Reviewerly/exHarmony huggingface dataset:
Responsive Webpage Using HTML CSS

When it involves building websites, the writing language will, initially look, seem unbelievably complicated. it's going to appear rather intimidating having to require all this new data on board, however by breaking down every of the various parts concerned, you'll notice that you just will be of these essential items of the puzzle. As you've read the brief abstraction above, I hope you all are able to understand what this paper is all about. There are types of operating systems are currently in use across the world having many versions of them. There are many types of devices having different screen sizes we all are using. So, it's quite difficult to develop a webpage or website which is compatible for all the types of OS and their different versions and device screen sizes. Through this paper you can learn all the essential things and concepts of how to develop a webpage/website which is compatible for every device and OS. If you're thinking of building an internet site, then you'll virtually definitely have stumble upon the acronym's markup language and CSS. These 2 essential tools type a number of the foundations of any webpage. However, though they're usually employed in conjunction with one another, they each serve terribly totally different functions once it involves net writing. The key variations exist their implementation, easy use, their numerous options, and the way they're structured, however at the tip of the day, each markup language and CSS square measure the keys to unlocking however the planet Wide networks. Let's dive into the small print of each markup language and CSS, what they're used for, and the way to find out markup language and CSS. Any role that needs writing will certainly desire a candidate with a minimum of some expertise with hypertext mark-up language and CSS, and therefore the opening to learning hypertext mark-up language and CSS is really knowing what they're and what they're used for
Analysis Of Cost Prediction In Medical Insurance Using Modern Regression Models

There are different kinds of insurances, but the most saturated is the medical (life) insurance domain. As a vast population invests in health insurance, it is hard to keep track of trends. The ineffective analysis of data results in cost overruns and insurance inequity, making its access difficult. There is a need to critically analyze the insurance data and make an insurance policy that is well adapted to the geographical and financial statuses of the insurers. This study aims to predict suitable medical insurance costs based on the patient's biological and demographic factors by using Machine Learning Regression techniques. Four models are applied on a US-based dataset. Gradient Boosting Regressor, AdaBoost Regressor, Lasso and Elastic Net Regression. Various loss functions were used to extract the best model on different parameters. Overall, the best performance in terms of maximum R2 and minimized loss were given by boosting techniques as compared to the regularization techniques. Proposed system will help organizations to design more public-oriented medical insurance policies which benefit the users and also improve the revenue of the organization.
Sign language recognition Using Python

It's generally challenging to speak with somebody who has a consultation disability. People with hearing and discourse disabilities can now impart their sentiments and feelings to the remainder of the world through gesture-based communication, which has permanently turned into a definitive cure. It works with and improves on the combination interaction among them and others. Nonetheless, essentially it is deficient to create gesture-based communication. This gift accompanies a ton of surprises. For somebody who has never learned it or knows it in an alternate language, the sign developments are oftentimes stirred up and befuddled. Notwithstanding, with the development of various ways to deal with computerizing the ID of sign movements, this correspondence hole that has persevered for quite a long time can now be connected. We give a Sign Language acknowledgment framework in view of American Sign Language in this paper. The clients ought to have the option to take photos of hand signals involving a web camera in this review, and the framework should expect and show the name of the gained picture. To recognize the hand motion, we utilize the HSV variety strategy and set the background to dark. The photographs are handled utilizing an assortment of PC vision procedures, including grayscale transformation, dilatation, and veiling. What's more, the district of interest is sectioned, for this situation the hand motion. The parallel pixels of the pictures are the highlights extricated. Convolutional Neural Network (CNN) is utilized to prepare and arrange the pictures. We have a decent degree of exactness in perceiving ten American Sign motion letter sets. Our model has an incredible precision pace of over 90
Improved Hybrid Model for Phishing Detection by Using Machine Learning

Modern word is revolutionized since decades due to the rapid increment in online technologies. As online transaction and business growing rapidly security problem also become a challenges for all over the world. Among the different security threats phishing is most popular and dangerous threats that need to be tackle as soon as happened. Attacker steals the personal information of the user through phishing websites about which the user is unaware. Many mechanism has been proposed to tackle with these phishing attacks but there is still need an approach that perform best in detection of that attacks. In this paper a hybrid model is proposed for the detection of phishing attacks and datasets are taken from UCI repository. In this hybrid model different algorithms are applied on the datasets through Weka and Rapid Minor software. After the results of individual we compare the performance the best features algorithms in order to take less error rate and high accuracy.
Digital Transformation and Disruptive Technologies: Effect of Blockchain on Managing Construction Projects

