Reviewerly/exHarmony huggingface dataset:
Marker-Embedded Tactile Image Generation via Generative Adversarial Networks

Data-driven methods have been successfully applied to images from vision-based tactile sensors to fulfill various manipulation tasks. Nevertheless, these methods remain inefficient because of the lack of methods for simulating the sensors. Relevant research on simulating vision-based tactile sensors generally focus on generating images without markers, owing to the challenges in accurately generating marker motions caused by elastomer deformation. This disallows access to tactile information deducible from markers. In this letter, we propose a generative adversarial network (GAN)-based method to generate realistic marker-embedded tactile images in GelSight-like vision-based tactile sensors. We trained the proposed GAN model with an aligned real tactile and simulated depth image dataset obtained from deforming the sensor against various objects. This allows the model to translate simulated depth image sequences into RGB tactile images with markers. Furthermore, the generator in the proposed GAN allows the network to integrate the history of deformations from the depth image sequences to generate realistic marker motions during the normal and lateral sensor deformations. We evaluated and compared the positional accuracy of the markers and image similarity metrics of the images generated via our method with those from prior methods. The generated tactile images from the proposed model show a 28.3% decrease in marker positional error and a 93.5% decrease in the image similarity metric (MSE) compared to those generated by previous methods, validating the effectiveness of our approach.
Elderly Fall Detection using Deep Learning Techniques

The elderly should be especially cautious about falls because old age people frequently cause fatalities and serious injuries. As a result, there has been an increase in interest in creating fall detection systems that can instantly identify falls and notify caregivers or emergency services. Deep learning approaches have demonstrated considerable promise in solving this issue. For the elderly to remain safe and healthy, fall detection is a crucial responsibility. Because deep learning approaches can automatically identify specific elements from data, these methodologies have recently demonstrated promising results in the detection of falls. In this research, a deep learning-based method for fall detection makes use of an biLSTM network and Convolutional Neural Network (CNN). The suggested method employs a CNN to identify features in the raw sensor data from a wearable device, such as an accelerometer. Then, based on the order of inputs, predictions are made using the biLSTM network to decide whether the incident has actually occurred.
Invading Deep Neural Information Management Systems by Adaptive Multi-Gradient Based Method

The applications of deep learning have been widely employed in many specific domains (e.g., computer vision and language processing). The research on the security and privacy of deep neural networks (DNNs) has drawn much attention related to adversarial attacks and defense on DNNs. To provide a simple yet effective adversarial learning resource for building adversarial security, we formulate imperceptible adversarial perturbations in an adaptive multi-gradient learning manner, which calls a few adaptive operators on gradient-based terms. First, the proposed work searches for the input gradient based on the source representation in deep neural models. Then, fusing three adaptive gradient-based mechanisms to form the perturbed terms, which improves the capability of the generated samples to search the decision boundaries between original and adversarial domains directly. We compare the proposed work to other gradient-based adversarial attack methods in fooling convolutional neural networks in the field of computer vision. Experimental results reveal that adversarial examples generated by the introduced approach show surpassing effectiveness in fooling deep neural networks. It manifests fewer pixel changes in generated adversarial examples both on and norm constraints with specific attack strategies, respectively.
Comparative Analysis of Different Machine Learning Algorithms on Different Datasets

Machine Learning is used to train models and machines without the help of any human interventions and guides. Here the models and machines are trained using algorithms. As it is difficult to train the algorithms one by one and get the analysis of the best algorithm. This research study intends to compare different algorithms used in different datasets to know the best algorithm according to different domains and the overall best algorithm. In this study, most popular supervised algorithms like SVM, Decision Tree, Random Forest, KNN, Logistic Regression, XGBoost, Adaboost, Voting Classifier (Decision Tree + Random Forest), Bagging Classifier (KNN + SVM) and Naïve Bayes. This research study has chosen three different domains for datasets. Different datasets were used to check the efficiency of algorithms. Comparative analysis of the classifiers shows that all algorithms outperform the existing methods with a high accuracy.
A Lightweight Neuromorphic CNN for Human Activity Recognition on Edge Device

Human activity recognition (HAR) is a rapidly growing field with numerous practical applications, such as healthcare, assistive living, and security. The majority of HAR applications are ideally suited for edge deployment. However, edge devices have limited memory and energy, making it challenging to deploy HAR tasks on them. To address this issue, a biological brain optimized neuromorphic Convolution Neural Network (biCNN-HAR) for HAR is developed. The performance of biCNN-HAR is evaluated on mHealth dataset, and its results are compared with baseline CNN models and state-of-the-art models. The findings demonstrate that biCNNHAR outperforms other models while utilizing fewer computational resources.
AI Camera for Tracking Road Accidents

