Reviewerly/exHarmony huggingface dataset:
Why Extending Actions through Time Can Violate a Moral Right to Privacy

Recently, Ingmar Persson and Julian Savulescu argued that an action that does not violate a moral right to privacy cannot violate that right if it is extended over time. Specifically, they argue that a moral right to privacy does not protect against gawking or stalking. In this reply the reverse position is defended. Specifically, it is argued that their arguments fails on according to their own definition of the right to privacy. Furthermore, it is argued and illustrated by examples that this conceptual conclusion holds for many other conceptions of the right to privacy. Moreover, it is argued that Persson and Savluescu fail to recognize the privacy-related harm extended to individuals through gawking or stalking. Lastly, the article ends by sketching how the role of privacy in public and consent relates to the given examples.
A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.
Word-Based Systolic Processor for Field Multiplication and Squaring Suitable for Cryptographic Processors in Resource-Constrained IoT Systems

Internet of things (IoT) technology provides practical solutions for a wide range of applications, including but not limited to, smart homes, smart cities, intelligent grid, intelligent transportation, and healthcare. Security and privacy issues in IoT are considered significant challenges that prohibit its utilization in most of these applications, especially relative to healthcare applications. Cryptographic protocols should be applied at the different layers of IoT framework, especially edge devices, to solve all security concerns. Finite-field arithmetic, particularly field multiplication and squaring, represents the core of most cryptographic protocols and their implementation primarily affects protocol performance. In this paper, we present a compact and combined two-dimensional word-based serial-in/serial-out systolic processor for field multiplication and squaring over GF(2m). The proposed structure features design flexibility to manage hardware utilization, execution time, and consumed energy. Application Specific Integrated Circuit (ASIC) Implementation results of the proposed word-serial design and the competitive ones at different embedded word-sizes show that the proposed structure realizes considerable saving in the area and consumed energy, up to 93.7% and 98.2%, respectively. The obtained results enable the implementation of restricted cryptographic primitives in resource-constrained IoT edge devices such as wearable and implantable medical devices, smart cards, and wireless sensor nodes.
Semi-automatic Construction of Financial Knowledge Graph Based on Web

Web pages about finance and economics on the Internet contain vast amounts of data with descriptions of various entities and their relationships. These HTML pages contain semi-structured as well as structured data, which can construct graphable knowledge acquisition. This paper proposes a semi-automatic construction method of financial knowledge graph based on Web. Populated template is used to construct the bait template for the data source. Knowledge graph is constructed by extracting triples data from multiple data sources by enticing templates. In addition, this paper designs an application system to build the knowledge graph for the financial field, which can significantly reduce the human participation and realize the construction of the knowledge graph quickly.
Automatic Summarization on Restaurants Opinion Mining

Innovation has been a fundamental piece of our lives. When going bent another cafe/restaurants or bistro, individuals normally use sites or applications to question close by spots and afterward select one hooked in to normal rating. Notwithstanding, the traditional rating is now and again insufficient to foresee the character of the café as individuals have alternate points of view and wishes while assessing an eatery. During this paper, an overview framework for sentiments in eatery audits is proposed. The framework may be a useful device for clients during a hurry to assist them improve decisions about the nature of a restaurant while saving their time. This is often finished via naturally and rapidly furnishing the clients with a synopsis of the sentiments within the café's/restaurants surveys. The proposed synopsis framework has been actualized during a versatile area based application with KNN Algorithm and Multi keyword search it accomplished a high convenience score.
Network Function Virtualization over Cloud-Cloud Computing as Business Continuity Solution

Cloud computing provides resources by using virtualization technology and a pay-as-you-go cost model. Network Functions Virtualization (NFV) is a concept, which promises to grant network operators the required flexibility to quickly develop and provision new network functions and services, which can be hosted in the cloud. However, cloud computing is subject to failures which emphasizes the need to address user’s availability requirements. Availability refers to the cloud uptime and the cloud capability to operate continuously. Providing highly available services in cloud computing is essential for maintaining customer confidence and satisfaction and preventing revenue losses. Different techniques can be implemented to increase the system’s availability and assure business continuity. This chapter covers cloud computing as business continuity solution and cloud service availability. This chapter also covers the causes of service unavailability and the impact due to service unavailability. Further, this chapter covers various ways to achieve the required cloud service availability.
Design Research on Visualization of Life Behavior and Rhythm

