Reviewerly/exHarmony huggingface dataset:
Path planning of a 5-dof robotic arm based on BiRRT-APF algorithm considering obstacle avoidance

In robotic applications, an efficient path planning in a prescribed environment is the central issue, which is a challenging task for multiple degree-of-freedom articulated robotic arms. In this light, the path planning for robotic manipulators based on the combined artificial potential field method and bidirectional rapidly exploring random tree (BiRRT-APF) algorithm is proposed in this paper, aiming to handle the problems of highly possible randomness and low search efficiency. In order to improve the attraction function to speed up the efficiency of searching path, the characteristics of the Bi-RRT algorithm (i.e., bi-directional growth of random trees), is integrated to reduce the search time. Subsequently, a path construction strategy is proposed to remove the redundant nodes of the path, for the increased smoothness of the path and reduced memory storages of the algorithm. The effectiveness of the proposed path planning algorithm is illustrated with a 5-dof robotic arm, and is verified with MATLAB TM and CoppeliaSim TM co-simulation, through a comparative study among different algorithms. The results show that the proposed BiRRT-APF algorithm can decrease the randomness of node growth and reduce the number of algorithm iterations, resulting in faster convergence of a smooth path for reduced computational burden.
Towards a Cryptography Benchmark: A View on Attribute Based Encryption Schemes

In many Internet-of-Things (IoT) scenarios - such as smart home, or intelligent traffic systems - groups of devices interact with each other. Such scenarios no longer require encryption for individual recipients but for groups of recipients, i.e., this is a shift towards 1-to-n and n-to-n encryption schemes. Developers must select the scheme that (1) offers the required functional encryption features, (2) has the non-functional requirements best fitting the specific use case, and (3) is the most performant in the specific group setting. In other research domains, benchmarks help to support researchers, but also practitioners, in the choice of the best technology for a specific use case. However, there is no benchmark for encryption schemes available that covers all different types of encryption schemes, like 1-to-n and n-to-n encryption. In this paper, we provide such a benchmark for the category of attribute-based encryption schemes (ABE), especially focusing on the workloads, measurement setup, metrics, requirements, and features for an attribute based 1-to-n encryption scheme benchmark. Additionally, we describe how to include n-to-n encryption schemes. Lastly, we apply the benchmark to compare attribute-based encryption schemes among and with n-to-n group encryption schemes. Our result indicate that (1) our benchmark is suitable for evaluating ABE schemes as 1-to-n and n-to-n ciphers, (2) there is no single scheme that performs best in all scenarios and (3) with ABE schemes extended to n-to-n encryption schemes, a combination of constant computation times and an efficient use of broadcast is possible, which is not the case with traditional n-to-n encryption schemes.
Framework for Breast Cancer Diagnosis Using Machine Learning and IoT

Breast cancer is the most frequent malignancy discovered in women across the world. The early and accurate diagnosis of breast cancer is critical for lowering the mortality rate and raising the odds of successful therapy. The goal of this paper is to provide a technique for conducting early breast cancer diagnosis via machine learning and IoT. The main aim of the paper is to provide an alternative to the conventional diagnosis technique by using several machine learning algorithms. Breast cancer diagnosis using machine learning is a non-invasive technique with high accuracy rate. The proposed technique showed accuracy of 92.98 percent, 96.49 percent,97.36 percent, and 98.24 percent using the decision tree, random forest, logistic regression, and eXtreme gradient boosting algorithms, respectively. It was evident through the obtained results that the eXtreme gradient boosting yields the highest accuracy.
Image and Video-based Graphical Password Authentication

Personal authentication is the procedure which is used numeral times around world by way of utilizing various methods and procedures. The best method for confirming an alphanumerical secret phrase is used for many years. Additionally, alphabet and passwords have enormous security issues like people forgetting key combinations due to difficult key combination selection. Additionally, when choosing a simple key aggregate, facilitates cybercriminals to decrypt their passwords more without problems. Regular passwords are susceptible to several sorts of attacks, for example, dictionary attacks, aggressive attacks, and malware. To offer a more secure and relaxed authentication method, a photographic authentication is presented on this page. Here we provide user protection and authentication. This paper includes parts, picture processing the usage of the selected click on vicinity and video processing the usage of click on durations, wherein your mixture of each will generate a password for the person to sign in. To log in, both combos have to be same. The user is allowed to pick his or her photograph and video alternatives and is saved on a notably non-public web site so that it isn't always accessible to different clients. The secret key produced for both previews and video is stowed away from the two clients and developers. This method is to be needed to save you unapproved get admission to large and selective information and to ensure it.
Experiments for students with built-in theory: ‘PUMA: Spannungslabor’ – an augmented reality app for studying electricity