Blockchain technology has major advantages in improving management processes in many industries, such as increasing the security and trust level, protecting data from forgery, and decreasing transaction costs. The paper discusses the impact of blockchain technology as a digital transformer on construction projects and improving management processes and its effects across the four elements: Business, Technology, Industry, and Society. The construction industry deals with many other entities with a large volume of transactions, creating a great environment to utilize blockchain technology to improve these processes. This study aims to determine the impact of blockchain technology on project management in the construction industry. Therefore, the collected data is from secondary data as there have been few cases discussing the implementation of blockchain within the construction industry, which significantly impacts project management through an efficient monetary exchange, building information modeling, smart contracts, and increased supply chain management transparency. As a result, blockchain technology has a positive impact and the ability to increase the efficiency of the construction project management process. This research has intelligibly discussed the importance of digital transformation in the present time, as well as the role of blockchain technology in the construction field along with project management.
Employee Hiring using Machine Learning

Hiring good employees is one of the vital components for a company's performance. In this study we have tested the recruitment / selection process on the performance of the candidate within the org. The purpose behind this model is to find-out the basic ways that are being used by the organizations to hire candidates. In today's competitive business world, it is important for an organization to take care of the requirements of the employee also. It is important for an organization to have a good recruitment policy to recruit best employees and to get the best result for the organization.
Qualitative Factors in Organizational Cyber Resilience

Cyber resilience moves organizations away from efforts to guarantee security of all systems, towards an approach that acknowledges that systems are bound to fail with a focus instead on the impact of that failure on business objectives. While the work on cyber resilience is evolving, there is a lack of studies using qualitative data for investigating the concepts and themes pertaining to cyber resilience in organizations. The purpose of this study is to uncover the non-technical organizational factors that contribute to better cyber resilience. By adopting a qualitative approach of analyzing factors of organizational resilience, this paper uses primary data collected through 25 interviews at senior leadership or board-level to point out the extent to which these factors facilitate or impede cyber resilience. The study illustrates a Leximancer map of each factor that characterizes organizational cyber resilience, based on insights from cyber practitioner communities through narrative interviews. This research contributes to a better theoretical and practical understanding of how cyber resilience within organizations can be improved. The findings show that cyber strategy and skilled people playa key role in adoption of cyber culture at the management level, while communication between boards and security leadership as well as a clear reporting structure are signals for building cyber resilience.
An Investigation on Data Center Cooling Systems Using FPGA-based Temperature Side Channels

As power and cooling cost has become a major factor in the total cost of ownership (TCO) of large-scale data centers, it is important to investigate how data centers run their cooling systems in practice. The data centers of Amazon Web Services (AWS) have been continuously expanding worldwide, and their restrictive security policies keep many management aspects of data centers private. In this paper, we make an attempt to explore the cooling systems of AWS data centers without privileged accesses. We first demonstrate PVT (process, voltage, and temperature) variations in AWS FPGAs (Field Programmable Gate Arrays) using time-digital converters (TDC). We further leverage the DRAM temperature side channel and improve the usage of the TDC to measure the temperature change accurately. We conduct a measurement on the daily temperatures of AWS data centers worldwide and find that temperature changes of some data centers are closely related to local weathers. Thus, we deduce they adopt free cooling techniques. This measurement study motivates us to re-think the vulnerability of data centers to power/thermal attacks.
Never Too Late: Tracing and Mitigating Backdoor Attacks in Federated Learning

The privacy-preserving nature of Federated Learning (FL) exposes such a distributed learning paradigm to the planting of backdoors with locally corrupted data. We discover that FL backdoors, under a new on-off multi-shot attack form, are essentially stealthy against existing defenses that are built on model statistics and spectral analysis. First-hand observations of such attacks show that the backdoored models are indistinguishable from normal ones w.r.t. both low-level and high-level representations. We thus emphasize that a critical redemption, if not the only, for the tricky stealthiness is reactive tracing and posterior mitigation. A three-step remedy framework is then proposed by exploring the temporal and inferential correlations of models on a trapped sample from an attack. In particular, we use shift ensemble detection and co-occurrence analysis for adversary identification, and repair the model via malicious ingredients removal under theoretical error guarantee. Extensive experiments on various backdoor settings demonstrate that our framework can achieve accuracy on attack round identification of ∼80% and on attackers of ∼50%, which are ∼28.76% better than existing proactive defenses. Meanwhile, it can successfully eliminate the influence of backdoors with only a 5%∼6% performance drop.
Research Paper on Path-finding Algorithm Visualizer