Traditional scientific research on visualization has gradually changed from emphasizing order and stability to disorder and changes of new science. The visual expression of lifestyle helps designers to understand people’s real life and create more ideal design works to meet people’s life demands. This essay discusses how to understand life behavior and rhythm by integrating philosophy, sociology and other interdisciplinary knowledge. It focuses on how to visually present people’s life behavior and rhythm through visual graphics, so as to understand the living habits and characteristics of a person and a group of people.
Understanding cybercriminals through analysis of penetration testing group dynamics

Cyberattacks are a major threat in the modern era, yet there is a lack of information on how cybercrime groups think and operate. This paper aims to better understand cyber adversaries by analyzing penetration testing teams during the 2018 and 2019 National Collegiate Penetration Testing Competition, in which groups of students performed similar actions as cybercriminals, attempting to identify and exploit system vulnerabilities. Using penetration testing teams as an ethical proxy for cybercrime groups allows the researchers to study group dynamics as well as factors impacting the rationality of cybercriminals. Themes identified in manually coded interview transcripts are compared to the existing literature on cybercrime groups. Similar to what is established in the prior research, themes emerged in the interviews on the group structure and dynamics of each team, featuring elements of leadership, division of labor, the role of each team member, the presence of partners and subgroups, communication within the team, and interpersonal team member relationships. Other apparent factors that specifically impacted the bounded, or limited, rationality of the team members included setbacks and problem solving, the competition environment, stress, and issues with morale. This comparison of penetration testing groups with cybercrime groups allows for the development of a better understanding of the operations and rational thinking of a criminal organization, which may lead to a better understanding of how to prevent or defend against cyberattacks, such as by improving response times of the security team or by increasing the difficulty of penetrating the technical environment.
Computational Hardness of Optimal Fair Computation: Beyond Minicrypt

Secure multi-party computation allows mutually distrusting parties to compute securely over their private data. However, guaranteeing output delivery to honest parties when the adversarial parties may abort the protocol has been a challenging objective. As a representative task, this work considers two-party coin-tossing protocols with guaranteed output delivery, a.k.a., fair coin-tossing. In the information-theoretic plain model, as in two-party zero-sum games, one of the parties can force an output with certainty. In the commitment-hybrid, any r-message coin-tossing protocol is $${1/\sqrt{r}}$$ -unfair, i.e., the adversary can change the honest party's output distribution by $$1/\sqrt{r}$$ in the statistical distance. Moran, Naor, and Segev (TCC–2009) constructed the first 1/r-unfair protocol in the oblivious transfer-hybrid. No further security improvement is possible because Cleve (STOC–1986) proved that 1/r-unfairness is unavoidable. Therefore, Moran, Naor, and Segev's coin-tossing protocol is optimal. However, is oblivious transfer necessary for optimal fair coin-tossing? Maji and Wang (CRYPTO–2020) proved that any coin-tossing protocol using one-way functions in a black-box manner is at least $$1/\sqrt{r}$$ -unfair. That is, optimal fair coin-tossing is impossible in Minicrypt. Our work focuses on tightly characterizing the hardness of computation assumption necessary and sufficient for optimal fair coin-tossing within Cryptomania, outside Minicrypt. Haitner, Makriyannia, Nissim, Omri, Shaltiel, and Silbak (FOCS–2018 and TCC–2018) proved that better than $$1/\sqrt{r}$$ -unfairness, for any constant r, implies the existence of a key-agreement protocol. We prove that any coin-tossing protocol using public-key encryption (or, multi-round key agreement protocols) in a black-box manner must be $$1/\sqrt{r}$$ -unfair. Next, our work entirely characterizes the additional power of secure function evaluation functionalities for optimal fair coin-tossing. We augment the model with an idealized secure function evaluation of f, a.k.a., the f-hybrid. If f is complete, that is, oblivious transfer is possible in the f-hybrid, then optimal fair coin-tossing is also possible in the f-hybrid. On the other hand, if f is not complete, then a coin-tossing protocol using public-key encryption in a black-box manner in the f-hybrid is at least $$1/\sqrt{r}$$ -unfair.
Introduction to the special section on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (VSI-gridl)

