Reviewerly/exHarmony huggingface dataset:
N-epitomizer: Enabling Semantic Offloading for Neural Network Inferences

Offloading neural network Inferences from resource- constrained mobile devices to an edge server over wireless networks is becoming more crucial as the neural networks get heavier. To this end, recent studies have tried to make this offloading process more efficient. However, the most fundamental question on extracting and offloading the minimal amount of necessary information that does not degrade the inference accuracy has remained unanswered. We call such an ideal offloading semantic offloading and propose N-epitomizer, a new offloading framework that enables semantic offloading, thus achieving more reliable and timely inferences in highly-fluctuated or even low- bandwidth wireless networks. To realize N-epitomizer, we design an autoencoder-based scalable encoder trained to extract the most informative data and scale its output size to meet the latency and accuracy requirements of inferences over a network. We also accelerate N-epitomizer by exploiting light-weight knowledge distillation for the encoder design and decoder slimming for the decoder design, reducing its overall computation time significantly. Our evaluation shows that N-epitomizer achieves exceptionally high compression for images without compromising inference accuracy, which is 21×, 77×, and 192× higher than JPEG compression, and 20×, 55×, and 86× higher than the state-of-the-art DNN-aware image compression GRACE [1] for semantic segmentation, depth estimation, and classification, respectively. Our results show N-epitomizer’s strong potential as the first semantic offloading system to guarantee end-to-end latency even under highly varying cellular networks.
HSCM: A New Framework For Handwritten Signature Verification Using ConvMixer

As nations attempt to recover from the coronavirus disease (COVID-19) epidemic, digital solutions facilitate economic change and put economies on the path to green growth. Therefore, identifying an individual based on biometric characteristics has rapidly become an urgent and essential subject. Banks, intelligence agencies, and high-profile institutions are frequently use handwritten signature verification to authenticate the identification of an individual. This paper introduces an innovative model (HSCM) for the authentication of online handwritten signatures based on the ConvMixer Network. The main idea of the proposed model is to divide the signature into several patches known as strokes, which are then processed using ConvMixer to generate signature features. These features are processed using Feed-Forward network. Experimental results on the real-world dataset show that the proposed model achieved 11.6% equal error rate (EER) compared with other competitors. This vindicates the efficacy and reliability of the proposed model in signature verification).
Innovative Methods of Conducting University Classes Aimed at Developing Communicative Skills (Using the Example of a Focus Group)

This article discusses innovative teaching methods in educational activities with a focus on the necessity of applying interactive approaches to enhance the quality of language-related disciplines. It is noted that the use of innovative technologies contributes to students' increased interest in speech culture by incorporating creative methods into the educational process, which fosters the development of diverse skills necessary for their future professional activities. The research aims to describe the application of non-traditional creative technologies during speech culture classes to enhance the effectiveness of the educational process. Using the example of one method (focus groups), the main stages of applying creative technologies in classes are demonstrated. The work characterizes the key features of the chosen method, which influence the formation of essential communicative competencies for successful student communication as future specialists.
LSTM deep learning model for Alzheimer’s disease prediction based on cost-effective time series cognitive scores

Alzheimer’s disease (AD) is a complex chronic neurodegenerative disease that propagates over time. Deep learning (DL) models can be used to learn time series data to extract deep temporal features and make robust decisions. The fusion of multimodal time series data has been proven to enhance model performance [1]. For instance, cognitive scores (CSs) including clinical dementia rating, and Alzheimer’s disease assessment scores have been integrated with other modalities like MRI to predict the future status of AD patients [2]. They enhanced the performance of DL models. These scores can be collected easily and in a cost-effective way in hospitals. No study in the literature has built a deep learning model to predict AD based on the cost-effective and multimodal time series data of CSs. In this study, we propose an LSTM-based DL architecture to predict AD based on multiple time series CSs. The proposed model has been optimized using a Bayesian optimizer to select the best architecture, and it has been compared with multiple classical machine learning models like random forest and others. The proposed LSTM architecture achieved better results than other models and provided stable and robust performance.
3D Small Object Detection from Cameras and Point Clouds Using Five-Head Attention in a Fusion Method

