Reviewerly/exHarmony huggingface dataset:
Functional Modeling and General Collective Intelligence as the Basis for Pervasive Healthcare

General Collective Intelligence has the potential to combine individuals into a single collective collective intelligence with general problem-solving ability (intelligence) that might be exponentially greater than that of any individual. In every software domain, including health and wellness, General Collective Intelligence and functional modeling have the potential to enable the definition of pervasive cognitive computing applications and platforms. In such cognitive apps, intelligent agents might provide services to the user that optimize their outcomes by independently executing functional operations in each software domain on whatever software best implements those operations, and independently incorporating any possible data available to the user in the best way available. And at the same time in such cognitive computing platforms, a GCI might orchestrate the process of gathering data from all such individual uses in order to optimize collective outcomes such as significantly increasing healthcare and wellness. And these models of individual and collective cognition suggest that such optimization might not be reliably achievable otherwise. For both of these cognitive computing approaches functional modeling is required to provide a universal mechanism for representing data and processes. Therefore, to achieve significantly increased healthcare and wellness outcomes both functional modeling and GCI might be required. Functional modeling has the potential to overcome the lack of consistency in type and format of data gathered and the lack of a mechanism for universally comparing and combining that data. This paper explores why functional modeling might not only be of critical importance to pervasive healthcare, but why it also might be critical to significantly improving capacity to diagnose and to make interventions.
Augmented latent Dirichlet allocation model via word embedded clusters for mashup service clustering

Abstract Along with the growing number of mashups, there have been many changes in their diversity as well. With the creation of a wide variety of mashup services, it has become difficult to select and recommend appropriate web‐based APIs or services to launch an application based on the principle of mashup technology. In keeping with the methods already in place, we realized that the input/output parameters and text description which are key features directly from WSDL specification documents are increasingly being used in the mashup service innovation process. The quality of these methods can be enhanced by capturing hidden topic content from WSDL documents through the LDA (latent Dirichlet allocation). In this paper, the performance as concerns the LDA model can be enhanced by using some auxiliary features rather than limiting it to some extent. The reason for this is that the word vectors achieved by the word2vec tool is of advanced quality than the word vectors of the word gained by the LDA model. So, we proposed to innovate the novel mashup service innovation and clustering method with better efficiency and higher accuracy through word2vec. Here developed an improved LDA model with a word embedded cluster (called AUG‐LDA) that has a major impact on word vector quality. In this proposed approach, the LDA's training process can be semimonitored by first acquiring the word vectors through the word2vec tool and then merging the word clusters with the K‐means++ algorithm. This will enable better representations for the distribution of mashup services. This can be obtained by crawling the dataset from the website called www.programmableweb.com to perform a comprehensive experiment and demonstrate the proposed method. It has been proven that there is a better increase by comparing our approach with different metrics and the clustering accuracy values obtained by other methods.
Design a Clustering Document based Semantic Similarity System using TFIDF and K-Mean

The continuing success of the Internet has led to an enormous rise in the volume of electronic text records. The strategies for grouping these records into coherent groups are increasingly important. Traditional text clustering methods are focused on statistical characteristics, with a syntactic rather than semantical concept used to do clustering. A new approach for collecting documentation based on textual similarities is presented in this paper. The method is accomplished by defining, tokenizing, and stopping text synopses from Wikipedia and IMDB datasets using the NLTK dictionary. Then, a vector space is created using TFIDF with the K-mean algorithm to carry out clustering. The results were shown as an interactive website.
FORMATION OF PROFESSIONAL COMPETENCE OF MASTERS OF PRESCHOOL EDUCATION: A GUIDE TO THE MODERN

The article considers the issue of professional training of future preschool teachers. The example of a new training course for the specialty 012 Preschool education "Art Education of Preschool Children" presents opportunities to improve the quality of education and the formation of information culture of applicants for the second (master's) level of training by the means of information and communication technologies. The current state of the problem is analyzed and the contradictions in the process of teaching professional methods between the existing theoretical basis of professional training of future teachers and the insufficient level of professional competence of graduates, which is partly explained by the low level of motivation of many students to work in preschool education. The scientific research devoted to the application of information and communication technologies in the educational process, the definition of levels of readiness of students to use information and communication technologies and skills of the teacher who has mastered information and communication competence are analyzed. The article presents the results of a study of the levels of formation of artistic competence of undergraduates by motivational, emotional, intellectual, activity criteria and relevant indicators; methods of pedagogical diagnostics are specified. The principles, methods, and forms of using information and communication technologies in the context of the discipline "Art Education of Preschool Children" are revealed. The correlation between forms and methods of interaction of the teacher with pupils based on the use of information and communication technologies is shown. Factors of efficiency of use of information and communication technologies are defined: increase of interest of students to academic discipline, motivation to self-improvement and self-development, modernization of content of educational disciplines by innovative forms of interaction, methods, and receptions of formation of corresponding professional competence at future masters.
Face Recognition System Based on Convolutional Neural Network (CNN) for Criminal Identification

