Reviewerly/exHarmony huggingface dataset:
Robust face recognition: How much face Is needed?

Face recognition systems are used in high security applications for identification, authentication and authorization.Being robust, is essential, not only towards Adversarial Examples, but also towards occluding accessories, such as facial masks, which become particularly relevant through the COVID19 pandemic.We have identified three inconspicuous facial areas to wear adversarial examples to attack face recognition.These are the mouth-nose section, the forehead and the eye area.In this paper, we will address the question of how much of a face needs to be present for successful identification and whether removing the identified critical regions is a viable countermeasure against adversarial examples.
Fast and Efficient Trajectory Generation for Ball Interception Using Motion Primitives

Ball-interception is an vital strategic operation for most soccer competitions. This paper proposes a trajectory generation method that combines trajectory prediction and motion primitives for a soccer-robot to perform ball-passing and interception operations during a RoboCup competition. Compared to existing algorithms, our method has better real-time replanning capability as well as better obstacle avoidance performance. Our method is thoroughly tested in both simulation and real-world environments, and the results show that our approach is both efficient and robust.
Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

Industrial Control Systems (ICS) can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate effectively.Internet of Things (IoT) integrates numerous sets of sensors and devices via a data network enabling independent processes.The incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things (IIoT), which find use in water distribution system, power plants, etc.Since the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection, an effective forensic investigation process becomes essential.This study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial infrastructures.The proposed strategy involves the design of manta ray foraging optimization (MRFO) based feature selection with optimal stacked autoencoder (OSAE) model, named MFROFS-OSAE approach.The primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial infrastructures.The MFROFS-OSAE approach involves several subprocesses namely data gathering, data handling, feature selection, classification, and parameter tuning.Besides, the MRFO based feature selection approach is designed for the optimal selection of feature subsets.Moreover, the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm (COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures. 2276
Remarks on Learning Inverse Kinematics of a Robot Manipulator Using a Quaternion Neural Network

In this study, the application of a quaternion neural network (QNN) for solving the inverse kinematics problem of a robot manipulator is investigated. To obtain the solution of the inverse kinematics problem, the QNN earns the mapping between the work space and the joint spaces of the robot manipulator through training on a dataset predetermined by the forward kinematics of the robot manipulator. A seven degree-of-freedom robot manipulator is employed in the computational experiments and the simulation results demonstrate the feasibility of using the QNN for this task.
Analysis of Security Challenges and Threats in Social Network and Cloud Computing

In recent days, social networks have changed rapidly the way a person interconnects and communicates. In social media, the user will be sharing personal data, photos, and videos. These are the most popular content which requires maximum space. All of the information will be kept in the cloud. Personal information is accessible on social media sites. Cloud-based social networks have an issue providing privacy and security to the user, even after deleting the personal data, the data is still stored on the networking site. Users may not be aware of these issues. The major issue in the cloud-based social network is security and privacy. Security and privacy issues raised by the use of cloud technology in social networks are investigated. The study also provides recommendations on precautions to be considered to secure sensitive data which is stored in the cloud.
An efficient three-dimensional prediction structure for coding light field video content using the MV-HEVC standard

Light field cameras have emerged in the consumer market as a technology that captures richer visual information than legacy cameras. While traditional photography captures only a 2D projection of the scene, the light field camera collects light intensity and direction. As a result, this technology opens new opportunities for applications such as remote surgery, autonomous driving, augmented reality, and digital health. However, one of the main problems with this technology is the size of the data captured which significantly increases the consumers' bandwidth requirements. Numerous solutions have been proposed that attempt to compress light field efficiently, but none of them fully evaluate the intricacies found in light field content. This paper proposes a three-dimensional prediction structure for compressing light field video content using the multi-view extension of HEVC (MV-HEVC). The inter-view structure exploits the correlations between the views in two directions and the high degree of resemblance between views around the centre of each frame. Experimental results show a BD-rate gain of 50.89% while subjective tests have shown a BD-rate improvement of 65.83% in mean opinion score over the state-of-the-art method. This means more visually appealing quality at a significantly reduced bitrate, thus facilitating practical implementations of the emerging technology.
ID3 Decision Tree for the Activity Recognition of the Healthy Elderly

