Reviewerly/exHarmony huggingface dataset:
Body orientation estimation through graph representation: Expanding accuracy with Data Augmentation and Gradient Boosting

Body Orientation Estimation (BOE) is important for a wide array of applications, including robotics, surveillance and consumer analysis. Although multi-sensor approaches are effective, they are not a viable option for in the wild scenarios; the usual approach in such cases is to use single camera images, with imprecise results. Some applications that deal with people benefit from obtaining 2D human skeletons for gesture recognition, and these skeletons bring valuable information about the person's pose. It is proposed to build a 2D skeleton via OpenPose and using its data as training data on XGBoost to detect BOE. To evaluate predictions considering real situations based on a single camera, the TUD Multiview Pedestrian dataset is used and extended considering that a single person is originally considered in images where more people were often identified. It is compared the proposed approach against various state-of-the-art methods and our results indicate better performance. Finally, it is proved that our method is viable for BOE in real-time scenarios by presenting case studies on simulated scenes.
Effective know-your-customer method for secure and trustworthy non-fungible tokens in media assets

Non-fungible tokens (NFTs) are becoming very popular in a large number of applications ranging from copyright protection to monetization of both physical and digital assets. It is however a fact that NFTs suffer from a large number of security issues that create a lack of trust in solutions based on them. In this paper, we provide an overview of some of the most critical security challenges in media assets in form of visual content and then propose a specific solution for one among them, namely, secure person identification used in the context of KnowYour-Customer (KYC) with emphasis on liveness detection. The solution includes an authentication procedure that matches a selfie photo to a photograph of an identity document (ID). The system runs through a series of steps. First, detection is applied to extract faces from the selfie and the ID. Then a face comparison is performed to assess if they belong to the same person. While these two procedures are standard in KYC, a liveness check is also included so as to increase the security. The latter ensures that the user undergoing identity verification is in front of the camera and not a fraudster attempting to impersonate another individual. The system instructs the user to perform gestures such as waving hands or tilting head in front of the camera. The algorithmic detection of these actions during the live feed will reveal whether or not the user is carrying out the instructed activities. Performance of the proposed solution is then assessed under varying conditions.
Gender and linguistic distinctions of information needs in the Chinese context: Conscious and formalized levels

Gender difference is a new perspective on understanding the levels of information needs proposed by Taylor in 1968. Drawn from Ruthven's research methodology, gender differences between conscious and formalized information needs using 4000 postings to four major online discussion topics were analyzed. Descriptive statistical analysis was based on text length and vocabulary usage. The analysis confirmed Ruthven's conclusion in the Chinese context that the statements describing conscious information needs are more narrative with more emotional and sensitive words. Females, on the other hand, frequently express their information needs consciously. Problematic situations generate information needs. Consequently, understanding the information needs levels should consider additional contextual aspects, and the initial stages of information needs are essentially the awareness in problematic situations. Language and gender distinction in the Chinese context is mainly helpful to refine the concept of information need levels and can also be an extension of Ruthven's research.
Automatic recognition system for document digitization in nuclear power plants

With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are requiblack to accurately model and pblackict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. We evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.
A new approach in modelling the circular data: circular ridge estimator

Different regression models that use circular data supported on the unit circle are rare. Regression parameters for circular data have mostly been estimated using the least-squares method. This paper addresses multicollinearity between the circular regressors. The ridge estimator is suggested as an alternative to the least-squares estimator in circular-linear regression model. The models fitted by the circular least-squares and circular ridge estimators are compared on real and simulated datasets. The mean squared error and the coefficient of determination are used to assess the models' adequacy. The findings demonstrated that the fitted models might not be significant if the circularity of the data is ignored. Circular regression on circular data shows the model to be significant. Although the two estimators' coefficients of determination for circular models are quite close, the circular ridge estimator with the optimum biasing parameter has a smaller scalar mean square error than the circular least-squares estimator.
Research on Obstacle Avoidance Technology of Fire Fighting UAV

