Reviewerly/exHarmony huggingface dataset:
Multi-Source Fusion Image Semantic Segmentation Model of Generative Adversarial Networks Based on FCN

At present, most of the methods used in the research of image semantic segmentation ignore the low-level feature information of image, such as space, edge, etc., which leads to the problems that the segmentation of edge and small part is not precise enough and the accuracy of segmentation result is not high. To solve this problem, this paper proposes a multi-source fusion image semantic segmentation model of generative adversarial networks based on FCN: SCAGAN. In VGG19 network, add super-pixel and edge detection algorithm, and introduce the efficient spatial pyramid module to reduce the number of parameters while adding the spatial and edge information of image; Adjust the skipping structure to better integrate the low-level features and high-level features; build a generation model DeepLab-SCFCN combining with the atrous spatial pyramid pooling to better capture the feature information of different scales of the target for segmentation; The FCN with five modules is designed as the discrimination model for GAN. It is verified on the data set PASCAL VOC 2012 that the model achieves IoU of 70.1% with a small number of network layers, and the segmentation effect of edge and small part is better at the same time. This technology can be used in image semantic segmentation.
A Status Update on Quantum Safe Cryptography

There is an ongoing effort to standardize asymmetric cryptography that should not be attackable by a quantum computer. The process is now into its final rounds. From 69 initial submissions from academia and industry, a small number of standardized schemes are expected in 2022-2024. The standards coming out from this work are expected to be used to secure civilian and military networks alike in the future, superseding current asymmetric techniques. In addition to providing an introduction to the process to this community, we also report on our own experiments with these schemes, highlighting which trade-offs one could prepare for.
Deep variational models for collaborative filtering-based recommender systems

Abstract Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems. Our proposed models apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing the variational technique in the neural collaborative filtering field. This method does not depend on the particular model used to generate the latent representation. In this way, this approach can be applied as a plugin to any current and future specific models. The proposed models have been tested using four representative open datasets, three different quality measures, and state-of-the-art baselines. The results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect. Additionally, a framework is provided to enable the reproducibility of the conducted experiments.
Grain-128AEADv2: Strengthening the Initialization Against Key Reconstruction

Properties of the Grain-128AEAD key re-introduction, as part of the cipher initialization, are analyzed and discussed. We consider and analyze several possible alternatives for key re-introduction and identify weaknesses, or potential weaknesses, in them. Our results show that it seems favorable to separate the state initialization, the key re-introduction, and the A/R register initialization into three separate phases. Based on this, we propose a new cipher initialization and update the cipher version to Grain-128AEADv2. It can be noted that previously reported and published analysis of the cipher remains valid also for this new version.
Leveraging Adversarial Learning for the Detection of Morphing Attacks

An emerging threat towards face recognition systems (FRS) is face morphing attack, which involves the combination of two faces from two different identities into a singular image that would trigger an acceptance for either identity within the FRS. Many of the existing morphing attack detection (MAD) approaches have been trained and evaluated on datasets with limited variation of image characteristics, which can make the approach prone to overfitting. Additionally, there has been difficulty in developing MAD algorithms which can generalize beyond the morphing attack they were trained on, as shown by the most recent NIST FRVT MORPH report. Furthermore, the Single image based MAD (S-MAD) problem has had poor performance, especially when compared to its counterpart, Differential based MAD (D-MAD). In this work, we propose a novel architecture for training deep learning based S-MAD algorithms that leverages adversarial learning to train a more robust detector. The performance of the proposed S-MAD method is benchmarked against the state-of-the-art VGG19 based S-MAD algorithm over 36 experiments using the ISO-IEC 30107-3 evaluation metrics. The proposed method has demonstrated superior and robust detection performance of less than 5% D-EER when evaluated against different morphing attacks.
Interaction-Aware Spatio-Temporal Pyramid Attention Networks for Action Classification

