Reviewerly/exHarmony huggingface dataset:
Explainable health prediction from facial features with transfer learning

In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.
Deep Convolutional Neural Networks-Based Sign Language Recognition System

Deaf individuals rely heavily on sign languages. They make use of them to communicate with others. Although deaf individuals are familiar with sign language, but it is not widely understood by the general public. In this article, sign language recognition through convolutional neural network (CNN) system is developed for those who aren't familiar with sign language. American sign language letters are utilized in this work. We tried to create a translator for these letters for people who do not know sign language, and we used GoogleNet, a CNN, using the transfer learning method. Our dataset was used to train the network. The network model and network weights are recorded for the test data set once network training is finished. The accuracy of this sign language recognition system is 91.02%.
Universal approximation theorem for a radial basis function fuzzy neural network

Radial basis function fuzzy neural network has one hidden layer. Inputs are used to find the mean and variance of the Gaussian activation function. In this article, the hidden layer weights which are taken as fuzzy numbers and the activation function values are used to determine the output function. The method of finding the output of this network is illustrated by an example. The output function of radial basis function fuzzy neural network act as a function approximator. Further, Universal approximation theorem for radial basis function fuzzy neural network is dealt with. This is proved using Stone Weierstrass Theorem.
Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Global Optimization

The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.
Federated Learning-based Power Control and Computing for Mobile Edge Computing System

This work considers a federated learning (FL) framework for enabling the energy consumption optimization in a mobile edge computing (MEC) system by optimizing the local computing and the transmit power jointly. Firstly, we prove that in the maximum power transmission case the optimal solution is one of the two modes: the local execution mode and the full offloading mode. Consequently, the corresponding analytical energy consumption expressions are provided. Secondly, in the general case, the objective function has the non-convex rate expression and it is difficult to obtain the optimal solution. However, it can be transformed by the alternative optimization method to simplify the original problem. Finally, we propose a FL-based power control and local computing scheme by analyzing the characteristics of the energy expression to reduce the energy consumption. Numerical results show that the proposed scheme outperforms the benchmark schemes with more than 35% performance gain.
A Novel Malware Traffic Classification Method using Semi-Supervised Learning

Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.
Towards an Automated Dissemination Process of Cyber Threat Intelligence Data using STIX

Cyber Threat Intelligence (CTI) is an actionable, evidence-based knowledge management system of cyber threats data. CTI consists of 3 (three) major parts, namely information gathering, analysis, and dissemination. To carry out dissemination in CTI, Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) have been developed as open and interoperable standards for the CTI dissemination. An effective CTI is a CTI that can be integrated into the organization&#x0027;s security operations processes. Many organizations today runs CTI with a mechanism for reporting and distributing information in the dissemination process or known as cyber threat information sharing which has significant effects on security operations performance. In this study, user requirements analysis and design of cyber threat information sharing processes were carried out. From the results of the requirement analysis, a suitable business process re-engineering were proposed to accelerate the automation of CTI, especially in the information sharing part. Lastly, the designed processes were tested by simulating the processes using STIX and TAXII-based platform. The results of this study indicate that the designed processes can be run on STIX and TAXII-based platforms, ergo a step toward to a fully automated CTI.
Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures

Finite Gamma mixture models have proved to be flexible and can take prior information into account to improve generalization capability, which make them interesting for several machine learning and data mining applications. In this study, an efficient Gamma mixture model-based approach for proportional vector clustering is proposed. In particular, a sophisticated entropy-based variational algorithm is developed to learn the model and optimize its complexity simultaneously. Moreover, a component-splitting principle is investigated, here, to handle the problem of model selection and to prevent over-fitting, which is an added advantage, as it is done within the variational framework. The performance and merits of the proposed framework are evaluated on multiple, real-challenging applications including dynamic textures clustering, objects categorization and human gesture recognition.
FlexFilt: Towards Flexible Instruction Filtering for Security

As the complexity of software applications increases, there has been a growing demand for intra-process memory isolation. The commercially available intra-process memory isolation mechanisms in modern processors, e.g., Intel’s memory protection keys, trade-off between efficiency and security guarantees. Recently, researchers have tended to leverage the features with low security guarantees for intra-process memory isolation. Subsequently, they have relied on binary scanning and runtime binary rewriting to prevent the execution of unsafe instructions, which improves the security guarantees. Such intra-process memory isolation mechanisms are not the only security solutions that have to prevent the execution of unsafe instructions in untrusted parts of the code. In fact, we identify a similar requirement in a variety of other security solutions. Although binary scanning and runtime binary rewriting approaches can be leveraged to address this requirement, it is challenging to efficiently implement these approaches.
Comparative Study of Machine Learning Based Classification Techniques for Credit Card Fraud Detection

