Reviewerly/exHarmony huggingface dataset:
A Feature Dimensionality Reduction Method with Lossless Accuracy in Vehicular Network Traffic Detection

In the Internet of vehicles, in-vehicle information systems are protected from malicious attacks by carrying network traffic detection. Existing grayscale-based traffic detection models are not suitable for in-vehicle gateways due to high time and space complexity. Thus, based on feature heat map statistics, this paper proposes a feature dimensionality reduction method with lossless accuracy (LAFDR) for in-vehicle network traffic. The LAFDR solves the problem that the high computational complexity of malicious traffic detection algorithm and the real-time requirements of in-vehicle gateways. The LAFDR uses the Grad-cam algorithm to obtain the feature grayscale figure corresponding to the heat map of network traffic, whose feature weights are counted by the mapping relationship between feature vectors and figure pixel locations. Under the accuracy constraint of the malicious traffic detection model, the method retains the features with high feature weights and eliminates the features with small feature weights. Eventually, on the basis of ensuring the detection accuracy, the LAFDR reduces the computational overhead of the traffic detection model by reducing the input volume of the model. The experiments conducted on the CICDIS2017 dataset, the effectiveness of the LAFDR is illustrated by comparative experiments on the CNN model. The results show that, under the constraint that the detection accuracy of the deep neural network model is no less than 99.0%, the LAFDR achieves the 49.38% reduction in feature dimensionality and the 40% reduction in time overhead.
ETANet: An Efficient Triple-Attention Network for Salient Object Detection

Salient object detection (SOD) is a critical vision task in ubiquitous applications. Most existing methods have complicated structure and large number of parameters, which prevents these methods to deploy on practical applications. In order to solve this problem, we propose an efficient triple attention network (ETANet), which consists of multiple attention mechanisms. In detail, we design a crossed spatial-channel attention mechanism to extract useful low-level features, an efficient branch to perceive high-level features based on self-attention through multi-scale receptive field. In addition, we propose a dilated criss-cross fusion mechanism to fuse low-level and high-level features in an efficient way. The experiment results show that our architecture achieved competitive performance and can trade off between the accuracy and efficiency compared to other heavy-weight methods.
Recommendation System Algorithms For Music Therapy

A recommendation system is an artificial intelligence or machine learning system that uses big data to suggest or recommend products to consumers. In this paper analysis of different types of recommendation system such as collaborative filtering & content filtering has been done through the help of implementations of different types of algorithms. Through those implementations an optimal algorithm which best suits our purpose of making a music recommendation system will be selected. Initially we will be looking at the different approaches implemented in the research papers and study them. In the paper implementations have been done for three algorithms i.e., Popularity based recommendation system, Item based recommendation system and Content filtering-based recommendation system. After the implementation of these approaches, it was found that item-based recommendation is the best model for the project and meets all our requirements. This paper also discusses most of the known approaches and gives a deep insight to their working & methodology.
Layer-wise Knowledge Distillation for Cross-Device Federated Learning

Federated Learning (FL) has been proposed as a decentralized machine learning system where multiple clients jointly train the model without sharing private data. In FL, the statistical heterogeneity among devices has become a crucial challenge, which can cause degradation in generalization performance. Previous FL approaches have proven that leveraging the proximal regularization at the local training process can alleviate the divergence of parameter aggregation from biased local models. In this work, to address the heterogeneity issues in conventional FL, we propose a layer-wise knowledge distillation method in federated learning, namely, FedLKD, which regularizes the local training step via the knowledge distillation scheme between global and local models utilizing the small proxy dataset. Hence, FedLKD deploys the layer-wise knowledge distillation of the multiple devices and the global server as the clients’ regularized loss function. A layer-wise knowledge distillation mechanism is introduced to update the local model to exploit the common representation from different layers. Through extensive experiments, we demonstrate that FedLKD outperforms the vanilla FedAvg and FedProx on three federated datasets.
Can I Squeeze Through? Effects of Self-Avatars and Calibration in a Person-Plus-Virtual-Object System on Perceived Lateral Passability in VR

