Reviewerly/exHarmony huggingface dataset:
Multi-Task Deep Learning Model for the Automated Segmentation of Neuroimages in Real-World Ct Scans

Segmenting head structures from medical images is a challenging task since they can be very heterogeneous. Multi-task learning is based on the premise that learning multiple related and shared tasks enhances the performance of a deep learning network. In particular, U-Net architectures has been widely used in neuroimaging segmentation. However, similarly to other deep networks, U-Net is not only subject to the degradation problem, but its performance is also degraded depending on the anatomical distortion present. In addition, learning a 3D shape leads to higher computational costs, requiring to evaluate its cost-effectiveness. Therefore, in this work we adapted a U-Net architecture (namely \textcyr{И}-Net) to an efficient and computationally lightweight architecture to perform the concurrent segmentation in 3D images of three head structures: intracranial space, lateral ventricles, and eyeballs. The DeepCTE3D multi-task model was developed using 559 real-world CT images from neonates to adults. The dataset includes typical and atypical cases (with anatomical distortions): enlargement of the lateral ventricles, skull distortions and eyeball abnormalities.Obtained Dice similarity coefficient (%) were: 98.13 (intracranial space), 80.76 (lateral ventricles) and 87.73 (eyeballs), considering the lateral ventricles as reference for the learning process. A comparison between single and multi-task network was made. For our multi-task approach, the training convergence was improved in the three classes of segmentation with similar performance observed from the single-tasks segmentation. The generalization in the multi-task DeepCTE3D model was shown to be particularly better at eyeball segmentation. Moreover, we bring to light a hypothesis regarding the morphological heterogeneity and segmentation complexity for the datasets composed of head structures used in this study, which may be generalized to other anatomical structures of the brain.
Weakly-Supervised Video Anomaly Detection with MTDA-Net

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.
Line Follower with Obstacle Avoiding Robot

An intelligent robot called a "line follower" finds and follows a visible line that is embedded in the floor. The predetermined route can be seen as a black line on a white background with sharply contrasted colours. It can recognize the existence of an obstruction and select a different route to its destination. The robot that detects a line and tries to move in the direction of the target can be programmed to follow instructions. Different sensors can be used to detect the line. The robot can identify the path it needs to take using infrared sensors. The ultrasonic sensor is a device that can detect the obstacle by measuring the distance using sound waves and avoiding collision.
RUJATRANS Application (Android-based Public Transportation Travel Route Information) using Waterfall Model

This research aims to help the public to provide public transportation information. By using this application, it is hoped that people can choose the right and efficient transportation to travel. Rujatrans (public transportation travel route information) uses waterfall model. The system includes an Android mobile app, which allows users to search for the right and efficient transport travel route while traveling. The four phases of the system are data collection, design, implementation, and testing. The system provides a user- friendly interface to provide public transport travel routes efficiently. This study uses a structured and iterative approach, resulting in solutions when you want to travel using public transportation that meets user needs. Rujatrans can provide solutions to reduce the use of private vehicles so as to reduce congestion on the highway.
Quantum process matrices as images: New tools to design novel denoising methods

Inferring a process matrix characterizing a quantum channel from experimental measurements is a key issue of quantum information. Noise affecting the measured counts could bring to matrices different from the expected ones and optimization methods usually employed, i.e. the maximum likelihood estimation (MLE), are characterized by several drawbacks. Lowering the noise could be necessary to increase the experimental resources, e.g. time for each measurement. In this paper, an alternative procedure, based on suitable Neural Networks, has been implemented and optimized to obtain a denoised process matrix and this approach has been tested with a specific quantum channel, i.e. a Control Phase. This promising method relies on the analogy that can be established between the elements of a process matrix and the pixels of an image.
A grounded theory of organizational structures for development and infrastructure professionals in software-producing organizations

DevOps and continuous delivery have significantly impacted the organizational structures of development and infrastructure groups in software-producing organizations. Our research revealed the different options adopted by the software industry to organize such groups, unveiling why different organizations adopt distinct structures and how organizations handle the drawbacks of each structure. By interviewing 68 carefully-selected, skilled IT professionals and analyzing these conversations through a Grounded Theory process, we identified theoretical assertions (conditions, causes, reasons to avoid, consequences, and contingencies) related to each discovered structure (segregated departments, collaborating departments, API-mediated departments, and single department). We, then, offer a theory to explain organizational structures for development and infrastructure professionals. This theory supports practitioners and researchers in comprehending and discussing the DevOps phenomenon and its related issues; it also provides valuable input to practitioners' decision-making.
A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

Background Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust. Objective This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes. Methods XAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app’s usability and influence on participants’ comprehension of machine learning predictions in real-world patient scenarios. Results A prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals’ comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool. Conclusions This study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care.
MG-SIN: Multigraph Sparse Interaction Network for Multitask Stance Detection

