Reviewerly/exHarmony huggingface dataset:
U-Net transfer learning backbones for lesions segmentation in breast ultrasound images

&lt;p&gt;Breast ultrasound images are highly valuable for the early detection of breast cancer. However, the drawback of these images is low-quality resolution and the presence of speckle noise, which affects their interpretability and makes them radiologists’ expertise-dependent. As medical images, breast ultrasound datasets are scarce and imbalanced, and annotating them is tedious and time-consuming. Transfer learning, as a deep learning technique, can be used to overcome the dataset deficiency in available images. This paper presents the implementation of transfer learning U-Net backbones for the automatic segmentation of breast ultrasound lesions and implements a threshold selection mechanism to deliver optimal generalized segmentation results of breast tumors. The work uses the public breast ultrasound images (BUSI) dataset and implements ten state-of-theart candidate models as U-Net backbones. We have trained these models with a five-fold cross-validation technique on 630 images with benign and malignant cases. Five out of ten models showed good results, and the best U-Net backbone was found to be DenseNet121. It achieved an average Dice coefficient of 0.7370 and a sensitivity of 0.7255. The model’s robustness was also evaluated against normal cases, and the model accurately detected 72 out of 113 images, which is higher than the four best models.&lt;/p&gt;
Sensitivity of Modern Deep Learning Neural Networks to Unbalanced Datasets in Multiclass Classification Problems

One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where, in fact, the last layers of the neural network are retrained. Therefore, the large datasets with highly unbalanced classes are not good for models’ training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2-6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes&amp;#039; classification also improved significantly – the TruePositive value can be increased by 0.3 and more. As result, the best approach to train considered networks on an initially unbalanced dataset was formulated.
Learning Environment Models with Continuous Stochastic Dynamics

Solving control tasks in complex environments automatically through learning offers great potential. While contemporary techniques from deep reinforcement learning (DRL) provide effective solutions, their decision-making is not transparent. We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent. However, for most control problems, automata learning is not scalable enough to learn a useful model. In this work, we raise the capabilities of automata learning such that it is possible to learn models for environments that have complex and continuous dynamics. The core of the scalability of our method lies in the computation of an abstract state-space representation, by applying dimensionality reduction and clustering on the observed environmental state space. The stochastic transitions are learned via passive automata learning from observed interactions of the agent and the environment. In an iterative model-based RL process, we sample additional trajectories to learn an accurate environment model in the form of a discrete-state Markov decision process (MDP). We apply our automata learning framework on popular RL benchmarking environments in the OpenAI Gym, including LunarLander, CartPole, Mountain Car, and Acrobot. Our results show that the learned models are so precise that they enable the computation of policies solving the respective control tasks. Yet the models are more concise and more general than neural-network-based policies and by using MDPs we benefit from a wealth of tools available for analyzing them. When solving the task of LunarLander, the learned model even achieved similar or higher rewards than deep RL policies learned with stable-baselines3.
Involving Problem-Based Learning as an Alternative for EFL Students’ Writing Improvement: A Method for Teaching Writing

In fact, writing is one of the language skills that is emphasized in learning English in Curriculum 2013. As one of the language skills, writing skills need to be improved because it is a productive and expressive language skill that is needed to fulfill daily communication needs. Seeing the crucial role of writing, this present study intent to examine the problem based learning as an alternative model for EFL students’ writing improvement. Classroom action research with two cycles was implemented to study this phenomena. Moreover, this study involved 18 students of class XI SMA YPPK YOS SUDARSO Merauke, in collecting the data, this study used observation sheet for both teacher and students’ activities, meanwhile to see students’ writing improvement, explanation text writing was applied in both cycles. As the result, Problem based learning could improve students’ writing, it could be seen in each cycles’ scores, where cycle I writing was 63.33% and improved in cycle II 81%. In cycle I, most of students did not pass the minimum completeness which was 75, meanwhile in cycle II most of students passed. The improvement based on the observation results in both cycles are also being the success category in this study, in cycle I both teacher and students obtained 67.85% (good category) and 57.14% (enough category), improved in cycle II which was 87.5 (excellent category) and 83.92% (excellent category).
A Blockchain Technology to Secure Electronic Health Records in Healthcare System

The study proposes a blockchain-based patient-centric EHR solution for India. The proposed framework would provide a secure health infrastructure system, make data tampering impossible, enable just-in-time availability of healthcare information, remove handwritten prescriptions, and of er end-to-end monitoring, and increase patient privacy and record management. The research connects to hospital databases to incorporate previous handwritten prescriptions into the new system. In the Hyperledger Fabric-based design, patients will view, write, and control authorization to their medical information through a web or mobile interface. During implementation, we used a network model with three companies and three peers. When write block duration is raised from 250 ms to 2s, 250 tps throughput improves by 4x. Block size 20 is 50% faster than block size 40, improving network performance. Since the network model's CPU utilization has remained steady over time, a drop in block size and an increase in block time will lead to a considerable decrease in network latency, boosting network performance.
Image Inpainting Using Deep Learning Techniques: A Review

