Reviewerly/exHarmony huggingface dataset:
Separation of scales and a thermodynamic description of feature learning in some CNNs

Deep neural networks (DNNs) are powerful tools for compressing and distilling information. Their scale and complexity, often involving billions of inter-dependent parameters, render direct microscopic analysis difficult. Under such circumstances, a common strategy is to identify slow variables that average the erratic behavior of the fast microscopic variables. Here, we identify a similar separation of scales occurring in fully trained finitely over-parameterized deep convolutional neural networks (CNNs) and fully connected networks (FCNs). Specifically, we show that DNN layers couple only through the second cumulant (kernels) of their activations and pre-activations. Moreover, the latter fluctuates in a nearly Gaussian manner. For infinite width DNNs, these kernels are inert, while for finite ones they adapt to the data and yield a tractable data-aware Gaussian Process. The resulting thermodynamic theory of deep learning yields accurate predictions in various settings. In addition, it provides new ways of analyzing and understanding DNNs in general.
AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.
Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines

Clean and safe drinking water is an integral part of daily living and is considered as a basic human need. Hence, this study investigated the suitability of the domestic water (DW) and groundwater (GW) samples with respect to the presence of metals and metalloid (MMs) in San Jose, Occidental Mindoro, Philippines. The MMs analyzed in the area of study for DW and GW were Arsenic (As), Barium (Ba), Copper (Cu), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn). The results revealed that Pb has the mean highest concentration for DW, while Fe is in GW resources in the area. Quality evaluation of DW and GW was performed using Metal Pollution Index (MPI), Nemerow’s Pollution Index (NPI), and Ecological Risk Index (ERI). The mean NPI value calculated for DW was 135 times greater than the upper limit of the unpolluted location category. The highest NPI observed was 1080 times higher than the upper limit of the unpolluted site category. That of the ERI observed in the area was 23.8 times higher than the upper limit for a “low” ERI category. Furthermore, the health risk assessment (HRA) of the GW and DW of the study area revealed non-carcinogenic health risks of the MMs analyzed in GW samples, and potential carcinogenic health risks from As, Cr, Pb, and Ni in DW. The use of machine learning geostatistical interpolation (MLGI) mapping to illustrate the PI and health risk (HR) in the area was an efficient and dependable evaluation tool for assessing and identifying probable MMs pollution hotspots. The data, tools, and the process could be utilized in carrying out water assessment, the evaluation leading to a comprehensive water management program in the area and neighboring regions of similar conditions.
Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Abstract Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes. The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network (CNN) and Transformer. The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data. The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN. To address these issues, a short‐term and long‐term memory self‐attention network is proposed. Firstly, a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN. Then, the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours. Finally, the multi‐layer reconstruction blocks will predict the tumour boundaries. Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation. Compared with the most competitive method, the proposed method provides Dice (82.4% vs. 76.6%) and Hausdorff distance 95% (HD95) (10.66 vs. 11.54 mm) on the KiTS19 as well as Dice (80.2% vs. 78.4%) and HD95 (9.632 vs. 12.17 mm) on the LiTS.
Post-quantum Cryptography: A Solution to the Challenges of Classical Encryption Algorithms

With the rise in quantum computing, there is an increase in the security concerns of existing cryptographic algorithms such as Rivest Shamir Adleman (RSA) and advanced encryption standard (AES) along with elliptic curve cryptography (ECC). The problem with RSA and ECC is that they depend on the challenges of integer factorization and discrete logarithms, respectively, whereas AES can be the target of brute force attack. In the recent years, there is a huge advancement in quantum computing due to which the existing encryption standards found vulnerable as they are weak to handle quantum attacks. Hence, to solve the problems with quantum computing, a new set of algorithms came into existence called as post-quantum cryptography (PQC) which is safe and secure from quantum attacks. In this review, firstly, the quantum computers and number of classical algorithms are introduced, afterward the classification of PQC algorithms has been shown. The aim of this review is to present the technical overview about PQC algorithms and their working procedures to protect classical cryptographic-based encrypted information from quantum attacks during online transactions and communications.
A Survey on Performance Analysis of Different Architectures of AES Algorithm on FPGA