The average number of vehicles on the road worldwide has increased as cars and other vehicles become more and more accessible. Our lives are now easier because of the technology and infrastructure that are developing quickly. The advent of technology has also enhanced the risks associated with traffic and Regular traffic accidents result in a number of accidents on this dynamic planet and significant loss of life and property due to inadequate emergency facilities. Accident victims suffer greatly and lose significant time and money as a result. It has been established after careful examination that the majority of accidents and in fatalities as a result of inadequate communication with the appropriate medical authorities and the following lack of urgent medical care. There are many deaths as a result of inadequate crisis management. As a result, this research study intends to provide crisis administrations to the person who meets with an accident as soon as time permits
SGDAT: An optimization method for binary neural networks

Stochastic gradient descent (SGD), one of the most popular neural network optimization algorithms, has a solid theoretical foundation as well as good generalization performance. However vanilla SGD performs catastrophically in Binary Neural Networks (BNNs). Many studies have identified this phenomenon without explaining its causes in depth. In this paper, we try to experimentally understand the possible reasons for this and significantly improve the performance of vanilla SGD in BNNs training by adjusting the training strategy to be comparable to Adam. We subsequently propose a new optimization method for training deep neural networks (DNNs) with binary weights. In the proposed SGD with Adaptive Threshold, referred to as SGDAT, we suppress the frequency of weights flipping by thresholds and adjust the threshold of each parameter according to the number of flipping to further reduce the network noise, stabilize the network training, and improve the network generalization ability. Also, we present a complete ablation study of the hyperparameters space, as well as experimentally analyze the impact of using adaptive thresholds. Furthermore, we conduct image classification experiments over the CIFAR10, CIFAR100 and TinyImageNet datasets using BinaryNet and ResNet-18 network structure. The experiments show that SGDAT outperforms other binary optimizers. Code is available at: https://github.com/gushan/SGDAT.
Online Prediction of Positive and Negative Emotionology Applying Machine Learning Technique

Owing to the meteoric rise of the Web, social media platforms have evolved into an essential part of the manner in which people express their thoughts and emotions through writing. Written words, visuals, sound, and moving pictures are just some of the many ways in which people share their ideas and opinions with the world. In the blink of an eye, a myriad of unstructured social media structures unexpectedly generat vast amounts of unstructured online data that can be used to better comprehend human psychology. Sentiment analysis, which determines the tone of a piece of writing, can be used for this purpose. Analyze the reviews that the individual has read and decide whether the author’s point of view is positive, negative, or neutral. Some critics argue that emotion recognition is necessary to select a person’s emotional/mental kingdom because emotion analysis is inadequate,- and that judgment of a person’s emotional/mental kingdom at the level of emotion evaluation, numerous emotional models, emotion studies, and feeling intensity is unreliable. Sentences that recognize your mannerisms. Evaluations of emotions and perspectives are difficult because of the difficulties encountered.
Mission Optimized Weather Nowcasting for Flight Planning

Weather nowcasts are highly efficient, short term forecasts that are widely used for tactical flight planning due to their ability to rapidly ingest and process recent weather data. Nowcasts are particularly important for uncrewed and remotely piloted aircraft that are required to navigate through hazardous weather conditions in a manner that maximizes the chances of mission success while minimizing risks of damage, fuel loss, or other mission failures. Recently, deep learning techniques have been widely applied to create highly efficient weather nowcasts that could help assist pilots and mission planners, however many of these neural network models are trained using loss functions based on metrics of image quality that do not necessarily align with operational mission needs. In this work we present a new paradigm for adapting DL-based weather nowcasts in a way that fine-tunes the weights of a deep neural network by optimizing directly on mission success criteria, characterized as a planned route through potentially hazardous weather conditions. We show this methodology improves accuracy over traditional nowcasting techniques when the forecast is evaluated using a mission success criterion.
Private Cloud Hybrid Architecture for Protected Data Communication

The IT sector is fast embracing cloud computing, a nextgeneration technology. Even though this technology offers many benefits, there are also some downsides. The most challenging aspect of this technology is security. We described a three-tier architecture for private cloud-based encrypted communication where developed a brand-new custom approach and combined steganography to provide an additional layer of protection to data transmission across the cloud. The suggested technique effectively serves as a road map for building a Cloud security architecture. Additionally, the key may be distributed to cloud users using efficient key management strategies, ensuring that only those who are authorized have access to the data.
Sliced Secure Boot an Accelerated Secure Boot Concept Compatible with Automotive Safety Controllers

