Reviewerly/exHarmony huggingface dataset:
Longevity of print book use at a small public university: a 30-year longitudinal study

This research uses the circulation history of print books in the University of Prince Edward Island’s (UPEI) collection to measure the longevity of usage using three different categories: Becher-Biglan (BB), major subject and academic department. Two sets of data provide for longitudinal analysis up to 30 years. About 10,000 books met the criteria for part 1, which had an average ten-year circulation longevity. About 14% had only one year of use and about 24% had less than five years’ longevity. There was little variation by BB, and the only major subjects that were noticeably different from the median were business and education, which were about 20% shorter. Part 2 included about 4,000 more recent books. 37% circulated for just one year and 64% circulated for at most a four-year range. There was very little variation in these results when broken down by BB, major subjects and academic departments. The one exception is business, which had a notably higher portion used only in one year. The circulation longevity of print books has significantly shortened over the last three decades at UPEI.
A Blogging Application Based on Facial Authentication

Blogging is something where you can share your knowledge with a large network and it also serves as a means to continue your passion. In this project, we have designed a blogging application which has features like facial authentication, social media integration along with Paytm integration. It has been developed by making use of the functionalities available in the Open-Computer-Vision (Open CV) library using Python. It has used Haar-Cascades for face detection purposes and Local binary pattern histograms (LBPH) recognizer for facial recognition.
Co-Designed Technology for Elderly Care: Mobile Robots and Passive RFID for Nighttime Safety

Ageing of the world's population is one of the significant challenges of the modern era. As the care resources are limited, new tools are needed for supporting elderly to live high quality life. Technology is identified as one of the potential tools. In this article, a combination of mobile robotics and RFID technology is used to provide a new tool for care homes, especially targeted for helping in night shifts. By combining passive RFID and mobile robots in a unique way, we create a system that can take actions in preventing potential accidents by identifying, alerting and helping a care home client at risk. The design process followed a co-design approach, where the use scenario and technical setup were created in a multidisciplinary collaboration. This was followed by building a laboratory version of the system, in which a mobile robot identifies and greets a person wandering in the care environment at night, and gently guides the person back to the room for a safe night. Although there are multiple technical details, which need to be developed further, the laboratory test results were very encouraging.
Macroharmonic Progressions through the Discrete Fourier Transform

This article examines macroharmony through the lens of the discrete Fourier transform (DFT) using computational analysis. It first introduces the DFT, giving an interpretive framework to understand the theory of chord quality first introduced by Ian Quinn (2007) before extending the theory to macroharmonies. Subsequently, the paper discusses different approaches—including different weighting and windowing procedures—to retrieving pitch data for computational analysis. An analysis of macroharmony in Domine Jesu from Maurice Duruflé’s Requiem, Op. 9 follows. I show that the DFT reflects intuition, reveals form-functional macroharmonies in the movement, and provides us with a perspective to find novel hearings.
Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion

The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach.
Multifactor User Authentication Mechanism Using Internet of Things

Internet of Things (IoT) is one of key areas of research, where number of smart devices is connected to internet. In IoT network Users operate various devices which process and exchange data with other devices and end users. User Verification is a major aspect in web based applications. One of the most well known evidence solutions used right now are the alphanumeric-based plans. Graphic critical solutions are offered to control the way users are ready to examine graphic styles pictures greater than reviewing alphanumeric secrets.Lexicon Attacks are focused on clients who select weak passwords. Graphical passwords offer enough security for such assaults. In this paper we suggest a two step authentication approach combining graphical passwords and Internet of Things.
Human Activity Recognition based on WaveNet

This paper makes a comprehensive study on human activity recognition based on CNN and RNN and proposes an alternative way to solve HAR based on WaveNet. The model is full probability autoregressive and can be used as a discriminant model. Therefore, this paper attempts to use WaveNet to solve HAR. By comparing the results of CNN, RNN and WaveNet models on the same data set, the experiment proves that WaveNet has strong superiority in some cases when dealing with HAR related problems, and the future development prospect is very optimistic.
Linear Fusion of Multi-Scale Transmissions for Image Dehazing