For CNN-based visual action recognition, the accuracy may be increased if local key action regions are focused on. The task of self-attention is to focus on key features and ignore irrelevant information. So, self-attention is useful for action recognition. However, current self-attention methods usually ignore correlations among local feature vectors at spatial positions in CNN feature maps. In this paper, we propose an effective interaction-aware self-attention model which can extract information about the interactions between feature vectors to learn attention maps. Since the different layers in a network capture feature maps at different scales, we introduce a spatial pyramid with the feature maps at different layers for attention modeling. The multi-scale information is utilized to obtain more accurate attention scores. These attention scores are used to weight the local feature vectors of the feature maps and then calculate attentional feature maps. Since the number of feature maps input to the spatial pyramid attention layer is unrestricted, we easily extend this attention layer to a spatio-temporal version. Our model can be embedded in any general CNN to form a video-level end-to-end attention network for action recognition. Several methods are investigated to combine the RGB and flow streams to obtain accurate predictions of human actions. Experimental results show that our method achieves state-of-the-art results on the datasets UCF101, HMDB51, Kinetics-400, and untrimmed Charades.
Ontology-Based System for Educational Program Counseling

Choosing the right university program can be very challenging for students.This is especially the case in developing countries such as India and Pakistan, where university admission depends on not only the program of interest but also other factors such as the candidate's financial standing.Since information on the Internet can be highly scattered, university candidates often need counseling from qualified people to decide their educational programs.Traditional database systems cannot effectively organize the large unstructured data related to university programs.It is challenging, then, for prospective students to acquire the information needed to make good decisions to consider factors such as personal preferences, available options, and the job market.This study proposes an integrated framework that takes advantage of the latest ontology-based semantic technologies.The proposed system can efficiently extract user-specific constraints in unambiguous queries and then retrieve more precise information.The system uses Web sources to ingest the right information and then extract explicit and implicit knowledge about educational domains and related university offerings.We believe the proposed system can effectively and efficiently choose an educational program by incorporating user constraints.
UAV Flight Path Planning When Considering Coverage Radius of UAV

This paper defines the coverage radius of Unmanned Aerial Vehicle(UAV) as its own attribute such as detection radius, overlooking radius, attack radius and so on when UAV is regarded as a fixed particle at a certain height. Generally, when UAV reconnaissance, attack or material delivery to multiple targets, its flight path planning problem is transformed into Traveling Salesman Problem(TSP), but the coverage radius of UAV is not considered, and there may be path intersection and targets covered repeatedly in the planned path. Aiming at the problem of UAV path planning when considering UAV coverage radius, Ant Colony Optimization (ACO) and Density-Based Spatial Clustering of Applications with Noise(DBSCAN) are proposed to solve UAV flight path planning problem combined with geometric judgment, which can better solve the problem of path intersection in TSP, and the problem of targets covered repeatedly without considering UAV coverage radius, to achieve the goal of no intersection path and shorter path length. Python programming simulation is carried out for the proposed method, and the experimental results show that the proposed method can effectively avoid the problems flight path intersection and targets covered repeatedly, and greatly shorten the flight path length of UAV, which will greatly improve the flight efficiency of UAV.
Predicting length of stay using regression and Machine Learning models in Intensive Care Unit: a pilot study

Healthcare Associated Infection (HAI) is a major health problem in several departments of the hospital sector. These infections cause prolonged length of stay (LOS), complications, and increased hospital costs. In this paper, medical record data of 415 patients admitted to in the Adult and Neonatal Intensive Care Unit (ICU) where there was a high risk of contracting HAIs, were used collectively. The aim was to create models capable of predicting LOS, measured in days, considering preoperative clinical information. Multiple linear regression analysis and Machine Learning (ML) regression analysis were performed. Subsequently, the LOS was grouped by weeks and classified with the ML classification algorithms. Multiple linear regression was implemented with IBM SPSS, the coefficient of determination (R2) was equal to 0.343. A regression with ML algorithms is performed with the Knime analysis platform. The best R2 was obtained from the Random Forest (R2 =0.414) and Gradient Boosted Tree (R2 =0.382) algorithms. Regarding the classification analysis, the RF and Multi-Layer Perceptron algorithms showed accuracy respectively 49.398% and 46.988%, an error of 50.602% and 53.012%. The goal was to create a model to support physicians in evaluating the hospitalization of patients at risk of HAI in the ICU. (H. De Koning, 2006)
Dynamic 3D point cloud streaming