Abstract With the help of augmented reality apps objects and text can be added virtually to the physical world (e.g. physical experiments) in real time. The augmented reality (AR) app ‘PUMA: Spannungslabor ’ enhances simple electric circuits experiments for students with virtual representations based on the electron gas analogy including visualisations of interior processes in various components such as lamps and resistors. This opens up new possibilities for connecting theory and experiment in secondary school physics teaching. While using the AR app students are enabled to acquire qualitative and semi-quantitative knowledge about the basic concepts of current, voltage, potential, and resistance as well as the laws of series and parallel circuits easier and more directly. This holistic approach of learning through experiments can facilitate a deeper and more interconnected understanding of the topics covered in physics lessons.
An access control and authentication scheme for secure data sharing in the decentralized cloud storage system

Data privacy and data availability remain the major concern in centralized cloud storage, which could be handled effectively through the concept of decentralized data storage. However, the secrecy of data sharing is a rising concern in the decentralized data storage for which the effective access control and data sharing scheme, hash and session key-dependent attribute-based encryption (HS-ABE) is proposed in this research. Accordingly, the multi-level access control and sharing strategy authenticates the genuine requestors and assures access to the requestors upon the authentication of their attribute sets. In this perspective of access control and secure data sharing, Ethereum blockchain, decentralized Interplanetary File System (IPFS), HS-ABE are organized. The data owners (DOs) provide the secret attribute set for the requestors ensuring fine-grained access over the data. The performance of the developed HS-ABE scheme is investigated and compared with the conventional strategies, where the memory usage of the proposed scheme is 5812 Kbps, which proclaims a well-defined secure access control and authentication in the Blockchain-based cloud storage.
Design of an Augmented Reality Based Platform with Hand Gesture Interfacing

Augmented Reality (AR) is a young area that is rapidly developing. Its goal is to bring the virtual and real worlds together. By incorporating virtual things into our view of the actual world, AR aims to improve our experience of it. This paper introduces a new gesture controlled AR system, which performs hand gesture recognition using novel acquisition devices such as the Leap Motion and Kinect sensors. And using these devices, it is feasible to collect a highly thorough description of the hand posture, which is used to identify exact gestures. To recognize the executed gestures, an ad-hoc feature set based on the location and orientation of the fingertips is generated and inputted into a Random Forest classifier. In order to increase recognition performance, a collection of features is taken from the depth computed by the Kinect and coupled with the Leap Motion ones and a recognition rate around 81 &#x0025; is achieved. And those gestures are interfaced with the Unity 3D in order to implement a hand gesture based human machine interaction system.
Sports Information Needs in Chinese Online Q&amp;A Community: Topic Mining Based on BERT

The online Question and Answering (Q&amp;A) community has grown globally, allowing users to ask, discuss, and answer questions based on shared interests. As a gathering place for people’s knowledge production, collaboration, and dissemination in the current Internet scene, the online Q&amp;A community can intuitively reflect the public’s information needs and behavior. It also collects many sports-related data and becomes an effective vehicle for comprehending mass sports information needs and disseminating sports knowledge. However, sports-related studies on the online Q&amp;A community have rarely been reported. This study took the sports information in Zhihu, the largest Q&amp;A community in China, as the research object to explore the public needs for sports information in China. We introduced the BERT model through a self-compiled python program and collected 391,092 sports-topic answers in the online Q&amp;A community of Zhihu. Then, we explored the topic content, evolution trend, and user attributes of these answers. We found that the overall trend of sports information needs in Zhihu can be divided into three cycles: the London 2012 Olympic period, the Rio 2016 Olympic period, and the Tokyo 2020 Olympic period in general. The diversified content of information needs included 40 second-level themes and eight first-level themes. Male and female users had similarities and differences in sports information needs. The male and female users had the same information needs for fitness-related information. However, men were more concerned with confrontational solid sports such as basketball and football; women were more likely to care about weight loss, shape effect, and self-protection while doing sports activities. In addition, compared with men, women preferred to emphasize their gender attributes when expressing their needs for sports information to obtain more practical knowledge. In conclusion, our finding reveals that the sports community formed by the current online Q&amp;A community in China is still a male-dominated information field.
Face image synthesis from facial parts

