Reviewerly/exHarmony huggingface dataset:
Implementation of a knowledge‐based decision support system for treatment plan auditing through automation

Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross-campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members.A knowledge-based automated anomaly-detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution.A total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre-processed. A knowledge-based anomaly detection algorithm, namely, "isolation forest" (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto-populated parameters were used to guide the manual auditing process and validated by two plan auditors.The two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year.iForest effectively detects anomalous plans and strengthens our cross-campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently.
Novel Development of Fuzzy Controller Based Multi-Agent System for Efficient Navigation of Autonomous Robots

This study investigates a fuzzy controller technique for autonomous robot navigation in both the static and dynamic environmental conditions and an excessive number of pathways to the destination. The design and implementation of a novel obstacle avoidance technique for autonomous robots are developed using the fuzzy controller-based multi-agent system. This method allows the Robot to identify dynamic or static unidentified objects while directing the Robot to prevent collisions and advance toward the objective. The Robot is capable of moving in a variety of environments. The Robot may communicate and travel in dynamic space by perceiving its surroundings and pursuing a free-collision route. This study covers creating a multi-agent system that includes fuzzy logic to regulate the robotic movements along a path reactive for effective Navigation. This project aims to develop an algorithm that allows the Robot to do distinct tasks to accomplish a unified objective, autonomous Navigation in a slightly unfamiliar environment. Under such a situation, the usage of a multi-agent system is advantageous. As a result, we created a framework made up of four agents responsible for sensing, Navigation, dynamic, and static obstacle avoidance. These agents communicate with one another via a coordinating mechanism.
CNN — Forest Based Person Identification and Head Pose Estimation for AI Based Applications

Face recognition and head posture estimation have aroused a lot of academic interest recently since the inherent information improves the performance of face-related applications such as face alignment, augmented reality, healthcare applications, and emotion detection. The proposed work explores the challenges of identifying people and determining head posture. An analysis of the features produced at intermediate layers by limiting the number of kernals is performed and improved the performance of detecting the person. In addition, the learned features are sent to forest trees in order to determine the exact head attitude of the detected person. The proposed Forest CNN (FCNN) architecture is tested for head pose estimation methods on the Pointing 04 and Facepix benchmark databases.
Cricket and Football Detection Using YOLOV5 Algorithm

A cutting-edge technology in artificial intelligence and machine learning is called object detection. It is crucial to understand about object localization before moving on to object detection. For being able to localize object first, we need to know what the object is in the image. Then we need to assign bounding box to that specific object we want to detect in the image or video. Finding a single item's location in an image or video is called object localization. Finding numerous objects' locations in an image or video is called object detection. The use of Object Detection has been increased drastically within last decade. And, it is being used in many areas for refining efficacy in the task. object detection is being used in home automation, agriculture, Automated or self-driving cars, Surveillance industry, traffic tracking system, Activity Recognition, defense systems, sports, industrial work, automobile industries, robotics, aviation industry and many other fields. Object detection can be performed consuming various set of rules like R-CNN, Fast R-CNN, Faster R-CNN, Single Shot detector (SSD) and You Only Look Once(YOLO). For this project an assessment of R-CNN and YOLO algorithms will be performed and also their results as well as performance will be studied. The performance and accuracy should be supreme vital in examining the algorithms.
Plot extraction and the visualization of narrative flow

Abstract This article discusses the development of an automated plot extraction system for narrative texts. Acknowledging the distinction between plot, as an object of study with its own rich history and literature, and features of a text that may be automatically extractable, we begin by characterizing a text’s scatter plot of entities . This visualization of a text reveals entity density patterns characterizing the particular telling of the story under investigation and leads to effective scene partitioning. We then introduce the concept of narrative flow , a graph representation of the narrative ordering of scenes (the syuzhet) that includes how entities move through scenes from the text, and investigate the degree to which narrative flow can be automatically extracted given a glossary of plot-important objects, actors, and locations. Our subsequent analysis then explores the correlation between subjective notions of plot and the information extracted through these visualizations. In particular, we discuss narrative structures commonly found within the graphs and make comparisons with ground truth narrative flow graphs, showing mixed results highlighting the difficulty of plot extraction. However, the visual artifacts and common structural relationships seen in the graphs provide insight into narrative and its underlying plot.
Systematic Review on the Recent Trends of Cybersecurity in Automobile Industry