We present a study on the impact of packet loss on dynamic 3D point cloud streaming, encoded with MPEG Video-based Point Cloud Compression (V-PCC) standard. We show the distortion when different channels of V-PCC bitstream are lost, with the loss of occupancy and geometry data impacting the quality most significantly. Our results point to the need for better error concealment techniques. We end the paper by presenting preliminary thoughts and experimental results of two naive error concealment techniques in the point cloud domain, for attributes and geometry data, respectively, and highlight the limitations of each.
AR Heart: A Development of Healthcare Informative Application using Augmented Reality

Abstract The utilization of Augmented Reality (AR) in industrial transformation is very well aligned with the fourth industrial revolution’s values. AR innovation can improve the perceptual knowledge of humans and enable us to communicate with a large amount of data. Therefore, this project takes the opportunity to create an AR healthcare app related to the human heart. The scopes are focusing on the human heart and provide information about the heart to the public besides improving their knowledge. Society nowadays is less concerned about heart health. There is a lot of information about the heart in newspapers, televisions and social media but most of the public still ignore it. Therefore, the information needs to deliver attractively. AR technology will be one of the interesting ways to spread information. The main goal is to provide information about the human heart in an AR environment. The other goals are to provide exposure for the users on how the insides of the human heart work and to make the users familiarize themselves with AR technologies. By using Unity and Vuforia Software Development Kit (SDK), this application lets users easily scan the provided marker anywhere and anytime using their smartphone to see the virtual heart.
Watermarking Scheme for using YCbCr Based On 2-Level DWT

Abstract Multimedia data for both personal and commercial purposes is now accessible to everyone due to the rapid development of the Internet. Consequently, the issue of copyright protection has surfaced and has triggered the development of several techniques for multimedia copyright protection. Such techniques include digital watermarking in which the important information contained in the host media is concealed by embedment in carriers such as images, videos, or audios. In this paper, the adaptive color image watermarking technique is proposed for the satisfaction of both imperceptibility and robustness demands. There are two main stages involved in this technique – coding/embedding and decoding/extraction. Prior to the coding stage, imperceptibility and robustness are preserved by first converting the host image from RGB to YCbCr color space before selecting the Cb component to apply the DWT embedding technique. Once more, the selected quadrant of the hosted image is decomposed using DWT before extracting the watermarked image. The robustness and efficiency of this technique were proved by exposing the watermarked image to six types of attacks, namely Median filter, Gaussian noise, Sharpening filter, Salt &amp; Pepper Noise, JPEG Compression, and Rotation. The results of the study were benchmarked against other methods that deploy DST on the same images. From the benchmarking process, the proposed algorithm was found to withstand the six types of attacks earlier mentioned and achieved a better performance compared to the DST approach. The quality of the watermarked image was also preserved in the proposed method.
Core to the Learning Day: The Adaptive Instructional System as an Integrated Component of Brick-and-Mortar, Blended, and Online Learning

Advanced technologies for individualized learning present intuitively appealing opportunities to provide students with exactly what they need, when they need it. However, observed challenges with implementations of adaptive instructional systems in K–12 education to date point to a need to better articulate the theory of change for the incorporation of individualized pathways and teacher-led grade-level learning, removing incongruities. This paper first examines separate exemplar theories of change underlying each of the adaptive instructional system and blended learning models. It then discusses associated pedagogical and setting considerations, identifying several areas of incongruity to be addressed when bringing these models together. An exemplar theory of change for integrating the adaptive instructional system as a component core to the learning day is proposed. Finally, the paper reflects on research required to test the proposed model and changes in interactions, pedagogy, and curriculum that may be required.
A machine learning algorithm for sorting online comments via topic modeling

This article uses a machine learning algorithm to demonstrate a proof-of-concept case for moderating and managing online comments as a form of content moderation, which is an emerging area of interest for technical and professional communication (TPC) researchers. The algorithm sorts comments by topical similarity to a reference comment/article rather than display comments by linear time and popularity. This approach has the practical benefit of enabling TPC researchers to reconceptualize content display systems in dynamic ways.
Few-shot Fall Detection using Shallow Siamese Network

