Reviewerly/exHarmony huggingface dataset:
Privacy Concerns and Acceptance Factors of OSINT for Cybersecurity: A Representative Survey

The use of Open Source Intelligence (OSINT) to monitor and detect cybersecurity threats is gaining popularity among Cybersecurity Emergency or Incident Response Teams (CERTs/CSIRTs). They increasingly use semi-automated OSINT approaches when monitoring cyber threats for public infrastructure services and incident response. Most of the systems use publicly available data, often focusing on social media due to timely data for situational assessment. As indirect and affected stakeholders, the acceptance of OSINT systems by users, as well as the conditions which influence the acceptance, are relevant for the development of OSINT systems for cybersecurity. Therefore, as part of the ethical and social technology assessment, we conducted a survey (N=1,093), in which we asked participants about their acceptance of OSINT systems, their perceived need for open source surveillance, as well as their privacy behavior and concerns. Further, we tested if the awareness of OSINT is an interactive factor that affects other factors. Our results indicate that cyber threat perception and the perceived need for OSINT are positively related to acceptance, while privacy concerns are negatively related. The awareness of OSINT, however, has only shown effects on people with higher privacy concerns. Here, particularly high OSINT awareness and limited privacy concerns were associated with higher OSINT acceptance. Lastly, we provide implications for further research and the use of OSINT systems for cybersecurity by authorities. As OSINT is a framework rather than a single technology, approaches can be selected and combined to adhere to data minimization and anonymization as well as to leverage improvements in privacy-preserving computation and machine learning innovations. Regarding the use of OSINT, the results suggest to favor approaches that provide transparency to users regarding the use of the systems and the data they gather.
Does Governance in MENA Countries Attract Migrants' Remittances?

Over the past years, international remittance of migrants to the Middle East and North Africa (MENA) has increased dramatically and become more resilient external financing sources than other capital flows, such as FDI, portfolio investment, and foreign aid. Nevertheless, the magnitude of remittances flowing into that region is less than expected. The research, hence, attempts to analyze the impact of governance on the volume of remittances in receiving economies in MENA from 1996 until 2020 by using the pooled mean group, mean group, and dynamic fixed effect methods. The results show that poor governance in MENA stimulates migrants to remit more to support and assist their relatives with their deplorable living conditions. Consequently, policymakers in the MENA region must strive to identify the underlying causes of weak governance and provide applicable solutions, as migrants' remittances cannot entirely eradicate the damage caused by the poor quality of governance. They also should provide an attractive environment for international remittances by applying policies pushing migrants to remit more, such as accelerating administrative procedures for business and increasing the interest rate on foreign currency deposits.&#x0D;
Security protection method of power system database based on cloud platform

With the progress and development of the power system, the automation, information, and intelligent technology of the power system have received more powerful technical support. However, as an important "network battlefield" in many countries, the security problem of the power monitoring system is becoming more and more prominent. Aiming at the problem that the threat situation of power databases is obviously rising under the action of malicious attacks, this paper proposes a security protection method for power system database based on the cloud platform. A public key and a private key are set by using an RSA encryption algorithm to encrypt the database; based on the Amazon Web Services of the cloud platform, a large prime number is selected from the Simple Storage Service of the cloud platform as a client key in a random selection mode. The database judges the legality of the access according to the relationship between the received verification key and the public key and private key set by the RSA encryption algorithm and makes feedback on the access after judging that the access is legal. The test results show that after the implementation of the design method, the threat situation value of the database is always less than 0.50 under different attacks, which can play a role in security protection.
Support Vector Machines

Let us imagine a linearly separable training data set, characterized by only two class labels. In such a situation, the binary classification task can be accomplished by determining a linear separator for the training points. Given that an infinite number of possible linear separators exist in general, methods such as the Perceptron algorithm find just any of the existing separators, while other methods search for the “best” linear separator, according to some criterion. Support Vector Machines (SVMs) aim to find a decision surface that is maximally far away from any data point, with the objective of maximizing classification accuracy and robustness, and generalization ability. In this chapter, SVMs are first introduced for binary classification and for linearly separable problems. Then, the concepts are extended to nonlinearly separable problems and multiclass classification.
Genetic Programming