Stance detection on social media aims to identify if an individual is in support of or against a specific target. Most existing stance detection approaches primarily rely on modeling the contextual semantic information in sentences and neglect to explore the pragmatics dependency information of words, thus degrading performance. Although several single-task learning methods have been proposed to capture richer semantic representation information, they still suffer from semantic sparsity problems caused by short texts on social media. This article proposes a novel multigraph sparse interaction network (MG-SIN) by using multitask learning (MTL) to identify the stances and classify the sentiment polarities of tweets simultaneously. Our basic idea is to explore the pragmatics dependency relationship between tasks at the word level by constructing two types of heterogeneous graphs, including task-specific and task-related graphs (tr-graphs), to boost the learning of task-specific representations. A graph-aware module is proposed to adaptively facilitate information sharing between tasks via a novel sparse interaction mechanism among heterogeneous graphs. Through experiments on two real-world datasets, compared with the state-of-the-art baselines, the extensive results exhibit that MG-SIN achieves competitive improvements of up to 2.1% and 2.42% for the stance detection task, and 5.26% and 3.93% for the sentiment analysis task, respectively.
Performance Analysis of the IOTA Chrysalis on Heterogeneous Devices

Existing Distributed Ledger Technologies (DLTs) based models like blockchain pose scalability and performance challenges for IoT systems due to resource-demanding Proof of Work (PoW), slow transaction confirmation rates, and high costs. Against a need to adopt a viable approach, especially for low-power IoT devices, IOTA emerges as a promising technology, leveraging the Direct Acyclic Graph (DAG) based approach called Tangle for IoT-focused applications. In this paper, we design a system enabling secure data exchange between IoT devices on IOTA Chrysalis, the latest version. We perform extensive experiments on two machines, a Workstation PC and Raspberry Pi, to demonstrate the performance gap between powerful and low-power devices. Our findings show that even low-power devices, such as Raspberry Pi, perform well with small payload sizes on the Chrysalis network but face challenges with larger payloads. We observe that variation in transmission time increases as payload size grows, indicating the impact of PoW complexity, but it still is feasible for Raspberry Pi. We further validated our experimental setup to ensure the validity and accuracy of our approach through discussions with the IOTA Foundation’s technical team.
Implementation of BYOD Security Policy in Malaysia Institutions of Higher Learning (MIHL): An Overview

The BYOD Security Policy (BYOD-SP) constitutes formal documentation protecting sensitive data and information during BYOD implementation in an organization. The BYOD environment in an organization might face threats if information assets security is not prioritized before initializing BYOD. Releasing BYOD-SP is one of the key strategies to safeguard safety in BYOD implementation and to ensure users adhere to the BYOD-SP. It can also increase the understanding of ways to protect the BYOD environment from any risk. Hence, this study explores the current situation of BYOD, specifically in Malaysian tertiary education institutions, and the compulsory components that should be highlighted in the BYOD-SP. The implementation of (BYOD-SP) in Malaysian Institutions of Higher Learning (IHL) has led to a sense of discouragement, with only a few institutions being able to clearly outline their implementation strategy, in contrast to what has been done by foreign universities. This has resulted in a lack of information and understanding about the components of BYOD-SP in the Malaysian environment, making it difficult to make deeper comparisons. Understanding the current practice of BYOD can provide a more comprehensive study of a suitable model, cybersecurity framework, or other features to prepare a comprehensive BYOD-SP documentation on campus. As the BYOD implementation is underway, this study could increase awareness of all aspects of BYOD to aid organizations in formulating the BYOD-SP document. This could encourage BYOD players to achieve maximum information security protection.
A theory for the sparsity emerged in the Forward Forward algorithm

This report explores the theory that explains the high sparsity phenomenon \citep{tosato2023emergent} observed in the forward-forward algorithm \citep{hinton2022forward}. The two theorems proposed predict the sparsity changes of a single data point's activation in two cases: Theorem \ref{theorem:1}: Decrease the goodness of the whole batch. Theorem \ref{theorem:2}: Apply the complete forward forward algorithm to decrease the goodness for negative data and increase the goodness for positive data. The theory aligns well with the experiments tested on the MNIST dataset.
A Novel Deep Kernel Incremental Extreme Learning Machine Based on Artificial Transgender Longicorn Algorithm and Multiple Population Gray Wolf Optimization Methods