In this research paper, we give emphasis on the face recognition system (FRS) and the evolution of the various approaches adopted for FRS by making use of the CNN model. From recent study, we can conclude that recognized face recognition systems (FRS) were easily attacked and tricked by using faked images of the person whom they are targeting obtained from various social networks like Facebook, Instagram, Twitter, etc. The main task which is involved in image classification is of extracting important features. By using the important features of image processing methods like scaling, thresholding and contrast enhancement which is based on deep neural networks, the face recognition system can classify the results more efficiently and achieve high accuracy. This paper gives emphasis on extracting various important features required for face recognition using a CNN model and gives a brief overview of various techniques employed by researchers in this area. In this research, we have used Flickr-Faces-HQ Dataset (FFHQ) and test images mixed with some real time images captured from camera devices like CCTV, mobile camera, and when appropriate match is found, it gives information according to the matched faces. Accuracy obtained by using this CNN model using Google Colab is 96%.
Experimental multi-state quantum discrimination through optical networks

Abstract Developing strategies to effectively discriminate between different quantum states is a fundamental issue in quantum information and communication. The actual realization of generally optimal protocols in this task is often limited by the need of supplemental resources and very complex receivers. We have experimentally implemented two discrimination schemes in a minimum-error scenario based on a receiver featured by a network structure and a dynamical processing of information. The first protocol implemented in our experiment, directly inspired to a recent theoretical proposal, achieves binary optimal discrimination, while the second one provides a novel approach to multi-state quantum discrimination, relying on the dynamical features of the network-like receiver. This strategy exploits the arrival time degree of freedom as an encoding variable, achieving optimal results, without the need for supplemental systems or devices. Our results further reveal the potential of dynamical approaches to quantum state discrimination tasks, providing a possible starting point for efficient alternatives to current experimental strategies.
Design and realization of an NLP application for the massive processing of large volumes of resumes

Information and communication technologies (ICTs) today impact all aspects of the life of businesses and organizations. This is particularly the case with the recruitment process, with the establishment of e-recruitment platforms. These platforms allow candidates to post their information online, including their resume. This undeniable progress has resulted in an explosion of applications for offers posted by companies. This is how large organizations today receive up to hundreds of thousands of applications for certain vacancies. E-recruitment platforms make it easy to eliminate applications that do not meet the mandatory conditions (hard skills) by using simple calculations (SQL queries). But after this first step, there are still hundreds of thousands of applications to process. At this level, the platforms show their limits. Indeed, you have to read each resume and detect the soft skills of the candidates to decide between them. We propose to design and implement a solution based on natural language processing (NLP) technologies to allow the recruiter to select the best candidates using their resume.
CERTAIN STATUS AND LABOR LAW ISSUES OF ELECTRONIC SPORT IN THE REPUBLIC OF SERBIA

The development of sports law has fundamentally changed the angle of view on the relationship between sports and law, which has long prevailed and manifested itself through the concept of denying the existence of the need to create special sports norms. With a critical approach and theoretical consideration, sports law has separated over time into a special branch of law that seeks to explain the essence of sports specificity, proving that the identity of sports norms arises precisely from the need for a broader view of relationships that arise in sports. As a distinctly social phenomenon, digitalization is beginning to have a strong influence on traditional sports concepts and the principles on which it is based, creating new forms of existence and practice of sports and leading to the emergence of e-sports. Due to the increasingly important role of e-sports in society, the work will be focused on the legal analysis of the position of e-sports in the sports system of the Republic of Serbia. Also, the authors will address the issue of the possibility of application or the need to change traditional principles in the field of sports to e-sports, which means pointing out the need to harmonize certain legal institutes related to e-sports in some places. The research will also include a review of labor and legal aspects of electronic sports, such as the possibility of signing contracts with persons under the age of 15, the possibility of signing contracts with athletes, etc. To answer the questions, we will start the analysis by defining the concept of sports and e-sports in the Republic of Serbia.
Rancang Bangun Keamanan Port Secure Shell (SSH) Menggunakan Metode Port Knocking