We present you a Path finding Visualizer, and the visualizer finds a path from a source to a destination and the root and terminal is set by the user only. We had used graph theory approach in this project. In this paper we have tried to use graph theory and various graph algorithms which we have studied in the chapter of data structures and algorithm and that is all in the backend. The front end is designed using Pygame and this project reflects the application of data structures. We had used different Pathfinding algorithms with a different approach of each algorithm to obtain the shortest path.
Accelerating Privacy-Preserving Image Retrieval with Multi-Index Hashing

With the explosive growth of data, a large amount of image data is stored on cloud servers. However, cloud servers can easily collect sensitive information about stored images, which brings serious privacy issues. Although uploading encrypted images to cloud servers could solve the privacy problem, most of the existing privacy-preserving schemes inevitably reduce the accuracy and efficiency of image retrieval. To address the above challenging issues, we propose a privacy-preserving content-based image retrieval scheme based on multi-indexed hashing (MIH) in this paper. To improve the retrieval precision, the ViT model is first used to extract feature descriptors of images and ITQ method is utilized to downscale the feature vectors into binary vectors. Subsequently, based on additive secret sharing, we propose a new secure Hamming distance calculation protocol to perform similarity measure, which protects the data privacy of image features. Finally, we design a secure multi-index hash structure to filter the dataset to improve the search efficiency. Experiments on the dataset demonstrate the efficiency and security of the scheme.
Reinforced Sample Selection for Graph Neural Networks Transfer Learning

Graph neural networks (GNNs) have become a practical paradigm for learning graph-structured data, which can generate node representations by recursively aggregating information from neighbor nodes. Recent works utilize self-supervised tasks to learn transferable knowledge from source domain graphs and improve the GNNs performance on target domain graphs. However, there are considerable low-quality and incorrect-labeled graphs in the source domain, which leads to the negative transfer problem in target domain graphs. To tackle this challenge, we propose RSS-GNN, a reinforced sample selection for GNNs transfer learning. The critical insight is that RSS-GNN attempts to use reinforcement learning (RL) to guide transfer learning and narrow the graph divergence between the source and the target domain. We leverage a selection distribution generator (SDG) to produce the probability for each graph and select high-quality graphs to train GNNs. We innovatively designed a reward mechanism to measure the quality of the selection process and employ the policy gradient to update SDG parameters. Extensive experiments demonstrate that our approach can be compatible with various GNNs frameworks and yields superior performance compared to state-of-the-art methods.
Intent-aware Prompt Learning for Medical Question Summarization

Consumer health question summarization aims to generate concise questions expressing the minimum information needed to find the correct answer to the original user queries, which can significantly improve the success rate of retrieving relevant answers in automated medical question answering systems. Recently, prompt learning has emerged as a new paradigm of leveraging pre-trained language models (PLMs) with promising results in downstream tasks. Existing prompt-based methods assume that all samples in a task share the same prompt. However, the health questions raised by consumers in search engines focus on different types of medical entities and cover different purposes. It is not sufficient to simply use the same prompt regardless of consumer intent awareness, which likely leads to meaningless or substandard quality summaries. In this paper, we propose an intent-aware prompt learning (InPL) method, which consists of a question intent recognition module, a dynamic prompt module and a medical summarization generation module to model the medical summarization task in a unified transformer. Our InPL architecture generates high-quality summaries capturing more consumer health intent and encourages machines to generate more factually correct summaries. Extensive experiments on the popular medical summarization datasets demonstrate the superiority of our approach.
Comparing three <scp>XR</scp> technologies in reviewing performance‐based building design: A pilot study of façade fenestrations

Abstract Extended reality (XR), including augmented reality (AR), mixed reality (MR), and virtual reality (VR), can make complex information more intuitively understandable. However, it still needs to be determined which XR technology is the most suitable for reviewing performance‐based building design, during which architects check whether design strategies increase energy efficiency, indoor environment quality and so forth. It requires comprehensive design and simulation information to be vividly represented. The authors develop three XR apps (AR, MR, and VR) to interactively visualize façade fenestration geometries and indoor illuminance simulations. Then XR technologies are assessed by 120 students and young architects, from task performance and engagement level two aspects. The task performance is measured by correct rate and time consumption two indicators, while the engagement level is measured by usability and interest two indicators. Evaluation results show that compared to AR and VR, MR is the best XR technology for this aim. VR outperforms AR on three indicators except for usability. By exposing three different XR technologies' performances in aiding fenestration design, this study increases the practical value of applying XR to the building design field.
A Cross-Attention Fusion Based Graph Convolution Auto-Encoder for Open Relation Extraction

