Reviewerly/exHarmony huggingface dataset:
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation

Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks. U-shaped neural networks with encoder-decoder are prevailing and have succeeded greatly in various segmentation tasks. While CNNs treat an image as a grid of pixels in Euclidean space and Transformers recognize an image as a sequence of patches, graph-based representation is more generalized and can construct connections for each part of an image. In this paper, we propose a novel ViG-UNet, a graph neural network-based U-shaped architecture with the encoder, the decoder, the bottleneck, and skip connections. The downsampling and upsampling modules are also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most existing classic and state-of-the-art U-shaped networks.
Unsupervised Multiple Virtual Histological Staining from Label-Free Autofluorescence Images

Clinical histopathological analyses usually require hematoxylin-and eosin-(H&E) as regular staining to visualize various tissue types and morphological changes, whereas some special stains are also essential to provide auxiliary information on particular components. However, it is infeasible to simultaneously implement diverse histological staining on the same tissue section. In this paper, we propose a multiple histological staining model that enables arbitrary staining image generation from label-free autofluorescence images. We use AdaIN to fuse styles into the image reconstruction process for source image content preservation. Moreover, direct image match loss is proposed to replace image reconstruction loss. Experimental results on mouse kidney tissue demonstrate the efficiency and advantage of our model compared to the baseline frameworks. Furthermore, we also validated the superior performance of the proposed model using mouse liver and heart tissues, which confirms that our method is generally applicable to multiple organs.
Complex Scene Segmentation With Local to Global Self-Attention Module and Feature Alignment Module

It is challenging to accurately mode the local and global context during complex scene segmentation. To solve this problem, a scene semantic segmentation network contains local to global self-attention module and feature alignment module is proposed in this paper. The local to global self-attention module is designed to combine the local and global features, in which the transformer backbone treats all patches equally in the global scope, to extract high-level features. The improved masked transformer with feature alignment module (MtFAM), which combines the masked transformer and feature alignment module to form a new decoder structure, is designed to fuse the features obtained from the vision transformer backbone and the local to global self-attention module. Experimental results demonstrate that the proposed structure show better performance, which can improve the value of mIoU by 3.63% on the ADE20K validation dataset compared to the Vit-Tiny. In particular, it can obtain 2.23% higher mIoU value than the segmenter method using the same transformer backbone on the challenging scene segmentation benchmark.
Personality Perceptions of Conversational Agents: A Task-Based Analysis Using Thai as the Conversational Language

Recently there has been a tremendous growth in the popularity of artificial intelligence (AI) based conversational agents (CA). Their support for anthropomorphism and human-likeness makes them popular. However, being anthropomorphic raises a question - do these agents have a personality? Moreover, what effect may personality have on the different tasks these agents perform? Through this research, we aim to answer these two questions by focusing on Thai as the communication modality between the users and the CAs. We use a multi-model approach involving human, brand, and website personality frameworks for proposing our CA personality model. We use a series of steps right from creating the initial pool of personality traits to the final set of personality traits through a systematic approach. Our proposed personality model has 7 dimensions across the two-dimensional continuum ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">calm - neuroticism, maturity - juvenility, intelligence - ineptness, openness - reserved, sociability - seclusion, self-control - instability, and aesthetics - unaesthetics</i> ). For examining the effect of personality type on the nature of tasks, we identified two primary task categories (social and functional) and used a multi-criteria decision-making approach to examine the corresponding impacts. Social tasks are impacted most from the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maturity - juvenility</i> ) dimension, whereas functional tasks are mostly impacted from the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intelligence - ineptness</i> ) dimension. Based on the results we provide suitable recommendations for future research.
Image Encryption Based on Hyperchaotic System and Improved Zigzag Diffusion Method

