Reviewerly/exHarmony huggingface dataset:
Abstract P3-01-13: Comparing portable and clinical ultrasound systems using 3D printed breast phantom inserts

Abstract Background: Late-stage breast cancer rates in the Pacific where mammography services are limited are exceedingly high. Therefore, alternative accessible breast cancer screening technologies such as portable ultrasound is needed. However, little is known about the performance of portable ultrasound when compared to clinical ultrasound for use in breast cancer screening. By utilizing 3D printing technology, it is possible to design breast phantom inserts to replicate various types of lesions. In this study, we utilized 3D printed breast phantom inserts to compare portable and clinical ultrasound lesion detection performance. Methods: Four different breast inserts were designed using FreeCAD (version 0.19) to replicate different lesion detection properties. The first insert compares lesion shape, the second insert investigates depth and size, the third insert looks at fiber diameter, and the fourth insert looks at clusters. The four inserts were printed using a photopolymer resign (Formlabs Inc Rigid resign, Somerville, MA, USA) and then placed in a gelatin-based breast phantom designed for ultrasound use. Using the portable ultrasound (GE Vscan Extend) and clinical ultrasound (Philips EPIQ 5G), various images were captured of identical angle and orientation for both devices. The number of lesions visualized were counted and presented as a percentage of lesions detected. Results: The portable ultrasound had a 100% lesion detection rate for breast insert 1, 90.3% for breast insert 2, 70% for breast insert 3 and 55.8% for breast insert 4. Clinical ultrasound had 100% lesion detection rate for breast insert 1, 93.1% for breast insert 2, 76.6% for breast insert 3, and 99% for breast insert 4. Conclusion: Portable ultrasound shows comparable lesion detection capabilities to clinical ultrasound in 3 of the 4 breast phantom insert tests. Portable ultrasound may have potential as a capable accessible breast cancer screening device in areas without mammography. Citation Format: Dustin Valdez, Jami Fukui, Thomas Wolfgruber, Lambert Leong, Gertraud Maskarinec, John Shepherd. Comparing portable and clinical ultrasound systems using 3D printed breast phantom inserts [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-01-13.
Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations

As the most successful network structure in biomedical image segmentations, U-Net has presented excellent performance in many medical image segmentation tasks. We argue that the skip connections between the encoder and decoder layers pass too many redundant information, and filtered out the unnecessary information may be helpful in improving the segmentation accuracy. In this paper, we proposed a contrast attention mechanism at the skip connection, and proposed a contrast attention U-Net for KiTS21 challenge. The proposed method is able to achieve better performance over nnU-Net models with both low resolution and full resolution inputs in the 5-fold cross validation in the training set. In the final unrevealed testing set, our method achieves a mean sampled average Dice coefficient of 0.8815 and a mean sampled average surface Dice coefficient of 0.8007, which ranked the 5-th in the KiTS21 challenge.
Using CMAF to deliver high resolution immersive video with ultra-low end to end latency for live streaming

Immersive video with 8K or higher resolution utilizes viewport-dependent tile-based video with multi-resolutions (i.e. low-resolution background video with high-resolution video). OMAF defines how to deliver tiled immersive video through MPEG DASH. But End-to-End latency is a consistent problem for the MPEG DASH solution. Solutions using short segment with 1 sec duration will reduce latency, but even in those cases, without CDNs, the end-to-end latency is still 5 secs or more. And in most cases, massive segment files generated every second harden CDN, leading to much longer latencies, such as 20 secs or more. In this paper, we introduce a solution using Common Media Application Format (CMAF) to deliver tile-based immersive video to reduce the end-to-end latency to sub-3 secs. Based on CMAF: We enabled long duration CMAF segment with shorter End-to-End Latency by using long duration CMAF segmentation reduce CDN pressure since it reduces the amount segment files generated. In addition, we re-fetch relative CMAF chunks of high-resolution segments via our own adaptive viewport prediction algorithm. We use a decoder catching-up mechanism for prediction-missed tiles to reduce the M2HQ (Motion-To-High-Quality) latency while viewport changed within chunks. As we will show, this leads to an overall sub-3 seconds End-to-End latency with ~1 second Packager-Display Latency and average 300ms M2HQ latency can be reached with 5 seconds segmentation in non-CDN environment.
STUDENT-TEACHER FEEDBACK-LOOPS: A STEP BEYOND REFLECTIONS WITH ELECTRONIC PORTFOLIOS

