Reviewerly/exHarmony huggingface dataset:
Advanced hill cipher algorithm for security image data with the involutory key matrix

Abstract The security of important and confidential data is a top priority. Therefore, it is necessary to secure appropriate data to ensure confidentiality. Secrecy of data security can be done by randomizing the data in order to make the data is difficult to understand by anyone. One of the data security techniques that can be used is Advanced Hill Cipher cryptography. The purpose of this research was to create an application or software used to secure an image’s data by applying the Advanced Hill Cipher algorithm. A classic cryptographic technique, which use a matrix as a key in the process of encrypting and decrypting data with the type of key used is the symmetric key type. The data used in this study was an image data, while the application development used the language of MATLAB Programming. The method used in this study was the Advanced Hill Cipher Cryptographic Algorithm. Basically the Advanced Hill Cipher Cryptography algorithm is much more like the Hill Cipher algorithm. However, on Advanced Hill Cipher, algorithms are developed using a key matrix that always has inverse; so that the disadvantages of the hill cipher algorithm are that the key matrix does not always have inverse resolved. In this research, the key matrix used is a 2x2 order involuntary matrix with integer elements. The output of this study is anapplication that can be used to encrypting and decryptinggrayscaleand color image data with key matrix that always have inverse.
Integration of Health and Public Transport Data to Enable Decision Support for Seniors to Reduce Risk of Infection with Communicable Diseases

The ageing population puts challenges on countries worldwide. New concepts under the term Smart Cities are developed globally to face these challenges. The application of information and communication technology can be used as a driver for new innovative and more efficient processes supporting citizens in smart cities. An example is the optimization of everyday life processes like routine medical examinations of seniors during epidemic outbreaks like COVID-19. In this work we propose a novel standards-based IT system to enable evidence-based decision support for seniors, based on the integration of health and public transport data. The system supports seniors to take a safe path with the public transport in a smart city for their routine medical examinations during epidemic outbreaks like COVID-19. Furthermore, we describe the standardized integration of public transport data in the system as the first implementation step of the project. The requirements for the system were defined by a use case described through a sequence diagram, which was subsequently used to define the proposed system's IT architecture. As a first step of the prototype implementation, the integration of public transport data in the system was done using medical interoperability standards. Conformance tests were performed by applying the HL7 FHIR specification 3.0.1 through the official HL7 FHIR validator. The used standards could successfully be applied in the pilot. Semantic interoperability was supported through definition of code lists. The next steps of the prototype implementation include an update to HL7 FHIR 4.0.1 specification, the integration of the telemonitoring branch and the integration of the data from the Epidemiologic Reporting System based on HL7 FHIR.
Development of diagnostic procedures and tools for assessing information culture and competencies of cadets

In modern conditions of digitalization of society, the presence of information competencies and information culture is the key to successful professional activity in all areas, including in the military sphere. The studies carried out in recent decades indicate the need and possibility of the formation and development of information communication and information technology skills in the process of integrated development of computer science, humanitarian, socio-economic, general professional and special disciplines. At the same time, the methods for assessing the level of information culture formation as an integral personal education are not sufficiently developed. In this regard, it is necessary to study the existing methods, to develop diagnostic procedures and tools for assessing the information and technological competencies of military educational organizations cadets, as well as to test statistical hypotheses. The concept of the information support system for assessing the level of competence formation and the intermediate results of a large-scale experiment on the development of cadets' information culture are presented.
Implementation of organizational and pedagogical conditions for &#x0D; the development of social responsibility in the educational process of cadets

This article presents the results of the implementation of organizational and pedagogical conditions for the development of social responsibility of future officers of the National Guard of the Russian Federation. The author, in the course of conducting training sessions in various disciplines, creates organizational and pedagogical conditions that promote the development of social responsibility of the cadet's personality, modifying the traditional format of conducting classes: the technology of inverted class is introduced in the lecture classes; the technology of storytelling and the case study method are used in practical classes. In the seminar classes, the order of assessment of students is changed from individual to collective (inverted assessment). The pedagogical experiment has shown its effectiveness, which is confirmed by the analytical data presented in the content of the article. The organizational and pedagogical conditions tested during the educational process (the development of social responsibility as an important component of the professional training of future officers, the organization of a system of joint activities of the subjects of the educational process, the change in the system of evaluating the activities of cadets in the context of changes in the information presentation of material and the inclusion of various pedagogical technologies) can be recommended in pedagogical practice.
Agent Decision Processes Using Double Deep Q-Networks + Minimax Q- Learning