Abstract Redundant nodes in a kernel incremental extreme learning machine (KI-ELM) increase ineffective iterations and reduce learning efficiency. To address this problem, this study established a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM), which is based on a hybrid intelligent algorithm and a KI-ELM. First, a hybrid intelligent algorithm was established based on the artificial transgender longicorn algorithm and multiple population gray wolf optimization methods to reduce the parameters of hidden layer neurons and then to determine the effective number of hidden layer neurons. The learning efficiency of the algorithm was improved through the reduction of network complexity. Then, to improve the classification accuracy and generalization performance of the algorithm, a deep network structure was introduced to the KI-ELM to gradually extract the original input data layer by layer and realize high-dimensional mapping of data. The experimental results show that the number of network nodes of HI-DKIELM algorithm is obviously reduced, which reduces the network complexity of ELM and greatly improves the learning efficiency of the algorithm. From the regression and classification experiments, its CCPP can be seen that the training error and test error of the HI-DKIELM algorithm proposed in this paper are 0.0417 and 0.0435, which are 0.0103 and 0.0078 lower than the suboptimal algorithm, respectively. On the Boston Housing database, the average and standard deviation of this algorithm are 98.21 and 0.0038, which are 6.2 and 0.0003 higher than the suboptimal algorithm, respectively.
Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism

Accurate photovoltaic (PV) generation forecasts can reduce power redeploy from the grid, thus increasing the supplier's profit in the day-ahead electricity market. However, the PV process is affected differently by various factors under different weather conditions, resulting in significantly different energy output curves. In this context, this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism. We propose a multi-stream attention fusion network (MSAFN) which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions. The proposed conditional decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions. Notably, the attention modules adeptly learn patterns under diverse conditions, while simultaneously, the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time. We compare the state-of-the-art decomposition algorithms (VMD, EEMD and MSTL, etc.) and prediction models (BPN, LSTM, XGBoost and transformer, etc.) commonly used in PV prediction. The results show that the MSAFN model is more accurate than the models above, which has a noticeable improvement compared to other recent day-ahead PV predictions on DKASC.
Blockchain and Machine Learning in EHR Security: A Systematic Review

Background: The rapid development of modern technologies renders a convenient and efficient solution to implement Electronic Health Records (EHRs) systems. The rapid growth of healthcare data has a distinctive attribute of digital transformations. The big datasets of healthcare, their complexity and their dynamic nature have posed severe challenges associated with the analysis, pre-processing, privacy, security, storage, usability and data exchange. Material and Methods: We have performed the Systematic Literature Review (SLR) and followed the Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. SLR refers to the methodology that discovers, analyses and accesses recent research literature related to the subject field. The research papers were searched from academic repositories like IEEE, WOS, Scopus and PubMed for the previous five years on March 2023. Results: The designed search string provides 199 research articles in total. We filter the research articles based on inclusion-exclusion strategies and quality assessment metrics. Six main criteria for research inclusion-exclusion for SLR are formulated. These works of literature insight into 1) the issues associated with interoperability and security of EHRs by using the Blockchain (BC) technology, 2) different frameworks and tools to improve privacy and security in the healthcare domain, 3) the open issues of using BC technology in the electronic healthcare domain, 4) the standardized ways to store EHRs, 5) various ways to handle the big data using the BC systems and 6) the usage of Federated Learning (FL) to preserve the privacy of EHRs in the healthcare domain. We acquired 46 research articles based on the criteria (inclusion-exclusion) that investigate the above-mentioned issues. Conclusion: The SLR will serve as the state-of-the-art (SOTA) for future researchers in the field of BC in healthcare. Additionally, the paper provides insights to the new researchers to revolutionize the healthcare domain by adopting the latest digitalized technologies. The proposed study identified various reflections. It analyzed the architectural mechanism that supports the security and interoperability of EHRs. Secondly, the study described different tools and frameworks to improve the privacy and security of EHRs using the BC. Thirdly, the open issues of storing and preserving the EHRs using BC in the healthcare system were determined. Fourth, it analyzed and provided a detailed view of using standardized ways for storing and handling big data by using the BC system. Lastly, the usage of FL to preserve the privacy of EHRs was analyzed.
AQUILA: Communication Efficient Federated Learning With Adaptive Quantization in Device Selection Strategy

The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typically arising from the transmission of large-scale models. Existing adaptive quantization methods, designed to mitigate these overheads, operate under the impractical assumption of uniform device participation. Additionally, these methods are limited in their adaptability due to the necessity of manual quantization level selection and often overlook biases inherent in local devices' data, thereby affecting the robustness of the global model. In response, this paper introduces AQUILA ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qu</u> antization in dev <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> ce se <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u> ection str <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> tegy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL. AQUILA integrates a sophisticated device selection method that prioritizes the quality and usefulness of device updates. Utilizing the exact global model stored by devices enables a more precise device selection criterion, reduces model deviation, and limits the need for hyperparameter adjustments. Furthermore, AQUILA presents an innovative quantization criterion, optimized to improve communication efficiency while assuring model convergence. Our experiments demonstrate that AQUILA significantly decreases communication costs compared to existing methods, while maintaining comparable model performance across diverse non-homogeneous FL settings, such as Non-IID data and heterogeneous model architectures.