M.C. Blanco-Gandía 1, G. López-Crespo1, C. Ferrer-Pérez1, N. Sánchez-Pérez1, M. Alcaraz-Iborra1, T.I. Jiménez-Gutiérrez1, S. Valdivia-Salas1, S. Montagud-Romero2 1University of Zaragoza (SPAIN) 2University of Valencia (SPAIN)
Best design practices &amp; strategy to launch your own NFT project

The term ‘non-fungible’ is used in economics to denote the possession of unique objects and to describe things that cannot be replaced by others because they have a set of unique properties. A ‘token’ as a unit of account is a record in a distributed blockchain that is controlled by a computer algorithm of a smart contract, in which the values of the balances on the accounts of token holders are recorded, making it possible to transfer them from one wallet to another.
Research Ideas and Analysis of The Electronic Archive Data Erasure and Destruction Platform

The large number of electronic archives which are stored in various storage media, provide a great convenience for electronic archives work. However, the risk of data leakage is faced, due to the storage media being retired or sent out for maintenance. In order to solve the problem of leaking electronic file data and improve the confidentiality, the development of a erase and destroy platform for electronic archive data are analysed and the application of equipment are described in detail. The equipment developed using the guiding ideas of this paper has been practically applied and good results have been obtained. The equipment developed using the ideas in this paper has been practically applied and good results have been obtained. The design ideas in the design process of the platform are dissected. Some working tips and technical key points are detailed in the process of using the platform. For other similar applications, a good solution is provided.
Social Networks as a Means of Combating Gender-Based Violence

Cyberbullying of women and girls is not just a violation of human rights, it is a global phenomenon that is destroying the lives of women and their families around the world. Using a hermeneutic documentary methodology, the objective of the study was to determine the legal mechanisms for the use of social networks as a means of combating gender violence and thus outline the main problems of their application of the law. Everything indicates that social networks, as an effective means of forming positive public opinions, can form a positive image of women and the appropriate attitude of men towards them, and as well as contribute to combating violence on the Internet. The system for combating gender-based violence consists of prevention, protection, criminal responsibility, and a comprehensive gender policy. It concludes that international gender policy standards have been found to be based on a model of "real equality", which is ensured through the achievement of equal opportunities, equal access to opportunities and equal performance.
Efficient Secure Medical Image Transmission Based on Brownian System

This article introduces an automated area of interest (ROI) based encryption method for applying security to medical images. The benefit of utilising this strategy is that it is resistant to a variety of attacks, including Median, Wiener, Gaussian, and differential attacks. The suggested technique is divided into three parts: an automatic ROI detection system, a permutation process, and a diffusion process. The position of each pixel in the ROI image that was changed using the Arnold map sequence and the pixel values are manipulated using the Brownian map. The results obtained with the proposed strategy showed that it has a promising performance.
736 REAL-TIME MOBILITY DATA FROM WEARABLE DEVICES PREDICTING FALLS RISK IN OLDER ADULTS

Abstract Introduction Falls are a common cause of injuries in people over 65 years, usually resulting in life-threatening or life-altering sequalae for affected individuals. Falls risk assessment typically requires a face-to-face evaluation delivered by a trained professional in a clinical setting, thus limiting their application for many older people with falls. Therefore, there is a need for a remote monitoring system (RMS) that captures mobility data to assess comprehensively an older individual’s true falls risk undertaking their everyday activities in their usual environment. Methods A pilot study was conducted with 16 participants over 65 years classified as high (n = 9) or low falls risk (n = 7) based on their fall’s history. A RMS was developed, which combines inertial measurement unit (IMU) data from a wearable device and online real-time Machine Learning (ML) to generate a two-stage fall risk assessment. In stage one, a ML activity motion classifier identified raw IMU data from participants as either, walking, resting or transitional movements (turning or sitting to standing). In stage two, previously classified motion segments were analysed by a ML fall-risk classifier, providing an objective risk assessment based on gait characteristics during everyday motion. Falls incidence was recorded prospectively at one year follow-up. Results The motion classifier achieved an 94.8% accuracy in differentiating between resting, walking and transitional movements. The fall risk classifier initially trained with retrospective fall history data achieved an 87.5% accuracy with 88.9% sensitivity and specificity, which is comparable to other accurate classifiers in published literature. The fall risk classifier retrained with prospective one year follow-up data achieved a 73% accuracy which was superior to the 67% accuracy of the Timed Up and Go test for falls prediction. Conclusion Our study shows the potential of a machine-learning remote monitoring system using information from mobility data to classify an older person’s falls’ risk.
The use of robotized inclinometric system in Early Warning System. The case study of a large landslide monitoring.

