Reviewerly/exHarmony huggingface dataset:
Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.
Research on Character Behavior Recognition Based on Local Spatio-temporal Relationship in Surveillance Video

Abstract Because of its wide application value, behavior recognition has long been one of the research hots pots in the field of computer vision and pattern recognition. At present, the method based on local features and word packet model has been widely used in the field of behavior recognition. However, this method does not consider the temporal and spatial relationship between features, and the local temporal and spatial relationship between features is very important for behavior representation and behavior recognition. In view of the above problems, this paper proposes a modeling method of character behavior recognition based on local spatio-temporal relationship in surveillance video. Firstly, each part of the proposed network model is introduced in detail, and then the proposed model is compared with the advanced skeleton action recognition methods in recent years on several skeleton data sets. Finally, the effectiveness of the proposed method is verified. The experimental results show that, compared with the recognition results of related literatures, the features extracted by choosing the starting point of trajectory have better recognition performance under the fusion framework.
Time Optimal Trajectory Generation with Obstacle Avoidance by Using Optimal Control Theory for a Wheeled Mobile Robot

The design of the mobile robot path is important when obstacles are present in the environment. In the present study, the theory of optimal control for path design and obstacle avoidance via simultaneous minimization of the time and kinetic energy is proposed. Nonlinear equations of robot motion without simplification are considered in optimum control problems, and in order to prevent collisions, the potential functions are utilized. In the next phase, the cost function is proposed that includes velocity inputs, time, and the potential function for obstacle avoidance, in which the nonlinear equation of the motion of the mobile robot is deemed as a constraint. The final equations are numerically solved, and the capability and effectiveness of the presented method will be presented via different simulations on the mobile robot.
Drunkenness Face Detection using Graph Neural Networks

The fatalities associated with driving while intoxicated (DWI) are on the rise, leading to a staggering twelve thousand people dying from it and nine lakh people getting arrested every year. DWIs are usually confirmed with the use of breathalyzers, which require the subject to blow into the machine. In light of the current pandemic caused by COVID-19, a susceptible individual may deny blowing into the machine. Thus, the need for a contactless method to detect if someone is drunk arises, so that suspects are prevented from taking advantage of the situation. This also assists law enforcement in the detection of DWI cases. The proposed study is the method to detect intoxication in a given suspect through Graph Neural Networks using facial landmarks. We also present a labeled dataset as a complementary dataset for intoxication detection. This dataset is the first graph-based data available for the detection of alcohol intoxication. Extensive experiments were carried out to validate this approach.
Fault Diagnosis Method of Analog Circuit Based on CEEMD-ELM

An analog circuit fault diagnosis method based on complementary ensemble empirical mode decomposition and extreme learning machine (CEEMD-ELM) algorithm is proposed. The test signals of the analog circuit were decomposed into multiple intrinsic mode functions (IMF), and then the features of each IMF, such as relative entropy, kurtosis, RMS, margin factor, were obtained to establish the fault feature vectors. The established fault feature vectors were used as input to train the ELM to realize the fault diagnosis of the analog circuit. Simulation results show the effectiveness and accuracy of the proposed method.
Sentimental Analysis for E-commerce Site

The world is rapidly transforming due to digital innovations. The usage of social media and e-commerce sites is expanding on a daily basis. The users’ primary sources of creating large amounts of text data include social networks, e-commerce sites, and so on. Sentiment analysis technology is used to efficiently find the sentiments associated with an event or some products. Despite the hype, sentiment analysis also faces several challenges, and also during identification, the sentiments within the text will be automatically identified. The primary task of the proposed analysis is to find the accurate meaning of the sentiment, and further, it is also used to detect the polarity. In the proposed system, machine learning emotional classification method and Support Vector Machine (SVM) classification have been used for data classification. Now, the deep learning model is used to increase the accuracy and encompass the labeled data along with the classifier, which improves the accuracy. Then, the comparative analysis has been performed on the dataset that shows the accuracy of sentiment analysis. Henceforth, the proposed research work attempts to find the sentiment associated with the review of dataset and obtain a higher accuracy.
Federated Learning Using Tensor Flow