Encryption is the primary way for ensuring communication security. The symmetric key method, often known as Advanced Encryption Standard (AES), is a well-known technique in the field of security. AES can be implemented in either software or hardware. In the current study, Field Programmable Gate Arrays (FPGAs) are utilized to implement AES. Number of studies have been done on experiments of AES using FPGAs. Till date, no study has been done on the architectures that are being utilized to implement AES on FPGA. This paper provides an in-depth examination of several hardware implementation of AES on FPGA in terms of through put and performance. This survey article enables the engineers to choose the best architecture of FPGAs to implement AES algorithm in terms of design as per the requirement. The surveyed architectures are sequential, pipelined, iterative, and parallel. Parallel architectures with pipelining between rounds have shown an excellent throughput. Certain improved S-box and key expansion approaches have also been employed by the researchers to reduce the hardware areas.
Operating Frequency Prediction of Annular Ring Microstrip Antenna Using Extreme Learning Machine

This article presents, Machine Learning-based proficient and quick, Extreme Learning Machine (ELM) procedure to predict the operating frequency of the Annular ring compact microstrip antenna (ARCMA)in the range of 0.95–5.76 GHz. As per UHF and SHF bands, 100 data samples are collected through simulation using CST by varying the proportion of inner radius to an outer radius of ARCMA’s. Arbitrarily 90 samples were utilized to set up the ELM model, and the remaining 10 data samples were utilized to test the model. It was observed that the simulated and predicted operating frequencies of the ARCMA’s are exceptionally near one another and can be used to design an annular ring microstrip antenna for UHF and SHF band applications.
Revisiting the resource-based view (RBV) theory: from cross-functional capabilities perspective in post COVID-19 period

ABSTRACTABSTRACTDue to ever-changing business context, researchers and academicians revisit established theories to examine their validity underdifferent evolving scenarios. Therefore, this study aims to examine the cross-functional capabilities of the organizations by revisiting the resource-based view (RBV) theory, especially focusing on thepost-COVID-19 period. This study also investigates the moderating role of the organization strategic management ability and itsinfluence on the performance of the organizations. After reviewing existing literature and applying RBV theory, a theoretical model was developed conceptually. Later, the conceptual model was validated using the structural equation modeling (SEM) technique with a survey of 342 usable respondents from 21 organizations. The study found thatRBV theory is useful and relevant for explaining organization cross-functional capabilities even in the post-COVID-19 period. We found that cross-functional capabilities have positively impacted overall organization capability, which then positively influences organization performance. The study also found that there is apositive moderating impact of strategic management ability onorganization performance.KEYWORDS: Strategic managementcross-functional capabilityresource-based vieworganization capabilityorganization performance Disclosure statementNo potential conflict of interest was reported by the authors.
Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

The wavelet transform is widely used in the task of hyperspectral image compression (HSIC). They have achieved outstanding performance in the compression of a hyperspectral (HS) image, which has attracted great interest. However, transform based hyperspectral image compression algorithm (HSICA) has low-coding gain than the other state of art HSIC algorithms. To solve this problem, this manuscript proposes a curvelet transform based HSIC algorithm. The curvelet transform is a multiscale mathematical transform that represents the curve and edges of the HS image more efficiently than the wavelet transform. The experiment results show that the proposed compression algorithm has high-coding gain, low-coding complexity, at par coding memory requirement, and works for both (lossy and lossless) compression. Thus, it is a suitable contender for the compression process in the HS image sensors.
Improved Feature Selection Based on Chaos Game Optimization for Social Internet of Things with a Novel Deep Learning Model

The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. Thus, in this work, we propose a framework to tackle the high dimensionality of transferred data over the SIoT system and improve the performance of several applications with different data types. The proposed framework comprises two parts: Transformer CNN (TransCNN), a deep learning model for feature extraction, and the Chaos Game Optimization (CGO) algorithm for feature selection. To validate the framework’s effectiveness, several datasets with different data types were selected, and various experiments were conducted compared to other methods. The results showed that the efficiency of the developed method is better than other models according to the performance metrics in the SIoT environment. In addition, the average of the developed method based on the accuracy, sensitivity, specificity, number of selected features, and fitness value is 88.30%, 87.20%, 92.94%, 44.375, and 0.1082, respectively. The mean rank obtained using the Friedman test is the best value overall for the competitive algorithms.
Interoperability Benefits and Challenges in Smart City Services: Blockchain as a Solution

