Reviewerly/exHarmony huggingface dataset:
Aspectos da depressão na velhice: revisão integrativa de literatura / Aspects of depression in old age: integrative literature review

Introduction: Depression is a psychological disease that can affect the human being in any disease of his life, but there are no challenges encountered in the treatment of an elderly person with depression, as this condition is always accompanied by other pathologies.Objective: This study has as its theme the aspects of coping with depression in old age, the symptoms and causes for this pathology.Methodology: This is an integrative literature review study that has a documentary and descriptive character.Results and thinking: Depression is a disease that does not depend on age, but in old age it develops this phase with all the factors of life, the prevalence is common.Conclusion: Depression in speed does not have a determining cause, but several factors that senescence can cause depression to have a high rate of involvement among the elderly, bringing this age group of death and feeling of worthlessness.
Role of Deep Learning in Medical Image Super-Resolution

Recently, deep learning-based convolutional neural networks method for image super-resolution has achieved remarkable performance in various fields including security surveillance, satellite imaging, and medical image enhancement. Although these approaches obtained improved performance in medical images, existing works only used a pre-processing step and hand-designed filter methods to improve the quality of medical images. Pre-processing step and hand-designed-based reconstructed medical image results are very blurry and introduce new noises in the images. Due to this, sometimes medical practitioners make wrong decisions, which are very dangerous for human beings. In this chapter, the authors explain that the hand-designed as well as deep learning-based approaches, including some image quality assessment metrics to open the gate to verify the images with different approaches, depend on the single image approach. Furthermore, they discuss some important types of medical images and their properties.
Real Time Body Orientation Recognition for Customer Pose Orientation

Background/Purpose: One of the most significant areas in marketing is consumer position analysis. Retailers can assess the extent of client interest in the goods based on customer pose data. Due to occlusion and left-right similarity difficulties, pose estimation is problematic. We describe a CNN-based solution that includes the body orientation and visibility mask to overcome these two challenges. It provides global information about posture configuration using simple gaits in a retail setting. When a person looks to the right, for example, the left side of his or her body is hidden by the body orientation. In the same way, the person faces the camera, the right shoulder will most likely be on the image's left side. A novel Deep Neural Network design is used to merge body orientation and local joint connections. Second, the visibility mask simulates each joint's occlusion state. Because body orientation is the major source of self-occlusion, it is inextricably tied to it. Detecting an occluding object (such as a shopping cart in a retail setting) might provide give visibility mask prediction clues. Global body position, local joint connections, client mobility, and occluding obstructions are all taken into account in the final advised method. Finally, we run a number of comparison tests to see how effective our technique is. Objective: This work presents customer posture estimation, and a visibility mask to build a prototype for inner and self-occlusion. It also concentrates on local joint connections, global body orientation, and customer mobility. Methodology: The suggested technique is depicted in its entirety in Figure-2. To figure out which portion of the human picture is concealed, we employ stance markers to identify the viewable areas. The landmarks in the occlusion zone have a lower confidence score when we extract posture landmarks. As a result, it is possible to obtain visible masks incorporating occlusion information. We employ visible signs to assist the hidden individual in three ways. To begin, visible masks are utilized to detect viewable parts and to construct spatial masks that filter noise caused by occlusions. Findings/Results: The proposed method outperforms to overcome left-right similarity difficulties, the network incorporates body orientation information, and the visibility mark layers are introduced into the network to enhance the efficiency of occluded joints. Conclusion: For customer pose estimation, A novel architecture using the concepts of Deep Learning is proposed and the occluding object detection clearly provides inter-occlusion by object cues. As a result, local joint connection, the global body orientation, and occluding object and human motion. Paper Type: Research article.
The Empirical Face Mask Detection Test

PC Vision can handle numerous between expansive applications that may go from agribusiness to clinical consideration. It can in like manner be executed to handle various issues that individuals demonstrated unfit. Regardless, during this outrageous COVID-19 pandemic situation, Computer Vision are often accustomed be used to contain this original Covid. As of shortly ago, there has been no useful vaccination to repair this affliction. In any case, the potential outcomes of transmission can almost be negated expecting that there is an expansive spread usage of covers, proper cleansing, and maintaining social isolating. This paper bases on making an application that perceives whether or not a personal is wearing a facial covering with Open Source Computer Vision library OpenCV using Python. Here, the client picture is gotten from the video move, then, at that time, preprocess it and later apply a couple haar flood classifiers to acknowledge face, eyes, nose, and mouth from the image. Considering the characteristics got, we then, apply decision reasoning to work whether a cover is accessible. This application can be applied during a few purpose cases, as an example, present day utility where there’s a motivation for utilization of canopy.
Multimodal Complex Road Network and Its Traffic Organization using GIS