With the popularity of Virtual Reality (VR) on the rise, creators from a variety of fields are building increasingly complex experiences that allow users to express themselves more naturally. Self-avatars and object interaction in virtual worlds are at the heart of these experiences. However, these give rise to several perception based challenges that have been the focus of research in recent years. One area that garners most interest is understanding the effects of self-avatars and object interaction on action capabilities or affordances in VR. Affordances have been shown to be influenced by the anthropometric and anthropomorphic properties of the self-avatar embodied. However, self-avatars cannot fully represent real world interaction and fail to provide information about the dynamic properties of surfaces in the environment. For example, pressing against a board to feel its rigidity. This lack of accurate dynamic information can be further amplified when interacting with virtual handheld objects as the weight and inertial feedback associated with them is often mismatched. To investigate this phenomenon, we looked at how the absence of dynamic surface properties affect lateral passability judgments when carrying virtual handheld objects in the presence or absence of gender matched body-scaled self-avatars. Results suggest that participants can calibrate to the missing dynamic information in the presence of self-avatars to make lateral passability judgments, but rely on their internal body schema of a compressed physical body depth in the absence of self-avatars.
Robust Federated Learning with Local Mixed Co-teaching

Federated Learning paradigm ensures basic data privacy of local clients through an iterative aggregation of model parameters. The success of a global model in federated learning depends on local models that are trained on self-labeled client data. However, all participating clients have their own personal bias and different expertise level that leads to label noise. Hence, a federated learning model should be robust to noise and deliver consistent output. To deal with this issue, we here propose a robust federated learning approach that focuses on a local model training phase of clients. We simultaneously train two deep networks using normal and augmented inputs and mix up their predicted classes to minimize entropy before using a noise tolerant loss function. Further, we add a simple knowledge distillation technique to enhance the performance of the network. We test our proposed method with CIFAR-10 and Fashion-MNIST datasets in both IID and non-IID data distribution settings to showcase its robustness to noise.
A Novel Dimensionality Reduction-based Software Bug Prediction using a Bat-Inspired Algorithm

The major intention of software bug identification is to predict the defects in the software modules for increasing the performance of testing. It helps in analyzing the bugs before starting the real testing through a few fundamental project resources. It is highly preferable for identifying the bugs in the early stages owing to the expensive cost of achieving flawless codes in the testing phase. Therefore, various approaches and techniques are designed in recent years for predicting the defects in software. Thus, machine learning is vastly utilized in bug prediction models. It is broadly applied for giving precise analysis and results. In the primary stage, the software modules are collected from the standard public benchmark datasets. Then, these data are pre-processed and given to dimensionality reduction for increasing the performance of software bug prediction. Here, the Improved Fisher's Linear Discriminant (IFLD) technique is utilized and this parameter optimization is done through the Bat-Inspired Algorithm (BA). The software bug prediction is done by using the Enhanced Deep Neural Network (EDNN). Here, the improvement in DNN is tuned by the same BA technique. The analysis is carried out for investigating the comparative evaluation of various statistical approaches like Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA), Kernel Support-Vector Machine (K-SVM), etc.
Ensembled Multi-detector Aggregation For Disaster detection (EMAD)

The most popular method for analysing a text is sentiment analysis. It is quite helpful for social media monitoring since it enables us to get a broad sense of what the general population thinks about particular issues. However, it is also handy for business analysis and various other situations in which the text needs to be analyzed. In this paper we present a solution up to 90% correctly classify whether a tweet is associated with a disaster. Using a multi-detector ensembled approach, we create a reliable model that can predict accurately in situations powered by multiple CNNs and BERT detectors.
Twitter Bot Detection using Machine Learning Models

In this fast-digitizing world where social media is ruling our lives, Twitter is among the most influencing social networking sites. Almost every day bundles of tweets are floated here and this platform allows users to express themselves. But the main motive of this paper is to analyze that is it only the humans expressing themselves or the bots are involved as well. With the increasing number of users on Twitter, it becomes necessary to know who is genuine and who is not. It is very important to detect the bots that harm the internet by spreading false information and messages. Any false information or spamming may give rise to a serious security breach for the involved parties. This paper proposes the best possible algorithm for classifying an account as a bot or non-bot. The analysis here is done on the basis of the accuracy received from each algorithm that is how efficiently it detects a bot or a non-bot account.
Hands-up-go: Development of gas efficient Blockchain event DApp