The activity management of the elderly is of great significance to their physical health. However, the traditional manual monitoring method requires a lot of manpower and material resources. Therefore, in this work, we predicted the activity status of elderly people living alone based on the data collected by wearable devices. The data used were collected by battery-free wearable sensors of 14 healthy elderly people aged 66 to 86 provided by Shinomoto Torres. Firstly, we analyzed the effectiveness of features based on Spearman. In order to capture different activity states, we designed a decision tree based on entropy to distinguish behaviors with different speed states. Compared with other advanced models, the decision tree has the best performance in K-fold, with an accuracy rate of 98.98%.
A 3D AUV Path Planner Based on Modified Bacterial Foraging Algorithm

Path planning is a vital component to realize the intelligent navigation system of the Autonomous Underwater Vehicle (AUV). In order to find an optimal path in the underwater environment, the slice model of the underwater topography and the multi-objective optimization model with multiple constraints of the path planning problem are proposed. The slice model is significant to reduce storage space and the influence of resolution. The optimization model considers the length of path, the energy consumption, and the smoothness in the vertical direction into objective function, and the constraints contain the terrain, the maximum yawing angle, and pitch angle. Based on these models, we propose a modified bacterial foraging algorithm with variable step length and simulated annealing mechanism. For the convenience of performing the simulation experiment, the virtual terrain described by superposing several Gauss functions with different parameters is designed to simulate the real underwater topography. The experiment results show that the proposed slice model, optimization model, and modified BFO algorithm are efficient to handle the complex underwater path planning problem.
Tecnologías digitales para la formación en empresas y entidades de la economía social

The pandemic derived from COVID-19 has generated new ways of being and being in society. One of the areas where its impact was most notable was education, making it necessary for institutions to generate new teaching and learning modalities that would allow them to continue with the training processes and making technology a sine qua non condition for developing them. From this point of view, the aim of this paper is to find out what role technology has played specifically in this transition towards new training modalities in higher education. To this end, we propose a systematic review of the literature collected in the Dialnet database between 2020 and 2021. The results show that there is a growing scientific production in different contexts, that the possibilities of technology are diverse and that there is no clear perception of the way in which the different agents have experienced this change.
The non-actualization meaning in -eya ha- modal sentences

In this paper, I discuss the non-actualization inference which arises from the interaction between a modal expression -eya ha- and a past tense morpheme. After discussing the general properties of the Korean priority modal -eya ha-, I show that the non-actualization inference we often find in -ess-eya ha- modal sentences does not belong to a traditional classification. By adopting Tonhauser et al.’s (2013) classification which uses a distinction between at-issue and not-at-issue meanings, I show that the non-actualization inference in -ess-eya ha- modal sentences is a not-at-issue entailment. I argue that, in this way, we can differentiate various subclasses of inferences in a unified way.
Investigating Information Seeking Process Using Think-Aloud Protocol of Students Living in Rural Areas

In this study, we investigated how students in a rural secondary school searched for online information for their school assignments, and we employed the think-aloud protocol to reveal students’ information seeking processes. In this study, three students were given three search tasks that were similar to their school assignments and they were asked to search the web for the tasks. To obtain data from each student’s web search, we used screen-capture software and collected audio and video records. Recordings were subsequently transcribed and analyzed. Results showed that while the students completed the easy and medium-level search tasks to a great extent, they had difficulty in completing the difficult task. In addition, students performed the tasks with various steps and durations. Emotions such as hesitation, satisfaction, frustration and anxiety were observed in the students while they were performing the tasks. The results were also discussed in terms of the characteristics of the information seeking process.
Text Classification in Organizational Research – A Hybrid Approach Combining Dictionary Content Analysis and Supervised Machine Learning Techniques