Image inpainting is a significant research area in the field of computer vision, with a diverse range of applications in image processing. Traditional image inpainting techniques proved to ineffective in generating better inpainted results, as they tend to produce inconsistent and multiple image semantics. Due to the advancement of Deep learning (DL) in recent years, this can be employed to address the issues with conventional image inpainting techniques. Currently, researches are mainly focused on image inpainting techniques based on deep learning using CNN, GAN etc. In this article, several deep learning based image inpainting techniques are been reviewed.
Exploring the Role of Information Technology in Supporting Sustainability Efforts in Saudi Arabia

This study explores the role of information technology (IT) in supporting sustainability efforts in Saudi Arabia. Through an analysis of four case studies, we examine how technology has been implemented by Saudi Arabian companies to advance sustainability goals. The findings demonstrate a notable shift towards green initiatives in the country, with the adoption of technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence. These technologies have been instrumental in enhancing energy efficiency and waste reduction. The study highlights the significance of policy interventions and public-private partnerships in achieving both environmental sustainability and economic growth. To ensure widespread adoption of sustainable practices, it is crucial for the government to focus on building digital capacity in companies and removing technological barriers. The case studies of four companies serve as illustrative examples of sustainable practices and the corresponding technological support in Saudi Arabia. The research underscores the importance of leveraging information technology and collaboration to accelerate progress towards the nation&amp;#039;s sustainability goals.
Hybrid quantum algorithms for flow problems

For quantum computing (QC) to emerge as a practically indispensable computational tool, there is a need for quantum protocols with an end-to-end practical applications -- in this instance, fluid dynamics. We debut here a high performance quantum simulator which we term QFlowS (Quantum Flow Simulator), designed for fluid flow simulations using QC. Solving nonlinear flows by QC generally proceeds by solving an equivalent infinite dimensional linear system as a result of linear embedding. Thus, we first choose to simulate two well known flows using QFlowS and demonstrate a previously unseen, full gate-level implementation of a hybrid and high precision Quantum Linear Systems Algorithms (QLSA) for simulating such flows at low Reynolds numbers. The utility of this simulator is demonstrated by extracting error estimates and power law scaling that relates $T_{0}$ (a parameter crucial to Hamiltonian simulations) to the condition number $\kappa$ of the simulation matrix, and allows the prediction of an optimal scaling parameter for accurate eigenvalue estimation. Further, we include two speedup preserving algorithms for (a) the functional form or sparse quantum state preparation, and (b) \textit{in-situ} quantum post-processing tool for computing nonlinear functions of the velocity field. We choose the viscous dissipation rate as an example, for which the end-to-end complexity is shown to be $\mathcal{O}(\textrm{polylog} (N/\epsilon)\kappa/\epsilon_{QPP})$, where $N$ is the size of the linear system of equations, $\epsilon$ is the the solution error and $\epsilon_{QPP}$ is the error in post processing. This work suggests a path towards quantum simulation of fluid flows, and highlights the special considerations needed at the gate level implementation of QC.
Higher order orthogonal polynomials as activation functions in artificial neural networks

Activation functions are used in Artificial Neural Networks to provide non-linearity to the system. Several different activation functions in use are very well known by almost any AI practitioner however this is not the case for polynomial activation functions. Increasing attention to these valuable mathematical functions can encourage more research and help to fill the gap. During this work, Chebyshev and Hermite orthogonal polynomials were used as activation functions. Calculations were conducted on 3 different datasets with different hyperparameters. According to the results, calculations done by Chebyshev activation functions take less time, but Chebyshev can be more fragile depending on the solved problem. On the other hand, Hermite shows a more robust and generalized behavior, it is less dependent on the problem type, and it improves by necessary adjustments.
Deep learning optimal quantum annealing schedules for random Ising models

Abstract A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training.
CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System

Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after.
Innovative Method for Predicting Vehicle Speed Detection using Fuzzy Local Information Means and CNN

Thanks to technological advancements, now have a number of options for obtaining traffic data. Yet, the precision with which various technologies measure drivers’ actual speeds varies greatly. It is a widespread complaint among transportation researchers and professionals that they lack information of device accuracy. This proposed approach employs video data image processing alongside the Euclidean distance approach to estimate vehicle speeds from a variety of perspectives. To begin, the proposed approach applies preprocessing to the frames we retrieved from the video data in order to reduce the shadow impact. Then, they use a Gaussian Mixture Model (GMM) to pull out the foreground from the background. The next step is to apply a median filter on the resulting foreground. Using Haar Wavelet decomposition for feature extraction, appearance-based hypothesis verification ensures the correctness of hypotheses based on their outward appearance. After that, FILM-CNN is used to train the model which outperforms k-Means and CNN methods.
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO.It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5 MB memory available for storing convolutional neural network (CNN) weights.The proposed quantized network architecture with 422 k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators.In particular, the proposed network has been deployed on the MAX78000 microcontroller achieving high frame-rate of up to 180 fps and an ultra-low energy consumption of only 196 µJ per inference with an inference efficiency of more than 106 MAC/Cycle.TinyissimoYOLO can be trained for any multi-object detection.However, considering the small network size, adding object detection classes will increase the size and memory consumption of the network, thus object detection with up to 3 classes is demonstrated.Furthermore, the network is trained using quantization-aware training and deployed with 8-bit quantization on different microcontrollers, such as STM32H7A3, STM32L4R9, Apollo4b and on the MAX78000's CNN accelerator.Performance evaluations are presented in this paper.
A Survey on Optimizing Mobile Delivery of 360° Videos: Edge Caching and Multicasting