Traffic engineering is the branch of engineering that deals with the planning, geometric design, and networks of these elements, as well as their connections to other forms of transportation, in order to enable safe and convenient movement of people and commodities. The safe and efficient movement of people and products is determined by traffic flow, whichis in turn determined by traffic characteristics. The current road infrastructure will not be able to meet future needs without adequate planning and traffic management. The present traffic studies are being carried out in Salem. Several blackspots have been detected with data and mapped using GIS, according to MORTH (Ministry Of Road Transport and Highway). Because it is a popular tool for the display and hotspot analysis of accident data. GIS technology is widely employed in transportation. Existing road networks must be optimised, and road safety measures must been enhanced, and GIS can be a significant tool in reducing the risk of road accidents. Users using GIS mapping may be able to avoid unwanted traffic and accidents. The study makes a significant contribution to a clear picture of traffic volume at key intersections and its importance.
A New Secure Crowdfunding Transaction Scheme Based on Blockchain

Online crowdfunding, an innovative model based on “[Formula: see text]”, is a hot spot for financing via Internet. Crowdfunding based on blockchain is an emerging economic phenomenon and becomes one of the most advanced risk financing strategies. However, crowdfunding transactions face security threats due to identity leaks, quantum attacks and the untraceable nature of blind signatures, which facilitate criminal activity. Different from the previous works, which ignored the importance of traceability, in this paper, we establish a blockchain-empowered secure crowdfunding architecture and propose an anti-quantum partially blind signature algorithm based on the verifiable identity of both sides. Specially, for one thing, the private key decided by user identity is generated by lattice-based sample matrix, and the privacy of user identity can be ensured and traced by the rejection sampling theorem. For another thing, we design an improved krill herd algorithm (IKHA) to increase the credit factor of fundraisers for dealing with project investment issues. The simulation evaluates the correctness and effectiveness of our theoretical analyses. Compared with the current popular schemes, the proposed IKH algorithm has a higher convergence speed and can optimize investment efficiency.
A Systematic Study of Bulletin Board and Its Application

Any person can post arbitrary strings on the bulletin board. Following publication on bulletin board, a party receives a "evidence" that the intended data were posted. The bulletin board is open to the public, which means that anyone can view its contents. The fundamental security criteria for a BB are that its contents cannot be deleted, and that no evidence of publication can be falsified. BB was commonly perceived as a trusted and publicly verifiable channel. It formed the backbone of many important protocols such as e-voting, secure multi-party computation etc. and quite often it was assumed to exist to support the execution of the main protocol. After the advent of blockchain technology, BB systems has found a strong footing regarding its implementation in a distributed manner. In this paper, we aim to provide a concise summary of the evolution of bulletin board, its implementation issues and security analysis with an emphasis on its impact to e-voting systems.
Exploring the Impact of Online Teaching on Academic Identities

The contextual changes in teaching and learning that have been accelerated by the COVID-19 pandemic have highlighted the need for an enhanced understanding of the intersection between the academic work of teaching and well-being. This chapter aims to explore how academics build and sustain their identities in digital spaces and what key conceptual solutions they hold to enable their own effective workplace learning. Semi-structured interviews with 11 academics of all ranks involving part- and full-timers working in a private university in the Republic of Cyprus were conducted. The findings reveal complexities and uncertainties facing academics in digital spaces, which led to their transformative learning, as the threshold concepts of their education philosophy were challenged. The chapter concludes by summarising suggestions for academics and institutions on how to support the development of academic digital competencies, ones that might be adopted in an attempt to create a more purposeful provision of ongoing workplace learning for academics in a post-pandemic era.
Social and Emotional Context of Fraud Scams in Cyberspace