Images acquired in inclement weather conditions (haze, mist, fog, rain, etc.) suffer from various degradation problems such as low contrast, diminished visibility and color distortions. These low-quality images do not meet the demand for computer vision applications like object recognition, smart transportation, remote sensing, weather forecasting, etc. To restore the haze-free image, we require two parameters to estimate. The first parameter is an estimation of the transmission and second is the atmospheric light. Existing work focused on estimation of the transmission. However, two issues halo artifacts at the sudden change of the depth and over enhancement are unresolved in the dehazed image due to inaccurate estimation of the transmission. The traditional methods utilized two methods: pixel-wise and patch-wise while estimating the transmission. However, these two methods pixel-wise and patch-wise suffer from the problem of over-saturation or loss of details and halo artifacts in the dehazed images respectively. This paper suggests a linear fusion of multi-scale transmissions to overcome these problems. Experiments are performed on various hazy images and qualitative and quantitative results are presented. These qualitative and quantitative results reveal that the proposed method has successfully overcome these problems.
Depression Level Prediction For Students Using Machine Learning In The Context of Bangladesh

Depression represents a psychological illness or disorder which is very common nowadays. It's serious and has a growing number of problems that affect one's way of life and interferes with the functioning of the environment. Moreover, it has many detrimental impacts on society as well as the country, which lead to deteriorating the society. So, it's a matter of fact to taking proper steps to forecast the level of depression. Very few works have been done in this context. Our aim is to forecast the level of depression which helps us to get rid of this problem at the early stage. We have selected various factors which are responsible for these problems. These factors are determined with the help of a psychologist. We have employed eight machine learning classifiers for experiments and various performance evaluation matrices to evaluate our work. In the end, we have found that SMO performs better than any other classifiers, which is promising for our work.
SEEV Modeler: A GUI-Based Tool for the SEEV Model Simulation

The SEEV model of visual scanning offers a quick and easy way of evaluating the attentional demands of various tasks and displays. A SEEV model can be developed without relying on complicated mathematical software or background, making the conceptual model highly accessible. Implementation of SEEV modeling can further be improved by easing the process of running simulations and providing actionable information. In this paper, we showcase the SEEV Modeler, a GUI-based prototype of the computational SEEV model that lowers the technical barriers for human factors practitioners. Furthermore, the prototype’s ability to predict eye movements in dynamic driving scenarios was tested, with an emphasis on the impacts of the attention shifting effort and inhibition of return (IOR) on the model’s prediction performance. The SEEV Modeler produced model fits comparable to those of previous mathematical modeling approaches but also revealed limitations and practical issues to be addressed in the final version.
Learning ‘Learning Curves’ with R Shiny

Learning curves are fundamental in understanding individual task performance, with ubiquitous implementation in task assignments, worker scheduling, team formulation, etc., in domains bridging from manufacturing to healthcare. With a broad range of applicability, it is critical that students conceptualize, visualize, and build learning curves to activate that knowledge for effective decision-making. This paper describes a hands-on experiential approach for teaching learning curves that utilizes building LEGO® sets with mathematical formulation and data visualization in an open-source R Shiny application. The R Shiny application was designed to educate and inform students of their curve status while automating the power curve fitting calculations. The proposed methodology appeals and applies to students of all ages and was preliminarily field-tested in two collegiate courses and a K-4 after-school program. This paper introduces this approach and the R Shiny app, while future work includes quantifying improved learning.
Semantic library LibMeta as a tool for building the knowledge space of a scientific subject area

The paper considers an information system designed to represent a subject area related to science and its features. Highlighted general concepts for formal description of such a subject area in the knowledge base of the semantic library. The peculiarity of these areas is that the data structure is subject to frequent changes. Therefore, the means of organizing knowledge, which is a semantic library, should be sufficiently universal and not require deep technical knowledge. The paper describes the functionality of the system and its use.
Summarization of Student Feedback using Sentiment Analysis: Case Study