Abstract Recently, inspired by the growing power of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs), facial image editing has received increasing attention and has produced a series of wide-ranging applications. In this paper, we propose a new and effective approach to a challenging task: synthesizing face images based on key facial parts. The proposed approach is a novel deep generative network that can automatically align facial parts with the precise positions in a face image and then output an entire facial image conditioned on the well-aligned parts. Specifically, three loss functions are introduced in this approach, which are the key to making the synthesized realistic facial image: a reconstruction loss to generate image content in an unknown region, a perceptual loss to enhance the network's ability to model high-level semantic structures and an adversarial loss to ensure that the synthesized images are visually realistic. In this approach, the three components cooperate well to form an effective framework for parts-based high-quality facial image synthesis. Finally, extensive experiments demonstrate the superior performance of this method to existing solutions.
Transfer Learning with Deep Representations is Used to Recognition Yoga Postures

Human activity identification is the automated interpretation of the movements happen in a video done by a human. Iterative Due to its wide applications in fields such as autonomous driving, biomedical imaging, and machine intelligence vision, among others, recognizing human activity in an image remains a tough and crucial research subj ect in the field of computer vision. Deep learning techniques have recently advanced, and models for image identification and classification, object detection, and speech recognition have been successfully implemented. Only a few examples include different aspects of human structure and movement, diffraction, a busy background, and so on. Moving cameras, changing lighting conditions and changing perspectives are all things to think about. Yoga is an excellent kind of physical activity. It's critical to maintain proper yoga posture. This research provides a unique technique for yoga asana detection based on feature extraction and representation Using a deep CNN model that has already been trained, followed by yoga asana recognition using a hybrid Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifier. With the constrained training datasets, it was discovered that previously learned CNN-based representations on large-scale annotated datasets may be applied to yoga asana recognition tasks. In real-time datasets, the suggested approach is tested on seven yoga asana (Pranamasana, Dhanurasan, Dandasana, Gomukhasan, Garudasana, Padmavrikshasana and Padmasan). The results show that the proposed scheme outperforms the state of the art methods.
A Novel Literary Translation Text Classification Method Based on Distributed Incremental Sequence Data Mining Algorithm

With the rapid development of electronic information technology and Internet technology, people's ability to generate and collect data is also increasing. With the rapid development of international exchanges, the number of literary translation texts has also increased dramatically. The information contained in the huge data of literary translation texts is huge, but these data are disorganized at this stage. Therefore, the classification of literary translation texts has become the key to efficient management of translated text information. Literary translation text classification is the process of classifying a given text as one or several of several predetermined text categories according to the content of the text. As one of the key steps in processing huge amounts of text data, text classification is generally regarded as the orderly organization of text sets, that is, grouping similar and related texts together. In this way, the problem of information clutter can be solved to a greater extent, and the efficiency of users' discovery, filtering, and analysis of text information resources can be effectively improved. At present, the basis of text classification is mainly based on the characteristics of words in the article, to analyze the correlation between words and categories. This approach ignores information such as word order and collocations in literary translation texts. To solve this problem, this paper introduces the distributed incremental sequence data mining algorithm into the classification of literary translation texts. This method can fully mine the features of syntactic order and other features based on considering the words and phrasing characteristics of articles in different application fields. The text classification effect is strengthened by discovering more effective information. The experimental results show that the method can improve the classification performance of literary translation texts.
A systematic overview of live virtual machine migration methods

Abstract Virtualization is regarded as one of the key technologies used in cloud computing by means of which virtual resources and dynamic resources are provided for users. Power management, load balancing, online system maintenance are remarkable benefits of virtualization. Such advantages are attributed to the feature of Virtual machine (VM) migration. Many research studies have been recently conducted on live migration and many researchers are seeking to reduce migration time. Prolonged migration time results in some problems especially in WANs which are geographically far from data centers. Hence, the speed of VM transfer from the source to the destination is reduced. There are many cases which can affect the migration of the VM. To the best of the researchers' knowledge, there is no comprehensive overview of studies conducted on live VM migration. This paper is aimed at providing a systematic overview and at investigating the literature on live migration. It includes 50 selected research reports which were published between 2011 and 2020. These papers are categorized by year of publication, publisher, number of citations, authors, immigration methods, techniques used, and effective parameters, and different results have been obtained from them, which we have been discussed in the paper.
Design of Pseudo-Random Number Generator Using Non-Linear Feedback Shift Register