The increasing integration of technology in automobiles has raised industry concerns about cyber-security. This paper provides an overview of the most recent developments in automobile cyber-security practices, pointing out the most important areas of cyber security that need more research in the context of self-driving cars and the automobile industry. Considering the different communities of cybersecurity mapped out by previous researchers, we explored each of these communities with respect to automobiles. The community of cybersecurity is subjected to intrusion detection, cryptography, sensor networks, information hiding, intrusion detection, biometrics, authentication, usable security, and access control. From the systematic review done, we found out the bulk of security research in the automobile industry is geared towards intrusion detection. The use of chaos-based picture encryption, visual cryptography, biometric fingerprinting, keystroke dynamics, background subtraction, gait identification, provable security, provable data possession, block ciphers, differential power analysis, hardware trojans, physical unclonable functions, etc., although used in the industry, has yet to be explored with respect to the security of vehicles, their networks, and the automobile industry. The results show that some of the different communities of cybersecurity towards automobiles are still in their infancy with opportunities for novel work.
An Intelligent Group Recommender System using Henry Gas Solubility Optimization Algorithm-based Adaptive Deep Belief Network

Group Recommender Systems (GRS) have recently gained attention for use in various applications. The GRS model is useful in any scenario that involves a decision-making process for a group of users. However, existing approaches are not able to provide high quality recommendations due to their inability to handle traditional problems such as cold start, sparsity, and dealing with large dimensional information. To address these issues, a novel deep learning-based adaptive model is proposed. Raw data is first collected from four different data sources and cleaned using stemming, stop word and punctuation removal methods. Data preprocessing is followed by the feature extraction phase. A Text Convolutional Neural Network (TCNN) is employed to extract efficient features from group reviews. The efficient features are fed into an Adaptive Deep Belief Network (ADBN) to provide the recommendation output. It is further optimized using the Henry Gas Solubility optimization (HGSO) algorithm. Through extensive experiments it is found that the performance of the proposed system outperforms existing standard algorithms, hence, solving the traditional GRS issues.
Datasets in OCR for Handwriting Recognition: A Survey

Languages across the globe have been used in expressing and writing thoughts, information, and discoveries. Aiming to store and preserve these contents in digitized form is served by the Optical Character Recognition mechanisms. The performances of these OCR engines depend a lot on the type of data given as input to learn, the model used for training, and the various evaluation matrices used for the purpose. Though literature is flooded with reviews and surveys on tools, models, and evaluation methods used for the purpose, very less is witnessed about the datasets available for the same. With this thought, we proceeded to present a survey of the various datasets created and used to recognize handwritten texts. This survey will definitely enlighten fellow researchers in this field to understand the characteristics and forms of the various datasets available to date. It will also motivate them in developing new and complex datasets which will eventually contribute to the creation of accurate and efficient learning models helpful for various OCR tasks.
An Novel Algorithm for Cloud Secure Storage Using Cloud Dispersion and Block Chain System

The rapid development of cloud computing has been greatly aided by the popularity and power of cloud storage. Nonetheless, there are still serioussecurity incidents that, as a consequence of malicious attack and management negligence, cause widespread sensitive data exposures at the cloud storage tier. To preserve the privacy of cloud data, this study created (CSSM). By combining data dispersion with dispersed storage, CSSM was able to provide encrypted, chucked, and scattered storage, preventing data intrusions at the storage layer. In addition, CSSM used a system of management levels and combined user passwords with secret sharing to avoid any unauthorized access to cryptographic materials. The experimental results show the proposed method can successfully store enormous amounts of cloud data without introducing a substantial latency cost, and is also suitable for securing data at the storage layer from leaking. Customers may upload/download a 5G file using CSSM in just 646 secondsand 269 seconds, which is acceptable.
Design of Infinite Impulse Response Filter Using Particle Swarm Optimization and its Invariants

In this paper, Particle Swarm Optimization (PSO) and its invariant techniques are used in designing the low-pass digital Infinite Impulse Response filter. The invariants that are taken into consideration are modified PSO, PSO with Constriction factor and inertia weight Approach (PSO-CFIWA), and Craziness based PSO. The modified PSO is obtained by varying the value of the velocity of the particle. PSO-CFIWA is derived by adding a construction factor. In craziness-based PSO, a signum function is added to the velocity of the particle. The Effectiveness and performance of this algorithm in digital filter design have been presented and compared
Real-time classification of haze and non-haze images on Arduino Nano BLE using Edge Impulse

Image dehazing is of utmost research interest and has several significant applications. However, the technology of image dehazing can only work for hazy images. Therefore, classification of images into haze and non-haze becomes a very essential precursor for the technology of image dehazing. Relying on human visual perception to classify the images is often trivial, empirical, and unrealistic. As a result, there is a requirement for a suitable algorithm that can perform this classification efficiently. This paper aims to meet such requirement and classifies images presented as ‘clear’ or ‘hazy’. Arduino Nano 33 BLE coupled with a Tiny Machine Learning Shield has been used for the implementation of the algorithm. Edge Impulse has been used for training, testing and validation of the Convolutional Neural Network (CNN) model. The image to be classified is displayed to the camera in real-time. Subsequently, the captured image is given to the model, which then classifies the image. The output of the classification is displayed on the serial monitor.
Enhancing BERT-based Passage Retriever with Word Recovery Method