&lt;div&gt;The secure boot has successfully protected systems from executing untrusted software (SW), but low-power controllers lack sufficient time to check every memory cell while satisfying real-time functional safety requirements. Automotive controllers need to maintain security through multiple cycles of remote, unsupervised operation and safely reach a secure state when an anomaly is detected. To accelerate the boot time, we propose Sliced Secure Boot: build fingerprints by slicing orthogonally through memory blocks, protect each cell with a reusable fingerprint using a reproducible pattern with sufficient entropy, and randomly check one fingerprint pattern during boot. We do not claim that sampling offers equivalent protection to exhaustive checks but demonstrate that careful sampling can provide a sufficient level of detection while maintaining compatibility with both startup time and functional safety requirements.&lt;/div&gt;
Sub-Entity Embedding for inductive spatio-temporal knowledge graph completion

Existing large-scale knowledge graphs generally contain rich spatial and temporal information. Knowledge graph completion has gained wide attention with a massive number of models proposed for inferring missing knowledge. Despite the fact that numerous approaches to knowledge graph completion exist, they often overlook the importance of simultaneously modeling spatial and temporal information, resulting in limited capacity to infer knowledge related to time and location. An intuitive solution to this issue is to use quintuple representation for spatio-temporal facts. To reduce the complexity of learning quintuples, in this paper, we propose sub-entity to tokenize every entity, relation, time stamp and location with a fixed-size vocabulary. Meanwhile, we design Spatio-Temporal Message Passing layer to learn the latent feature vectors of a knowledge graph. We conducted entity link prediction, relation link prediction, time prediction and location prediction experiments. The quantitative results demonstrate the effectiveness of our model in both predicting missing knowledge under both transductive link prediction and inductive link prediction. The visualization results also show that our model can capture meaningful time and location information.
Perbandingan Algoritma Machine Learning Dalam Mendeteksi Serangan DDOS

Ddos is an attack method by sending a lot of packets into a network that causes the device not to run according to its function. This attack will result in machine or network resources cannot be accessed or used by the user. Various methods are used to detect DDOS attacks on SDN [4] , namely statistical methods, machine learning, SDN architecture, blockchain, Network Function Virtualization, honeynets, network slicing, and moving target defense. Because so many people use machine learning to detect DDoS attacks, it is necessary to do further research to find out which one is the best and has high accuracy. Therefore, a research entitled “Comparison of Machine Learning Algorithms in Detecting DDoS Attacks was made. In this study, three machine learning algorithms will be compared, namely XGBoost, Decision Tree and ANN. The methods used are data acquisition, data understanding, data preparation, modeling, performance evaluation, and conclusions. In this study it can be said that for accuracy, the highest model is XGBoost in determining attacks, but to execute it requires the longest time among other models tested. While Decision tree also has high accuracy, slightly below XGBoost, but the time required to execute is fast or short. Therefore, in this study it can be said that the Decision Tree is the best model in detecting and classifying DDoS attacks.Keywords: Ddos Attack, Machine Learning, Decision Tree, XGBoost, ANN.
XRAE: Extended Reality Acuity Examination

Research and real world use cases have shown that extended reality (XR) can provide immense benefits to a wide range of applications both in the commercial, private, and government sectors. XR has been shown to increase accuracy, decrease errors, and improve training in a variety of applications. Many of these XR implementations include text based user interfaces (UI) to instruct or present data to a user. However, text readability is a major limitation in these XR environments due to a variety of factors including device resolution, vergence accommodation conflict (VAC), and user visual acuity. This paper presents the Extended Reality Acuity Examination (XRAE) application as a comprehensive solution to generate text optimized for an XR environment, its user, and the associated XR hardware. A two-step approach was developed and studied. Step one was the creation of a virtual Snellen Chart with options to customize different text characteristics. The Snellen Chart has been widely used by optometrists as a quick and effective means to judge a user’s visual acuity. A user simply selects the text they can read clearly and comfortably from several options presented. Step two was the real-time generation of a user-specific text (i.e., size, font, and other characteristics) and distribution to all UI elements. The net result of XRAE is text-based UI elements customized to a user and associated XR environment that can be easily implemented in a wide range of use cases.
CHICKEN BANDA PERFORMANCE IMPROVEMENT UTILIZING NEURO-FUZZY LOGIC TECHNIQUE