Classical two-dimensional chaotic systems are not safe enough due to few control parameters and limited chaotic range. To cope with this problem, a new wide-range 2D-Logistic-Sine hyperchaotic map (2D-LSHM) is proposed in this paper. By analyzing the bifurcation map and Lyapunov exponent of 2D-LSHM, the results prove that the map has good ergodicity and unpredictability. In addition, this paper proposes a 2D-LSHM-based image encryption scheme, LSHM-IES, to solve the problem that the dislocation diffusion algorithm based on specific rules is vulnerable to attacks. The scheme uses a 3×3 convolutional kernel to replace the pixel values in the dislocation process. An improved Zigzag transform is developed in the diffusion phase to make the algorithm more secure and the key space larger. Experimental results from a variety of performance tests on different images indicate that the LSHM-IES encryption scheme possesses favorable encryption performance, low time cost, and high robustness against data missing attacks and noise effects.
Local Path Planning: Dynamic Window Approach With Q-Learning Considering Congestion Environments for Mobile Robot

In recent years, autonomous mobile robots have significantly increased in prevalence due to their ability to augment and diversify the workforce. One critical aspect of their operation is effective local path planning, which considers dynamic constraints. In this context, the Dynamic Window Approach (DWA) has been widely recognized as a robust local path planning. DWA produces a set of path candidates derived from velocity space subject to dynamic constraints. An optimal path is selected from path candidates through an evaluation function guided by fixed weight coefficients. However, fixed weight coefficients are typically designed for a specific environmental context. Consequently, changes in environmental conditions such as congestion levels, road width, and obstacle density could potentially lead the evaluation function to select inefficient paths or even result in collisions. To overcome this challenge, this paper proposes the dynamic weight coefficients based on Q-learning for DWA (DQDWA). The proposed method uses a pre-learned Q-table that comprises robot states, environmental conditions, and actions of weight coefficients. DQDWA can use the pre-learned Q-table to dynamically select optimal paths and weight coefficients that better adapt to varying environmental conditions. The performance of DQDWA was validated through extensive simulations and real experiments to confirm its ability to enhance the effectiveness of local path planning.
RFS-Based Multiple Extended Target Tracking With Resolved Multipath Detections in Clutter

In the literature, the problem of point target tracking with multipath detections has been studied. However, the case of extended target tracking in a multipath environment (e.g., tracking a submarine using a high resolution sonar, tracking a vehicle in an urban environment using an imaging radar) has not been adequately addressed. If the multipath detections from a single target can be modeled and used properly, better tracking performance can be obtained in terms of accuracy, false tracks and computing time. By integrating the Random Matrix (RM) theory and the random finite set (RFS) theory, an extension of the Probability Hypothesis Density (PHD) filter, called MP-ET-PHD, is proposed in this paper to address the multitarget tracking problem with an unknown number of targets in an uncertain multipath environment with clutter. In the proposed framework, a novel multipath measurement update equation is formulated and derived. Also, a Gaussian Mixture (GM) implementation of the proposed MP-ET-PHD is presented for practical applications. Simulation results show that the proposed MP-ET-PHD can effectively handle multipath detections and yield improved tracking performance over the traditional single-path extended target trackers.
Proactive Fault Prediction and Tolerance in Cloud Computing

Commercial cloud services have strengthened online and digital businesses by providing additional benefits such as maximal computing with minimal capital investment, resource reliability, elasticity, and scalability. The reliance of cloud users on third-party resources has grown dramatically. Cloud Service Provider follows the Service Layer Agreement and assumes responsibility for the availability and security of services, cloud outages occur due to variable and dynamic resource use. Some unexpected downtimes may occur due to cloud outage. Even the failure of cooling systems causes a reduction in airflow, and the subsequent thermal spikes throughout the data center may stave off the performance of network compute and storage devices. To avoid such downtimes and failures, it is beneficial that the system is regularly monitored and see that no issue occurs. The proposed system predicts the occurrence of any failure of a Physical Machine in advance and takes some measures to avoid the downtime. The fault prediction model has been trained and achieved satisfactory accuracy.
Bayesian-Based Parameter Estimation to Quantify Trust in Medical Devices