PT. Banten Tour Mandiri is a tour and travel service provider, the need for internet use in companies is very much needed so that services in the service sector can be carried out comfortably and effectively. This study aims 1. To design a remote system using port knocking to monitor and supervise internet network access at PT. Banten Tour Mandiri. 2. Overseeing the internet network at PT. Banten Tour Mandiri. 3. Make it easier for companies to manage, supervise when using the existing internet network in the corporate environment. To develop the stability of the existing internet network, but there are several things that have not been considered and implemented, namely security and supervision of the internet network, it is necessary to have a network security system that simplifies security and supervision, by performing the Secure Shell (SSH) Port Security Design Port Knocking Method At PT. Banten Tour Mandiri. The results of this study show that the administrator has successfully performed remote access to the internet network server using the SSH port. Monitoring and management of internet network security can be done using a different PC located in the PT. Banten Tour Mandiri
Evaluating the effectiveness of fine gold extraction technologies on the example of titanomagnetite beach placers of the western coast of Kamchatka

The material on the gold content of coastal-marine deposits of the western coast of Kamchatka is analyzed and the resource potential is briefly characterized. A review of gold technological estimates obtained from the study of gold-bearing samples taken in promising areas of the coastal-marine strip, from the Bolshaya Vorovskaya River in the north to Cape Levashova in the south, is presented. Stock materials supplemented by our studies were used. Gold-bearing deposits with very fine (0.05-0.1 mm) and fine (0.1-1.0 mm) gold amounting to 94.6 % were tested, as well as samples with gold up to 2 mm and more in addition to fine gold The content of freely recoverable gold reached 91.9 %. In the case of significant gold reserves in placers, prospects for their commercial development open up. experiments on the enrichment of gold-bearing coastal-marine deposits of Western Kamchatka indicate the possibility of extraction of free gold and other valuable components from them. Gravity technologies and modern equipment developed by ОАО Irgiredmet and JSC ITOMAK are the most effective for their extraction. enrichment of gold-bearing formations with simultaneous extraction of several useful components from them, which is possible with the use of fluoride technology, can also become economically advantageous.
Digital literacy competency indicator for Indonesian high vocational education needs

In the face of the COVID-19 pandemic, education in Indonesia has shifted from face-to-face learning to online learning via the internet and digital technology. Therefore, digital literacy is a skill needed by vocational students and teachers today. This study aimed to identify and analyze digital literacy competency indicators to improve the quality of learning in high vocational education. This research employed a systematic review method, especially a qualitative meta-ethnographic approach, by reviewing various studies related to digital literacy competencies from various journal articles and conferences to then carry out the synthesis process. Meta-ethnography as part of the systematic review method integrates data across studies to obtain new theories and concepts with a deeper and more thorough understanding. The results showed four competency factors and 28 indicators to improve digital literacy competence. This research can be the basis of creating a digital literacy assessment model for high vocational education in Indonesia.
Motion Control for Aerial and Ground Vehicle Autonomous Platooning

In this paper a navigation control for a platoon composed by a ground and an aerial vehicle is proposed. While the first one (a mobile robot, equipped with a LiDAR) visit desired points avoiding obstacles, the second one (a cheap quadrotor equipped with a vertical camera pointing to the floor) follows it both in position and orientation. The problem is related to the perception layer, autonomous visual control of the drone and autonomous control of the ground robotic vehicle. To control the quadrotor, we propose in this paper an aerial visual-servoing methodology based on an Image Based Visual Servoing (IBVS) algorithm applied to the image given by onboarded camera. A new variant of the Dynamic Window Approach methodology was developed in order to control the motions of the ground robotic vehicle. Preliminary experimental results presented show the efficacy of our aerial-ground platooning methodology.
Fraud Detection on Bank Payments Using Machine Learning

This paper consists of fraud detection and measures to automate it fully. For every bank, it has become essential for Fraud detection. Fraud is rising significantly, which ends in many damages for the banks. Transactions create unique challenges for fraud exposure due to the lack of short-term processing. The foremost task is a feasibility study of chosen fraud detection methods. With the help of models, these transactions are to be tested individually and further proceeded. We first define a detection task: attributes of the dataset, the metric choice, and any techniques to control such unbalanced datasets. This leads to the fact that the underlying pattern generating the dataset results: For example, cardholders may improve their purchasing habits over periods, and fraudsters may change their tactics. Later, we highlighted several methods used to obtain the sequential features of credit card transactions.
VERGE in VBS 2022