We live in the digital era where paying by credit card has become an essential part of our life. With the increasing demand for credit cards, it has also become a challenge to maintain the integrity of users. Credit card fraud is a malignant activity that causes financial loss that costs consumers as well as banks. Nowadays, fraudsters use distinct methods to commit fraud. To identify credit card fraud, it is important to understand the type of fraud and past transaction history. To solve credit card-related issues there should be a system for fraud detection that can detect fraud activities before they occur and also in an accurate way. In this paper, we implement six widely used machine learning techniques for credit card fraud detection. For each machine learning technique, a confusion matrix is prepared for performance analysis of the algorithm. Their efficacy is analyzed based on the parameters such as accuracy, precision, recall, specificity, misclassification, and F1 score. Results show that machine learning techniques are helpful for credit card fraud detection. Although results show a high level of precision and recall for each algorithm, we strongly recommend using multiple machine learning techniques for fraud detection before they occur or in the process of occurrence.
Bleeding contour detection for craniotomy

Bleeding impairs observation during neurosurgery, and excessive bleeding endangers the life of a patient. Thus, hemostasis is important during neurosurgery. The detection of bleeding areas is a prerequisite for hemostasis. To the best of our knowledge, this paper is the first to present results on the detection of neurosurgical craniotomy bleeding scenarios, i.e., scalp incision bleeding, skull incision bleeding, and dura matter-incision bleeding. This is realized via a workflow that combines craniotomy image data preparation and a Mask R-CNN framework. Bleeding images on a porcine skin tissue with a simulated blood injected by a syringe are taken by a visible light camera, and the video frames of the scalp incision, skull incision, and dura matter-incision bleeding are extracted from neurosurgical videos. The precision of bleeding areas detection for the simulated bleeding scenario and the three craniotomy phase scenarios were 94.40%, 84.44%, 89.48%, and 90.46%. The contours of the neurosurgical craniotomy bleeding regions can be detected along with the bleeding areas. It is beneficial for neurosurgeons to identify the bleeding areas by sending prioritized alerts for bleeding events. Furthermore, it is valuable for a task-level medical robot designed for a neurosurgical procedure, such as craniotomy, or a high-level robot designed for an entire neurosurgery procedure.
VLSI Architecture Design for Adder Convolution Neural Network Accelerator

Convolution Neural Network (ConvNet) achieved good performance in a variety of image processing tasks. How-ever, a large number of multiplication operations in convolution layers affect mobile device deployment of the ConvNet. Recently, an Adder Convolution Network (AdderNet) was proposed to reduce the multiplication operations of common convolutional neural networks. In this paper, we analyzed differences in calculation processes between the AdderNet and the ConvNet and proposed the VLSI architecture of the AdderNet. In addition to analyzing resource consumption of adder convolutional layers, we also built the whole LeNet neural network with the adder convolutional layers and calculated inference latency. Experiment results showed the proposed VLSI architecture of the AdderNet reduced the latency by 29.26%. Compared with the multiplication convolution layer, the resource consumptions of DSP, Flip-Flop, and LUT for the adder convolution layer were reduced respectively by 6.25%, 0.31%, and 0.86%.
A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method.
Real-Time Tracking of Human Neck Postures and Movements

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.
Detection of traffic anomaly in highways by using recurrent neural network

Using deep learning in all fields has experienced spectacular dynamics in recent years. Among these fields are road traffic in cities and highways. The sensors placed on these routes generate a mine of data for analysis and exploration. In this article, we propose a model based on recurrent neural networks and Multi-Agent Systems (MAS) to find anomalies in vehicle flows on highways compared to normal flows. This model is designed around the events and contexts of cities. By event, we mean periodic events such as cultural events, and political events, etc., and by context, we mean tourist towns, mountain towns, etc. The vehicle flows during city events and contexts plus daily vehicle flow results in univariate time series, hence the choice of recurrent neural networks “Long Short-Term Memory” LSTM which is useful for learning periodic data and which has the ability to hold data in long-term and short-term memory. The different datasets, representing the different time series, will be generated by agents who receive the daily flow of vehicles from sensors placed on the highways at the entry and exit of cities. Each agent represents an event and/or a city context to generate a single time series. These datasets, considered initially as normal data, will be used for training and testing our LSTM model. By this model, we want to look for two cases of anomalies: a decrease or an excessive increase in the flow of vehicles on the highways compared to the normal value. To test our model, we used the predictive error called the standard error of prediction.
Data encryption method using CP-ABE with symmetric key algorithm in blockchain network