Ethereum’s smart contract is advantageous for proof of existence and content because it has transparency, anonymity, and irreversibility, which are the characteristics of blockchains. Ethereum’s DApps are developed using smart contracts. The developed DApp continuously uses a specific smart contract, and such long-term uses lead to duplicate usage, using one contract repeatedly. Smart contracts require a cost called 'gas' to deploy and use. It is necessary to design a smart contract and develop a DApp so that the gas cost is low, as it is crucial both from an environmental and user point of view. In this paper, a gas-efficient event, DApp named HandsUp-Go, is developed. Hands-up-go supports anonymous event holding and participation through the features of blockchain. In addition, due to the transparency of blockchain, fraud that exploits anonymity can be prevented. We design Hands-up-go first. Then, the gas efficiency test of the contract developed using the private network is carried out. Experiments are classified into a method in which one event is included in a single contract (Multiple Deploy) and a method in which multiple events are included in one contract (One-time Deploy). And we analyze using equations and graphs which method is more efficient. Experimental results show that Onetime Deploy is gas-effective when a smart contract contains more than one event. Based on the results, Hands-up-go is developed and tested.
Sign Language Recognition System Using Deep Learning

Deafness and mutism tend to place those individuals in the limelight owing to their inability to hear or communicate with the outside world, attempting to distinguish them from the rest of society. The communication is mostly done in ASL (American Sign Language) or ISL (Indian Sign Language), with various motions signifying a certain letter, number, or phrase. Not all deaf people utilize sign language; some use hearing aids; nevertheless, few can afford a hearing device. All these issues are addressed by sign language. It is more efficient, simple, and adaptable; hence, the research focuses on creating a system for hand gesture recognition that delivers the message of hope for the entire society and its significance. The goal of this paper is to detect the sign gesture using deep learning and to improve existing systems to attain a efficient software with proficient and prominent software.
Analyzing the Performance of a DAPP Using Blockchain 3.0

Blockchain is the most cutting-edge technology nowadays. Most organizations are trying to achieve decentralization using blockchain technology. But storing information in such a system incurs some charges due to the creation of new blocks which results in gas fees, and requires some processing which takes some time. Each transaction that executes in the blockchain uses some gas that the user must pay. Frequent updation/creation of data results in the creation of a new block resulting in gas fees, to reduce this transaction cost and the time to process the data, the user can cache multiple data in memory and push all the data in the blockchain in a single transaction which results in less gas usage as compared to pushing each data individually. The author has created a cachingmechanism to store multiple details in the memory and the user can push these stored data into the blockchain in a single click. In this implementation, the author has used a local blockchain provided by ganache which works on a local host. A dialogue box is shown to the user after a successful transaction showing the transaction and time taken to push the data to the blockchain. The gas fees and the current block number can be viewed in the ganache console.
Group Counselling Optimization-derived Software Bug Prediction Framework by Optimal Feature Selection-based Dimensionality Reduction

Recently, Software Bug Prediction (SBP) has become a serious issue in maintaining as well as developing the software that concerns the overall success of the software. In this case, the prediction of the bug in the software in its earlier phase may enhance the efficiency, quality, and reliability of the software as well as reduce the cost. Moreover, designing a robust software bug prediction model has become a challenging issue as well as several techniques have been recommended in the literature. Initially, the data from the relevant dataset is gathered. Then, the gathered data is fed to the pre-processing stage. The pre-processed data is fed to the optimal feature selection phase, in which the features are optimized with the aid of the Group Counseling Optimization (GCO) algorithm. Then, the Improved Long Short Term Memory (ILSTM) technique is used for prediction and the parameters in LSTM are also optimized with the same GCO algorithm. Then, the bug- predicted outcomes are attained by comparing the model with other conventional techniques including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel-based Support Vector Machine (SVM), etc.
Key generation and management method using AI generated Rubik’s cube states

The current encryption system operates as a hybrid encryption method that uses a combination of the symmetric-key method and the asymmetric-key method to generate and manage keys for encryption communication. Despite the fast processing speed of symmetric key encryption methods, due to the problem in key exchange, hybrid encryption solves the security problem of key exchange using asymmetric key method. The asymmetric-key method is difficult to provide smooth services due to the decrease in processing speed caused by computational complexity. The hybrid method is designed to take advantage of the fast processing speed of the symmetric-key, and the secure exchange method of the key used in the symmetric-key operates by using the asymmetric-key. However, with the advent of quantum computer technology, encryption techniques using the asymmetric-key method are changing in the same way as increasing the key size used. Therefore, we will explain the AI application method using the state change of Rubik’s cube to generate keys in the latest state in the symmetric-key encryption system. The application of the Rubik’s cube state change algorithm, which operates on the AI base described in this paper, has the advantage of inducing and using the used key without exchanging the pre-shared key. Because of this key induction method, malicious attackers have little information on the keys used for encryption and decryption, so it is judged that the confidentiality and integrity of key generation and management can be secured more safely. We intend to improve the problem of key exchange and management in existing encryption systems by generating and managing keys through the Rubik’s cube state change algorithm, which operates based on AI.
Modern Trends in Quantum AI: Distributed and High-Definition Computation