Recently, there has been an ever-growing demand for virtual reality (VR) and 360° video applications. Different from conventional 2D videos, 360° videos take users into an immersive experience by providing them with a navigable panoramic view. However, achieving adequate quality of experience (QoE) levels poses significant network challenges, especially in mobile delivery setups. Despite the tremendous improvements offered by 5G and beyond mobile networks, streaming 360° videos in a similar fashion to 2D videos is suboptimal, while scaling at high numbers questions the feasibility of the endeavor. This paper explores the utilization of caching and multicasting solutions for the mobile delivery of VR and 360° videos. First, an overview of immersive technologies and their distinctive characteristics is provided. Then, we discuss the network challenges associated with 360° videos and the role of implementing robust caching and multicasting schemes that exploit the unique features of 360° videos and capitalize on the correlations among end-users’ viewports. Having established the foundations and challenges of 360° video streaming, we continue with a comparison of the state-of-the-art literature, while focusing on video streaming optimization aspects. We conclude our work by discussing the status and future research directions.
Social world knowledge: Modeling and applications

Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike. As of today, there exist many knowledge bases that represent factual world knowledge. Yet, there is no resource that is designed to capture social aspects of world knowledge. We believe that this work makes an important step towards the formulation and construction of such a resource. We introduce SocialVec, a general framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks. In this framework, entities correspond to highly popular accounts which invoke general interest. We assume that entities that individual users tend to co-follow are socially related, and use this definition of social context to learn the entity embeddings. Similar to word embeddings which facilitate tasks that involve text semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor. In this work, we elicited the social embeddings of roughly 200K entities from a sample of 1.3M Twitter users and the accounts that they follow. We employ and gauge the resulting embeddings on two tasks of social importance. First, we assess the political bias of news sources in terms of entity similarity in the social embedding space. Second, we predict the personal traits of individual Twitter users based on the social embeddings of entities that they follow. In both cases, we show advantageous or competitive performance using our approach compared with task-specific baselines. We further show that existing entity embedding schemes, which are fact-based, fail to capture social aspects of knowledge. We make the learned social entity embeddings available to the research community to support further exploration of social world knowledge and its applications.
Augmented Reality Awareness and Latest Applications in Education: A Review

Several advances in technology have touched the educational sector, where improvements and enhancements are always a surge for innovative learning. Augmented Reality (AR) is considered an efficient and promising technology capable of enhancing the educational sector. AR is a visualization technology that allows human interaction by providing users with a perception of reality using virtual information. In other words, AR adds to the existing real-world environment some extra virtual information generated by computer techniques that can enhance the overall experience. This promising technology can be applied in different sectors such as education, healthcare, banks, tourism, etc. Despite the great work available in the literature about this topic, deep insights and extensive research are still needed before the actual implementation in the real world. The aim of this work is to highlight the importance of AR in improving the educational sector and increase awareness of using such an approach. A review is conducted to prove the validity of AR in increasing learning outcomes, students’ motivation, and engagement in classrooms, as well as improving the understanding level. Several papers were analyzed based on different application areas under various interdisciplinary databases. As a result, the papers were collected in a table-based format to provide researchers with better and easier insights on how to improve the use of AR in education before real implementation. This study helped in highlighting potential future work, discussing limitations, advantages, disadvantages, and the latest advances in this technology.
Factors Influencing Consumers Intention towards Online Food Purchasing in Phnom Penh

The purspose of this study is to indentify factors influencing the consumer intention towards the online food purchasing in Phnom Penh. The conceptual frameworkis developed from previous study which includes perceived convenience, customer control, advertsing, technolgy eagerness, online purchase attitude, and online purchase intention. The quantitative method was applied to distribute questionaires to 400 respondents. Multi-stage sampling technique was conducted in nonprobability sampling approach, using judgmental sampling to select relevant respondents in Phnom Penh, quota sampling to calculate the sample size of the targeted respondents, and convenience sampling to distribute questionaires online, using Google-based surevy form. Item-Objective Congruence and Cronbach’s Alpha were employed to validate the constructs. Confirmatory Factior Aalysis (CFA) was used to verify conventient and discriminant validity, and goodness of fit indices. Structural Equation Model (SEM) was carried out to test the relationship among variables. The results from the analysis revealed that advertisng and technology eargerness have signigicant influences on online purchase attitude, and online purchase attitude has significant influence on purchase intention. However, the customer control and perceived convenience do not have any influences on the online purchase attitude.