This paper proposes a method of encrypting data stored in the blockchain network by applying ciphertext-policy attribute-based encryption (CP-ABE) and symmetric key algorithm. This method protects the confidentiality and privacy of data that is not protected in blockchain networks, and stores data in a more efficient way than before. The proposed model has the same characteristics of CP-ABE and has a faster processing speed than when only CP-ABE is used.
Optimal CNN–Hopfield Network for Pattern Recognition Based on a Genetic Algorithm

Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the FC layers contain most of the parameters of the network, which affects memory occupancy and computational complexity. For many real-world problems, speeding up inference time is an important matter because of the hardware design implications. To deal with this problem, we propose the replacement of the FC layers with a Hopfield neural network (HNN). The proposed architecture combines both a CNN and an HNN: A pretrained CNN model is used for feature extraction, followed by an HNN, which is considered as an associative memory that saves all features created by the CNN. Then, to deal with the limitation of the storage capacity of the HNN, the proposed work uses multiple HNNs. To optimize this step, the knapsack problem formulation is proposed, and a genetic algorithm (GA) is used solve it. According to the results obtained on the Noisy MNIST Dataset, our work outperformed the state-of-the-art algorithms.
Safety and Security Beyond Industry 4.0

As open standards like OPC UA were introduced to facilitate the interoperability of devices, safety and security standards were only gradually incorporated into Industry 4.0 designs. Hence, safety and security issues have eventually emerged with the growing complexity of such systems, and they need to expand outside of their premises to be integrated into larger set-ups. Typical examples thereof are power grids, whose dependability was guarded for long times by strict rules and restrictive communication protocols. Once they were exposed on the Internet, however, their vulnerability has increased to a point where their safety could be compromised. Hence, there is an urgent need to implement effective safety and security mechanisms into OPC UA to provide systems with appropriate levels of functional safety as well as data security. With increasing levels of integration, e.g. with Industry 5.0, these standards need to evolve to provide for sustainability in a globally interconnected world.
Digital Image Forgery Detection Using Local Binary Pattern (LBP) and Harlick Transform with classification

Images are a natural carrier of information. Images are used in an immense range of applications nowadays, including military purposes, surveillance systems, insurance processing, the internet, television, advertising media, forensic investigation, and so on. However, because powerful, low-cost image editing tools are readily available, these images can be easily tampered with. Therefore, the authenticity of images has become questionable. In this age of advanced PC innovation, digital picture and video have high significance in our everyday life. For editing or modifying the original multimedia contents, a range of low-cost multimedia content handling tools, techniques, and applications with various advanced features are available on the Internet, such as Adobe Photoshop. To handle this issue, numerous investigations have been centered around how to identify, this kind of controlled media. Existing computerized fraud identification techniques are grouped into two significant classes: active and passive. This paper proposes a technique of copy-move forgery detection in which feature extraction is done by Local Binary Patterns (LBP) and Harlick features. For the verification of the authenticity of the image various supervised machine learning classifiers like Support Vector Machine (SVM), Random Forest (RF) & Gradient Boost classifiers are used and the performance of forgery detection is based on these classifiers are analyzed.
Latent Dirichlet Allocation for Medical Records Topic Modeling: Systematic Literature Review

The fast growth of Electronic Medical Records (EMR) has improved its functionalities and increase its use in secondary functions. EMRs can be used to improve the quality and capacity of physicians and medical students. It is done by using EMRs as a data source for researches and learning. A lot of studies have been done in this area. Various methods are also proposed. One of the methods used in medical records retrieval is Latent Dirichlet Allocation (LDA). Thus, research on LDA for medical records has also been carried out quite a lot. Unfortunately, those researches are still scattered and make it difficult to know the state of the arts of utilizing LDA in the medical records field. Our research intends to explore the studies of the LDA method progress in the medical field. To better present responsible literature study, our research used Systematic Literature Review as a research methodology. The literature review was conducted on studies from 2015 to 2021. The result of the literature review showed that the LDA method had been used in several research topics. And most of the literature used private datasets in their experiments. Some researchers also added a modification in the LDA method. Even though the LDA method proved a great method in medical fields, it has several limitations that need to be overcome.