Reviewerly/exHarmony huggingface dataset:
A Survey on Phishing Attacks and Their Counter-Measures

There has been a significant rise in the issues related to data security. Though most of the attacks are being performed by script kid- dies, yet there are some expert hackers who, instead of putting their knowledge and skills to help others, are also involved in the activities of stealing information. Phishing is also one such type of methodology in which information about certain individual, a group, or an organization is stolen. Phishing is a cybercrime in which telephones, emails, text messages, personal identification information, banking details, credit card details, and other passwords are targeted. Social Engineering skills and technical subterfuge are used by the phishers to steal victims’ personal data and account details. This chapter gives a fair idea of phishing attacks and their variations, the procedure through which the attacks are performed, traditional targets, detection and preventive measures of it.
Pretrained Language Model for Medical Recommendation System (PLM2RS) Using Biomedical and Electronic Health Record Text Summarization

Biomedical text summaries are essential in interpreting full text biomedical documents to understand useful insights about disease prediction or medicine recommendation decisions. In this paper, we have developed an approach for creating a unified medical language model from biomedical documents for medicine suggestion and clinical decisions. Online medicine recommendation systems have become the need of the hour in recent covid-19 outbreak. In this paper, for developing the medical recommendation system, how the medical literature generated over the period is managed and how the Medical SubHeadings are framed with in the literature and how the hierarchal structure of medical documents created based on medical text summaries and unique topics is discussed. Specific disease related medicine prediction require specific knowledge source mapping in recommendation systems and the summaries generated through this pretrained medical embeddings helpful in getting accurate recommendations. This proposed model in this paper has reached 95.7% efficiency in predicting the correct medical recommendations for neuro based diseases compared with all other existing models.
Breast Cancer Prediction by Levaraging Machine Learning Algorithm and Using Adaptive Voting Ensemble Method

Breast cancer is extensively stirring cancer in females in global and is it mostly related to high humanity. The main detach of this work was too contemporary the numerous tactics to examine these applications we used multiple algorithms of machine learning techniques. Technologies in medical world include preserve and retrieval of health care records of patients and devices involved. Tumours identification is always becoming a challenge to medical department so to minimize that we are trying to introduce a technique by using machine learning to detect the breast cancer in humans. Conferring to the Breast Cancer Institute (BCI), Breast cancer is ace of the most hazardous viruses for women in the world which is identical effective. In this chapter for diagnosing the breast cancer, we proposed ensemble voting method which is adaptive by using the dataset of Wisconsin Breast Cancer and Kaggle repository by using these datasets we are try to identify the tumours in the human breast tissues. ML is actually based on four step by step process. Assembling the data model, selecting the type module, training the module, testing the model. And also, by using different machine earing algorithms we predict and diagnose the breast tumours in patients. The main algorithms we are going use in our paper are k-nearest neighbours (KNN), Artificial Neural Network (ANN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM). After comparing all algorithms our worked proved that ANN algorithm and Random Forest along with logistic regression could provide best accuracy.
Content-Based Video Retrieval Using Deep Learning Algorithms

Due to the expansion of multimedia data types and accessible bandwidth, there is an increasing demand for video retrieval systems as consumers move away from text-based retrieval systems and towards content-based retrieval systems. Regardless of the video attributes used, the choice of extracted features is essential in content-based video retrieval. Over the past ten years, researchers have studied the content-based video retrieval (CBVR) system, which is based on numerous applications, advancements, and technologies. There is still a need in video retrieval systems for the extraction of high-level semantic elements as well as the processing of low-level materials. Researchers are actively focusing on the importance of feature extraction and processing speed in large-scale video retrieval systems. This study seeks to stabilise accuracy while reducing the processing time required to extract the movies using existing deep learning models. Deep learning models will be fine-tuned to extract features and blend them with manually constructed features. We separated the movies to locate elements that matched the human-defined interest in order to fulfil the goal of content-based video retrieval. The primary role of video analysis is object detection and classification. To obtain the true result, we separate the foreground object from the background, perform localisation on each extracted frame from a video, measure the intensity histogram of 8-oriented frames, and then use the active appearance model (AAM), convolutional neural network (CNN), Gabor filter, and Haar cascade algorithms. We integrated more than 2000 films and used two datasets—YouTube and SegTek—as well as cluster computing to get the most sophisticated object detection and segmentation results. We provide a method for training and testing text-to-video retrieval (T2VR) and video-to-text retrieval (V2TR) using a video captioning dataset in this study.
Structure of the Study and Methodology

