Reviewerly/exHarmony huggingface dataset:
Enhancing Drift Detection and Model Uncertainty Handling in Imbalanced Streaming Data using Autoencoder-based Approach

In today’s digital era, numerous applications are generating data in the form of data stream. The data streams are a continuous massive amount of data generated in real-time. Handling the data streams in real-time environments is a critical challenge. The data stream classification becomes more challenging when the statistical properties of the features (changes in the underlying data distribution) or their relationship with the target label are changing and the phenomenon is known as concept drift. When concept drift occurs, a machine learning model trained on previous data may no longer predict or generalise to new incoming data accurately. This can lead to a decrease in performance, poor accuracy, and unreliable predictions. In this paper, we propose an approach for drift detection using an Auto-encoder-based model that incorporates KL divergence and reconstruction loss, along with class imbalance handling and integrating the Monte Carlo Dropout. The Auto-encoder learns to capture the inherent patterns and regularities, facilitating drift detection by computing the reconstruction loss. KL divergence is an additional loss term, which encourages the Auto-encoder to learn a latent space that is sensitive to changes in the data distribution. Borderline-Synthetic Minority Oversampling Technique (Borderline-SMOTE) is used to address the challenge of class imbalance in drift detection, which generates synthetic minority samples. Integrated the Monte Carlo Dropout, a probabilistic regularization technique, to enhance the model’s uncertainty estimation. The proposed model was evaluated on real-world datasets. The results showed that the proposed model outperformed the other state-of-the-art methods.
OPT-MobileNet: A Deep Learning Approach for Carcinogenic Classification of Human Skin mole

The elementary symptom of skin cancers primarily appears as a spot, scaly area on the skin that changes colour, size or shape over a period of time. The detection of skin pigmentation and growth is an early sign of visual cues to identify any indication towards skin cancer. Of all cancers, melanoma is arguably one of the most straightforward to detect early because most melanomas present on visible areas of the skin and mucosa. This pigmentation may be caused for various other reasons which may not lead to carcinogenic structural growth. In many cases, these pigmentations are misunderstood or lately identified as any kind of harmful cell division. The ambiguous visual structure of skin moles may create false positives or negatives to come to a conclusion for identifying the exact disease being it to be benign or malignant. Manual identification of such microscopic slide image is a time and effort constraint activity. Automated AI (Artificial Intelligence)-based computer-aided diagnostics have proved to be reliable in many such cases making healthcare facilities more accessible. In this paper, the research gaps have been addressed and based on these, a new optimum CNN classifier OPT-MobileNet (Optimum MobileNet) has been proposed. A range of state-of-the-art NN (Neural Network) models are compared with the proposed technique for verifying the efficiency and reliability in perfect carcinogenic structure identification. The performance of the proposed technique has been illustrated numerically and graphically for related reference. During the experiment, the proposed methodology has confirmed to identify the skin mole type with in an optimum amount of time and classify the type of skin mole to its exact class. This proposed method of skin mole analysis can serve as a state of Decision Support System (DSS) in medical pathology.
Absolute Randomness as a Key Part of Information Security

The paper describes a technology for sending and assembling data packets based on a random selection of servers. It is this approach that avoids a lot of problems in the field of information security. The given solution is considered for an abstract industry, and can be adapted to any needs. In the final part of the article, a description of the software solution developed for the implementation of this technology is presented.
Prototype for Creating Educational Courses in Virtual Reality

New technologies are applied in education to enhance the learning process. Technologies used to visualize and enable interaction with a virtual environment are combined to create virtual reality (VR). Virtual reality aims to improve, inspire, and stimulate students' understanding of certain events while also enabling them to engage in practical learning. The main aim of the presented work is to design a learning package for biology course as a template for both teachers and students using Virtual Reality technology which provides a description in simple terms of the cardiac cycle, digestive system, and cells. Hoping to improve the enjoyment and appeal of the educational process for students. Using headset and joystick. Students may learn and work together more effectively with the aid of the headset. Since visuals aid in learning, Virtual Reality combines text with images to help students comprehend a wide range of subjects more rapidly.
Study on Influence of Seasonal Freeze-thaw Environment on Crack Evolution of Expansive Soil in Subgrade Based on Genetic Algorithm