&amp;lt;p&amp;gt;Geohazard monitoring is a key component in an early warning system (EWS). The implementation of monitoring actions provides data for the acquisition of variables related to the landslide, its triggering and kinematic; additionally, it can provide insights of its evolution in time in order to plan mitigation actions, including alarms and warnings. The use of high-frequency systems can also provide such data in the shortest time thus optimizing the aforementioned actions. In the last decades, numerous surface monitoring systems were developed, with various features, providing punctual information, like GNSS or Robotized Total Stations, at high frequency or large-scale data, i.e. Remote Sensing, at lower temporal resolution. The choice of the best one is related to the goals to be fulfilled but, independently from the selected method, surface techniques monitor only a displacement resulting from the sum of all the deep-seated ground deformations. To properly detect the subsoil behavior of a landslide, the use of subsurface sensors is necessary. To couple the subsoil survey with high frequency monitoring a robotic inclinometric system was developed, and patented, by the Geohazard Monitoring Group (GMG) of CNR-IRPI. The instrumentation features the operational characteristics of the manual inclinometric measures (reliability, double readings 0/180&amp;amp;#730;&amp;amp;#8230;) but integrates the advantages of the robotization (accuracy, measurement frequency&amp;amp;#8230;), too. The robotized instrumentation also called &amp;amp;#8220;Automated Inclinometer System&amp;amp;#8221; (AIS) allows the automatic exploration of all the borehole length (up to 120 meters in the standard configuration) with a single probe. The AIS is remotely connected by a 4G modem so it is possible to define the acquisition parameters, download measured data and check the device functioning parameters. The instrumentation was deployed, at the beginning of December 2021, in a borehole located in Passiria Valley (northeastern Italy) to monitor a large and slow-moving landslide involving the whole mountain face; thanks to instrumentation modularity, the AIS is ready to measure after only 4&amp;amp;#247;5 hours of installation time. Concurrently with the main installation, a GNSS benchmark was positioned and surveyed to provide, with the next measurement campaigns, a crosscheck with the AIS results. After 10&amp;amp;#247;15 days of monitoring at 1 measurement/day the landslide&amp;amp;#8217;s sliding surface, its depth and deformation rate, were clearly identified, thus confirming the capability of the AIS to perform early detection of the landslide kinematic. This result is key information in the risk reduction chain as it shortens the time necessary to achieve the numerical parameters describing the landslide and, consequently, plain the following, mitigating, actions.&amp;lt;/p&amp;gt;
Analysis of Eyebrow Motion for Micro-Expression Recognition

Facial expression recognition is a field of emotion recognition that has received considerable attention over the last decades. However, most of the research has been focused on the recognition of macro-expressions that are generally posed and easy to imitate. On the other hand, micro-expressions are much subtler and localized therefore by definition harder to replicate, thus making them valuable for medical diagnosis and criminal investigations. Normally, micro-expressions are detected and recognized by studying the entire face. We believe that the eyebrows contain the most relevant information pertaining to micro-expression recognition. Therefore, in this paper, we propose to recognize facial micro-expressions based solely on the motion of the eyebrow. The proposed method starts with the preprocessing and cropping of image sequences based on the eye area. Next, feature extraction is carried out using Histogram of Oriented Optical Flow (HOOF), and Local Binary Patterns on Three Orthogonal Planes (LBP-TOP). Validation of our methodology is carried out on the SMIC-HS and CASME II databases. We achieved promising recognition accuracy of 54% with magnification and 64% without magnification on the SMIC-HS database. In the case of CASME II database we achieved an accuracy of 57% with magnification, and 59% without magnification.
Profile analysis with reconstruction robustness for measurement data subject to outliers

