper files to organizations, reducing recruitment time, etc.In recent years and especially with the pandemic, the job offers increased across the web with a great diversity of professional platforms and networks offering these offers.This increase in the number of job openings and the number of web platforms presents difficulties for candidates.The candidates must register and create an account on each recruiting platform.This implies a considerable waste of time for candidates to manage their accounts and to find suitable jobs according to the candidate's profile on each platform.Most recommendation systems are oriented towards the jobs platform and not towards the candidate, and depend heavily on the jobs platform.So candidates can apply on job offers specific to their specialties and skills, we propose a recommendation system for the online job offers to allow the classification of job offers to correspond to the profiles (e-portfolio) of graduates or candidates according to their skills.Our research work is carried out from extracting to calculating similarity and ranking passing by specific processing for the preparation of our data set and the classification of job offers and e-portfolios according to the skill specialty.
A Study of Prediction Model for Capture Fisheries Production in Indonesian Sea Waters Using Machine Learning

The potential for capture fisheries in Indonesia is a priceless wealth. This wealth has not been explored optimally. Fisheries resources are included in the category of renewable resources whose sustainability needs to be considered. This is important in maintaining food security which will increase over time, due to population growth. Capture Fisheries Production Prediction Model is needed to find out what determining variables affect capture fisheries production. There are many methods for predicting, the method that is widely used today is using machine learning since it ability to handle complex jobs with large input data. This research is a literature study, which aims to: (1) identify and analyze machine learning methods that are suitable for predicting capture fisheries production, and (2) identify variables that can affect capture fisheries production. The results of the study show that the Neural Network method is most widely used as a predictive model. In addition, the Random Forest and Linear Logistics methods provide better accuracy results. The results of the study also succeeded in finding 12 determining variables for the capture fisheries production prediction model.
Pengaruh Metode Pembelajaran E-Learning untuk Meningkatkan Motivasi Belajar Siswa

Lately there is a learning method that is often used when carrying out the process learning, namely the learning method of e-learning. This learning method is a method learning by implementing electronic digital learning systems that can be accessed anywhere at any time you want it. Therefore there is an e-learning learning method that combines learning with technology that has been highly developed in this generation and we as the next generation must be able to take advantage of existing technology as best as possible. This change in learning methods has various impacts negative impact on users such as educators and students who have a tendency not to understand what is being taught, lack of learning about technology to be used and so on. As for all that we should be able to take advantage of technology that has been created in such a way as possible. So that's the main goal of this e-learning learning method is actually used to increase student learning motivation in order to have a new atmosphere in the learning process so that students can develop deeper and wider knowledge.
A State of Art Survey for Big Data Processing and NoSQL Database Architecture

Internet corporations and other types of enterprises are quickly implementing NoSQL technology rather than merely SQL because of the introduction of Big Data.The simplicity of the design, the capacity to scale horizontally, and tighter control over availability are some benefits.Because of the schema-free data model they use, more businesses are beginning to recognize that NoSQL databases are a superior method for managing bulk amount of structured, semi-structured, and unstructured data currently being collected and handled.As a result, NoSQL database is increasingly viewed as a viable alternative to relational databases.For instance, NoSQL database is regularly utilized to gather and store data about social networking sites.This article targets to introduce the ideas that underpin NoSQL, as well as a review of the pertinent literature, an overview of the many types of NoSQL databases, and reasons both for and against the use of NoSQL.To analyze the known advantages of NoSQL and demonstrate the distinctions between the SQL and NoSQL methods, a simple SWOT analysis was performed.In the final part of the article, we draw some comparative studies of the NoSQL database and conclusions to broaden the scope of our previous work.
Credit Card Fraud Detector Based on Machine Learning Techniques

The massive development of technology has affected commerce and given rise to e-commerce and online shopping. Nowadays, consumers prioritize e-shopping over the brick and motor stores due to numerous benefits, including time and transport convenience. However, this progressive upsurge in online payment increases the number of credit card frauds. Therefore, defending against fraudsters’ activity is obligatory and can be achieved by securing credit card transactions. The objective of this paper is to build a model for credit card fraud detection using Machine learning techniques. An innovative approach to credit card fraud detection grounded on machine learning is proposed in this study. Machine learning (ML) is an artificial intelligence subfield comprising learning techniques from experience and completing tasks without being explicitly programmed. Three ML techniques have been used: Support vector machine, logistic regression, Random Forest, and Artificial Neural network. First, the most significant features that affect the type of transaction (fraud or not fraud) have been selected. After that, the ML model was applied. The performance of the proposed approach is tested using a confusion matrix, recall, precision, f-measure, and accuracy. The proposed method is tested using accurate data that consists of 284807 transactions. The result shows the efficiency of the proposed approach.
Pentingnya Desain Interior terhadap Kenyamanan Pengunjung Perpustakaan

&lt;p&gt;Interior design is very important and needs to be considered when building a library. Efforts to present a library interior design that meets the needs and comfort of library visitors requires good understanding and planning. Library interiors that combine aesthetic values will affect the use of library services for users. This study uses the method of literature study or literature review through scientific concepts that discuss library science from several library journals and interior design. The research develops the main issues on how interior design can affect the comfort of library visitors and how to develop a good library interior design. The aim of the research is to find out how interior design affects the comfort of library visitors. This research is expected to be useful for library development and facilitate planning in the library.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Kemampuan Membaca Pemahaman Mahasiswa di Masa Pandemi

Reading proficiency among students during a pandemic demonstrated a lack of motivation in reading among students during online learning. Then, it is also absent in the observations made regarding the reader's comprehension of the text supplied during lectures. The offered reading took a lot of work for half of the 40 students to understand. The study aimed to describe the students in PGSD FKIP UBT's reading speed and reading comprehension of the reading material. Utilizing a qualitative approach, this investigation. The study's participants were 17 members of the 2019 batch of PGSD students, who were split up into different classrooms. This topic was picked because, as of July 2021, there were still pandemic conditions. The understanding test approach was employed in the data collection process. Based on the study's findings, information on students' reading comprehension and speed reading skills in the medium category was gathered. This is demonstrated by the typical student's reading comprehension score of 61%–70% and their speed reading score of 247.78 KPM, respectively. Based on the data collected, it can be said that the 2019 PGSD FKIP students do not have sufficient reading speed or comprehension because they are still unable to receive a score in the good group.
Self-supervised Multi-frame Monocular Depth Estimation with Pseudo-LiDAR Pose Enhancement

Depth estimation is one of the most important tasks in scene understanding. In the existing joint self-supervised learning approaches of depth-pose estimation, depth estimation and pose estimation networks are independent of each other. They only use the adjacent image frames for pose estimation and lack the use of the estimated geometric information. To enhance the depth-pose association, we propose a monocular multi-frame unsupervised depth estimation framework, named PLPE-Depth. There are a depth estimation network and two pose estimation networks with image input and pseudo-LiDAR input. The main idea of our approach is to use the pseudo-LiDAR reconstructed from the depth map to estimate the pose of adjacent frames. We propose depth re-estimation with a better pose between the image pose and the pseudo-LiDAR pose to improve the accuracy of estimation. Besides, we improve the reconstruction loss and design a pseudo-LiDAR pose enhancement loss to facilitate the joint learning. Our approach enhances the use of the estimated depth information and strengthens the coupling between depth estimation and pose estimation. Experiments on the KITTI dataset show that our depth estimation achieves state-of-the-art performance at low resolution. Our source codes will be released on https://github.com/IRMVLabIPLPE-Depth.
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular “Sea-Thru” algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.