In this paper, we focus on 3D point clouds, multimodal data fusion, and attention mechanisms. Through a survey of related research on 3D object detection based on multimodal data fusion, we identified three problems: (1) the detection accuracy of small objects, such as pedestrians and bicycles, is unsatisfactory; (2) the fusion training of two different models cannot match the efficiency of single model training; and (3) when there are long-range objects in the pseudo-image generated by the features, existing methods cannot maintain the original high accuracy, and the generalization ability of the model is weak. To solve these problems and improve the detection performance of single-modal based detectors, this paper introduces a new fusion network that mainly consists of a five-head attention module and a posterior decision fusion (CPFN) module. The five-head module suppresses noise interference by jointly considering channel, spatial, point, and voxel attention, while enhancing the understanding of key information about the object. Additionally, CPFNet uses a PointPillars network with an attention mechanism for decision fusion with a CascadeR-CNN network. Experimental results on the validation set of the KITTI dataset show that our proposed method far outperforms existing methods in the small sample category, whether compared to state-of-the-art fusion-based methods or point cloud neural networks.hods or point cloud neural networks.
Automated Evaluation of Karate Practitioners’ Style during the First Kata Performance using Deep Learning and Pose Estimation

Automated motion analysis of human body movements has attracted a lot of interest recently since it has the ability to offer unbiased evaluations of performance in a variety of sports and martial arts. This study focused on analyzing the manner in which karate practitioners perform the first Kata, a critical component that reveals their command of fundamental motions and technique proficiency. This study suggests an automated method for analyzing karate practitioners’ movements from recorded video using pose estimation and a deep Convolutional Neural Network (CNN) model. The experimental findings show that our approach achieved a high level of accuracy 97%, and 94% particularly when employing different evaluation window sizes. This strategy provides fair and accurate decisions in sports arbitration by removing subjective biases that may result from human observers.
Implementing and Measuring the Performance of PB, RR and PBRR Scheduling Algorithms on ATMega32A using FreeRTOS

This research paper discusses the Real Time Operating Systems; their types, software, benefits, and drawbacks, as well as how they work, including task states and control blocks. This research goes into great detail to explain many scheduling algorithms, including Round Robin, Priority Based, Earliest Deadline First, First Come First Serve, and Non-preemptive Deadline First. It also covers the performance monitoring tools and the parameters to be monitored while measuring the performance. The problem that we shed light on is the increment of death and incidents of Liquified Petroleum Gas (LPG) burns. In 2018, The researchers found that almost 81.03% of the total burns’ percentage were related to gas leaks and 2% to negligence in the kitchen. To solve this problem, we made an application using FreeRTOS. We used different scheduling algorithms such as PB and RR. Furthermore, we utilized three different time slicing intervals: (0.1 sec, 10m sec, 100μ sec) . Our application was implemented on ATMega32A with internal frequency 1 MHz. The application includes two sensors and an LCD. Additionally, We added a DC motor that functions as a ventilating fan, allowing us to take appropriate actions based on the sensor readings. The results show that the best scheduling algorithm with respect to time execution was PBRR with time = 0.0656 secs, while the worst was without RTOS taking 4.581 secs. However, the memory consumption of the application without RTOS was RAM of 8%, and ROM of 18.7%, while the PBRR memory consumption was RAM of 89.9%, ROM of 31.5%. Also, We simulated the execution of the tasks using SimSo for PB scheduling algorithm.
Semi-Fragile Watermark for the Authentication and Recovery of Tampered Images

In order to strengthen the safety of corporate multimedia assets, a semi-fragile watermarking method is developed, which makes use of the integer wavelet transform (IWT) and the discrete cosine transform (DCT) for tamper detection and recovery. In this paper, we produce two distinct kinds of watermarks: an authentication watermark and a recovery watermark. A tamper detection methodology is utilized at the receiving end to check the watermarked image for validity and detect any assaults. If the changes are determined to be malicious, the suggested tamper recovery method is used to restore the altered areas of the received image. Different incidental/content-preserving modifications and malicious assaults were used to evaluate the suggested tamper detection and recovery method’s performance. The suggested embedding method improved the Peak signal-to-noise ratio (PSNR) of the watermarked images as compared to existing semi-fragile watermarking methods. The suggested tamper detection and recovery method also outperformed the state-of-the-art methods by pinpointing the origin of the assaults and reclaiming the compromised components.
Graph Assisted Unsupervised Domain Adaptation for Machine Fault Diagnosis