In the surface profile analysis, there are often a few observations that contain outliers. Due to the existence of outliers, the application of non-robust reconstruction algorithms for measurement data will become a huge problem because these methods are often sensitive to outliers and the approximation effectiveness will be greatly aggravated. In view of this, this paper presents a novel angle-based moving total least squares reconstruction method, to the best of our knowledge, that applies two-step pre-treatment to handle outliers. The first step is an abnormal point detection process that characterizes the geometric features of discrete points in the support domain through a new angle-based parameter constructed by total least square. Then, the point with the largest anomaly degree is removed, and a relevant weight function is defined to adjust the weights of the remaining points. After pre-treatment, the final estimates are calculated by weighted total least squares (WTLS) based on the compact weight function. The detection and removal of outliers are automatic, and there is no need to set a threshold value artificially, which effectively avoids the adverse impacts of human operation. Numerical simulations and experiments verify the applicability of the proposed algorithm as well as its accuracy and robustness.
Optimized Voronoi-Based Algorithms for Parallel Shortest Vector Computation

This chapter addresses Voronoi cell-based algorithms, solving the Shortest Vector Problem, a fundamental challenge in lattice-based cryptanalysis. Several optimizations reduce the original algorithm's execution time. The algorithm suitability for parallel execution on both CPUs and GPUs is also shown. Optimizations are based on pruning, avoiding computations that will not improve the solution. The pruning criteria is related to the target vectors norm relative to the current best solution vector norm. Speedups up to 69× are observed. With a pre-process sorting step, which requires storing the norm ordered target vectors and therefore significantly more memory, speedup increases to 77×. On the parallel processing side, the optimized algorithm exhibits linear scalability on a CPU with up to 28 threads and keeps scaling, at a lower rate, with Simultaneous Multi-Threading up to 56 threads. The lack of support for efficient threads synchronization in GPUs precludes a scalable implementation of the pruning optimization. A parallel GPU version of the non-optimized algorithm is demonstrated to be competitive with the parallel non optimized CPU version, although the latter outperforms the former for 56 threads. GPUs are expected to outperform CPUs for higher lattice dimensions; this cannot be experimentally verified due to the limited memory available on current GPUs.
Dynamic Path Planning Algorithm for Unmanned Ship Based on Deep Reinforcement Learning

In order to enable the unmanned ship to have the ability of autonomous path planning in the complex marine environment, which can avoid obstacles and reach the destination accurately in the unknown environment, this paper combines the ability of deep learning to obtain information with the decision-making ability of reinforcement learning, and proposes an algorithm based on deep reinforcement learning. The simulation results show that after 3000 rounds of training in a given environment, the success rate of sailing to the end point in this environment is 100%. The path planning algorithm based on deep reinforcement learning performs well in the simulation experiment, achieves a very high accuracy after a large number of training times, and meets the needs of the operation and use of the unmanned ship on the water. This algorithm can be carried in the actual environment to realize the autonomous navigation of the unmanned ship on the water.
An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

This article proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets, which are tweets with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of 90% when analyzing complex Wikipedia data while the other algorithms did not achieve 85% when processing the same amount of data.
PG Recommendation

Abstract: When you plan of going to study far away from your comfort zone, one of the first things to do is to book a good place to stay. Booking a PG online can be an overwhelming task with thousands of PG to choose from.Inspired by the importance of these situations, we made a decision to apply our skills on the task of recommending PG to Students. We used a hotel recommendation dataset, which provided us with a variety of features that helpedus gain a deeper understanding of the process that makes a user choose certain PG. The aim of this PG recommendation project is to predict and recommend best PG to a user that he/she is more likely to book from the given hundred distinct choices.
A Patient-Centered Secured Approach to E-Health

Electronic health records (EHRs) have largely supplanted paper-based systems in order to improve the accuracy, reliability, and accessibility of patient data. Furthermore, EHR security is a major concern that puts patient privacy at risk. User privacy and data security are issues with the majority of third-party hosting solutions. As a result, access control regulations must be restricted and effective procedures for cloud-based EHR data must be developed. Under the suggested system, only authorised individuals will be allowed to update or examine some of the patient&#x0027;s data, ensuring the data&#x0027;s confidentiality. Each EHR record is encrypted by the controlling authority. The requesting user may receive dynamically changing permissions based on authentication and context factors. This paper is to provide security and patient-centric approach to e-health solutions. The patient can have control over their medical records. The potential of data theft grows when users&#x0027; data is outsourced, and attackers can compromise data. So, the authority verifies the request with the database and then generates the secret key. Then with the key provided, files are shared with the authorized user.