Student feedback improves the quality of the teaching and learning process. If the feedback is summarized, it is easier to understand. Despite the fact that many lecturers collect feedback from students, it is not summarized. It will be necessary for the teacher to read and analyze all of the feedbacks in order to provide a summary, which will be difficult in a large class. Therefore, an automated approach for collecting all students feedback and providing a concise description will be extremely beneficial. The analysis is done by taking students and teachers feedback and students performance data, using sentiment analysis the feedback is first summarized into various categories and then the analysis is carried out on the obtained data to obtain a rating on the understanding of students taking into consideration all of the students in the class and based on all of the topics, which is further analyzed with teacher feedback to obtain a rating on the difficulty of the topic which can used by the institutions to improve the teaching and learning experience for students
Human Trafficking Solution by Deep Learning with Keras and OpenCV

The entire world is currently facing problems related to higher rates of human trafficking today, and the research shows that the higher percentage of victims are among children. Although countless children are reported missing every year, many of them remain untraced. This paper presents a novel use of deep learning methodology for fighting against human trafficking and identifying the reported missing child from their photos, with the help of face recognition. The first step is to register the children in the common portal to be verified before crossing the border of their country of origin. Secondly, the public can upload photographs of suspected missing children into a common portal with landmarks and remarks. Uploaded photos are automatically compared with the registered photos of the missing children from the repository, and the classification of the input children’s images is performed. The photo with the best match will be selected from the database. To do that, a deep learning model is trained to correctly identify the missing child from the missing children image database provided, using the facial images uploaded by the public. The Convolutional Neural Network (CNN), a highly effective deep learning technique for image-based methodology, is adopted here for face recognition purposes. Face descriptors are extracted at 99.98% from the images using a pre-trained CNN model VGG16-Face deep architecture.
Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection

The data imbalance problem often occurs in nanometer VLSI applications, where normal cases far outnumber error ones. Many imbalanced data handling methods have been proposed, such as oversampling minority class samples and downsampling majority class samples. However, existing methods focus on improving the quality of minority classes while causing quality deterioration of majority ones. In this paper, we propose a two-stage classifier to handle the data imbalance problem. We first develop an iterative neural network framework to reduce false alarms. Then the oversampling method on a final classification network is applied to predict the two classes better. As a result, the data imbalance problem is well handled, and the quality deterioration of majority classes is also reduced. Since the iterative stage does not change any existing network structure, any convolutional neural network can be used in the framework. Compared with the state-of-the-art imbalanced data handling methods, experimental results on the hotspot detection problem show that our two-stage classification method achieves the best prediction accuracy and reduces false alarms significantly.
Movie Recommendation System using Weighted Average Approach

The recommendation system is a tool to assist in decision- making by providing items following user preferences. Recommendation systems are used in a wide variety of fields. Like in e-commerce, social media, ads, and others. The algorithm that is popular in making recommendation systems is collaborative filtering; however, the algorithm is less accurate if the amount of data is too small. Therefore, the use of the weighted average method can help to improve accuracy in providing recommendations. This study indicates that the user weighted average and the movie weighted average influence in providing film recommendations to the user. Furthermore, it shows that the level of accuracy of the recommendation system that uses the weighted average has higher accuracy than the recommendation system that uses collaborative filtering
Bayesian Variational Federated Learning and Unlearning in Decentralized Networks

Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions. Upon the completion of collaborative training, an agent may decide to exercise her legal "right to be forgotten", which calls for her contribution to the jointly trained model to be deleted and discarded. This paper studies federated learning and unlearning in a decentralized network within a Bayesian framework. It specifically develops federated variational inference (VI) solutions based on the decentralized solution of local free energy minimization problems within exponential-family models and on local gossip-driven communication. The proposed protocols are demonstrated to yield efficient unlearning mechanisms.
Multi-Terrain Rover Based on Rocker-Bogie Mechanism