Big Data is an emerging field in organizational research as it provides new types of data, and technologies like digitization and web scraping allow to study huge amounts of data. Since large parts of digital data consist of unstructured text, text classification - assigning texts (or parts of texts) to predefined categories - is a central task. Text classification not only allows to identify relevant texts in a jumble of data but also to extract information from texts, such as sentiments, topics, and intentions. However, large amounts of textual data require the use of automated text mining methods, which is mostly uncharted territory in organizational research. We, therefore, outline and discuss the two existing approaches to text classification, one originating from social science (dictionary content analysis) the other from computer science (supervised machine learning). Since both approaches have advantages and disadvantages, we combine ideas from both to develop a hybrid approach that reduces existing issues and requires significantly less knowledge in programming and computer science than supervised machine learning. To illustrate our approach, we develop a classifier that identifies critical media coverage of organizational actions.
The Effect Of The Application Of Nomograph Pros On The Learning Outcomes Of Elementary School Students Materials Of Counting Intelligence Operations

The purpose of this study was to determine the effect of the application of Nomograph teaching aids on student learning outcomes. This research was conducted at grade IV. The method used in this study is an experimental method with a one-group pre-test-post-test design. The instrument used in this study was a mathematics learning outcome test in the form of an essay. The test result data were analyzed by means of the t-test on the final result (post-test). Based on the analysis of the research data, there is an effect of the application of Nomograph teaching aids on learning outcomes.
Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signature samples collected from 200 individuals. This database is publicly distributed under the name of SIGMA for Malaysian individuals. The experimental results are reported as both error forms, namely False Accept Rate (FAR) and False Reject Rate (FRR), which achieved up to 4.15% and 1.65% respectively. The overall successful accuracy is up to 97.1%. A comparison is also made that the proposed methodology outperforms the state-of-the-art works that are using the same SIGMA database.
Differences in Ischemia Mechanism in Coronary Artery Disease and Cardiac Syndrome X

The Cardiac Syndrome X (CSX) includes patients with the triad of angina pectoris: a positive exercise-strain test and angiographically smooth coronary arteries. The most manifested clinical symptom is chest pain. The aim of the study is to explore the differences in ischemia mechanism in both groups – CSX and patients with Coronary Artery Disease (CAD), based on specific changes in Autonomic Cardiac Control (ACC). A novel approach for multicriteria decision making – InterCriteria Analysis (ICrA) is used to reveal relationships between 13 standard indicators for Heart Rate Variability (HRV) – in resting state (RS) and by sympathetic stimulation (handgrip test; HT) or parasympathetic stimulation (Valsalva manoeuvre; VM). Through these indicators the activity of ACC in two groups (patients with CSX or CAD), each one containing 40 patients, is evaluated. In the considered here three cases all pairs of the 13 indicators, without exception, do not show any dependencies. These results give reason to assume, that in the two groups there is a different response of the sympathetic autonomic system under conditions of stress with matching parasympathetic tone.
Analysis of Reversible Network Covert Channels

In the last years, the utilization of information hiding techniques for empowering modern strains of malware has become a serious concern for security experts. Such an approach allows attackers to act in a stealthy manner, for instance, to covertly exfiltrate confidential data or retrieve additional command & control payloads for the operation of malware. Therefore, the deep understanding of data hiding mechanisms is a core requirement, as it allows designing effective countermeasures. Unfortunately, the most recent evolution of information-hiding-capable threats enjoys <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reversible</i> properties, i.e., the abused network flow is restored to its original form. Hence, detection approaches based on the comparison of different traffic samples may not work anymore. In this paper, we further investigate various methods for performing reversible data hiding for network covert channels. Specifically, we extend our previous research by considering different scenarios focusing on IPv4 traffic and HTTP conversations. The results confirm that reversibility can be used in various network conditions and is not impaired by middleboxes. In addition, engineering countermeasures or mitigation techniques could be difficult, thus requiring to consider reversible mechanisms already in the early design stages of a protocol/deployment.
Smart healthcare service system for elderly based on AIoMT