Domain adaptation has emerged as a useful technology that leverages the knowledge learned from labeled data in the source domain to build an effective classifier for the target domain, given that the source and target data have different underlying distributions. Most of the existing techniques aim to address the marginal distribution discrepancy alone, ignoring the conditional distribution divergence that may exist between the two domains. In order to achieve good adaptation performance, both the marginal and conditional distribution of the source and target data need to be aligned. The problem becomes challenging when the data is limited, and no labels are available for the target domain data. To address this scenario, in this paper, we propose a novel formulation that utilizes the concepts of Class-wise Maximum Mean Discrepancy and label propagation over the graph for unsupervised domain adaptation. We have applied it to the challenging scenario of adaptation between physically different but related machines for fault diagnosis. Two publicly available bearing fault datasets have been employed for performance evaluation. The experimental results indicate the superior performance of the proposed method compared to the state-of-the-art methods showing efficient transfer learning capabilities for machine fault diagnosis.
Research on the Security Reinforcement Method of Industrial Internet Identification Data Based on Blockchain Technology

The industrial Internet identity resolution system provides coding, registration and resolution services for industrial equipment, machines, parts and products. Similar to the domain name resolution system for the Internet, the industrial Internet identity resolution system is the nerve center of the industrial Internet and an important infrastructure for the interconnection, resource scheduling and production coordination of the entire network. However, with the rapid development of the industrial Internet, the number of industrial Internet identifications has soared, and the number of concurrent parsing requests can reach tens of millions. Vulnerabilities in the architecture, protocol, operation, identity and other aspects of the identity parsing system have gradually emerged. Attackers can use the hierarchical tree architecture to carry out DDoS attacks, monitor the identity parsing session to carry out man-in-the-middle attacks, and carry out cache poisoning attacks through recursive parsing server mixed with malicious data, Due to the low latency, high concurrency, zero error and other business requirements of industrial production sites, traditional security protection measures are difficult to work. This paper proposes an industrial Internet identification data security reinforcement method IICC (Industrial Internet Identification Cache Chain) based on blockchain technology: build the industrial Internet identification recursive parsing service on the blockchain, design the recursive parsing node selection protocol based on the idea of load balancing, dynamically and randomly select any blockchain node to provide identification parsing service, and solve the problems of DDoS attack and single point failure; The identification data verification algorithm is designed based on the consensus mechanism, and the validity of the updated mapping record is verified through the positive and negative verification mechanism, which solves the cheating problem caused by the single-source resolution of industrial Internet identification such as buffer poisoning. This paper deploys and builds the above scheme based on Ethereum, and compares it with the baseline system to prove the availability, security and efficiency of the scheme.
Data-Free Backbone Fine-Tuning for Pruned Neural Networks

Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover from the performance drop caused by compression. However, the training data is not always available due to privacy issues or other factors. In this work, we present a data-free fine-tuning approach for pruning the backbone of deep neural networks. In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map. Afterwards, the pruned backbone can be combined with the original network head to make predictions. We generate synthetic images by back-propagating gradients to noise images while relying on L1-pruning for the backbone pruning. In our experiments, we show that our approach is task-independent due to pruning only the backbone. By evaluating our approach on 2D human pose estimation, object detection, and image classification, we demonstrate promising performance compared to the unpruned model. Our code is available at https://github.com/holzbock/dfbf.
Improved Auto-Encoding Using Deterministic Projected Belief Networks and Compound Activation Functions

In this paper, we exploit the unique properties of a deterministic projected belief network (D-PBN) to take full advantage of trainable compound activation functions (TCAs). A D-PBN is a type of auto-encoder that operates by “backing up” through a feed-forward neural network. TCAs are activation functions with complex monotonic-increasing shapes that change the distribution of the data so that the linear transformation that follows is more effective. Because a D-PBN operates by “backing up”, the TCAs are inverted in the reconstruction process, restoring the original distribution of the data, thus taking advantage of a given TCA in both analysis and reconstruction. In this paper, we show that a D-PBN auto-encoder with TCAs can significantly out-perform standard auto-encoders including variational auto-encoders.
RAFL: A Robust and Adaptive Federated Meta-Learning Framework Against Adversaries

With the emergence of data silos and increasing privacy awareness, traditional centralized machine learning provides limited support. Federated learning (FL), as a promising alternative machine learning approach, is capable of leveraging distributed personalized datasets from multiple clients to train a shared global model in a privacy-preserving manner. However, FL systems are vulnerable to attacker-controlled adversarial clients that potentially conduct adversarial attacks by uploading unreliable model updates or clients unintentionally uploading low-quality models leading to degraded FL performance and reduced resilience to attacks. In this paper, we propose RAFL: a new robust-by-design federated meta learning framework capable of mitigating adversarial model updates on non-IID data. RAFL leverages 1) a residual rule-based detection method and a Variational AutoEncoder (VAE) learning based detection method combined to distinguish adversarial clients from benign clients. 2) a similarity-based model aggregation method to reduce the likelihood of uploading adversarial models from adversarial clients. 3) multiple learning loops to collaboratively train multiple personalized detection models against adversaries effectively. Experimental results demonstrate that our proposed FL framework is robust by design and outperforms other defensive methods against adversaries in terms of model accuracy and efficiency.
An Evolutionary Algorithm for Adversarial SQL Injection Attack Generation