Most research on internet-frauds has focused on victims' cognitive and personality vulnerabilities and ignored that scammers often have been victims of financial cyber-crimes [1]. These victim-offenders expressed retaliation as a motive to offend and highlight the overlooked emotions and social learning contributing to cyber-scams [2]. A broader understanding of the motives, emotions and knowledge of victim-offenders, solely offenders and solely victims might improve awareness campaigns and security training [3]. In this talk, I use a life-course perspective of social learning [4] to examine media and social sources, prior victimization, and knowledge and attitudes about relationships contribute to committing internet frauds. Data are drawn from two large self-report surveys of victimization and perpetration of a wide range of internet frauds. Deviant friends and family members, mentors, online discussions, and contacts in the dark web increase support for retaliation and provide praise for perpetrating internet-fraud. Those who attended victim support groups and have knowledge of dating app etiquette have more accurate knowledge about suspicious communications on dating apps. Beyond low self-control, psychopathy and committing frauds in the real world, those with higher rates of victimization more often perpetrated cyber-frauds. The life-course perspective suggests a broader view of the emotional and social context of offending might improve the content and focus of awareness campaigns and security training. These campaigns and training often ignore how scammers learn manipulative tactics from friends, family, media, and online sources. This focus also might enhance AI tools to detect and intercept fraudulent messages on dating and social media sites.
Indoor Autonomous Mobile Bobot Navigation Method Based on Ant Colony Algorithm

The conventional ant colony algorithm exists in such problems as slow convergence velocity, feeble global search capability and tend to fall into local optimality. To cope with this, an indoor mobile robot path planning approach is presented in this study which is based on a modified ant colony algorithm. On the basis of the classical ant colony algorithm, two angle pieces of information are being introduced to enhance the directionality of the algorithm and overcome the local optimum problem. In view of accelerating the capability of global search and convergence speed of the robot, the fluctuation coefficient of the pheromone is adaptively scaled with the iteration number. Results of experiments indicate that our proposed algorithm can not only effectively raise the convergence speed and precision, but also achieve smoother paths.
Critical assessment of the EU law on resales of digital content

The transferability of digital content is not exclusively an IP topic. Contracts and digital architecture are equally relevant hurdles. Besides, the debate on data ownership needs to be taken into account. Neither IP nor property law, nor ‚Äòdata ownership' allows acquirers to transfer downloaded data to a third party without copyright-holders' authorization. Instead, the equal treatment principle is essentially the only valid ground for courts to allow such transfers anyway, as implicitly shows from CJEU case law. When putting transferability into practice, consumer law and national law might take the lead. Subscribers to streaming services and other online access rights are often not entitled to transfer those rights. Nonetheless, they do have an interest to do so. New consumer law and unfair terms law might prove interesting to channel these interests. Moreover, legislative action can be considered to safeguard our capacities to transfer acquired digital assets in times of servitization.
Hand Gesture Based Speed and Direction Control of DC Motor Using Machine Learning Algorithm

The gesture is a foremost non-touching reciprocal action between humans and computers. The hand gesture can be utilized in many types of work such as home appliance control, physically handicapped person, smart car control, and many more. In this paper, a system is proposed to control the speed and direction of the DC motor by hand gesture. An apparel device called a data acquisition unit adheres to the hand so that it can record the gesticulation of the hand. This device converts the hand gesture into a set of discrete values and transmits the data via Bluetooth to another circuit that is connected to the computer. A data set is created by recording the values obtained for different gestures to train several machine learning models. The most efficient model to recognize the hand gesture is identified based on the performance of the models. The Cubic SVM model shows the maximum classification accuracy for our data set. The accuracy of Cubic SVM is 97.8%. This classification model is used in the real-time operation of the system. The system shows 95% accuracy in real-time gesture recognition in controlling the speed and direction of the DC motor.
APKC '22

Public key cryptography plays an essential role in ensuring many security properties required in data processing of various kinds. The theme of this workshop is novel public-key cryptosystems for solving a wide range of real-life application problems. This workshop solicits original contributions on both applied and theoretical aspects of public-key cryptography.
New Multiparty Computational Model

Nakamoto introduced a mechanism for leader election via proof of work, and allowed this dealer to speak once. In this setting parties do not have inputs and they output a value which they calculated. Jing and Micali took this idea a step further and showed that Byzantine agreement, a function where parties have inputs but no private information, can be reached in a setting where each party speaks only once. This was achieved by introducing the notion of player replaceability. In our recent works we have shown that any function, including those where parties have secret inputs, can be computed in the YOSO (you only speak once) model.
Open Challenges of Malware Detection under Concept Drift