This chapter provides an in-depth understanding and background of the context in which this study took place and reviews the data collection processes and ethical considerations. Section 4.1 begins with a brief insight into the profile of the Company, its socio-economic background, its role in the national passenger transport system and the Company’s position in the minds of the customers, i.e., passengers and the wider public.
Imbalanced Classification: Challenges and Approaches to Handle

An imbalanced classification problem is a problem where circulated data all over the data sets are prejudice and jagged. A challenge for designing analytical model to solve the imbalanced classification problem, as most of the algorithms were designed to solve this kind of classification problem, to consider the hypothesis of, like each data set has equal number of samples. This paper presents the multiple challenges to deal with imbalanced classification as it is exclusively hard due to its strictly jagged data set distribution and jumbled costs. This paper reviews slight and severe imbalance classification and also reviews its techniques to deal with imbalanced classification which appears in various real-time areas such as fraud detection, advertising click-through, SaaS subscription churn, and disease screening. In this review paper, resampling, synthetic minority over-sampling technique (SMOTE), Balanced Bagging Classifier, and threshold-moving techniques are discussed to solve the imbalanced data for classification problems.
Healthcare Monitoring System for Covid People

Due to the persistent COVID-19 crisis, many people are confined to their homes. To continuously monitor health indicators, we will utilise biosensors such as a respiration sensor, a body temperature sensor, a heart rate sensor, and an oxygen sensor. The received values will be sent through wireless technology to a neighbouring medical centre or COVID Hospital. Notifications are also delivered to clinicians who engage with confined people via their mobile devices. The mobile notifies if any of the health metrics, such as the human body’s temperature, respiration rate, or heartbeat rate, exceeds or falls below its normal rate, and receiving authorities take appropriate preventive action. We use an Arduino Uno, an IoT device for data collection and reception. The decision tree algorithm is used to analyse health factors and make decisions. Our project allows for constant monitoring of COVID-affected people’s health and can assist them in the event of an emergency.
Technologically Mediated Interaction

This chapter provides insights into the context of technologically mediated interactions by conducting an in-depth review and critique of technologically mediated theories. It also outlines the relevant technological features and discusses the affordances and constraints associated with each of the three media examined in this study: telephone, email, and Facebook. Within this context, the chapter explores service encounters as a specific type of activity in which the medium utilized plays a significant role. Users not only adapt to the affordances of the medium but also exploit them to their advantage.
A Virtual Machine Protection Framework Against Compromised Hypervisor in Cloud Computing

Virtualization in cloud computing enables efficient management of physical resources leading to affordable cloud services but has security vulnerabilities. The existing solutions are at a different level of privileges to protect VMs from compromised HV. To overcome this problem, in this paper, we proposed a solution that is software-based and works in the same privilege level of HV. Toward this end, we proposed a framework known as Secure and Isolated Execution Environment (SIEE) for VM protection against compromised hypervisors in cloud computing. An algorithm known as page marking for memory isolation (PMMI) algorithm ensures that each page can only be owned by one VM or hypervisor. Another algorithm known as interaction monitoring for hypervisor resilience (IMHR) is proposed to monitor different interactions structured to realize the SIEE. The empirical study reveals that the proposed framework incurs negligible overhead but improves the security level of virtualization in cloud computing environments.
A Study of Deep Learning Techniques for Predicting Breast Cancer Types

