Reviewerly/exHarmony huggingface dataset:
MedSyn: Medical Image Synthesis of Brain Magnetic Resonance Images Based on Hybrid Information Aggregation

Magnetic resonance imaging (MRI) is widely used in clinical scenarios. However, acquisition of certain image modalities may be in limitation due to multiple considerations such as time duration and labor costs. Hence, medical image synthesis can resolve those limitations by estimating the desired target image modality without inducing an actual scan. In this paper, we proposed a novel method which aggregated the hybrid information of MRI to improve the performance of medical image synthesis and addressed it as MedSyn. Specifically, the MedSyn adopted the GAN framework, and made some great improvement. In generator, we proposed a simple yet efficient Tri-Focus Visual Center (TVC) to activate contextual features by establishing inter-dimensional dependencies with the assistance of rotation operations and gaussian functions. Furthermore, we proposed a Hybrid Global-Local Feature Extractor (HGL) module to capture long-range dependencies and exploit potential relationships between spatial and channel dimensions to extract global-local information. Benefitting from the modules of TVC and HGL jointly, the generator could capture obvious image features and underlying semantic information. In discriminator, we proposed a Dynamic Multi-Scale (DMS) Discriminator which could enhance discriminative ability by combining features of different scales dynamically. To evaluate the performance of the proposed method, we conducted experiments on three brain datasets (the Meningioma dataset, the IXI dataset and BRATS dataset), and compared the results with some state-of-the-art methods. The experimental results showed that our MedSyn outperformed other state-of-the-art methods in terms of both quantitative evaluation and qualitative visual assessments.
Channel Estimation of OFDM System Based on BR-SRGAN Network

In the existing channel estimation methods of OFDM systems, a naive deep learning-based algorithm cannot fulfill the expected fidelity and high-frequency details are varnished at higher resolutions. A Generative Adversarial Network algorithm based on super resolution techniques and bilinear residual is proposed in this paper, to estimate traditional channel through the image processing. Firstly, the training dataset is generated by the Physical Downlink Shared Channel of the 5G New Radio standard. Then, converting the generated channel matrix of the pilot frequency position to a low-resolution image. And, utilizing neural network model to substitute the channel estimation interpolation processing. The MSE of our method is able to consistently reach 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> , indicating that the algorithm we proposed has outstanding performance and prominent robustness than the traditional algorithms, such as LS, Practical Channel Estimation, ChannelNet and ReEsNet network.
Dynamic Binary Decision Diagram Creation Using an Extended Apply Algorithm

Boolean functions are used in many areas of mathematics and computer science. An efficient representation of the function is an essential aspect of each practical application. Binary Decision Diagrams provide such a representation even for functions with numerous variables. A crucial aspect of Binary Decision Diagram utilization is the efficient creation of the diagram - the basis of which is the dynamic merger of diagrams. One of the algorithms we use for this purpose is called apply. Apply, in its basic form, merges two diagrams using a binary operation into a new diagram. In the article, we describe an extended version of the apply algorithm that can merge <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> diagrams. By experimental comparison, we show that this extended version can prove to be useful and convenient in practice.
Suppressing Laser Triangulation Sensors Optical Aberrations by Replacing the Lens with a Slit

This paper presents a new capture method for laser triangulation sensors consisting in replacing the lens with a slit—effectively making it a pinhole camera—and exploiting diffraction effects. This new method circumvents optical aberrations such as spherical, defocusing, coma, field curvature, and lens distortion, as well as lens flare to a point where it can be virtually ignored. Moreover, using a slit reduces the number of optical parts and simplifies the modeling of the laser spot to find its center. To test our proposed method, we generated data sets taking pictures with both a lens and a slit on different materials placed on a worm gear with its position controlled by a high-precision step motor. With this data, we extracted the center of the laser spot with different image processing algorithms. It was then possible to assess the goodness of fit of the triangulation curve using the sets of captured points by both methods. We show that not only we circumvent the adverse effects of optical aberrations and lens flares, we obtain a more accurate estimation of the actual known distance of the target.
Message from the IDAACS 2023 Co-Chairmen

It's our pleasure to welcome all attendees the 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), www.idaacs.net, which is running in Dortmund, Germany, 7-9 September, 2023 in a hybrid mode.
An Improved Bayesian Learner Based on Optimized Kernel Density Estimation and Wasserstein Soft Ensemble Strategy

Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the traditional Bayesian classifier in that the conditional possibility density is calculated by an assumed statistical model, our method estimated the possibility value without model assumption, they are obtained by kernel density estimator with optimized window width and the Wasserstein distance is used to solve the problem of how to cause the classifier difference in the integration process. The optimized window parameters are solveded by minimizing the Unbias Cross-Validation (UCV) objective function using Chaos-Particle Swarm Optimization (CPSO). Wasserstein distance is innovational used to evaluate the similarity between possibility distributions and generates weights of base classifiers. The final ensemble outputs are calculated by a soft voting strategy. The experimental results illustrate the effectiveness and improvements of the proposed method in terms of density curve fitting, ensemble ability, and overall classification accuracy.
Green litchi automatic learning based on YOLOX-S, Faster-RCNN, SSD deep learning algorithm

In the past 10 years, many models of target detection based on convolution networks have been proposed, which are mainly divided into single-stage target detection models and two-stage target detection models. The main feature of single-stage is fast detection speed, and the main feature of two-stage is high detection accuracy. The purpose of this experiment is to select the most suitable target detection model for Qinglitchi by comparing the performance of the single-stage target detection model and the two-stage target detection model. After many and sufficient model training, the experimental results show that the YOLOX-S model has the fastest detection speed, and FPS is 37.965; The average accuracy of detection is the highest, with an average accuracy of 89.98%. Therefore, the YOLOX-S model is selected as the target detection model of green litchi.
UDL for Inclusive Teaching

This qualitative case study investigates the impact of the universal design for learning (UDL) framework paired with multiple educational technologies on student engagement and sense of inclusion and belonging in the classroom. Research has indicated that a sense of belonging has a direct impact on success among undergraduate students. I saw increased evidence of belonging among students in my class after incorporating choice for my students in all the ways they interact with me, each other, and the course content. This article summarizes the revisions made to the course, the educational technologies used to implement those changes, and qualitative data that supports my hypothesis that a technology-supported UDL approach can have a positive impact on feelings of belonging among college students.
Isolation and Antibiogram Pattern of Bacteria Isolated from Automated Teller Machine (ATM) at Abuja Campus, University of Port Harcourt, Nigeria

The growing use of automated teller machines (ATMs) has sparked worries about possible microbial contamination on ATM keypads, which might be harmful to the general public's health. This study examines the antibiotic resistance characteristics of the bacterial infections linked to ATM keypads at the University of Port Harcourt's Abuja campus in Nigeria. Tests for antimicrobial susceptibility, bacterial isolation, and identification were carried out on swab samples obtained from different bank ATMs. Common contaminants found in the research were Staphylococcus, Bacillus, Enterobacter, Aerobacter, Photohabdus, and Micrococcus species. With a 40% prevalence, Bacillus species predominated, followed by Staphylococcus species at 30%. The range of total heterotrophic counts was 3.38–4.69 Log Cfu/ml. The necessity for efficient biocides was highlighted by the antimicrobial susceptibility tests, which showed different resistance patterns in Gram-positive and Gram-negative isolates. This study demonstrates the rise of antibiotic-resistant bacteria and emphasizes the importance of microbial contamination on ATM keypads for public health. Advocating for the slowing of the spread of resistant microbes, research promoting frequent surveillance, hygiene education, and the creation of antimicrobial measures is endorsed. All things considered, insightful information on the microbial environment of often-touched surfaces was provided by the results, emphasizing the advocacy for taking preventative measures to reduce the health hazards related to ATMs.
Un frammento di metafisica descrittiva: i presupposti dell’atteggiamento naturale

The combinatory restriction known in linguistics as selection restrictions are generally assumed to be a kind of linguistic structures, either syntactic or semantic, or at best cognitive structures. The idea discussed in this paper is that selection restrictions, although relevant for the description of complex meanings of linguistic expressions, do not belong to the structure of either language or cognition. Instead, they are criteria for conceptual consistency. They form a layer of shared presuppositions that lie at the grounds of consistent human behaviour and therefore of consistent thought and expression and belong to a natural ontology shared far beyond the boundaries of a given linguistic community.
Enhancing information retrieval with semantic query expansion: a Word2Vec-based approach

A significant area of research in the field of information retrieval is query expansion. To address the issue of inaccurate user query descriptions and mismatches with query terms during the information retrieval process, a semantic query expansion method based on Word2Vec is proposed. The method involves training low-dimensional word vectors using the Word2Vec distributed neural language probability model, selecting a candidate set of expansion words, and employing a query vector generation approach that focuses on the expansion words to filter the candidate set. This ensures that the selected expansion words effectively reflect the semantic and syntactic relevance of the entire query. Experimental results demonstrate that the semantic query expansion method based on Word2Vec improves both recall and precision, making it a valuable approach in the field of query expansion.
Data-Flow-Based Normalization Generation Algorithm of R1CS for Zero-Knowledge Proof