The security community today is increasingly using machine learning (ML) for malware detection for its ability to scale to a large number of files and capture patterns that are difficult to describe explicitly. However, deploying an ML-based malware detector is challenging in practice due to concept drift. As the behaviors of malware and goodware constantly evolve, the shift in their data distribution often leads to serious errors in the deployed detectors. In addition, such dynamic evolvement further adds to the pressure of labeling new malware variants for model updating, which is already an expensive process. In this talk, I will introduce our recent exploration of the challenges introduced by malware concept drift and the potential solutions. I will first discuss the problem of detecting drifting samples to proactively inform ML detectors when not to make decisions. We explore the idea of self-supervision for drift detection and design the corresponding explanation methods to make sense of the detected concept drift. Second, to facilitate malware labeling and model updating, I will share our recent results from combining cheap unsupervised methods with the existing limited/biased labels to generate higher-quality labels. Finally, I will discuss the emerging threat of poisoning and backdoor attacks that exploit the dynamic updating process of malware detectors, and potential directions to robustify this process.
Design of Public Voluntary Carbon Market Mechanism based on Crosschain and Token Economics

Climate change is currently a key issue of humankind. As a crucial policy tool to reach net zero targets, carbon credit mechanism has developed to over 18,000 registered projects globally since the Kyoto Protocol, but is far below the desired environmental and social impacts. This paper aims at the public sector and proposes a public carbon voluntary market mechanism that involves extensive participation of the public and contributes to public emission reduction in the modern cities. To enhance the effectiveness, a dual token economic model is established as the incentive mechanism. Additionally, a crosschain method of relaychain technology is adopted to achieve a certain degree of isolatability of different scenario operators while ensuring the system security. Finally, the logical architecture of the public voluntary carbon market integrating work flow, info communication and value stream is presented.
Representation Uncertainty in the Earth Sciences

Abstract The first Joint Workshop on Representation Uncertainty in the Earth Sciences was held in March 2021. This brought together the Earth observation, data assimilation and forecast verification and post‐processing communities, alongside metrologists to discuss the definition and quantification of representation uncertainty within the Earth sciences. The aim of the workshop was to facilitate cross‐disciplinary discussion, establishing where existing methodologies could be shared and to foster future collaboration. A key outcome of the workshop was a working definition of representation uncertainty applicable across all the Earth science communities, which is presented in this white paper. The cross‐disciplinary discussions at the workshop highlighted the need for scientists to work with metrologists to establish a common vocabulary for uncertainties, accessible to Earth science applications. Further recommendations included regular workshops to discuss progress in defining and quantifying representation uncertainty and awareness of cross‐disciplinary funding opportunities to further address representation uncertainty issues.
Processing capabilities and low-pass filtering effect in a delay-based photonic reservoir computer

We explore the performance of a photonic reservoir computer based on a semiconductor laser with bandpass filtered optoelectronic feedback, focusing on how changing the low-pass filtering affects the system’s processing capabilities: the computational ability (CA) and the memory capacity (MC). Analysis of the system’s eigenvalue spectrum lets us find the correlation between the distance from the imaginary axis to the nearest eigenvalue and the MC of the reservoir computer. The maximum of the MC is observed when many eigenvalues can be characterized as having small damping, and the overall spectral shape can also be considered as a relatively flat top. We introduce a measure that evaluates the average distance between the real parts of the pseudo-continuous (PC) spectrum and the imaginary axis. The average distance is well-correlated to the MC, and the largest values of MC correspond to the smallest values of the average distance. Despite the significant decrease in the system’s bandwidth, the maximal CA remains sufficiently unchanged with variations in the low-pass cut-off frequency. However, while MC is maximized when the average distance is minimal, the CA reduces and vanishes in the vicinity of the Hopf bifurcation borders. It can be attributed to the interference of the previous inputs kept in the reservoir’s memory. Changing the low-pass cut-off frequency changes both the memory capacity and the computational ability of an optoelectronic feedback RC. Reducing the distance between the real parts of the pseudo-continuous spectrum and the imaginary axis correlates with an increase in the memory capacity of an optoelectronic feedback RC and a decrease in its computational ability.
Electronic Phenotyping to Identify Patients with Arrythmia Disease from a Hospital Information System

Electronic phenotyping is an important method to identify a disease group by collecting clinical data from hospital information systems. This study aimed to extract accurate cases of supraventricular arrythmia, ventricular arrythmia, and bradycardia from clinical data of a hospital information system. The electronic phenotyping algorithm was improved using the machine learning method. Subsequently, it showed a higher area under the curve for prediction and higher specificity. However, the algorithm needs further improvement to classify each arrythmia disease accurately. In conclusion, phenotyping using clinical data from hospital information systems has some affinities and issues depending on the disease.