The widespread usage of smart devices with various city-centric services speeds up and improves civic life, in contrast to growing privacy and security concerns. Security issues are exacerbated when e-government service providers trade their services within a centralised framework. Due to security concerns, city-centric centralised services are being converted to blockchain-based systems, which is a very time-consuming and challenging process. The interoperability of these blockchain-based systems is also more challenging due to protocol variances, an excessive amount of local transactions that raise scalability and rapidly occupy memory. In this paper, we have proposed a framework for interoperability across various blockchain-based smart city services. It also summarises how independent service providers might continue self-service choices (i.e., local transactions) without overloading the blockchain network and other organisations. A simulated interoperability network is used to show the network’s effectiveness. The experimental outcomes show the scalability and memory optimization of the blockchain network.
Meteorological Variables Forecasting System Using Machine Learning and Open-Source Software

The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the design, implementation, and comparison of forecasting models for meteorological variables have been performed using different Machine Learning techniques as part of Python open-source software. The techniques implemented include multiple linear regression, polynomial regression, random forest, decision tree, XGBoost, and multilayer perceptron neural network (MLP). To identify the best technique, the mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics. The most efficient techniques depend on the variable to be forecasting, however, it is noted that for most of them, random forest and XGBoost techniques present better performance. For temperature, the best performing technique was Random Forest with an R2 of 0.8631, MAE of 0.4728 °C, MAPE of 2.73%, and RMSE of 0.6621 °C; for relative humidity, was Random Forest with an R2 of 0.8583, MAE of 2.1380RH, MAPE of 2.50% and RMSE of 2.9003 RH; for solar radiation, was Random Forest with an R2 of 0.7333, MAE of 65.8105 W/m2, and RMSE of 105.9141 W/m2; and for wind speed, was Random Forest with an R2 of 0.3660, MAE of 0.1097 m/s, and RMSE of 0.2136 m/s.
Review: A Survey on Objective Evaluation of Image Sharpness

Establishing an accurate objective evaluation metric of image sharpness is crucial for image analysis, recognition and quality measurement. In this review, we highlight recent advances in no-reference image quality assessment research, divide the reported algorithms into four groups (spatial domain-based methods, spectral domain-based methods, learning-based methods and combination methods) and outline the advantages and disadvantages of each method group. Furthermore, we conduct a brief bibliometric study with which to provide an overview of the current trends from 2013 to 2021 and compare the performance of representative algorithms on public datasets. Finally, we describe the shortcomings and future challenges in the current studies.
RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images

Automated segmentation techniques for cardiac magnetic resonance imaging (MRI) are beneficial for evaluating cardiac functional parameters in clinical diagnosis. However, due to the characteristics of unclear image boundaries and anisotropic resolution anisotropy produced by cardiac magnetic resonance imaging technology, most of the existing methods still have the problems of intra-class uncertainty and inter-class uncertainty. However, due to the irregularity of the anatomical shape of the heart and the inhomogeneity of tissue density, the boundaries of its anatomical structures become uncertain and discontinuous. Therefore, fast and accurate segmentation of cardiac tissue remains a challenging problem in medical image processing.We collected cardiac MRI data from 195 patients as training set and 35patients from different medical centers as external validation set. Our research proposed a U-net network architecture with residual connections and a self-attentive mechanism (Residual Self-Attention U-net, RSU-Net). The network relies on the classic U-net network, adopts the U-shaped symmetric architecture of the encoding and decoding mode, improves the convolution module in the network, introduces skip connections, and improves the network's capacity for feature extraction. Then for solving locality defects of ordinary convolutional networks. To achieve a global receptive field, a self-attention mechanism is introduced at the bottom of the model. The loss function employs a combination of Cross Entropy Loss and Dice Loss to jointly guide network training, resulting in more stable network training.In our study, we employ the Hausdorff distance (HD) and the Dice similarity coefficient (DSC) as metrics for assessing segmentation outcomes. Comparsion was made with the segmentation frameworks of other papers, and the comparison results prove that our RSU-Net network performs better and can make accurate segmentation of the heart. New ideas for scientific research.Our proposed RSU-Net network combines the advantages of residual connections and self-attention. This paper uses the residual links to facilitate the training of the network. In this paper, a self-attention mechanism is introduced, and a bottom self-attention block (BSA Block) is used to aggregate global information. Self-attention aggregates global information, and has achieved good segmentation results on the cardiac segmentation dataset. It facilitates the diagnosis of cardiovascular patients in the future.
Identification, Exploration, and Remediation: Can Teachers Predict Common Wrong Answers?