The introduction of zero-knowledge proofs (ZKPs) has had a profound impact on the blockchain and distributed ledger communities. ZKPs require the utilization of Rank-1 Constraint Systems (R1CS), which serve as verifiers for bi-linear equations. However, the flexibility of R1CS representation leads to notable variations in the compiled R1CS forms derived from circuit language programs with identical semantics. To tackle this challenge, this paper proposes a data-flow-based R1CS paradigm algorithm, producing a standardized format for different R1CS instances with the identical semantics. By adopting the normalized R1CS format circuits, the complexity of circuits’ verification can be reduced. Furthermore, this paper presents an R1CS normalization algorithm benchmark, and our experimental evaluation demonstrates the effectiveness and accuracy of our methods.
PoTR: Accurate and Efficient Proof of Timely-Retrievability for Storage Systems

The use of remote storage has become prevalent both by organizations and individuals. By relying on third-party storage, such as cloud or peer-to-peer storage services, availability, fault tolerance, and low access latency can be attained in a cost-efficient manner. Unfortunately, storage providers may misbehave and violate Service-Level Agreements (SLAs). In this paper, we propose, implement and evaluate a new Proof of Timely-Retrievability (PoTR) that aims at assessing whether a provider is able to retrieve data objects with a latency lower than some SLA-specific threshold δ. We leverage Trusted Execution Environments (e.g., Intel SGX) to ensure that the proof is produced by the node being audited and to reduce the communication between the auditor and the audited node. We have experimentally evaluated our design considering a challenging edge computing setting, where storage services are provided by resource-constrained fog nodes, and the distance between the auditor and the audited node can be large. Despite the variance in edge network delays, we show that the auditor is able to effectively detect SLA violations.
Non-Homogeneous Haze Image Formation Model Based Single Image Dehazing

Image dehazing is an image processing technique that involves removing the haze effect from a given image. Restoration of hazy images is currently an established computer vision issue due to its applications in the real world, like automated vehicles, surveillance, etc. This paper proposes a single image dehazing model by estimating atmospheric light and transmission map simultaneously using two networks. Atmospheric light and transmission map are estimated by a non-homogeneous light estimation network and a novel refined transmission map estimation network. The non-homogeneous light estimation network has two subnetworks: a white-balanced network and a deep convolutional neural network called Global Atmospheric Light Estimation Network. The white-balanced error network for correction of white-balanced error in a hazy image and a Global Atmospheric Light Estimation Network for estimation of global atmospheric light in a corrected white-balanced error hazy image. A refined transmission map estimation network is used to create a refined transmission map of the hazy image. The design of a refined transmission map estimation network is based on U-Net with skip connections, which estimates and refines the transmission map of the input haze image. The non-homogeneous haze image formation model takes both estimated global atmospheric light and a refined transmission map as input to restore the hazy-free image. The global atmospheric light estimation network and refined transmission map estimation network are trained using mean square error (MSE) and hybrid loss functions, respectively. In terms of structural similarity index Matric (SSIM) and peak signal-to-noise ratio (PSNR), the proposed model is equal to the cutting-edge approaches on the NR-Haze, I-Haze, and O-Haze datasets.
Machine Learning Techniques for Hate Speech Detection on Social Media

social media are computer-based and internet-based technologies that offers a platform for the concept and distribution of information, thoughts, concerns, and point of view on somethings going in the world and expression through virtual communities and networks. People use social media like Instagram, Facebook, Twitter for sharing their views and their perspective towards things happening all around the world. Some people make offensive comments and videos on these platforms that may affect the other people and can change into conflicts. Hate Speech is one of the major reasons of the conflicts in the world. Conflicts can be in the individual-level or country-level.
BigHeuFIM: A Heuristic Apriori Algorithm for Extraction of Frequent Itemsets on Big Data

The association rule mining is the method to mine the patterns associated with the data in transactions. Setting the minimum support to a lower value increases space complexity particularly when the dataset is sizable. There are various methods that struggle to scale because of their high time complexity or memory requirements. This situation arises need for approximate results in contrast to accurate results. This work proposes a divide-and-conquer approach based heuristic strategy, BigHeuFIM, that exponentially reduce time and space complexity. The experiments demonstrates that the BigHeuFIM significantly outperformed existing algorithms in terms of speed by processing items in 80% lesser time.
C. L. Liu – A Leading Authority and Role Model in Sino-American Academia