Color image watermarking has achieved great success in various applications such as copy protection, content authentication, tamper detection and recovery, etc. The present paper provides a detailed overview of watermarking along with its characteristics, evaluation metrics, and current applications. We then provide a comprehensive survey of different approaches along with their merits and limitations. Furthermore, we summarize each of the approaches in detail, including objectives, goals, embedding location, evaluation metrics, and weaknesses, and discuss recent challenges and their possible solutions. We believe this paper will provide readers with comprehensive insight into the field of watermarking research.
Human Motion Capture Technology Based On Multi-Binocular Vision And Image Recognition

This paper proposes a human motion capture system based on multi-binocular vision and image recognition. The system which relies on the principle of binocular imaging and stereo matching of the same name points of the human joints in the left and right views to obtain the 3D coordinates of the human joint points in real time, uses multiple binocular cameras placed at different angles for simultaneous shooting, expanding the integrity of the original binocular vision system. At the same time, the detection model of 3D abnormal estimation points of human joints under multi-binocular vision is established, so that the system can more accurately identify the 3D abnormal estimation points of human joints due to occlusion. Finally, the average coordinates of the non-abnormal human 3D joint points obtained by other binocular cameras are dynamically selected as system's final estimated coordinates, thereby further improving the accuracy of human motion capture.
A Survey for Conventional Regression- and Deep Learning-based Face Alignment Methods

Face alignment, as an important part of facial tasks, will affect the final efficiency and accuracy. Face alignment is to locate the exact shape of a detected face bounding box. There are amount of challenges in face alignment because of large poses, occlusions and illuminations in real-world conditions. The approaches to tackle these challenges can be categorized in methods based on regression, which require operators in feature extraction, and methods based on deep learning, in which the feature extraction is data driven.
Virtual Experiment Platform For Middle School Entrance Examination

The virtual experiment platform of the middle school entrance examination is a platform for junior high school students to simulate the experiment. It integrates the experimental knowledge of the middle school entrance examination into the virtual experiment, so that students can have a clearer understanding of the experimental phenomena and conclusions, and master the basic use of the experimental instruments of the middle school entrance examination. And cultivate students' ability of observation, practical ability and the ability of linking experiment with theoretical knowledge.
Strategic Mineral Mapping in the Altiplano-Puna Volcanic Complex Using Orbital Multispectral and Hyperspectral Imageries

We have developed a method that can effectively identify and map the distribution and semi-quantitative abundance of minerals in a volcanic terrain using NASA's spaceborne spectral instruments. Our method synergistically combines spatially extensive but spectrally low-resolution Landsat multispectral imageries with spatially narrow but spectrally high-resolution Hyperion hyperspectral imageries. This approach profits from the advantages of the two types of data while compensating the disadvantages, leading to retrievals of reasonably detailed mineralogical information within a practical processing timeframe. We mapped the mineralogy of the Altiplano-Puna volcanic province in the Central Andes and detected ferric iron minerals, alunite, and kaolinite. This contributes to the ongoing efforts to better understand the Altiplano-Puna region for scientific and industrial purposes, as well as a demonstration for analysis of other planetary surfaces using similar datasets.
C-PO: A Context-Based Application-Placement Optimization for Autonomous Vehicles

Autonomous vehicles are complex distributed systems consisting of multiple software applications and computing nodes. Determining the assignment between these software applications and computing nodes is known as the application-placement problem. The input of this problem is a set of applications, their requirements, a set of computing nodes, and their provided resources. Due to the potentially large solution space of the problem, an optimization goal defines which solution is desired the most. However, the optimization goal used for the application-placement problem is not static but has to be adapted according to the current context the vehicle is experiencing. Therefore, an approach for a context-based determination of the optimization goal for a given instance of an application-placement problem is required. In this paper, we introduce C-po, an approach to address this issue. C - PO ensures that if the safety level of a system drops due to an occurring failure, the optimization goal for the successively executed application-placement determination aims to restore the safety level. Once the highest safety level is reached, C-po optimizes the application placement according to the current driving situation. Furthermore, we introduce two methods for dynamically determining the required level of safety.
Development of a 3D visualization system for the cerebral aneurysm coil embolization