GAs, studied in Chap. 3 , are capable of solving many problems and simple enough to allow for solid theoretical studies. Nevertheless, the representation of individuals that characterizes GAs (i.e., the fact that individuals in GAs must be strings of a previously fixed length) can be a limitation for a wide set of applications. In these cases, the most natural representation for a solution is a hierarchical computer program, rather than a string of characters of a fixed length. For example, strings of a static length do not readily support the hierarchical organization of tasks into subtasks typical of computer programs, they do not provide any convenient way of incorporating iteration and recursion, and so on. But above all, GA representation schemes do not have any dynamic variability: the initial selection of string length limits in advance the number of internal states of the system and limits what the system can learn.
Particle Swarm Optimization

Particle Swarm Optimization (PSO) [Kennedy, 2010, Kennedy and Eberhart, 1995] is an optimization algorithm designed for continuous optimization. Like GAs, it is a population-based stochastic method, but unlike GAs it does not take its inspiration from the Theory of Evolution of Darwin, but from the social behavior of bird flocking or fish schooling [Reynolds, 1987]. For instance, one may imagine a flock of birds flying over an area, to find a point to land. In such a situation, defining where the whole swarm should land is a complex problem, since it depends on many pieces of information, such as, for instance, maximizing the availability of food or minimizing the risk of existence of predators.
Artificial Neural Networks

Artificial Neural Networks (ANNs) are computational methods that belong to the field of Machine Learning [Mitchell, 1997, Kelleher et al., 2015, Gabriel, 2016]. The aim of ANNs is to implement a very simplified model of the human brain. In this way, ANNs try to learn tasks (to solve problems) mimicking the behavior of the brain. The brain is composed of a large set of specialized cells, called neurons. Each single neuron is, in itself, a very simple entity, and the power of the brain is given by the fact that neurons are numerous and strongly interconnected, by means of connections called synapses. The brain learns because neurons are able to communicate with each other. A picture of a biological neuron and its synapses is shown in Figure 7.1. Biological neurons can receive stimuli and, as a consequence, emit (electric) signals, which can stimulate other neurons. When a biological neuron emits its signal, we say that it “fires”.
Bayesian Learning

Bayesian learning (Tipping, 2004; Barber, 2012) is the name commonly used to identify a set of computational methods for supervised learning based on Bayes’ Theorem. Broadly speaking, Bayes’ Theorem deals with the modification of our perception of the probability of an event, as a consequence of the occurrence of one or more facts. For instance, what probability are you assigning to the event “somebody stole my car” at the moment? Of course, this can depend on many different factors, but on a normal day, one may argue that that probability is generally rather low. Now, imagine that you go looking for your car, and the car is not in the place where you remember that you parked it. What is now the probability of the event “somebody stole my car”? The fact that the car is not where it was parked clearly changes the probability that it was stolen. This property is general: the realization of some events can modify the probability of others. This property can be exploited to tackle Machine Learning tasks, for instance classification: data, interpreted as events, can be used to change the probability that a given observation belongs to a given class. Before studying this mechanism in detail, let us first present Bayes’ Theorem and its most immediate use in Machine Learning.
Testing quantum computers with the protocol of quantum state matching

Abstract The presence of noise in quantum computers hinders their effective operation. Even though quantum error correction can theoretically remedy this problem, its practical realization is still a challenge. Testing and benchmarking noisy, intermediate-scale quantum (NISC) computers is therefore of high importance. Here, we suggest the application of the so-called quantum state matching protocol for testing purposes. This protocol was originally proposed to determine if an unknown quantum state falls in a prescribed neighborhood of a reference state. We decompose the unitary specific to the protocol and construct the quantum circuit implementing one step of the dynamics for different characteristic parameters of the scheme and present test results for two different IBM quantum computers. By comparing the experimentally obtained relative frequencies of success to the ideal success probability with a maximum statistical tolerance, we discriminate statistical errors from device specific ones. For the characterization of noise, we also use the fact that while the output of the ideal protocol is insensitive to the internal phase of the input state, the actual implementation may lead to deviations. For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume, while for random inputs they show a more similar performance.
Healthcare Multimedia Data Analysis Algorithms Tools and Applications

In the domain of information retrieval, there exists a number of models which are used for different sorts of applications. The extraction of multimedia is one of the types which specifically deals with the handling of multimedia data with different types of tools and techniques. This chapter provides a complete insight into the audio, video, and text semantic descriptions about the multimedia data with the following objectives: i) methods ii) data summarization iii) data categorization and its media descriptions. Upon considering this organization, the entire chapter has been dealt with a case study depicting feature extraction, merging, filtering, and data validation.
FIP2DMP: Linking data management plans with FAIR implementation profiles