Aerospace battle scenarios represent a challenging modeling effort, often requiring large, continuous, and simultaneous state and/ or action spaces with imperfect information. We model a battle as a Multi-Stage Markov Stochastic Game (MSMSG) and facilitate agent decision making using a Double Deep Q-Network (DDQN) paradigm with Minimax Q-Learning. We demonstrate our model performance in contrast with a DDQN agent trained using a traditional Q-learning algorithm in a 1D dynamic battle environment. Preliminary findings suggest that the DDQN + Minimax-Q agent is more robust to parameter tuning and can learn true optimal mixed strategies compared to its traditional Q-learning counterpart.
Learning Molecular Classes from Small Numbers of Positive Examples Using Graph Grammars

We consider the following problem: A researcher identified a small number of molecules with a certain property of interest and now wants to find further molecules sharing this property in a database. This can be described as learning molecular classes from small numbers of positive examples. In this work, we propose a method that is based on learning a graph grammar for the molecular class. We consider the type of graph grammars proposed by Althaus et al. [], as it can be easily interpreted and allows relatively efficient queries. We identify rules that are frequently encountered in the positive examples and use these to construct a graph grammar. We then classify a molecule as being contained in the class if it matches the computed graph grammar. We analyzed our method on different known groups of molecules defined by structural properties and show that our method achieves low false-negative and low false-positive rates.
Deep Learning Models

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Thea Ability Requirement and Promotion Strategy of Professional Teachers in Newly-built Universities in the Context of New Engineering Disciplines

New engineering disciplines is very important to the successful implementation of the new technology revolution and the new industry revolution in China. In this paper, the new requirements for the teaching staff of new engineering disciplines is studied. And also, the common problems existing in the teaching staff of newly-built university, the strategies of building high-quality teaching staff for universities and the strategies of improving the quality for individual teachers are studied.
A Hybrid Hierarchical Model for Accessing Physical Activity Recognition towards Free-living Environments

Driven by the new revolution in healthcare, the importance of understanding Physical Activity (PA) in the uncertain, dynamic, free-living environments has been drawing growing attention. However, there is a lack of holistic investigation on how to improve the accuracy of PA recognition in free-living environments using cost-effective wearable devices with feasible algorithms. In this paper, we design a two-layer hybrid hierarchical model for accessing and evaluating cost-effective wearable intelligence approaches for PA recognition in free-living environments. The hypothesis of this model first suggests utilising less-attached on-body consumable wearables like belt and wristband devices, and then building up a PA dataset collected in free-living environments like an elderly home, hospital, office and gym. The model is then defined with components of lightGBM (LGB) and Artificial Neural Networks (ANN) for coarse and fine-grained classification, parameters of achieving high recognition rate including time window sizes, features and activation functions. The experimental results indicate that our model has superior ability over other state-of-the-art algorithms in classifying three typical types of PA (dynamic, sedentary, and transitional) with an average accuracy up to 84%. Specifically, our model performs good results of PA recognition with the ageing populations including 5 Mild Cognitive Impairment (MCI) and 17 Parkinson's disease (PD) patients.
Auto-Off ID: Automatic Detection of Offensive Language in Social Media

Abstract As the popularity of social media grows, computer-mediated anonymity allows users to engage in activities that they would not do in real life. This makes users vulnerable to abuse through Internet platforms. Due to the enormous number of social media data, it is not possible to manually filter out the overflow of abusive content in online communities and social networking sites. The research work proposes a multi-level classification model that deploys various machine and deep learning models to effectively identify offensive content in a tweet. The proposed Auto-Off ID system is designed to build a system that classifies tweets as offensive or non-offensive; filters out and classifies offensive tweets as either targeted or non-targeted; filters out targeted tweets and identify mentions of individuals and organizations who have been bullied. The study is supported by the text analysis features with lexicon features using LIWC, POS tags for primary and secondary users, Twitter Tag Scores (TTS). This system is evaluated using a diverse choice of machine learning and deep learning models from which it is proved that C-LSTM outperform with an accuracy of 91.72% for offensive language identification; LDA + Logistic Regression training with SVM accuracy of 90.87% for offensive tweet classification.
Integration of Hierarchical Clustering method and Dendrogram method with Expectation Maximization for identification of the best player cluster

In the last century, football has been the world's most common sport, but very little is understood about its structure. There are many incidents in each season that neither experts nor fans can reliably classify or forecast, despite the sport being continuously discussed and reported worldwide. We propose in this paper the use of two databases to help analyze football, the FIFA football video game dataset and Stats Bomb Open data. We have incorporated the clustering capability of two unsupervised learning clustering methods: Hierarchical and Expectation Maximization Clustering. To visualize a link between player positions using Principal Component Analysis, we have combined all clustering approaches for football player positions (PCA). We use 4 and 11 clusters in these visualizations that lead to player positions in the field. We use purity and the silhouette score to interpret Hierarchical and Expectation Maximization clustering. Results show that Hierarchical Clustering classifies the data better than Expectation Maximization.
A Survey on Conversational Question-Answering Systems