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models.
TempRank: Tempo-Textual Ranking of English Text Documents

Ranking is an important aspect of any information retrieval system in order to serve the best suitable document to user. In the today’s era, amount of digital information is increasing drastically, and retrieving relevant information as per user’s need has become more challenging task. After retrieving relevant documents that are matching with user’s query, identifying most relevant or suitable document is very important. If user’s need is based on the specific time, then embedding time in the ranking algorithm becomes necessary. In this paper, I have proposed a ranking algorithm that combines the textual relevance and temporal relevance of document and query to rank the document.
Comments on 'Canadian Guardrails for Generative AI – Code of Practice'

Innovation, Science and Economic Development Canada (ISED) recently announced their intent to create a national Code of Practice for the development, deployment, and operation of generative AI (GAI) systems. As part of this initiative, ISED launched a series of invitation-only roundtables and welcomed members of the public to submit their comments on this initiative as part of a consultation process. This document contains my brief comments on the substance and process of ISED’s Code of Practice initiative. It is encouraging that ISED recognizes the importance of affording the public a voice in their AI governance initiatives. It is also encouraging that ISED recognizes the importance of providing flexible guidance “across the value chain” to GAI developers, deployers, and operators to help them better adapt to the rapid pace of social and technological change caused by GAI. However, there are two significant gaps in the substance and process of this initiative in its current state: (1) the narrow scope of principles, practices, and AI value chains observed in the current Code, and (2) the inadequate consultation process launched as part of this initiative. My comments describe those gaps in greater detail and suggest a tiered guidance framework and public awareness and consultation campaigns as solutions to address those gaps.
Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality

Augmented reality (AR) has become a powerful tool for assisting operators in complex environments, such as shop floors, laboratories, and industrial settings.By displaying synthetic visual elements anchored in real environments and providing information for specific tasks, AR helps to improve efficiency and accuracy.However, a common bottleneck in these environments is introducing all necessary information, which often requires predefined structured formats and needs more ability for multimodal and Natural Language (NL) interaction.This work proposes a new method for dynamically documenting complex environments using AR in a multimodal, non-structured, and interactive manner.Our method employs Large Language Models (LLMs) to allow experts to describe elements from the real environment in NL and select corresponding AR elements in a dynamic and iterative process.This enables a more natural and flexible way of introducing information, allowing experts to describe the environment in their own words rather than being constrained by a predetermined structure.Any operator can then ask about any aspect of the environment in NL to receive a response and visual guidance from the AR system, thus allowing for a more natural and flexible way of introducing and retrieving information.These capabilities ultimately improve the effectiveness and efficiency of tasks in complex environments.
Efficient sampling from the PKBD distribution

In this paper we present and analyze random number generators for the Poisson Kernel-Based Distribution (PKBD) on the sphere. We show that the only currently available sampling scheme presented in Golzy and Markatou (2020) can be improved by a better selection of hyper-parameters but still yields an unbounded rejection constant as the concentration parameter approaches 1. Furthermore, we introduce two additional and superior sampling methods for which boundedness in the above mentioned case can be obtained. The first method proposes initial draws from angular central Gaussian distribution and offers uniformly bounded rejection constants for a significant part of the PKBD parameter space. The second method uses adaptive rejection sampling and the results of Ulrich (1984) to sample from the projected Saw distribution (Saw, 1978). Finally, both new methods are compared in a simulation study.
Calibrated Interpretation: Confidence Estimation in Semantic Parsing

Abstract Sequence generation models are increasingly being used to translate natural language into programs, i.e., to perform executable semantic parsing. The fact that semantic parsing aims to predict programs that can lead to executed actions in the real world motivates developing safe systems. This in turn makes measuring calibration—a central component to safety—particularly important. We investigate the calibration of popular generation models across four popular semantic parsing datasets, finding that it varies across models and datasets. We then analyze factors associated with calibration error and release new confidence-based challenge splits of two parsing datasets. To facilitate the inclusion of calibration in semantic parsing evaluations, we release a library for computing calibration metrics.1
Robust Registration of Optical and SAR Images Using Multi-Orientation Relative Total Variation Structural Representation