In the cold seasonal frozen soil area, expansive soil is in the environment of alternating wet and dry and freezing and thawing cycle for a long time, so it is easy to form cracks, which greatly impacts its strength, permeability and deformation characteristics. Usually, the frost heave deformation of subgrade is unevenly distributed along the length of the road, so the smoothness of the road surface is poor, the flexible road surface is prone to bulge and crack, and the rigid road surface is prone to break, which affects the normal use of the road. In view of this situation, this paper discusses the influence of seasonal freeze-thaw environment on crack evolution of subgrade expansive soil based on GA(genetic algorithm). By studying the crack volume fraction distribution under wet-dry and wet-dry freeze-thaw coupling cycles, it is found that the crack volume fraction of the sample under the first coupling cycle is 6.09%, and it gradually stabilizes to 10.6% after the 15th cycle, which is about 1.24 times that of the wet-dry cycle. It can be seen that GA can effectively improve the crack evolution of subgrade expansive soil caused by seasonal freezing and thawing environment. Based on the different frost heaving rates of fill in seasonal frozen soil area of GA, this paper analyzes the distribution and evolution of subgrade deformation and stress during freeze-thaw cycle, and further discusses its influencing factors, revealing the occurrence mechanism of subgrade diseases in depth.
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning

In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL. Our approach uses available information about agent timescales to define and learn periodic multi-agent policies. In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy. To learn such policies, we propose a policy gradient algorithm that parameterizes the actor and critic with phase-functioned neural networks, which provide an inductive bias for periodicity. The framework's ability to effectively learn multi-timescale policies is validated on a gridworld and building energy management environment.
Robust Filtering Technique for CNN-Based Image Classification under High-Density Salt and Pepper Noise Interference

The performance of deep learning convolutional neural network (CNN) could suffer severe misclassification and performance degradation under high-density salt and pepper noise interference. The investigation studies the classification performance of AlexNet Deep Learning Network (DLN) which is a type of deep learning CNN consisting of 8-layer deep. Conventionally median filtering (MedF) could be applied to filter out the salt-and-pepper noise to improve the image classification performance of the AlexNet. However, MedF may fail to work under high-density salt and pepper noise interference. Hence, a robust recursive weighted myriad filtering (RWMyF) for solving the problem is proposed in this work. A novel and robust filtering system model for CNN-based image classification is devised to mitigate the high-density salt and pepper noise interference for better and more reliable performance. Overall, higher peak signal-to-noise ratios (PSNRs) are obtained from the robust RWMyF filtering for Alexnet with more precise classifications obtained for those images severely distorted by high-density salt and pepper noise.
IHCNet: Advancing Multi-Stage HER2 Status Detection in Breast Cancer Using Interpretable Robust Transfer Learning Model from IHC Images

Human Epidermal Growth Factor Receptor (HER2) has become one the most dangerous subtype of Breast Cancer (BC). According to the guidance of CAP/ASCO, HER2 expression can be classified between 0 and 3+ range in terms of its severity level. it can be detected from immunohistochemical (IHC) of several classes such as 0, 1+, 2+, and 3+. Early diagnosis can alleviate and help pathologists to take early treatment. Utilizing artificial intelligence (AI) in digital pathology, a robust transfer learning (TL) model has been utilized known as IHCNet based on DenseNet201 to diagnose BC from the distinct expression of HER2 subtypes. This model is introduced with a fine-tuned combination of Global Average Pooling (GAP), Batch Normalization (BN), Dense Layers with Swish activation functions, Dropout layers, and a Dense classifier with a softmax function. IHCNet exceeded all other state-of-art models with an Accuracy of 93.45%, Precision of 94.01%, and Recall of 93.14%, on the BCI dataset, maintaining promising improvement. Finally, Grad-CAM has been incorporated into the proposed model to explain the model more intuitively. Grad-CAM explains IHCNet performances in different convolution layers by generating heat maps.
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach

Reinforcement Learning (RL) has demonstrated impressive performance in various areas such as video games and robotics. However, ensuring safety and stability, which are two critical properties from a control perspective, remains a significant challenge when using RL to control real-world systems. In this paper, we first provide definitions of safety and stability for the RL system, and then combine the Control Barrier Function (CBF) and Control Lyapunov Function (CLF) methods with the actor-critic method in RL to propose a Barrier-Lyapunov Actor-Critic (BLAC) framework which helps maintain the aforementioned safety and stability for the system. In this framework, CBF constraints for safety and CLF constraint for stability are constructed based on the data sampled from the replay buffer, and the augmented Lagrangian method is used to update the parameters of the RL-based controller. Furthermore, an additional backup controller is introduced in case the RL-based controller cannot provide valid control signals when safety and stability constraints cannot be satisfied simultaneously. Simulation results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code can be found in the GitHub repository: https://github.com/LiqunZhao/A-Barrier-Lyapunov-Actor-Critic-Reinforcement-Learning-Approach-for-Safe-and-Stable-Control show that this framework yields a controller that can help the system approach the desired state and cause fewer violations of safety constraints compared to baseline algorithms <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> For a more comprehensive version of this paper, please refer to [1] (https://arxiv.org/abs/2304.04066) which includes additional details and an extra “Simulated Cars” task..
Partial Rank Similarity Minimization Method for Quality MOS Prediction of Unseen Speech Synthesis Systems in Zero-Shot and Semi-Supervised Setting

This paper introduces a novel objective function for quality mean opinion score (MOS) prediction of unseen speech synthesis systems. The proposed function measures the similarity of relative positions of predicted MOS values, in a mini-batch, rather than the actual MOS values. That is the partial rank similarity is measured $(\mathcal{P}RS)$ rather than the individual MOS values as with the L1 loss. Our experiments on out-of-domain speech synthesis systems demonstrate that the $\mathcal{P} R S$ outperforms L1 loss in zero-shot and semi-supervised settings, exhibiting stronger correlation with ground truth. These findings highlight the importance of considering rank order, as done by $\mathcal{P}RS$, when training MOS prediction models. We also argue that mean squared error and linear correlation coefficient metrics may be unreliable for evaluating MOS prediction models. In conclusion, $\mathcal{P} R S$-trained models provide a robust framework for evaluating speech quality and offer insights for developing high-quality speech synthesis systems. Code and models are available at github.com/nii-yamagishilab/partial_rank_similarity/
Extending Self-Distilled Self-Supervised Learning For Semi-Supervised Speaker Verification

In this study, we extend self-distillation with no labels (DINO), a successful self-supervised learning framework, by combining it with supervised classification (SC) for semi-supervised speaker verification with limited labeled data. We introduce a transfer learning framework that pre-trains and fine-tunes the encoder using DINO and SC, respectively, and a multitask learning framework that shares the encoder while having separate projection layers for both methods. To achieve lower inter-speaker similarity, we propose a joint learning framework sharing both the encoder and projection layer for DINO and SC. We also propose an auxiliary contrastive loss between embeddings derived from labeled and unlabeled utterances and introduce a two-stage learning strategy to apply margin penalty effectively. Experimental results on the VoxCeleb corpus indicate that the joint learning framework outperforms the other frameworks and is closest to achieving the performance of fully supervised learning.
Vulnerability Analysis of Electronic Voting Protocols

Electronic voting is a complex but widely demanded technology. The generally accepted opinion is that electronic voting guarantees the anonymity of voters and at the same time allows verify the correctness of registration of votes, ensuring transparency of the electoral process. Attempts to use electronic voting technologies in elections began a long time ago, but, as practice has shown, it has not yet been possible to create a secure system that meets all the election principles. This article analyzes the shortcomings and vulnerabilities of online election systems used in various countries. We consider the methods of protecting the electronic voting process based on an additively homomorphic Shamir secret sharing scheme, which allows voters to jointly calculate the results of voting, operating with encrypted ballots without revealing their own vote.
Towards a Blockchain-Based Agricultural Ecosystem: A systematic review