In this paper, two basic aspects of multi-rotor UAV, the air braking capability and obstacle avoidance function, are analyzed and studied on a certain type of fire-fighting UAV. Firstly, based on the motion model, the force analysis of the UAV is carried out to explore the factors that affect the braking ability; the mathematical relationship is established between the influencing factors and the braking distance, and the braking problem is solved in PYTHON. After that, the obstacle avoidance function of the multi-rotor UAV is developed based on the ROS environment. Several commonly used obstacle detection technologies and path planning algorithms are introduced and their advantages and disadvantages are compared. Under the ROS environment, simulating the advanced obstacle avoidance strategy according to VFH algorithm, and using PX4 external control mode in Gazebo simulation environment to verify the feasibility of the designed obstacle avoidance strategy. The results show that in actual flight, when the braking pitch angle is 30°, the UAV can slow down and hover within a safe distance of 10 m, meanwhile the advanced strategy based on VFH algorithm can be realized in simulation environment.
Dubins Path Generation and Tracking in 3D for UAVs

Excessive maneuvering may pose risk of damage to the aircraft. This paper presents methods to solve this problem. Specifically, the shortest path between two given points in the 3D space of an unmanned aerial vehicle (UAV) is generated, and the generated path is traced. Moreover, the designed controller is introduced. The path generation algorithm is based on the existing path generation algorithm pertaining to the Dubins curve. The path generated by this algorithm can satisfy the constraints regarding the minimum allowable turning radius; moreover, the algorithm involves rapid computation and can be applied for path generation in real time. Notably, in this algorithm, the shortest path to fixed initial and final points is geometrically obtained in a 2D plane; however, real aircraft path generation problems must be implemented in 3D. Therefore, a path in 3D space is generated by deriving a solution through a numerical technique, using the same principle as that of the problem in 2D. Furthermore, the line-of-sight guidance algorithm corresponds to basic waypoint guidance. The proposed 3D path tracking approach is implemented using a nonlinear controller, resembling those of line-of-sight guidance algorithms. The controller guarantees, under certain assumptions, asymptotic tracking of the path, in terms of both the position and attitude. Finally, we adopt a sliding mode controller, which is a robust controller that can ensure high control performance and stability even under uncertainties. The proposed controller satisfies the constraint through the saturation function, specifically, by introducing the constraint on the angular velocity to the existing sliding mode controller. The stability of the designed controller is verified considering Lyapunov stability, and the performance of the proposed algorithm is verified through numerical simulation.
Study on Close Air Support System Based on Information Integration

Aimed at defects such as low efficiency, lengthy request-response processes, and proneness to collateral damage associated with the traditional map/voice-guided CAS [1], a new design of an Information Integration Close Air Support (ICAS) system is proposed here. The functional requirements of the ICAS system are analyzed to present the overall framework. The software architecture of the ICAS system is devised, and the principle and implementation of the software’s core functions are studied. In addition, the workflow of the ICAS system is presented. Finally, the feasibility and effectiveness of the ICAS system are proved in a simulation. The method provided in this paper defines the situation information sharing among various combatants and allows ICAS missions to be completed more rapidly and more accurately.
Predictive Artificial Potential Field for UAV Obstacle Avoidance

A method to generate sequential obstacle avoidance trajectories for Unmanned Aerial Vehicles (UAV) is proposed. The proposed Predictive Artificial Potential Field (P-APF) method can generate smoother and less time-delayed avoidance trajectories in real time while maintaining the same high avoidance performance and low computational load as the conventional APF method. It is expected that the use of UAVs in transportation systems will increase, and the proposed P-APF method is highly effective in situations where multiple UAVs perform respective operations in the same flight envelope. The P-APF method generates a potential field and repulsion vectors based on current information and also on predicted future UAV trajectories and observed obstacles. Thus, since prediction is incorporated into the trajectory generation process, the proposed method can generate trajectories that start avoiding obstacles earlier than the conventional APF method. To evaluate the performance of the proposed method, simulations of a UAV avoiding static or dynamic obstacles were conducted using the proposed method and the conventional method. The results confirmed that the P-APF method can maintain safety and take evasive action with a smaller delay than the conventional method.
A Comparative Analysis of Deep Learning-Based Frameworks for Face Vitality Detection