A Pseudo-random sequence generation is an important part of security and encryption operations. Some of the most significant requirements for encryption and security applications are sequences with a lengthy duration and a high level of output complexity. Random number generators use a variety of topologies and components, including pseudorandom chaos generators, linear feedback shift registers, and non-linear feedback shift registers In this research, a novel design is described by evaluating and comparing the features of each structure to develop a non-linear feedback shift register and a pseudo-random chaos generator that generates high-quality random numbers. With a decent autocorrelation function and the emergence of a -function, the output signal is noisy. When compared to previous studies, the presented design has a suitable random characteristic.
Ensemble Techniques to improve the performance of the High Dimensional MultiClass Algorithms

Ensemble plays a major role in machine learning algorithms, and it can improve the performance of the single model by combining two or more models. It can be able to combine a number of different models and comes out with a promising result. There are several ensemble techniques such as bagging, boosting, and stacking each of which performs in its own way and produces the results. In this work the different techniques of ensembling are being explored and has been tested its working on the sample dataset. The results are varying in performance and suits well for the taken data points. Keywords: ensemble, stacking, boosting, bagging, ensemble learners
A Multistage with Multiattention Network for Single Image Dehazing

For single image dehazing, an end-to-end multistage with multiattention network is proposed in this paper. The network contains two different stages, in which the first stage uses an encoder-decoder subnet to obtain contextual features, and the second stage adopts a single-scale pipeline to provide spatial image details. At each stage, ground-truth supervision is provided, and an attention mechanism is used between the two stages, so the features learned from the previous stage will be refined before passing to the next stage. A basic multiattention unit that combines channel attention, spatial attention, and pixel attention is designed to earn more weight from important features, and a positional normalization that normalizes exclusively across channels is used in the multiattention unit to learn more weight from important features. Experimental results in several benchmarks indicate that the proposed network outperforms the state-of-the-art methods both quantitatively and qualitatively.
Deception Detection in Cyber Conflicts

Deception is a strategy that has been widely used in cyber conflicts. How to detect deception in a timely manner is always a challenge, especially for a cyber commander who is at the point of making decisions with respect to the actual target to go after, the exact location of the target, the starting and ending time of a cyber operation, the type of cyber operation, the way of launching the cyber operation, and the amount of resources and support needed. It is absolutely important for a cyber commander to know for sure that he/she is not deceived by an adversary so he/she will be able to make right decisions. Varied solutions do exist. However, they are either too narrow or too broad. The solutions represented by signature technology are narrow in scope, so that they are not capable of dealing with the deception that they have not handled before. The solutions represented by behavioral analysis are relatively broad, so that they require extra time to re-adjust their focuses, incorporate contextual information, and combine heterogeneous data resources in order to get to what is exactly needed. In addition, the use of contexts in analysis is at random and not in a systematic way in most cases. Even when contexts are included in analysis, their relations with the relevant events are not well explored in all these solutions. To address these issues, this paper proposes a new strategic and systematic solution applying the Operational-Level Cybersecurity Strategy Formation Framework. This new solution employs purpose analysis, contextual analysis, and risk analysis. A case study is provided to test the effectiveness of this solution in detecting deception in a timely manner. The benefits and limitations of this solution are discussed. The capabilities of the Operational-Level Cybersecurity Strategy Formation Framework are evidently proved via this use case.
Hierarchical Clustering

Hierarchical clustering methods produce a series of partitions moving from the trivial partition with all singleton clusters to the trivial partition with a single cluster including all units or vice versa. The former case defines the subclass of agglomerative hierarchical clustering procedures, while the latter denotes divisive hierarchical clustering procedures. The number of clusters should not be fixed in advance; rather, it can be selected according to a specific criterion once all the partitions are obtained. In this chapter, hierarchical clustering methods will be briefly introduced from a theoretical point of view and their implementation will be presented in detail by application to specific case studies.