This chapter provides information on the usage of federated learning and how it works. Federated learning is a very fast growing technology with rapid development in every field. Google and many big companies are trying to utilize the power of federated learning. Federated learning can be achieved using tensor flow. We will discuss the theoretical usage of the tensor flow for federated learning. Keras can be used to achieve federated learning using tensor flow. In this chapter, we discuss federated learning API and federated core API in detail.
Tail Time Defense Against Website Fingerprinting Attacks

In the past few years, many defense mechanisms have been proposed against website fingerprinting attacks. Walkie-Talkie (WT) built on top of the Tor network is known to be one of the most effective defense mechanisms. However, we observed that WT significantly increases the page loading time (time overhead) although the bandwidth overhead is not high. This paper analyzes the cause of the increased page loading time and presents a defending approach called Tail Time (TT), which addresses the problem by limiting the maximum time for which a pending request can block subsequent requests. Our experimental results indicate that the proposed TT defense can significantly reduce the page loading time while keeping the defense performance on par.
Differentially Private Deep Learning with Iterative Gradient Descent Optimization

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://github.com/CGCL-codes/DPDLIGDO.git .
Multi-party computation enables secure polynomial control based solely on secret-sharing

Encrypted control systems allow to evaluate feedback laws on external servers without revealing private information about state and input data, the control law, or the plant. While there are a number of encrypted control schemes available for linear feedback laws, only few results exist for the evaluation of more general control laws. Recently, an approach to encrypted polynomial control was presented, relying on two-party secret sharing and an inter-server communication protocol using homomorphic encryption. As homomorphic encryptions are much more computationally demanding than secret sharing, they make up for a tremendous amount of the overall computational demand of this scheme. For this reason, in this paper, we demonstrate that multi-party computation enables secure polynomial control based solely on secret sharing. We introduce a novel secure three-party control scheme based on three-party computation. Further, we propose a novel n-party control scheme to securely evaluate polynomial feedback laws of arbitrary degree without inter-server communication. The latter property makes it easier to realize the necessary requirement regarding non-collusion of the servers, with which perfect security can be guaranteed. Simulations suggest that the presented control schemes are many times less computationally demanding than the two-party scheme mentioned above.
Artificial Immune Algorithm-based Airplane Path Planning under Complicated Environment

This paper mainly studies the flight route planning in complex and uncertain environments. The improved artificial immune algorithm is used to complete the flight route planning in different complex environments. The experimental results show that the improved artificial immune algorithm has a higher success rate of route planning than the common genetic algorithm in complex environments; therefore, it also shows that this method has better adaptability in complex environment
Collection and Classification of Human Posture Data using Wearable Sensors

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. It is important to understand if a person is healthy (in terms of his everyday posture) or is suffering from a joint/bone disease as reflected by his incorrect posture. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. The aim of the proposed work is to classify the human posture during three different activities (standing, sitting and sleeping/lying) as a healthy or an unhealthy one. This is done by applying machine learning techniques on a large posture dataset which is collected with the help of MPU-6050 sensors mounted on multiple positions on the body. The performance evaluation of the proposed work reveals that the proposed work is efficient enough to classify the postures accurately.
The State-of-the-Art of Heterogeneous Graph Representation

In this chapter, we give a comprehensive review of the recent development on heterogeneous graph representation (HGR) methods and techniques. In the method aspect, according to the information used in HGR, existing works are divided into four categories, i.e., structure-preserved HGR, attribute-assisted HGR, dynamic HGR, and application-oriented HGR. In the technique aspect, we summarize five commonly used techniques in HGR and categorize them into shallow model and deep model. In addition, we also provide some public sources, e.g., benchmark datasets, source code, and available tools.
Using Trust Statements and Ratings by GraphSAGE to Alleviate Cold Start in Recommender Systems