Passage retrieval is a fundamental task in information retrieval research and has received extensive attention on academia and industry in recent years. BERT-based passage retriever adopts a dual-encoder architecture to learn dense representations of queries and passages for semantic matching, which has become an indispensable component of passage retrieval system. However, BERT-based passage retriever tends to ignore phrases and entities mentioned in sentences, which are critical of retrieval. To address this problem, we propose a word recovery method that introduces word-granularity information into BERT-based paragraph retriever. We evaluate our model on three public datasets in different domains including E-commerce, Entertainment Video and Medical. By augmenting BERT-based passage retriever with word recovery method, the retriever achieves consistent improvements in MRR@10 and Reca11@1000, of which MRR@10 increased by 1.2% in Entertainment Video, and Reca11@1000 increased by 0.6% in Ecommerce.
SEPHWIR: Search Engine Parsing for Hidden Web Information Retrieval

The world wide web consists of web pages and databases from which web pages can be generated on demand. It is presumed that the information stored in the databases is of better quality than the one published onto already created static webpages. The web search engine architectures are tuned to access static webpages and index and rank them in their results against the query. This results into the accessibility to the kind of information that is not of better quality. This paper talks about the approach to parse such results of the search engines and find the entry points to the databases to fetch the better-quality information.
Study on fairness of group recommendation based on stochastic model checking

With the increasing development of social networks, group recommendation has gradually become a hot research topic in the field of recommendation technology. The core of improving the quality of group recommendation lies in coordinating the preference differences among group members and improving the fairness among group members. The feedback loop formed by the user-system interaction can amplify the fairness bias in the system. In this paper, we address this common problem by formally modeling the general group recommendation framework into a DTMC model based on feedback loops and verifying fairness; we propose a dynamic fairness threshold detection framework based on stochastic model testing to detect the fairness of the system and make adjustments in time. The improved framework is simulated and tested, and its fairness is significantly improved.
Development of an Image Processing Techniques for Vehicle Classification Using OCR and SVM

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.
Human Fall Detection Using Machine Learning and Deep Learning Techniques: A Survey

Wearable airbag systems manifest an important role in the senior population, where falls have become a serious health problem. As a result, these devices are increasingly being utilized by elderly people in order to safeguard them from falling, particularly those with chronic diseases. This paper reviews about the previous research on human fall detection systems based on Machine Learning (ML) and Deep Learning (DL) techniques using different sensors that are employed in the various positions of the body. Apart from ML and DL techniques, few researchers analyzed other techniques and micro mechanical devices are designed for the prevention and detection of falls as mentioned in this research paper.
STEM outreach activities for pre-university generation to be performed by the extra-curriculum project team of undergraduate students

The extra-curriculum project team organized by undergraduate students has performed various STEM outreach activities for pre-university generation in local community for almost 20 years. Small age differences are believed to be effective for realizing active communication during the activities, which is in turn believe to be effective for stimulating interests of younger children and students toward various STEM fields. The extra-curriculum status of the project team is also effective for improved qualities of the activities.
Semantic Segmentation With Multiple Contradictory Annotations Using a Dynamic Score Function

Semantic Segmentation aims to partition an image into separate regions where each region conveys certain valuable information. In recent years, various deep learning models have achieved high performance in this task. However, when several ground truth segmentations are available, aggregating the information of these segmentations into a single ground truth becomes a crucial pre-processing step. This task can become challenging when the segmentations are contradictory and the existing classes in the segmentations are imbalanced. An elegant example is the grading of Prostate Cancer in the Gleason 2019 Challenge dataset. This dataset provides six annotations from expert pathologists for each image. The high inter-observer variability among pathologists has led to conflicting annotations for biopsy images. In addition, the low diversity of biopsy tissue patterns for all Gleason grades has also resulted in an imbalanced dataset. Previously proposed methods include the Majority Voting or the Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm for combining the expert annotations. In this paper, we point out that the outputs of these algorithms discard the semantics of the image in regions of high contrast which highly dissipates the performance of deep learning models trained on these ground truths. Moreover, we propose a dynamic score function to effectively solve the diversity among the annotations and balance the Gleason grading among the annotations in terms of variability and quantity. We further train a Pyramid Scene Parsing network on the final ground truth annotations and achieve a performance higher than the participating teams in the challenge.
Design and research of railway foreign material intrusion limit based on new sensor and network

foreign matter intrusion on train operation safety is becoming increasingly serious. Based on a large amount of research data, among many factors affecting the operation safety of high-speed railway, the detection of personnel and foreign matter intrusion is difficult, and the preventive measures are limited, mainly because of its strong randomness and complexity. This paper systematically introduces the development status of high-speed railway line protection technology at home and abroad, especially summarizes the development status of high-speed railway line monitoring technology in China, and proposes a new design of foreign object intrusion detection and alarm device based on new sensors and networks, which has certain reference value for high-speed railway line protection and railway bureaus and departments in related fields.