During this paper we have got projected to create in all-terrain mobile automation based Rocker Bogie Mechanism with Arduino as microcontroller which will move across alternative planet surfaces to seek out data and to require samples. The rovers of planetary motion have important part in exploring the areas of mars or any other planet when landed and they should work as planned for future missions. This Rover could be a sensible, reconfigurable all-terrain multi mission small Rover it was made to deal with problems of exploration of planet. Rover stands for remotely operated video increased receiver. Rocker bogie mechanism could be a mechanism primarily employed in the mars rovers to beat the rough terrains whereas maintaining stability. The look consists of a spring free suspension-based difference drive system that enables the bogie to maneuver over rocks, pebbles with ease.
A generalized stability estimator based on inter-intrastability of subsets for high-dimensional feature selection

Feature selection is an important preprocessing step in high-dimensional regression and classification problems because it helps to avoid the effect of noise, redundant, and irrelevant features on model performance. A variety of methods for feature selection have been proposed in the literature. However, small perturbations in the training data may produce highly different feature subsets; this is known as instability. Evaluating the stability of feature selection approaches has grown in importance and popularity in recent years. This paper introduces a novel stability estimator for measuring the internal and external stability of features subsets chosen using various methods in random subsampling experiments. The proposed estimator evaluates the similarity of features within selected subset as well as measuring the variation with respect to the number of selected features between selected subsets in different subsampling experiments. Furthermore, the asymptotic normality of the proposed stability estimator for large number of subsamples is also established. Experiments are carried out on both simulated and real-world datasets; where results demonstrate the usefulness of the proposed stability estimator.
PakeMail: Authentication and Key Management in Decentralized Secure Email and Messaging via PAKE

We propose the use of password-authenticated key exchange (PAKE) for achieving and enhancing entity authentication (EA) and key management (KM) in the context of decentralized end-to-end encrypted email and secure messaging, i.e., without a public key infrastructure or a trusted third party. This not only simplifies the EA process by requiring users to share only a low-entropy secret such as a memorable word, but it also allows us to establish a high-entropy secret key. This approach enables a series of cryptographic enhancements and security properties, which are hard to achieve using out-of-band (OOB) authentication. We first study a few vulnerabilities in voice-based OOB authentication, in particular a combinatorial attack against lazy users, which we analyze in the context of a secure email solution. We then propose tackling public key authentication by solving the problem of secure equality test using PAKE and discuss various protocols and their properties. This method enables the automation of important KM tasks such as key renewal and future key pair authentications, reduces the impact of human errors and lends itself to the asynchronous nature of email and modern messaging. It also provides cryptographic enhancements including multi-device synchronization, and secure secret storage/retrieval, and paves the path for forward secrecy, deniability and post-quantum security. We also discuss the use of auditable PAKEs for mitigating a class of online guess and abort attacks in authentication protocols. We present an implementation of our proposal, called PakeMail, to demonstrate the feasibility of the core idea and discuss some of its cryptographic details, implemented features and efficiency aspects. We conclude with some design and security considerations, followed by future lines of work .
The Price Scraping Bot Threat on E-commerce Store Using Custom XPATH Technique

Scraping bots or scrapers are computer programs that automatically fetch data from the Internet. The price of products is illegally copied from different e-commerce stores and pasted into their sites for their benefit. Various web traffic surveys show that automated programs account for approximately half of all website traffic. However, an E-commerce website concerns many security issues that remain unresolved despite a significant increase in E-commerce development. One of the most damaging attacks on E-commerce sites is the price scraping when it comes to competitors. This paper aims to uncover the fact that both humans and bots are involved in stealing the prices of products from different e-commerce stores without the permission of original sites. This paper discusses the well-known price scraping tools and techniques. First, we have performed real-time price scraping attacks through custom XPATH, and we have obtained results which show that some platforms are still vulnerable for bots e.g., Alibaba and eBay. These sites do not restrict the copy of XPATH. Whereas, Amazon and daraz.pk blocks the price code from HTML document for the scraper to copy the custom XPATH. Moreover, we propose solutions to mitigate price scraping attacks on different e-commerce stores.