With the growing volume of information being expanded by product and service providers, recommender systems have become a tool to prevent information overload. One of the most popular types of recommender systems is collaborative filtering. The issue of user cold start is the main challenge in this approach. Cold start means the lack of information to predict ratings of a user accurately. Because the user's prior experiences in the system are essential in trusting the recommendations, making the proper recommendations is very important in the early stages of interaction. In this paper, the aim is to solve the problem of partial user cold start by gathering the information of the trust network and users ratings. In this approach, the trust network information and user ratings are first aggregated by the GraphSAGE neural network algorithm to extract the user's hidden features vector. Then, user ratings are predicted in each cluster of users. This method, which has been evaluated on two data sets, in the best case, improves the accuracy of predicting non-existing ratings for partially cold start users in terms of mean absolute error by 0.9% and root mean squared error by 1.1% compared to previous methods. Also, due to the inductivity of the GraphSAGE algorithm, if a new user (a user who was not available in the data set during the training process) enters, there is no need to retrain the model, and its embedding vector is created with the existing model.
Character Segmentation from Handwritten Gujarati isolated words using Deep Learning

Information retrieval from scanned handwritten digital copies is a very challenging task especially in Indian scripts like Gujarati due to the presence of joint and conjuct characters as well as matras, cursive nature and varying size of the characters. There are two methods namely recognition-based and recognition-free for document image retrieval. The difference in both approaches lies in the level of segmentation. There are two levels of segmentation namely Fine and Coarse Grain. In Fine-Grain segmentation, the base character and the matras are considered as separate and are two different units of segmentation. In Coarse-Grain segmentation, the base character and matras are considered as a single unit of segmentation. The accuracy of the segmentation highly affects the result of information retrieval. The research here heads towards addressing these issues. Deep learning has been very effective in many domains but has not been used much in this domain. In this research, we propose a Coarse Grain segmentation method using the object detection model Faster RCNN and a Fine Grain segmentation method using a combination of Connected Component Analysis and Faster RCNN. The annotation of the dataset for training these models has been carried out manually using LabelImg tool.
VLSI Implementation of A Robust Threshold-Based Earthquake Early Warning System

The earthquake-early-warning-system (EEWS) is one of the effective tools to mitigate seismic hazards in real-time. In previous studies, the product of two parameters viz. ground motion period (τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> ) and high-pass filtered vertical displacement peak amplitude (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> ) has been extensively used to establish threshold-based EEWS. However, the threshold-based approach by employing parameter τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> × P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> suffers from error and generates false alarms for low-magnitude events data corrupted with high background noise. We have proposed a robust threshold-based methodology where noise-removal by employing adaptive filter is performed to increase the SNR of events data. Moreover, owing to the fact that τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> exhibits uncertainty in predicting low-magnitude events though reliably predicting high-magnitude ones, whereas P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> can reliably predict low-magnitude events, we have used a two-step threshold-based approach, where in step I, if the estimated magnitude based on parameter P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> does not cross the predetermined threshold value, then low-magnitude event is predicted; next in step II, if the estimated magnitude based on parameter τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> overshoots the threshold value, then high-magnitude event is predicted and warning is generated. In this paper, a novel VLSI architecture of the proposed two-step τ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> and P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> estimation unit has been presented and realized in both FPGA and ASIC platforms.
Tensor-based Subspace Factorization for StyleGAN

In this paper, we propose TGAN a tensor-based method for modeling the latent space of generative models. The objective is to identify semantic directions in latent space. To this end, we propose to fit a multilinear tensor model on a structured facial expression database, which is initially embedded into latent space. We validate our approach on StyleGAN trained on FFHQ using BU-3DFE as a structured facial expression database. We show how the parameters of the multilinear tensor model can be approximated by Alternating Least Squares. Further, we introduce a stacked style-separated tensor model, defined as an ensemble of style-specific models to integrate our approach with the extended latent space of StyleGAN. We show that taking the individual styles of the extended latent space into account leads to higher model flexibility and lower reconstruction error. Finally, we do several experiments comparing our approach to former work on both GANs and multilinear models. Concretely, we analyze the expression subspace and find that the expression trajectories meet at an apathetic face that is consistent with earlier work. We also show that by changing the pose of a person, the generated image from our approach is closer to the ground truth than results from two competing approaches.
OPECUR: An Enhanced Clustering-Based Model for Discovering Unexpected Rules