FIP2DMP is a mapping resource between a FAIR Implementation Profile (FIP) and a Data Management Plan (DMP) template implemented in the Data Stewardship Wizard. The main benefit of this resource is to facilitate easier adoption of community choices from the FIP by a researcher in their DMP.
INCREASING THE SPATIAL ACCURACY OF THE LAND USE MAP USING FUSION OF OPTICAL AND RADAR IMAGES OF SENTINEL AND GOOGLE EARTH ENGINE

Abstract. Nowadays, exact and real-time land use and land cover (LULC) maps are imperative to supply precise data for energetic observing, arranging and arrive administration. With the appearance of cloud computing frameworks, time arrangement includes extraction methods, and machine learning classifiers, unused openings emerge in more precise and larger-scale LULC mapping. In this research, the aim is to obtain a land use map with a spatial resolution of 10 meters by combining Sentinel-1 radar images and Sentinel-2 optical images. The processing was done in Google Earth Engine cloud computing system. Also, in this research, NDVI map and NDBI map were used to increase classification accuracy. Classification was done using the decision tree method and in eleven classes. To evaluate the classification accuracy and the final map, randomly collected points were used in the study area. The results of the comparison of the ground collection points and the final classified map showed that the classification accuracy in this study was around 94%. The results obtained from such as research can be optimally used in urban planning and crisis management.
Interpretable Neural Symbol Learning Methods to Fuse Deep Learning Representation and Knowledge Graph: Zhejiang Cuisine Recipe Intangible Cultural Heritage Use Case

Deep learning (DL) is difficult to provide explanations verified by non-technical audiences such as end-users or domain experts. This paper uses symbolic knowledge in the form of an expert knowledge graph, and proposes an interpretable neural-symbol learning (RF-YOLOv5) method, designed to learn symbols and deep representations. Finally, the deep learning representation and knowledge map are integrated in the learning process, so as a good basis for interpretability. Among them, the RF-YOLOv5 method involves specific two aspects of interpretation, respectively in reasoning and training time (1) YOLOv5-EXPLANet: experts alignment explained part of the auxiliary network architecture, combined convolutional neural network, using symbol representation, and (2) interpretable artificial intelligence training process, correct and guide the DL process and such symbol representation form of knowledge graph. The camera is placed above the refrigerator to detect the variety of ingredients, and then used in the RF-YOLOv5 method recommended by Zhejiang cuisine recipes, and demonstrates that using our method can improve interpretability while improving interpretability.
SISTEM INFORMASI UNTUK MENENTUKAN PENERIMAAN BANTUAN PROGRAM KELUARGA HARAPAN DI KELURAHAN PASAR MERAH TIMUR

The Family Hope Program (PKH) is a social assistance program for underprivileged and vulnerable families by the Ministry of Social Affairs of the Republic of Indonesia with various conditions. This program aims to help the poor as well as a mechanism for social protection and poverty alleviation. The selection process that took place at the Pasar Merah Timur Kelurahan office still had several drawbacks, because the selection process was still manual and there was an element of subjectivity in determining potential PKH beneficiaries, this would result in PKH beneficiaries not being on target. To solve this problem, a web-based information system for determining Family Hope Program Assistance (PKH) was designed. The existence of this web-based system can help make it easier to determine the PKH assistance process using the information system.
Pelatihan Pembuatan Logo Produk guna Mengoptimalkan Peluang Bisnis pada Remaja RT 02 RW 13 Kelurahan Rangkapan Jaya Lama Kota Depok

The program partners are teenagers from RT 002 Rw 013, Rangkapan Jaya Lama Village, Depok City. The main problem for our partners is that hospitals still have the human resources to prepare them as well as possible for the challenges of the time. Design is the design of a product or object to be manufactured, and graphics is the science of forming dots and lines to provide information and form images related to the printing process. This graphic design training can be taught from the high school level, so soft-skills are relevant to creating product logos. With these soft-skills, teens can even turn it into a source of income by offering photo editing services. Additionally, this skill will enable students to easily edit photos in an ever-evolving technology environment. The hope is to kickstart entrepreneurship by launching graphic design services to support future steps in building the creative economy. Allowing you to start early to improve your skills and mindset to build.
Advertising in the Metaverse: Opportunities and Challenges