In this paper, we propose a data-driven approach to estimate Bayesian parameters when trust needs to be quantified in the domain of wearable medical devices (WMD). Our approach extracts the probability of a trust determinant (e.g., reliability or robustness) being in a specific state from the data. Then, we use the Bayesian approach to estimate the parameters for the intermediate nodes in the network and ultimately compute the trust score. The trust score we compute is used as a relative measure of trustworthiness between different WMDs evaluated in the same test conditions and with the same Bayesian network (BN). To evaluate our approach, we develop a BN for the trust quantification of similar wearable medical devices from two manufacturers under identical test conditions. The results demonstrate the learnability and generalizability of our data-driven parameter estimation approach.
Investigation of Drift Detection for Clinical Text Classification

Today, machine learning models are applied in various healthcare applications in productive use. The availability of extensive patient information in electronic formats makes it possible to utilize them and develop machine learning-based models for data analysis. However, the performance of an operational model is continuously subject to degradation due to unforeseen changes in the input data flow. Therefore, monitoring data drift becomes essential to maintain the desired performance of the trained models. In the context of monitoring and drift detection, statistical hypothesis testing enables us to examine whether incoming data deviate from training data. Recent studies show that Kernel Maximum Mean Discrepancy (KMMD) and Kolmogorov–Smirnov (KS) can reliably measure the distance between multivariate distributions, hence drift detection. In this work, we conduct a case study on drift detection based on textual data from drug reviews and propose the sub-sampling method to stabilize drift detection. The results of our experiments show that both KMMD and KS detect changes in the text reviews with a limited number of these reviews in both the reference and test data.
Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients

The advancement of Electronic Health Records (EHRs) and machine learning have enabled a data-driven and personalised approach to healthcare. One step in this direction is to uncover patient sub-types with similar disease trajectories in a heterogeneous population. This is especially important in the context of mechanical ventilation in intensive care, where mortality is high and there is no consensus on treatment. In this work, we present a new approach to clustering mechanical ventilation episodes, using a multi-task combination of supervised, self-supervised and unsupervised learning techniques. Our dynamic clustering assignment is explicitly guided to reflect the phenotype, trajectory and outcomes of the patient. Experimentation on a real-world dataset is encouraging, and we hope that we could someday translate this into actionable insights in guiding future clinical research.
Proving Almost-Sure Innermost Termination of Probabilistic Term Rewriting Using Dependency Pairs

Abstract Dependency pairs are one of the most powerful techniques to analyze termination of term rewrite systems (TRSs) automatically. We adapt the dependency pair framework to the probabilistic setting in order to prove almost-sure innermost termination of probabilistic TRSs. To evaluate its power, we implemented the new framework in our tool .
A Uniform Formalisation of Three-Valued Logics in Bisequent Calculus

We present a uniform characterisation of three-valued logics by means of bisequent calculus (BSC). It is a generalised form of sequent calculus (SC) where rules operate on the ordered pairs of ordinary sequents. BSC may be treated as the weakest kind of system in the rich family of generalised SC operating on items being some collections of ordinary sequents. This family covers several forms of hypersequent and nested sequent calculi introduced to provide decent SC for several non-classical logics. It seems that for many non-classical logics, including some many-valued, paraconsistent and modal logics, this reasonably modest generalization of standard SC is sufficient. In this paper we examine a variety of three-valued logics and show how they can be formalised in the framework of bisequent calculus. All provided systems are cut-free and satisfy the subformula property. Also the interpolation theorem is constructively proved for some logics.
A Semi-Supervised Network Framework for low-light image enhancement

Existing supervised learning-based low-light image enhancement algorithms treat all degradations as a whole, resulting in limited enhancement performance. It is difficult for fully unsupervised learning to recover more hidden details, making the augmented results unsatisfactory for visual needs. To overwhelm the limitations of supervised and unsupervised learning, we propose a Semi-Supervised Network Framework (SSNF) to enhance low-light images. Specifically, we decouple the low-light image enhancement task into two stages. In the first stage of the SSNF, we employ methods based on information entropy and Retinex to improve the visibility of images. It is worth mentioning that this stage is a lightweight self-supervised network, which only needs to input low-light images and undergo minute-level training to achieve brightness improvement. In the second stage of the SSNF, we utilize U-Net and residual networks to remove problems such as noise and degradation existing in the first-stage enhancement results, thereby improving the visual properties of the enhanced images. It overwhelms the challenge of dealing with low-light images directly. We conduct extensive experiments on datasets such as LOL, synthetic, DICM, etc. The experimental results show that SSNF exhibits better visual effects and outperforms other advanced methods in performance metrics.
Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models

Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.
An efficient and privacy-preserving blockchain-based secure data aggregation in smart grids

Smart Grids (SGs) present a number of advantages over traditional grid like reduce energy cost, reduce energy wastage, and increase reliability and transparency. However, it also introduces a number of security and privacy issues not foreseen before. These are very serious issues from consumer's perspective who do not want any malicious entity to infer any personal information from the consumption data. Data aggregation plays a significant role in protecting user's consumption data. SGs deploy a single entity, called an aggregator, to collect and aggregate end users' encrypted consumption data. While a number of secure data aggregation schemes have been proposed, many existing schemes suffer from a single point of failure in the aggregation process. Recently, some blockchain-based data aggregation schemes have been proposed to overcome this problem. While overcoming the failure issues, these schemes do not provide the necessary security and privacy requirements of smart grids. Furthermore, these schemes suffer from high computation and communication cost due to the use of RSA-based signatures. In order to solve these issues, this paper proposes a decentralized secure data aggregation scheme using blockchain which preserves privacy, integrity, authentication, and confidentiality of individual consumption data. Experimental results show that the proposed scheme effectively protects end user consumption data.
Face Super-Resolution via Joint Edge Information and Attention Aggregation Network

Face super-resolution (SR) is a specialized image super-resolution problem that is particularly relevant in various intelligent transportation scenes such as video surveillance and identification systems. Deep learning-steered solutions for face images often leverage facial priors (i.e., face parsing and landmark) to restore intricate facial components and have demonstrated promising performance. However, these methods typically rely on abundant manually labeled data and require lengthy training times. In this work, we introduce a meaningful approach called the Joint Edge Information and Attention Aggregation Network (JEANet) for the face SR problem. JEANet is established on our carefully designed Face Attention Aggregation Module (FAAM), which incorporates parallel connections of both channel-wise and spatial-wise features. Specifically, we integrate an attention fusion strategy into the residual blocks and elaborately employ edge blocks to extract edge information. Additionally, we introduce adaptive shortcuts that interpolate reconstruction parts at multiple scales. With the aid of adaptive shortcuts, JEANet effectively restores detailed images textures by leveraging contextual features optimally. These considerations enable the convolutional operations to flexibly distill information related to primary facial structures and progressively incorporate edge information to assemble local and global meaningful structures. Qualitative and quantitative evaluations demonstrate that our proposed method excels in recovering highly realistic face images compared to several competitive face SR methods.
Digital exposure notification tools: A global landscape analysis

As the COVID-19 pandemic continues, digital exposure notification systems are increasingly used to support traditional contact tracing and other preventive strategies. Likewise, a plethora of COVID-19 mobile applications have emerged. Objective: To characterize the global landscape of pandemic related mobile applications, including digital exposure notification and contact tracing tools.The following queries were entered into the Google search engine: "(*country name* COVID app) OR (COVID app *country name*) OR (COVID app *country name*+) OR (*country name*+ COVID app)". The App Store, Google Play, and official government websites were then accessed to collect descriptive data for each application. Descriptive data were qualified and quantified using standard methods. COVID-19 Exposure Notification Systems (ENS) and non-Exposure Notification products were categorized and summarized to provide a global landscape review.Our search resulted in a global count of 224 COVID-19 mobile applications, in 127 countries. Of these 224 apps, 128 supported exposure notification, with 75 employing the Google Apple Exposure Notification (GAEN) application programming interface (API). Of the 75 apps using the GAEN API, 15 apps were developed using Exposure Notification Express, a GAEN turnkey solution. COVID-19 applications that did not include exposure notifications (n = 96) focused on COVID-19 Self-Assessment (35·4%), COVID-19 Statistics and Information (32·3%), and COVID-19 Health Advice (29·2%).The digital response to COVID-19 generated diverse and novel solutions to support non-pharmacologic public health interventions. More research is needed to evaluate the extent to which these services and strategies were useful in reducing viral transmission.