Web application security poses ongoing challenges for organizations, and researchers have increasingly turned to machine and deep learning techniques to address vulnerabilities such as SQL injection and Cross Site Scripting. While these studies have made notable progress, the vulnerability to adversarial attacks remains a significant challenge. In this work, we aim to explore the impact of adversarial examples on machine and deep learning applications for detecting web vulnerabilities. We propose an evolutionary algorithm to create effective adversarial samples for bypassing various detection systems by iterative exploration of the search space. The effectiveness of our proposed approach is validated through rigorous testing on different diverse SQL injection detection systems.
Research on an Artificial Intelligence Based Diagnosis Algorithm of BDS Telemetry Anomaly

The traditional satellite telemetry data anomaly detection method based on threshold judgement and expert knowledge base has the characteristics of a wide diagnostic threshold range and large constraint by experience, and the accuracy of the detection results cannot fully meet the application requirements of high reliability of navigation satellites. Therefore, it is urgent to develop an automatic system for satellite anomaly detection. In this work, the Auto-Encoder algorithm (AE) and Prophet algorithm are applied to BDS telemetry anomaly detection and prediction for the first time, and typical satellite anomaly telemetry mode is selected for analysis and verification. The result shows that the satellite's abnormal fluctuation trend predicted by the Prophet algorithm is consistent with the actual data abnormal distribution. This algorithm can support accurate satellite anomaly detection, it can correctly predict the changing trend of satellite telemetry data and ensure the effectiveness of anomaly detection. It will be of great significance for the timely and effective diagnosis of satellite system telemetry data anomalies in the future.
KNN and the CNN for Handwritten Digit Recognition: A comparative study

The automatic identification of handwritten digits by computers or other devices is referred to as "handwritten digit recognition technology," and it has a wider range of potential applications in the processing of bank bills, financial statements, and postage stamps. This study examines the methods KNN, and CNN, and their use in handwritten digit recognition using samples from the MNIST handwritten digit database. In order to achieve the best results for each method, this work rewrites KNN with Python and CNN with Tensorflow throughout the training phase. The benefits and drawbacks of the two AI based techniques employed for handwriting recognition the results has been in terms of recognition rate.
Enhancing Breast Cancer Diagnosis with Vision Transformer-based Ultrasound Image Classification

Around the world, millions of individuals are impacted by the awful disease known as breast cancer. Early detection is essential for more successful therapy. Deep learning techniques provide enormous potential for the detection and categorization of breast cancer in medical images, according to a recent study. This study examined the accuracy with which a Vision Transformer model could distinguish between different types of breast cancer. The model showed a 95% classification accuracy after being trained on a collection of ultrasound pictures. According to the study, radiologists may be able to make more accurate diagnoses since the Vision Transformer model can classify breast cancer more precisely than the CNN model.
RT-YOSO: Revisiting YOSO for Real-time Panoptic Segmentation

Panoptic segmentation, a crucial computer vision task for scene understanding, simultaneously combines semantic segmentation and instance segmentation to classify pixels and identify object instances in images. Despite significant progress in this field, real-time performance with deep learning methods has yet to reach its full potential. In this paper, we present a novel adaptation of the "You Only Segment Once" (YOSO) architecture, designed for real-time panoptic segmentation. Our primary contribution involves substituting YOSO’s original ResNet50 backbone with the time-efficient STDC networks. Our "Real-time-YOSO" (RT-YOSO) model significantly improves real-time performance, demonstrating its compatibility with robotic systems and autonomous driving scenarios. Thorough experiments on the Cityscapes dataset confirm the robustness and accuracy of our model, achieving 59.2% and 51.2% PQ with corresponding frame rates of 18.5 and 27.8 FPS, respectively. This establishes the best accuracy-speed trade-off by maintaining comparable accuracy while delivering an average of 50% higher FPS. The source code is publicly available at https://github.com/AbdallahReda/RT-YOSO