The emergence and application of blockchain technology can provide a certain degree of solution to many problems in the agricultural sector, especially in the sub-sectors of agricultural supply chain management, agricultural product traceability, and agricultural finance. The development of blockchain technology has increased significantly, and the scope of application has continued to expand. For that, it becomes an effective tool to promote agricultural development in many countries, especially in agriculturally developed countries. This paper presents a systematic review of the current implementation of blockchain in agriculture. Furthermore, the paper critically assesses the limitations, constraints, and unresolved issues surrounding the integration of blockchain technology within agricultural systems. This review is a resource for researchers and stakeholders interested in the prospects and challenges of building a blockchain-based agricultural ecosystem. It sheds light on the transformation of blockchain technology while addressing the need for further research and development to unlock its full benefits within the dynamic landscape of agriculture.
The Development of Breast Cancer Classification Methods: Healthcare Professionals Early Detection

The aim of this research is to improve the classification performance of the Random Forest algorithm in breast cancer diagnosis through the application of feature optimization techniques. By focusing on the Breast Cancer Coimbra dataset, the study conducts experiments to assess the classification accuracy before and after feature optimization. The findings demonstrate that the Random Forest algorithm achieves an accuracy of 75.91% without feature optimization. However, after implementing feature optimization using the genetic algorithm, the accuracy significantly improves to 81.29%. A comparative analysis with other feature optimization methods reveals the superiority of the genetic algorithm in enhancing classification accuracy. These results highlight the effectiveness of the proposed optimization approach in improving the Random Forest algorithm's performance for breast cancer classification. The research contributes to the advancement of breast cancer diagnosis methods, offering valuable insights for healthcare professionals in early disease detection. Furthermore, the application of data mining concepts aids in understanding the factors influencing breast cancer, providing a basis for future investigations into the utilization of genetic algorithms in classifying other diseases.
On the Security of Randomly Transformed Quadratic Programs for Privacy-Preserving Cloud-Based Control

Control related data, such as system states and inputs or controller specifications, is often sensitive. Meanwhile, the increasing connectivity of cloud-based or networked control results in vast amounts of such data, which poses a privacy threat, especially when evaluation on external platforms is considered. In this context, a cipher based on a random affine transformation gained attention, which is supposed to enable privacy-preserving evaluations of quadratic programs (QPs) with little computational overhead compared to other methods. This paper deals with the security of such randomly transformed QPs in the context of model predictive control (MPC). In particular, we show how to construct attacks against this cipher and thereby underpin concerns regarding its security in a practical setting. To this end, we exploit invariants under the transformations and common specifications of MPC-related QPs. Our numerical examples then illustrate that these two ingredients suffice to extract information from ciphertexts.
Evaluation of cloud simulators for energy-related applications

Simulation tools play a critical role in various domains of cloud computing. They offer researchers and infrastructure designers a virtual environment to work in, eliminating the expenses associated with physical infrastructure, so those platforms could offer determinant support to analyze the performance of cloud systems and the related data centers. For those reasons, many cloud simulators have been developed. They are currently used by academics and practitioners, but only a few of these provide useful features in order to perform effective simulations of cloud systems for energy-related issues. The goal of this work is to help academics and professionals interested in energy-aware simulations of cloud systems to select the most suitable simulation platform for their purposes. The first part of this paper reviews the five simulators that hold the most promising features regarding energy issues. Subsequently, an evaluation metric has been introduced, based on five qualitative indexes: Last update, Popularity, Availability, Granularity, and Performance profile. The evaluation aims to provide researchers with suggestions for the best simulator choice.
Velocity Estimation for Motorcycles Using Image-to-Road Mapping

The authors propose a visual-inertial approach to estimate the body-fixed lateral velocity of motorcycles traveling along extra-urban roads. The approach comprises the following steps: First, a monocular camera takes video of the road ahead. Key features from sequential images of the road surface are extracted using the Harris corner detector and matching features are identified using the Fast retina keypoint descriptor. The locations of these features on the road surface are determined using a mapping based on an intuitive ray-casting approach. Next, the feature locations on the road, the angular velocity measurements and the optical flow of the feature projection locations on the image plane are used to formulate the egomotion of the motorcycle as a system of linear equations from which a velocity estimate is solved for using the least-squares method. Finally, this estimate is fused with readings from an inertial navigation system using a Kalman filter to produce a filtered estimate and correct integrator drift. The approach is validated against simulation data generated using BikeSim and the results are compared against state observer approaches and previously published visual-inertial approaches from the authors.