With the continuous upsurge of virtual and augmented reality, technology is seen precipitously evolving introducing new innovations that would have been formerly unbelievable. One of these innovations is the metaverse, a distinctive and immersive virtual world, a network of 3D virtual environments resided by avatars of actual people that focuses on social connections. This virtual world would continue to evolve and develop based on consumers&amp;rsquo; choices and interactions within this space, synchronized with the real world that has no end. The metaverse can be described as an indefinite universe that continues to swell as more and more users are involved, merging reality and virtuality in one.&#x0D; &#x0D; In the field of digital advertising and marketing, advertising agencies and strategists need to keep up with the speed of the latest artificial intelligence developments, with a full understanding of the metaverse and its potential. Keeping in mind the main target audiences, Gen Z and millennials, as they have been already spending time in virtual worlds and participating in a range of metaverse behaviors through virtual games such as Roblox, and other virtual reality technologies. This research aims to explore the potential of advertising within the metaverse universe, the challenges it would face, the virtual strategies that can tie in with the real world, and how brands can forge their own virtual pathways in relation to consumer behavior.
Estimation of One-Repetition Maximum, Type, and Repetition of Resistance Band Exercise Using RGB Camera and Inertial Measurement Unit Sensors

Resistance bands are widely used nowadays to enhance muscle strength due to their high portability, but the relationship between resistance band workouts and conventional dumbbell weight training is still unclear. Thus, this study suggests a convolutional neural network model that identifies the type of band workout and counts the number of repetitions and a regression model that deduces the band force that corresponds to the one-repetition maximum. Thirty subjects performed five different exercises using resistance bands and dumbbells. Joint movements during each exercise were collected using a camera and an inertial measurement unit. By using different types of input data, several models were created and compared. As a result, the accuracy of the convolutional neural network model using inertial measurement units and joint position is 98.83%. The mean absolute error of the repetition counting algorithm ranges from 0.88 (seated row) to 3.21 (overhead triceps extension). Lastly, the values of adjusted r-square for the 5 exercises are 0.8415 (chest press), 0.9202 (shoulder press), 0.8429 (seated row), 0.8778 (biceps curl), and 0.9232 (overhead triceps extension). In conclusion, the model using 10-channel inertial measurement unit data and joint position data has the best accuracy. However, the model needs to improve the inaccuracies resulting from non-linear movements and one-time performance.
Research on Digital Image Intelligent Recognition Method for Industrial Internet of Things Production Data Acquisition

The real scene data of production images and videos collected by the perception layer of the industrial Internet of Things, which are shot under the conditions of lack of illumination, underexposure and insufficient contrast, need to be fully and efficiently utilized to ensure the smooth progress of the follow-up supervision, monitoring, detection and tracking of the industrial Internet of Things. Therefore, this paper studies the intelligent recognition method of digital images on production data collected by industrial Internet of Thing. Firstly, the video or image data collected by the industrial Internet of Things monitoring platform are preprocessed to achieve the purpose of image clarity and targeting. It includes constrained least square restoration and Lucy-Richardson restoration for image blur caused by defocus, and blind deconvolution restoration for image motion blur caused by vibration. The adaptive histogram equalization algorithm is described in detail, and it can enhance the global contrast of digital images collected by industrial Internet of Things while retaining the details of the target area as much as possible. Based on U-net convolution network, the target recognition model of digital images collected by industrial Internet of Things is constructed, and spatial convolution pooling pyramid and improved convolution module Inception are introduced to optimize the model. Experimental results verify the effectiveness of the model.
Research on English Teaching Model in Universities under the Perspective of Big Data

The advent of the information age has led to dramatic changes in the way people live their lives, and education has been affected by a great deal of innovation.However, the era of big data is a brand new era, and we have just entered it and have not yet made the most of it.And now, the teaching of English in colleges and universities is also like this, with insufficient ability to handle the massive amount of information to guide English teaching well.Therefore, the teaching mode of English in colleges and universities should be constantly innovated and reformed in order to meet the development trend of the information age and big data and to cultivate more English talents for our country.However, further research is needed on how to better carry out the teaching of English in colleges and universities under the vision of big data.The following is my opinion on how to solve this problem for reference.