Prior work analyzing tutoring sessions provided evidence that highly effective tutors, through their interaction with students and their experience, can perceptively recognize incorrect processes or "bugs" when students incorrectly answer problems. Researchers have studied these tutoring interactions examining instructional approaches to address incorrect processes and observed that the format of the feedback can influence learning outcomes. In this work, we recognize the incorrect answers caused by these buggy processes as Common Wrong Answers (CWAs). We examine the ability of teachers and instructional designers to identify CWAs proactively. As teachers and instructional designers deeply understand the common approaches and mistakes students make when solving mathematical problems, we examine the feasibility of proactively identifying CWAs and generating Common Wrong Answer Feedback (CWAFs) as a formative feedback intervention for addressing student learning needs. As such, we analyze CWAFs in three sets of analyses. We first report on the accuracy of the CWAs predicted by the teachers and instructional designers on the problems across two activities. We then measure the effectiveness of the CWAFs using an intent-to-treat analysis. Finally, we explore the existence of personalization effects of the CWAFs for the students working on the two mathematics activities.
Learning transformer-based attention region with multiple scales for occluded person re-identification

Occluded person re-identification(Re-ID), with the aim of matching occluded person pairs under cross-camera, remains challenging due to incomplete information and spatial misalignment. The state-of-the-art (SOTA) methods usually include a two-stage architecture based on the existing pose estimation models or the attention mechanism to generate human masks to extract features, which complicate the model and introduce additional biases. To address this issue, we propose a novel end-to-end transformer-based occluded person Re-ID model. Specifically, our model contains two crucial components: (1) the features of global and non-occluded person regions are extracted by two independent Transformer-based feature extraction networks respectively; (2) the distribution of common non-occluded human regions is learnt via a multiheaded self-attention mechanism, and then the Minimized Character-box Proposal (MCP) is utilized to generate accurate shared non-occluded crops. In our model, non-occluded human regions are not annotated and only weakly-supervision of ID labels with multiheaded self-attention are employed to jointly learn the distribution. Further, the human feature contains multi-scale information which is extracted from our dual-branch architecture. Extensive experiment results on four benchmarks of person Re-ID for two tasks (occluded, partial) demonstrate the effectiveness of our proposed framework which achieves the SOTA or the comparable performance on all benchmarks.
Learning Stance Embeddings from Signed Social Graphs

A challenge in social network analysis, is understanding the position, or stance, of people on a large set of topics. While past work has modeled (dis)agreement in social networks using signed graphs, these approaches have not modeled agreement patterns across a range of correlated topics. For instance, disagreement on one topic may make disagreement (or agreement) more likely for related topics. Recognizing topics influence agreement and disagreement, we propose the Stance Embeddings Model (SEM), which jointly learns embeddings for each user and topic in signed social graphs with distinct edge types for each topic. By jointly learning user and topic embeddings, SEM can perform cold-start topic stance detection, predicting the stance of a user on topics for which we have not observed their engagement. We demonstrate the effectiveness of SEM using two large-scale Twitter signed graph datasets that we open-source. One dataset, TwitterSG, labels (dis)agreements using engagements between users via tweets to derive topic-informed, signed edges. The other, BirdwatchSG, leverages community reports on misinformation and misleading content. On TwitterSG and BirdwatchSG, SEM shows a 39% and 26% error reduction respectively against strong topic-agnostic baselines.