Despite abundance of applications of facial biometric systems in human authentication, they are defenseless to attack presentations. With the advancements in attack generation techniques that provides realistic look for fake face presentations, it becomes a progressively challenging task. Hence, determining the root of bonafide or artificial face presentations has received a substantial attention. Recently, deep learning (DL) has made breakthroughs in the field of presentation attack detection (PAD) or face vitality detection (FVD). The DL-based face PAD approaches are better compared to traditional methods, as they do not require the user to choose the image features beforehand. Although, handcrafted feature-based techniques were the prominent ones over the past decades but since 2014, the research in this field is shifting toward the features extracted through deep neural networks. Hence, in this work, we present a review of various deep networks that are been successfully designed for face PAD mechanisms. It is well known that DL-based techniques, being able to extract significant patterns automatically, and deliver good detection accuracies in unknown attacking scenarios too. Additionally, the live face samples closed to a specific attack group such as makeup, obfuscation, and transparent mask has made the task of vitality detection as more challenging. The increased training overhead and the requirement of large training data is a trade-off for the substantial growth of these techniques. Consequently, the notion of transfer learning (TL) aims to transfer the existing learnt knowledge with larger dataset to new smaller database. Therefore, TL in face PAD mechanism is feasible solution that deserves more attention with an aim of increasing face vitality detection accuracies.
Methods Used to Improve Generalization Performance

Historically, the intuition behind developing classification algorithms was often to identify a hypothesis that minimizes training error. A major problem encountered with this approach is overfitting, which occurs when the hypothesis becomes too complex in comparison to the size of training data. In such cases, it is likely that an algorithm minimizing training error will find a hypothesis that fits the training data very well, but generalizes poorly to previously unseen data. Good generalization here refers to low generalization error which is defined as the difference between the training error and true error.
A Survey: Secure Cloud Data Storage and Access Control System Using Blockchain

The recent companies are able to get benefits with designing cloud and IoT solutions for security. The security of any system is considered the robust when it is able to avoid unauthorized access of data in cloud and IoT. In this paper, it is discussed various access control methods for cloud and IoT systems. Various papers are studied and presented in literature review. The access control is able to provide secure data storage using blockchain technology. Various access control methods are discussed and compared in this paper. The study is able to derive the pros and cons of access control methods for blockchain technology.
Secure device control scheme with blockchain in a smart home

The Internet of Things (IoT) and blockchain technologies characterizing the era of the fourth industrial revolution have enabled smart home networks to support their various systems and services. In a blockchain-based smart-home network environment, all connected IoT devices must be controlled safely and efficiently. Nevertheless, existing block-chain-based smart-home IoT systems pose a delay issue due to the necessary block generation time. In addition, IoT devices installed in smart homes should be able to prevent forgery attacks such as spoofing because they are often directly associated with personal information. In this study, we proposed an enhanced method to control smart home devices safely and efficiently by applying the zero-knowledge proof combined with a blockchain-based IoT system to protect the public keys of home network devices and the communication among them. The proposed model was approximately 10 s faster than the block generation-based model when it communicated three times in rinkeby, which is one of the test networks of Ethereum.
Forensic Analysis of Images on Online Social Network: A Survey

In today’s world as the use of social media sites increases, the sharing of information also increases. This often impacts the new generation socially as well as politically. Digital images play a major role in providing a proof for any event. Forensic experts use digital images in their forensic inspection. Manipulation of digital images is very easy with the help of photo editing software such as Corel paint shop pro, Cameraman 360, Photoshop, and Adobe image shop. An ordinary person can make changes to an image in a very sophisticated manner by utilizing these software that are available online as well as offline. Thus, forged images can also be used as fake or planted evidence in criminal cases. This paper represents a detailed literature survey of digital image forensics. First, we will cover the overview or introduction to detect the various types of image forgery detection methods, including active as well as passive methods. It will also present a detailed survey of passive image forgery detection which is also called as blind image forgery detection. Also, the motive of this paper is to help the researchers in future for proposing appropriate structured algorithm for detecting the originality of digital image.
Learning body part‐based pose lexicons for semantic action recognition