Open Relation Extraction (OpenRE) aims at clustering relation instances to extract relation types. By learning relation patterns between named entities, it clusters semantically equivalent patterns into a unified relation cluster. Existing clustering-based OpenRE methods only consider the information of the instance itself, ignoring knowledge of any relations between instances. Therefore, a Cross-Attention Fusion based Graph Convolution Auto-Encoder (CAGCE) method for Open Relation Extraction is proposed. The Auto-Encoder learns the semantic information of the sentence instance itself, and the Graph Convolution Network learns the relational similarity information between sentences. Then, the two heterogeneous representations are crossed and fused layer-by-layer through a cross-attention fusion mechanism. Finally, the fused features are used for clustering to form the relation types. A comparison with baseline models using the FewRel and NYT-FB datasets shows the effectiveness and superiority of the proposed method.
Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction

Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing high-quality biomedical annotation data is not only time-consuming but also requires a high level of knowledge in the biomedical field. To alleviate this problem, Semi-supervised Biomedical Relation Extraction aims to extract relation facts from the limited labeled data and the more readily available unlabeled samples. Existing works can be roughly categorized as self-training methods and self-ensembling methods. The former aims to generate pseudo labels, which may lead to the gradual drift problem. The latter aims to encourage the output of one model to be consistent with the other model, where the acquisition of the model is tedious. To alleviate these issues, we propose a novel Uncertainty-Guided Mutual Consistency Training framework(UG-MCT) for semi-supervised Biomedical relation extraction. Specifically, our framework consists of two models with the same structure, which differ only when updating their weights, and then an intersecting pseudo-label mechanism is designed to convert the prediction discrepancies of the two models into mutual consistency training loss, thus promoting the consistency of model predictions. In addition, we utilize uncertainty as guided information to assist the model in focusing on the confident pseudo labels and mitigate the noise of inaccurate pseudo labeling during training. Thus, our model is very simple and efficient while mitigating the noise introduced by pseudo-labels. UG-MCT is evaluated on multiple datasets in different settings and the experimental results demonstrate that our method is highly effective in semi-supervised biomedical relation extraction compared to the state-of-the-art.
Measuring Employee Productivity Through Copresence Using RFID Within the Workplace

Measuring productivity of employees within the workplace is a challenging task for organizations. This research aims at using RFID technology to measure the effects of the time spent by employees’ copresence as a means to understand its effect on their daily work performance. Indeed, RFID tags were used to locate each employee within the office premises. The collected RFID data, coupled with the office floor plan, were used to spot the copresence of employees in collaboration/discussion scenarios which leads to measuring its impact on productivity. This research has been conducted on the employees of an organization based in Dubai during regular office hours. The collected data will enable the human resource department, in collaboration with the entity managers, to make decisions on enhancing the work environment, which will lead to improved employee performance.
How Well Apply Multimodal Mixup and Simple MLPs Backbone to Medical Visual Question Answering?

Although current methods have significantly improved the performance of medical visual question answering (Med-VQA), there are still two aspects worth exploring, namely the simplification of model structure and the effective model training on small-scale data. Different from the previous Med-VQA model, this paper only employs multi-layer perceptrons (MLPs) as the backbone network for feature extraction and modal fusion and designs a Med-VQA model on such basis, which achieves superior performance with a simple backbone network. To enhance model generalization, we design multimodal mixup (M-Mixup) to augment images and questions separately, which effectively alleviates the problem of insufficient training samples in the Med-VQA task. To prevent the destruction of the feature relationship when tokenizing the medical image, we design pooling tokens (PTs), a simple downsampling structure to capture fine-grained visual features without affecting the parameters and FLOPs of the entire model. Experimental results demonstrate that our model achieves state-of-the-art on the SLAKE, and obtains a remarkably competitive performance on the VQA-RAD. The source code and models are available at https://github.com/Alivelei/M-Mixup.
Performance Analysis on Interpolation-based Methods for Fingerprint Images

Image interpolation methods are a good approach to enhance or super resolve a digital image. But, there are surprisingly lack of use of these methods in biometric images especially, fingerprint images. In this research, we analysed is the most commonly known interpolation methods namely; nearest neighbour, bilinear, bicubic, and Lanczos interpolations to super resolve fingerprint images and compare the performance of each interpolation methods. Based on the results, the best interpolation is the Lanczos interpolation, followed by bicubic, bilinear and nearest neighbour interpolations.