In this study, a system has been developed to draw three-dimensional coordinates of a catheter in real time using Unity for cerebral aneurysm coil embolization, which is one of the endovascular treatments. The system detects the tip of a catheter by binarization and delineation from X-ray images taken from two directions (frontal and lateral views) and calculates the tip coordinates of the catheter in the 3D model of blood vessels generated using VTK from DICOM images. In this system, surgeons can more effectively visualize the catheter tip in 3D by using an head-mounted display, a virtual reality device. In the evaluation experiment, we compared the catheter tip detection with conventional methods and found that it was faster and more accurate than conventional methods. Future tasks include accurate positioning of 2D images and 3D models and drawing catheters other than the tip.
Towards Explainable Visionary Agents: License to Dare and Imagine

Since their appearance, computer programs have embodied discipline and structured approaches and methodologies. Yet, to this day, equipping machines with imaginative and creative capabilities remains one of the most challenging and fascinating goals we pursue. Intelligent software agents can behave intelligently in well-defined scenarios, relying on Machine Learning (ML), symbolic reasoning, and the ability of their developers for tailoring smart behaviors to specific application domains. However, to forecast the evolution of all possible scenarios is unfeasible. Thus, intelligent agents should autonomously/creatively adapt to the world’s mutability. This paper investigates the meaning of imagination in the context of cognitive agents. In particular, it addresses techniques and approaches to let agents autonomously imagine/simulate their course of action and generate explanations supporting it, and formalizes thematic challenges. Accordingly, we investigate research areas including: (i) reasoning and automatic theorem proving to synthesize novel knowledge via inference; (ii) automatic planning and simulation, used to speculate over alternative courses of action; (iii) machine learning and data mining, exploited to induce new knowledge from experience; and (iv) biochemical coordination, which keeps imagination dynamic by continuously reorganizing it.
Multi-Population Genetic Algorithm Based on Adaptive Learning Mechanism

Traditional genetic algorithm has some disadvantages, such as slow convergence, unstable, and easy to fall into local extreme. In order to overcome these disadvantages, an improved genetic algorithm is proposed in the present study. First, based on the analysis of advantages and disadvantages of learning mechanisms in literature, new improvements of learning mechanisms under the multi-population parallel GA are made. In previous studies, gene patterns from which other individuals can learn will be extracted from the excellent individuals of the population, this study improved the learning mechanism by adaptively changing the related control parameters, and dynamically controlling the process of the learning mechanism. Simulation results show that the new algorithm has a great improvement in many aspects of the global optimization, such as convergence speed, the accuracy of the solution, and stability.
Research on Social Innovation Design of SCD Pre-hospital Emergency Equipment Based on IoT Technology

This paper analyzes and summarizes the products in the current emergency market, and in view of the fact that there are few emergency products for sudden cardiac death in the market, and the mainstream emergency products have different degrees of disadvantages, such as poor timeliness, inconvenient transportation, difficult to use, high limitations, etc. Taking emergency equipment such as AED and automatic compression devices as examples, discuss the feasibility, necessity and specific methods of applying IoT technology to pre-hospital emergency equipment. Through the product design case, this paper puts forward the solution of the application of IoT technology in the pre-hospital emergency equipment for patients with sudden cardiac death.At present, medical emergency is an important social problem, the traditional first aid process and specific operation obviously can't deal with emergencies very well. However, it takes a long time to improve public awareness, skills training and operation process innovation, and the effect is very little. In contrast, it will be more effective and faster to make breakthroughs in the technical innovation, function improvement and social innovation design of emergency equipment. This study adheres to the concept of innovating the industry and designing to serve the society. It aims to compare the application of the existing pre-hospital emergency equipment, form a modern WITMED emergency mode through the combination of IoT and medical treatment, create perfect user experience, provide auxiliary equipment for pre-hospital emergency care, improve the survival rate of patients and improve the status of pre hospital first aid.