In the age of digitalization, digital technologies offer incredible opportunities to the healthcare sector. No matter where elderly people are, they have to interact with various digital devices in the real world. By using digital sensors, smart devices, Internet of Medical Things, big data, cloud/edge computing, Artificial Intelligence and other advanced technologies, the smart system can observe and analyze the lives of the elderly, dynamically access their information and data, connect people, materials and institutions, and then actively manages and responds to their demands in an intelligent manner. The system can promote interaction and cooperation among social institutions and all stakeholders, help organizations and individuals make informed decisions through information sharing, facilitate the rational allocation of resources, and finally ensure that elderly people can get the services they need. The smart system can integrate and coordinate social systems to realize the dynamic and refined management of the elderly.
Bhagavad-Gita to Boost AI Technology

Time-tested knowledge in ancient scriptures, though meant for leading a better life, has not been exploited much by technologists. This paper explores whether and how the knowledge given in the sacred text of Bhagavad-Gita can be used to solve Human-Challenges in a digital medium. The paper defines Human-Challenge. The questions to be answered by a Question Answering agent for addressing a Human-Challenge are identified as a new category of questions, namely, Challenge-Question. It has been observed that for Challenge-Question, the answering agent requires more contextual information about the user. Though the present Deep Learning technology-based models help in finding the user’s context, the inherent limitations and scaling difficulties of such models need to be addressed. The present work is towards exploring the use of alternative models based on Bhagavad-Gita knowledge to find users’ context needed for solving many Human-Challenges. The paper presents one such knowledge framework, namely SMIS-Model, along with the method and instrument developed to test the model’s effectiveness through a real-life application. Based on the encouraging results, a generic solution architecture has been proposed for solving Human-Challenges using Bhagavad-Gita.
Design and simulation of video monitoring structure over TCP/IP system using MATLAB

Video monitoring systems are undergoing an evolution from conventional analog to digital clarification to provide better rate and security over internet protocols. In addition, analog surveillance becomes insufficient to face enormous demand of security of system contains more than hundreds of camera often deployed in hotels environments far away from room control. This paper presents the design and simulation of a video monitoring scheme in excess of a transmission control protocol/internet protocol (TCP/IP) system using MATLAB. Sophisticated cameras could record directly high-definition digital videos based on digital technology which simply communicate the control room relaying on ordinary internet protocol infrastructure networks. This technology provides a flexible network interface over a wide variety of heterogeneous technology networks. Though, the acceptance of IP designed for video monitoring pretense severe difficulties in terms of power processing, system dependability, required bandwidth, and security of networks. The advantage of IP based video monitoring system has been investigated over conventional analog systems and the challenges of the method are described. The open research issues are still requiring a final solution to permits complete abandon against conventional technology of analog methods. In conclusion, the method to tackle the purpose of video monitoring in actual operation is proposed and verified properly by means of model simulation.
Distance Grabbing

In Chapter 6, I mentioned that the VRTK provides you with two standard ways to interact with an Interactable game object. Direct Interaction is the most common form of Interaction. The other form of Interaction is the ability to grab an object from a distance.
Protecting Your Privacy in a Data-Driven World

Journal of the Royal Statistical Society: Series A (Statistics in Society)Early View BOOK REVIEW Protecting your privacy in a data-driven world , Bowen, Claire McKay, 2021, Pages: 148, Price: $39.19, ISBN 9780367640743 Stefan Stein, Stefan Stein S.Stein@warwick.ac.uk University of Warwick, Coventry, UKSearch for more papers by this author Stefan Stein, Stefan Stein S.Stein@warwick.ac.uk University of Warwick, Coventry, UKSearch for more papers by this author First published: 05 April 2022 https://doi.org/10.1111/rssa.12847Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Comments on USD Central Bank Digital Currency

The Board of Governors of the Federal Reserve System has engaged the public in its consideration of a USD Central Bank Digital Currency (USD CBDC). The Board has framed USD CBDC issues principally around payment market access and efficiency, tending to overlook questions CBDC raises about the nature, size and scope of the Federal Reserve's operations and services. This paper re-frames the question by highlighting the implications of USD CBDC for the monetary base and expansion of the Federal Reserve safety net to include the general public. Alternative approaches are suggested for broadening public access to safe assets, should there be a need, and the Board is encouraged to prioritize issues related to the Federal Reserve balance sheet and the conduct of monetary policy.