Accurate registration of optical and synthetic aperture radar (SAR) images remains a challenging task because of the potential large modality differences across individual images. To improve the registration performance, this paper proposes a robust registration method for optical and SAR images based on a novel multi-orientation relative total variation (MORTV) structural representation. The MORTV model is designed by integrating multiple orientation strategy into the original RTV to extract the structural maps, which can capture more structural features while removing image noises and textures. Then, a novel feature descriptor called layer-wise multiscale histogram of oriented gradient (LMHOG) is constructed on the multiscale structural maps that are generated using the MORTV model with different parameters. The LMHOG can fully characterize structural features at different scales in a multilayer manner, further enhancing the robustness and distinctiveness of the descriptor without increasing its dimension. Comprehensive experiments on two large-scale optical and SAR image datasets validate that the proposed method obtains superior registration performance over several state-of-the-art methods.
The Impact of Occlusion on Depth Perception at Arm's Length

This paper investigates the accuracy of Augmented Reality (AR) technologies, particularly commercially available optical see-through displays, in depicting virtual content inside the human body for surgical planning. Their inherent limitations result in inaccuracies in perceived object positioning. We examine how occlusion, specifically with opaque surfaces, affects perceived depth of virtual objects at arm's length working distances. A custom apparatus with a half-silvered mirror was developed, providing accurate depth cues excluding occlusion, differing from commercial displays. We carried out a study, contrasting our apparatus with a HoloLens 2, involving a depth estimation task under varied surface complexities and illuminations. In addition, we explored the effects of creating a virtual "hole" in the surface. Subjects' depth estimation accuracy and confidence were a ssessed. Results showed more depth estimation variation with HoloLens and significant depth error beneath complex occluding surfaces. However, creating a virtual hole significantly reduced depth errors and increased subjects' confidence, irrespective of accuracy enhancement. These findings have important implications for the design and use of mixed-reality technologies in surgical applications, and industrial applications such as using virtual content to guide maintenance or repair of components hidden beneath the opaque outer surface of equipment. A free copy of this paper and all supplemental materials are available at https://bit.ly/3YbkwjU.
I am a Genius! Influence of Virtually Embodying Leonardo da Vinci on Creative Performance

Virtual reality (VR) provides users with the ability to substitute their physical appearance by embodying virtual characters (avatars) using head-mounted displays and motion-capture technologies. Previous research demonstrated that the sense of embodiment toward an avatar can impact user behavior and cognition. In this paper, we present an experiment designed to investigate whether embodying a well-known creative genius could enhance participants' creative performance. Following a preliminary online survey ( N = 157) to select a famous character suited to the purpose of this study, we developed a VR application allowing participants to embody Leonardo da Vinci or a self-avatar. Self-avatars were approximately matched with participants in terms of skin tone and morphology. 40 participants took part in three tasks seamlessly integrated in a virtual workshop. The first task was based on a Guilford's Alternate Uses test (GAU) to assess participants' divergent abilities in terms of fluency and originality. The second task was based on a Remote Associates Test (RAT) to evaluate convergent abilities. Lastly, the third task consisted in designing potential alternative uses of an object displayed in the virtual environment using a 3D sketching tool. Participants embodying Leonardo da Vinci demonstrated significantly higher divergent thinking abilities, with a substantial difference in fluency between the groups. Conversely, participants embodying a self-avatar performed significantly better in the convergent thinking task. Taken together, these results promote the use of our virtual embodiment approach, especially in applications where divergent creativity plays an important role, such as design and innovation.
Artificial Bee Colony Algorithm Based on Dimensional Memory Mechanism and Adaptive Elite Population for Training Artificial Neural Networks

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Lévy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30-and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.