This paper presents VERGE, an interactive video search engine that integrates multiple retrieval methodologies and also combines them with reranking and fusion techniques. Moreover, a user interface, implemented as a Web application, enables users to formulate queries, view the top retrieved shots and watch the respective videos, before submitting a shot to a VBS task, all in an efficient and easy manner.
QSRNet – Towards Quaternion-based Single Image Super-Resolution

Single-image super-resolution (SISR), which maps a low-resolution observation to a high-resolution image, has been extensively utilized in various computer vision applications. With the advent of convolutional neural networks (CNNs), numerous algorithms have emerged that achieve state-of-the-art results. However, the main drawback of CNN is the negligence in the interrelationship between the RGB color channel. This negligence further reduces crucial structural information of color and provides a non-optimal representation of color images. Furthermore, most of these CNN-based methods contain millions of parameters and layers, limiting the practical applications. To overcome these drawbacks, an endto- end trainable single image super-resolution method – Quaternion-based Image Super-Resolution network (QSRNet) that takes advantage of the quaternion theory is proposed in this paper. QSRNet aims at maintaining the local and global interrelationship between the channels and produces high-resolution images with approximately 4x fewer parameters when compared to standard CNNs. Extensive computer experimentations were conducted on publicly available benchmarking thermal datasets, including DIV2K, Flickr2K, Set5, Set14, BSD100, Urban100, and UEC100, to demonstrate the effectiveness of the proposed QSRNet compared to traditional CNNs.
VMSH

Lightweight virtual machines (VMs) are prominently adopted for improved performance and dependability in cloud environments. To reduce boot up times and resource utilisation, they are usually "pre-baked" with only the minimal kernel and userland strictly required to run an application. This introduces a fundamental trade-off between the advantages of lightweight VMs and available services within a VM, usually leaning towards the former. We propose VMSH, a hypervisor-agnostic abstraction that enables on-demand attachment of services to a running VM---allowing developers to provide minimal, lightweight images without compromising their functionality. The additional applications are made available to the guest via a file system image. To ensure that the newly added services do not affect the original applications in the VM, VMSH uses lightweight isolation mechanisms based on containers. We evaluate VMSH on multiple KVM-based hypervisors and Linux LTS kernels and show that: (i) VMSH adds no overhead for the applications running in the VM, (ii) de-bloating images from the Docker registry can save up to 60% of their size on average, and (iii) VMSH enables cloud providers to offer services to customers, such as recovery shells, without interfering with their VM's execution.
Determinants of human compositional generalization

Generalisation (or transfer) is the ability to repurpose knowledge in novel settings. It is often asserted that generalisation is an important ingredient of human intelligence, but its extent, nature and determinants have proved controversial. Here, we re-examine this question with a new paradigm that formalises the transfer learning problem as one of recomposing existing functions to solve unseen problems. We find that people can generalise compositionally in ways that are elusive for standard neural networks, and that human generalisation benefits from training regimes in which items are axis-aligned and temporally correlated. We describe a neural network model based around a Hebbian gating process which can capture how human generalisation benefits from different training curricula. We additionally find that adult humans tend to learn composable functions asynchronously, exhibiting discontinuities in learning that resemble those seen in child development.
Facial Identity Verification Robust to Pose Variations and Low Image Resolution: Image Comparison Based on Anatomical Facial Landmarks

Face comparison/face mapping is one of the promising methods in face biometrics which needs relatively little effort compared with face identification. Various factors may be used to verify whether two faces are of the same person, among which facial landmarks are one of the most objective indicators due to the same anatomical definition for every face. This study identified major landmarks from 2D and 3D facial images of the same Korean individuals and calculated the distance between the reciprocal landmarks of two images to examine their acceptable range for identifying an individual to obtain standard values from diverse facial angles and image resolutions. Given that reference images obtained in the real-world could be from various angles and resolutions, this study created a 3D face model from multiple 2D images of different angles, and oriented the 3D model to the angle of the reference image to calculate the distance between reciprocal landmarks. In addition, we used the super-resolution method of artificial intelligence to address the inaccurate assessments that low-quality videos can yield. A portion of the process was automated for speed and convenience of face analysis. We conclude that the results of this study could provide a standard for future studies regarding face-to-face analysis to determine if different images are of the same person.