The threat of falling down is significantly higher for the geriatric population and can lead to serious injuries including death. In the past, classical Machine Learning/Deep Learning-based methods have been successfully investigated for the detection of falls. However, most of these methods require a lot of data in order to be successfully trained for accurate detection. In this work, we propose a shallow architecture using 1 × 1 filters for use in a few-shot Siamese network. The proposed architecture was used in a Siamese network-based fall detection system. The proposed detection system is shown to effectively learn feature representations for the detection of falls when trained with few signals acquired from wearables containing inertial motion unit (SisFall dataset). The proposed system achieved a performance of 93% ± 7% and 72.5% ± 10% in 15 and 1-shot scenarios respectively. Performance comparisons with Siamese convolutional autoencoders and transfer learning¬based approaches demonstrated the superiority of the proposed few shot fall detection system.
Teaching-Learning in the Industrial Engineering Career in Times of COVID-19

Since cancellation of face-to-face education during the health emergency caused by COVID-19, higher education institutions, have had to guarantee the right to education through virtualization. Therefore, this work analyzes the teaching-learning process adapted to the virtual mode in the students of technical subjects of the professionalizing axis in the industrial engineering career a private university of Ecuador. Quantitative research is performed for this using surveys, performance index collection, and historical data. The method has a comparative scope between two academic periods, contrasting the data before and after the pandemic, the indicators are taken from students and teachers. The analysis of surveys is carried out in 3 components of teaching, in students are obtained: planning and methodology, with 81.1% considered as satisfied; teacher-assisted activities, with 79.1% content with the activities; and in practical activities, with 74.9% according to the tools used. Comparisons of indicators in different academic periods show minimal variations in performance, teachers improve the average performance score by 4.1%, and students drop academic performance by 2.6%. Finally, the correlation demonstrates a similar perception between students and teacher in the learning practice component.
A Modified Hybrid Blockchain Framework for Secured Data Transaction

Abstract Blockchain has recently become an essential tool which enables sensitive cloud services without the need for central confidence. For example, several different cryptocurrencies were permitted with public blockchains. Unfortunately, confidential details may be exposed on current public blockchain and smart contracts implemented there. Whilst some continuous work is under way to resolve these insecure knowledge leakage problems using advanced cryptography, they need major improvements on current and common Blockchain technology such as Ethereum and are typically costly in computing. On the other hand, blockchain applications were proposed to allow the data exchange among the pre accepted nodes/participants to be more efficient and privacy-preserving. While private blockchains respond to certain challenges of privacy by allowing only the particular community of participants to view sensitive data, they do not allow public transparency for communications because businesses are accepted by a known number of users also cannot be freely viewed. One natural problem is whether we should use public and private Blockchain networks in order to allow effective, improve privacy and accountable applications in view of these findings? In this work, we try in connection with digital auctions to face this challenge. In specific, we provide a newly designed blockchain architecture combined with private and open blockchains which enables sensitive offers to be opened up on a secluded blockchain so solitary the merchant can study the offers, and none of others. We also use shared blockchains to report the public sale winner and to make transfers responsible. Moreover, we demonstrate how we can promote sincere activity among auction participants by using intelligent contracts on public blockchains. Our detailed analytical findings suggest that it’s more cost effective compared to pure public auction implementations based on blockchain.
A near effective and efficient model in recognition

Neuro-fuzzy models have been applied in various domains, in which the issue of long time-consumption for optimizing parameters and less innovation in fuzzy method for feature extraction remains to be solved. Here, we present a novel cycle reinforce hierarchical model (CRHM) for effective and efficient recognition. The innovative strategies of CRHM consist of the hierarchical structure, the groups of fuzzy subsystems and the cycle mechanism. The hierarchical structure is innovatively built to extract features and transform the low-level features into advanced ones semantically, in which we adopt the groups of fuzzy subsystems as feature extraction units in each hidden layer, which ensures the diversity of features, avoids the fuzzy rules explosion, and reduces the time for clustering. The cycle mechanism is first proposed to connect the hierarchical structure and the output layer directly, transferring the tuned parameters again and again, to reinforce features gradually. To demonstrate the performance of CRHM, we have conducted extensive comparison with several state-of-the-art algorithms on benchmark 1D and 2D datasets. The experimental results show that the recognition rate of CRHM is higher than convolutional neural network (CNN), while the training time is only 5% of CNN's, which confirms that our approach provides a novel model for recognition, which can simultaneously improve the effectiveness and efficiency without the need of advanced equipment. In addition, the analysis results about the contribution of the core strategies to CRHM performance indicates that the contribution of the hierarchical structure is greater than that of the groups of fuzzy subsystems, which is superior than that of the cycle mechanism.