As the technology of quantum computers experienced major advancements in recent years, Quantum computing (QC) has also naturally received an unprecedented amount of attention and the number of research on the topic has greatly increased as well. This is because compared to classical computing, QC is able to solve complex problems quickly and possess impressive computational capabilities. One of the fields which can exploit the strengths of QC is quantum machine learning (QML). QML integrates the field of QC and machine learning by applying QC to various areas of machine learning e.g., deep learning, reinforcement learning, and distributed learning. Despite the noise intermediate scale quantum (NISQ) limitation of QC, QML models still show superior performance compared to their classical counterparts by leveraging the strengths of QC. This has been thoroughly corroborated via extensive simulation results in many works which demonstrate the full potential and importance of QML. Therefore, in this paper, we aim to explore various QML methodologies e.g., quantum deep learning, quantum deep reinforcement learning, and quantum distributed learning. Furthermore, the model structures of each methodology will be discussed, highlighting the possibilities and limitations of each model. Finally, the direction of future works of QML research will be discussed as well.
Hand Written Digits Recognition based on Concatenated LSTMs

Over the last few years, contactless technology has gotten much attention because of the influence of the coronavirus (COVID-19). We focused on the hand written digits using contactless devices, Leap Motion Controller, for an information input method to accommodate the rapidly increasing demand. In this study, we verify the effectiveness of the proposed learning model that divides the subject’s hand-tracking data according to the type of fingers and learn with multiple input layer. We set two kinds of evaluation methods. The normal recognition method is that specific subject data includes training and test data. The third party recognition method is that specific subject data does include training data. The classification accuracy calculated by the proposed learning model, the normal recognition method achieves a maximum of 96.7%, and the third party recognition method achieves a maximum of 80.1%.
Sentiment Recognition of Hinglish Code Mixed Data using Deep Learning Models based Approach

Sentiment analysis in social media content gained popularity over the past several years due to its various uses in understanding human computer interaction, understanding consumers behavior, psychology, smart system, etc. Due to the massive volumes of data available via social media, which is commonly used for expressing opinions and ideas, this issue has drawn a lot of attention. To identify emotions, this research will make use of a labelled Hinglish dataset. Deep learning-based techniques are utilized to identify emotions in tweets with mixed Hindi-English coding by utilizing transformer-based models and multilingual word embeddings derived from FastText methods. Various deep learning models, such as convolutional neural networks (CNN), long short term memory (LSTM), and bi-directional long short term memory (Bi- LSTM) have been used to analyze sentiments. In comparison to other models, the convolutional neural network (CNN) achieved the highest accuracy, 75.25%.
Is Puzzle-Based CAPTCHA Secure Against Attacks Based on CNN?

In recent years, unauthorized access to websites by bots has been on the rise. CAPTCHA, which distinguishes bots from humans, is widely used as a means of preventing automated access by bots. However, as bots have become more sophisticated, some bots have been able to break through CAPTCHAs with conventional difficulty. On the other hand, CAPTCHAs that prevent access by advanced bots are also difficult for humans, resulting in a trade-off between user convenience and the security against bots. In this paper, we verify that the instance of Capy Puzzle CAPTCHAs difficult for machines and easy for humans exists. As a first step, we designed a machine learning model that solves instances equivalent to Capy Puzzle CAPTCHA. This model detects the position of a piece from an instance image in which the piece is embedded in a background image. The model uses a Convolutional Neural Network (CNN) to detect the position of the piece from the instance image with an accuracy of 100%, which is higher than the accuracy of conventional methods. This shows the risk that the Capy Puzzle CAPTCHA can be easily broken through by machine learning. The instances that were considered difficult by the model were also difficult enough for humans.
Effects of Dimensionality Reduction on Classifier Training Time and Quality

Two important aspects when training a classifier are being investigated, training time and classifier quality. Two well-known dimensionality reduction techniques are performed on three groups of data to create smaller data sets for classifier training. The first two techniques are the principal component analysis (PCA) and its modification called adaptive PCA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{aPCA})$</tex> . The third one is the linear discriminant analysis (LDA). Three groups of data sets are trained to create classifiers with and without being pre-processed with these feature reduction techniques. Training time and accuracy scores obtained from each classifier are compared, and the experiment results show that sample size and number of features are two important factors to be considered before selecting dimensionality reduction techniques. If the compatible data reduction technique and data set is chosen, the training time can be reduced without compromising the quality of the classifier.