Rare association rules gained importance since they are widely used in critical real-life domains, such as medicine, fraud detection, malware attacks, recommender systems, and weather forecasting. In order to extract actionable rules that can be used in real-life scenarios, user confidence and an easy-to-use model with as few as possible tuning knobs are required. On top of this, an inherent imbalance of the data (e.g., in the medical domain, there are fewer ill people compared to healthy people) poses a severe challenge, which complicates the finding of rare patterns. Recently, an unsupervised clustering model was proposed to discover interesting rare rules. However, the performance of this model degrades in terms of time and accuracy. In this paper, we propose an efficient model to recover interesting rare rules. In this model, we employ machine learning-based classifiers to assess the performance of the generated rules. To evaluate the proposed model, we experiment with three real-life medical datasets. The experimental results show that our model outperforms the state-of-the-art model in terms of time and accuracy. Furthermore, we generate more accurate results, which means that the user is only confronted with the most important and compact rules. This reduces a user’s effort on postprocessing of associating rules significantly.
Research on Trusted Access of Ubiquitous Power Internet of Things Terminal Based on Hyperledger

Abstract The secure access of terminals is an important problem that hinders the construction of ubiquitous power Internet of things. This paper analyzes the requirements and problems of ubiquitous power Internet of things terminal access, introduces the alliance chain framework of blockchain technology, selects super Ledger as the basic framework, and introduces it into the ubiquitous power Internet of things terminal access authentication scheme, and proposes a trusted access scheme of ubiquitous power Internet of things terminal based on Super ledger. Firstly, based on the framework of fabric project in hyperledger, the terminal access framework based on Hyperledger is designed, and the overall framework is mapped into a classic five layer structure of blockchain for functional design. Then, the terminal access process is divided into two stages: terminal access and identity authentication to achieve the authority control and identity authentication of the terminal and ensure the safe access of the terminal. Finally, the feasibility of the proposed scheme is verified by comparing with the traditional scheme through simulation experiments.
An Enhancement to Caesar Cipher using Euler Totient Function

In this modern world every communication including financial transactions are taking place through the open unsecured network. In order to communicate securely through open networks the messages need to be concealed by using the cryptographic methods. The mono alphabetic traditional Cipher is Caesar cipher is the simplest and fairly popular among the available cryptographic algorithms but it is prone to brute force attack and frequency analysis test. In this paper we propose an enhancement to Caesar cipher by using the Euler Totient function to generate the key to encrypt a text file. The Euler Totient function is applied on the total number of lines of the plain text file. The key value is generated from the plain text hence for different plain text the key value is different; this makes it difficult for the adversary to guess the key thus making the encryption scheme robust. It needs to design secured key distribution schemes to be taken up as the extension work of this scheme.
DeltaFace: Fully Automatic 3D Facial Cosmetic Surgery Simulation

Before cosmetic surgery procedures, the patients are commonly involved in the consultation process using interviews and reference images. The reference images typically consist of pre-post surgery images of other patients, leading to misunderstandings between patients and the surgeon. We present fully automatic pipeline to simulate the whole face of post-surgery results. We first establish a 3D face registration and alignment based on the face surgery procedures of the current day then generate the delta image. We proposed the delta image to solve the lack of dataset dilemma of the pre-post surgery face images. We also propose a convolutional autoencoder model to select the most similar face. Furthermore, we simulate the post-surgery results using surgery procedures retargeting. In the last section, we validate the results using a subjective survey.