Abstract Semantic action recognition aims to classify actions based on the associated semantics, which can be applied in video captioning and human‐machine interaction. In this paper the problem is addressed by jointly learning multiple pose lexicons based on multiple body parts. Specifically, multiple visual pose models are learnt, and one visual pose model is associated with one body part, which characterises the likelihood of an observed video frame being generated from hidden visual poses. Moreover, multiple pose lexicon models are simultaneously learnt along with visual pose models. One pose lexicon model is associated with one body part that establishes a probabilistic mapping between the hidden visual poses and semantic poses parsed from textual instructions. To capture the temporal relations among body parts, a transition model is also learnt to measure the probability of the alignment transitioned from one position to another position. The body part‐based pose lexicon learning provides a novel method of cross‐modality semantic correlation, which can be applied in other spatial and temporal data. Action classification is finally formulated as the problem of finding the maximum posterior probability that a given multiple sequences of visual frames follow multiple sequences of semantic poses, subject to the most likely visual pose sequences and alignment sequences. Experiments were conducted on five action datasets to validate the effectiveness of the proposed method.
Study of Document Clustering Algorithms Applied on Covid Data

Suresh, SweetyKrishna, GopikaThushara, M. G.The advancement in the technology rises online unstructured data. As the data grow rapidly, tackling the information is becoming hard. There is a demand to maintain these unstructured data to gather important insights. Clustering of the text documents has become leading edge over Internet. Document clustering is mainly described as grouping of the similar documents. It plays vital role for establishing massive information. The paper shows an overview of study done on different clustering algorithms on covid data. The study of the semantic links between words and concepts in texts aids in the classification of documents based on their meaning and conception. The clusters were visualized using the k-means clustering technique, which was then evaluated using t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA).
Predictive Analytics of Logistic Income Classification Using Machine Learning

Accurate income data is one of the hardest piece of data to obtain across the world. Subsidy Inc. company delivers subsidies to individuals based on their income. We wish to develop an income classifier system for individuals. The income prediction model is designed using logistic regression classifier. The logistic regression model is a machine learning classification approach for predicting the likelihood of a categorical dependent variable. In this work, we also made an experiment to compare machine learning algorithms logistic regression with K-nearest neighbor on a dataset with dimension 473,421 rows and with 15 different number of columns or attributes. This experiment results show that logistic regression classifier performs better with 90% accuracy where as KNN gives an accuracy of 87%.
Khmer Sentiment Lexicon Based on PU Learning and Label Propagation Algorithm

The sentiment lexicon is an important tool for natural language processing tasks. In addition to being able to determine the sentiment polarity of words or phrases, it can assist attribute-level, sentence-level, and text-level sentiment analysis tasks. In light of the fact that tagging data and corpora for the Khmer language are scarce, where most resources related to sentiment lexicons are for English, this paper proposes a method for constructing a sentiment lexicon for Khmer based on Positive-Unlabeled learning (PU Learning) and the label propagation algorithm. Sentiment words are first extracted from a corpus using the Spy technique of PU learning method. The main idea is to purify the set of N-class examples, train the MLP classifier, and continuously delete spy words and increase the number of P-class words in the iterative process. Following this, the sentiment polarity of the candidate words is determined. By considering the problem of determining the sentiment polarity of the candidate words as one of calculating its probability distribution, a small number of labeled sentiment words and candidate words are used to construct a graph model. The contextual information of the candidate words is used to construct a simple supplementary graph model of the set of sentiment words through word co-occurrence and triangulation, where this enhances the correlation between data items. The sentiment polarity of the candidate words is then determined through the label propagation algorithm. The results of experiments show that the proposed method can be used to construct a Khmer sentiment lexicon with a small number of labeled data and a small corpus without requiring excessive manual labeling.
Smart Contract Assisted Public Key Infrastructure for Internet of Things

IoT devices use PKI to authenticate themselves in the network to manage the authenticity of network, data confidentially and data integrity. This paper proposes an approach to implement the smart contract-based Public Key Infrastructure (SC-PKI) for the Internet of Things. In the proposed approach, Smart contracts are used for public key life cycle management starting from the generation of the public key, distributing public key to the revocation of the public key in the form of X.509 certificate. SC-PKI does not have any third-party centralized certification authorities (CAs) to issue, validate, and revoke the certificates. All these activities are executed by the smart contract on a dedicated blockchain network. The proposed approach enhances the network’s security, avoids single-point failure, and increases the certificate verification performance.