Professor Chung Laung (David) Liu lived an extraordinary life and touched the lives of so many along the way. He was a great computer scientist and made exceptional contributions to the field of Computer Science and Engineering. He was an outstanding educator. In that capacity, he served not only as a well-respected university president but also as a treasured mentor to many of us. He is truly a role model in Sino-American Academia.
EgoMUIL: Enhancing Spatio-temporal User Identity Linkage in Location-Based Social Networks with Ego-Mo Hypergraph

Users tend to own multiple accounts on different location-based social network (LBSN) platforms, and they typically engage with diverse social circles on each platform within the same locations. Consequently, linking these accounts across separate networks becomes essential, playing a critical role in information fusion. Previous works accomplishing user identity linkage (UIL) utilize individual mobility records, which are significantly affected by the issue of data scarcity. In this paper, we propose EgoMUIL, a heterogeneous graph embedding approach specifically devised for information propagation, aiming to alleviate the scarcity problem to some extent. Considering that follow relations of respective networks also hold great significance for the UIL task, we are inspired to enrich individual limited mobility records through follow relations. Our preliminary research reveals that direct common follow relations are quite insufficient. Since the followers with the same spatio-temporal mode tend to have social connections, we first mine closely-related users for each user through topology and locality similarity, generating respective cross-domain ego-networks. Subsequently, we construct a heterogeneous ego-mo hypergraph consisting of mobility and ego-networks. We propose a novel graph convolutional network (GCN)-based approach to learn user representations, which enables the aggregation of information from surrounding nodes, incorporating topological similarities, stay locality similarities, and co-occurrence frequencies. The resulting embeddings provide comprehensive representations of users and locations, capturing their characteristics and relationships across platforms, which further facilitates the UIL task. Our experimental results on real-world check-in datasets from Foursquare and Twitter demonstrate that EgoMUIL outperforms the state-of-the-art methods on the UIL task. Notably, EgoMUIL exhibits superior performance in scenarios involving limited check-in records and follow relations.
61 Delayed Language Development in Mexican Toddlers Following Lockdown During COVID-19: The Case of Peer Socialization

Objective: Socialization is a crucial factor in children’s language acquisition. Lack of socialization could affect language development, causing a delay that can be spotted early by identifying neurological soft signs (NSS). This study aimed to compare NSS and language performance between two samples of children (pre and post-pandemic) since the lockdown carried out by Covid-19 restricted socialization in post-pandemic kids. Participants and Methods: Two groups of 30 children (aged 3 to 5 years old, ten children per age group; 50% boys and 50% girls) were assessed with the NSS and language subtest from the SNB-MX battery (Salvador, Tovar, Segura, Armengol &amp; Ledesma, 2019). The first group was selected and evaluated before the covid lockdown; the second group was selected and assessed after the lockdown. Hence the second group of children was less exposed to socialization since schools changed to digital format. We compared the language performance of both groups. Results: Results include the comparison between samples pre and post-pandemic. Post-pandemic children performed lower in language skills. We also found a correlation between the language and NSS. Conclusions: We conclude that socialization is an essential factor in language development. Also, identifying Neurological Soft Signs could help predict language delay. We thank project PAPIIT IN308219 for sponsoring this research.
The current status of AI-related curriculum offerings and the experiences of current teachers in early childhood teacher education programs

Objectives This study analyzed the current status of AI-related curriculum offered in early childhood teacher edu-cation programs and explored the experiments of current teachers regarding AI education.&#x0D; Methods To achieve this, the study analyzed the websites of 61 universities with departments of early childhood education that had established AI-related curriculum, frequency and percentages were calculated. Additionally, in-depth interviews were conducted with seven current teachers who had experience with AI education, using a qualitative data analysis approach to examine their experiences.&#x0D; Results The results of the study are as follows: Firstly, in terms of the current status of AI-related curriculum in early childhood teacher education programs, 49 courses were offered in four-year universities, and 45 courses were offered in three-year(or two-year) colleges. Both four-year universities and three-year(or two-year) col-leges offered the highest number of courses in the first semester of the first year. While four-year universities offered courses with major studies worth three credits, three-year(or two-year) colleges had more courses with general education worth two credits. Secondly, from the perspective of current teachers' experiences with AI ed-ucation, the study identified how current teachers get rid of a sense of vague apprehension, anxiety and negative perceptions, and specified the experiences of engaging with AI education through play.&#x0D; Conclusions This study contributes foundational data for the implementation of AI education in pre-service early childhood teacher education programs.