This study is on improvement of performance of the chicken Banda, using indoor change in environmental conditions for temperature control. The differential change in climatic conditions is technically used to put on the fan and the Banda so as to realize the right comfortable indoor conditions.&#x0D; The chicken chicks’ Banda Mathematical model is created, prototype designed, temperature controller to depict a two systems simulation of neuro fuzzy logic and fuzzy logic .The performance is analyzed by the use of Matlab Simulink latest edition. To monitor the temperature of the Chicken cage the neural fuzzy logic technique is utilized. As far as the prototype is concerned the chicks’ cage set temperature is fixed at 26.50C.&#x0D; The study will show that the reference input can be kept on track by the process controller hence proving the principle that the neural fuzzy control is much superior in optimizing performance compared to the fuzzy only controllers. The Back propagation (BP) and least square estimator (LSE) are the hybrid optimization methods which are used. For data training the gradient descent method (GDM) is used.&#x0D; The research reveal that there is drastic performance improvement in the behavior response where result show that there settling time is reduced from 0.75 to 0.48 seconds while the percentage overshoot is also reduced down from 29.9% to 0.9345%.
Cloud Steganography: An Intelligent Approach to Improve Data Security in the Cloud Environment

With the advent of big data, cloud computing has emerged as the most on-demand availability of computer system resources, a concept that changed the ways of computing. However, data security remains a problem in making cloud computing vulnerable. Multimedia objects like audio, video, and image are widely used to obscure secret information via steganography. A steganographic system for implanting confidential data in sound utilizing LSB-based calculation which can be used to protect data in the cloud is introduced in this paper. The use of the least significant bit (LSB) of a byte is done to replace a message, further replacing each bit of LSB with a binary message allows us to encrypt an extensive quantity of data. The current research work first carries out a detailed review of various popular steganography techniques, as discussed in the literature. The audio steganography using an improved LSB methodology to safeguard data on the cloud is introduced in the paper. The numerical example demonstrating the encoding and decoding procedure indicates that the improved LSB technique is easy to implement yet difficult to break for intruders.
Automated Breast Cancer Detection using Convolutional Neural Network based Computer Added Design System

As per WHO health dataset, worldwide the second most common type of cancer in women is breast cancer. It is increasing at an alarming rate. Study in 2012 concluded that about 12 percent cancer cases are breast cancer among all cancers and in women it is 25 % among all the cancers in them. Cause of Breast cancer lies in cells, when cells grow abnormally or overlap each other. A lump-like pattern is formed by the cells which are also known as tumors. These tumor can be clearly visible in x-ray. A tumor is called malignant i.e. cancerous when the cells grow into nearby tissues. They spread and become metastasized to different areas of the body. The main focus of this research is to develop a Convolution Neural Network that classifies input biopsy photos into two categories, Benign (non-cancer tumor) or malignant(cancer). We will need to be accurate as people's lives depends on it. We are trying to train a Convolution Neural Network(CNN) based model for automatic identification of breast cancer in women patients through biopsy images.
Blockchain and its dimensions: Future Security

Blockchain is an immutable ledger that records transactions and tracks assets in a business network. The asset can be tangible (a house, cash,etc.) or intangible (intellectual property, patents, branding). Blocks of information are connected in a "chain" to create blockchains. Depending on the type of blockchain, blocks can store different types of data, such as the sender and receiver when exchanging Bitcoin. Blockchain can be used to create trust and transparency for your data. It increases the security, efficiency and traceability of data shared across a business network. Blockchain is a decentralized system of data sharing that cannot be altered. These records are immutable and confirmed to be correct by an independent authority. Blockchain, an enterprising idea, is now being applied in the real estate industry. Governments and healthcare industries are now leveraging this technology to make their contracts smarter.
Adversarial Attack to Deceive One Stage Object Detection Algorithms

In this paper, we are focusing to fool one stage object detection algorithms and propose a black box method to generate adversarial example such that it is imperceptible to human eyes and can fool one stage object detectors. We are generating random perturbation and scaling it on the basis of unsuccessful attack and maximum number of iterations. The generated perturbation is added to original image to generate perturbed image. After that the output of one-stage object detector is compared. We have defined three success scenarios, hiding objects, misclassification and count of objects, if attack achieved one of these scenarios, it will be considered as successful attack. The proposed work is evaluated on the basis of perceptibility, average number of iterations and convergence rate. The results show that we have achieved 98.05% convergence rate on 4.7 average number of iteration with PASS score of 1.94*10-2 on RetinaNet, 98.73% convergence rate on 4.68 average number of iteration with PASS score of 1.58*10-2 on Single Shot multi-box Detection (SSD) and 77.11% convergence rate on 6.08 average number of iteration with PASS score of 2.04*10-2 on You Look Only Once version 3 (YOLO V3) which shows the effectiveness of proposed attack.