With the development of artificial intelligence technology and the increasing demand for rapid access to information, question-answering system has been widely studied. In recent years, due to the great achievements in the single round of Q&A, the research focus has shifted from the previous single round of Q&A to multiple rounds of Q&A. In this paper, we divide the Q&A system into task-oriented Q&A system and non-task-oriented Q&A system, and discuss the application of multi-round Q&A, respectively. Finally, we discuss the possible future direction of multi-round Q&A in the dialogue system.
Technology vs Humanity, a Detailed Lookback

The past few years have seen more technological advancements than in the past century maybe. Humans are relying more and more on machines and automation. But is this leading to a safe environment? Many activists fear if technology takes over humanity completely it would mean the end of humanity. There might be a possibility that these artificially intelligent robots become more intelligent than humans and start controlling the human race. The research aims to put forth data on how the automation is replacing humans in the present day. The research also includes the prospective future of humans with these AI machines and the most advanced technologies that are expected to come up and are being worked on.
Study subsets in Unite projective planes and their applications to Error-Correcting codes

Abstract The main objective of this paper is to classify the special sets in the projective planes PG (2, t) with the values of t= 17,31,37, and we found the arcs of the second degree (k,2) - arcs in PG (2, t) or third-degree (k,3)-arcs in PG(2, t), and the parameters of codes n, k, d was calculated and whether they are complete or incomplete, and the corresponding codes were formed.
Accelerated Moving Humans Detection Algorithm using Combined Global Descriptors on GPU Based on CUDA

Day by day, the ability to detect and to identify automatically the objects among images and videos without constraint has becoming more and more important. The security systems, robots, smartphones and smart devices need to know the semantic meaning of image. The increase in object detection and identification algorithms is essentially related to the increase in complex object specification and authentication techniques. This could be resolved only when using the parallel architectures that can support heavy parallel processing such as GPU. In this chapter, we propose to present an implementation of moving humans detection algorithm on GPU based on the programming language CUDA. We proposed an implementation of an algorithm to extract the image features using the Fourier descriptor on GPU. We have proposed a second implementation to extract the image features based on the HOG descriptor on GPU. To detect the moving objects, we have implemented a background subtraction algorithm based on the GMM: Gaussian Mixture Model on GPU. In order to integrate these implementations in the main moving humans detection algorithm, the use of preprocessing and filtering techniques is necessary at this level as well as the CCL: Connected Component Labeling method which allows extracting the Moving objects from the rest of the image. The implementation of such kind of algorithm on GPU allows a great performance in terms of execution time.
Crookies

HTTP cookies provide stateful and reliable cross-domain tracking capability to web technologies including e-commerce. Affiliate marketing (AM) enables businesses to generate visitor traffic at a relatively low cost, but some advertising models of AM are prone to large-scale fraud, such as “click-fraud,” which can allow rogue affiliates to earn commissions fraudulently. Cost-per-acquisition (CPA) appeared as the silver bullet against AM fraud, as the e-commerce site does not pay for “clicks,” but only for monetary outcomes. The discovery of “cookie stuffing” fraud shows that CPA is not the silver bullet that it was thought to be. The researchers designed and developed AMNSTE, a simulation platform to discover new vulnerabilities such as load-time click, conversion hijacking, conversion stealing, conversion faking, which are presented in detail in this article. It also presents technical solutions to mitigate some of the vulnerabilities, which will help practitioners to implement new solutions or re-examine their existing security strategies.
Bach and Busoni Essentials in A Chaotic World

The study analyses the manifestation of structural change from the Bach Violin Chaconne (BWV1004, c.1720) to the Bach-Busoni piano transcription (c.1897). This article explores the use of two-dimensional music abstraction for vertical (pitch height) and horizontal (time) and its signal insignificant identified bariolage sections in music. Bariolage excerpts are chosen for they are implied sounds captured in repeatability and recontextuality. The first part of the article offers an excerpt of the Bariolage from violin at bars (113-120) and its parallel piano transcription at bars (118-125). Significant expressions of registral change utilizing different voice parts (Soprano, Alto, Tenor) offer a wider expansion with piano. These excerpts were chosen after reviewing the original Bach Chaconne and its essentials in analytical aesthetics for the projection of beauty in symmetry-asymmetry-chaos in composition. This study captures the aesthetics of the beautiful from the original score of the violin Chaconne at (bars 89-96) and (bars 113-120) for Bach-Busoni piano transcription. Further, there are recommendations for future studies to explore vertical spaces and mathematical sequences embedded in the music in other sections. The findings of this study were implied through new music analytical formats to be applied in composition, pedagogy, and performance practice.
The Impact of Population Aging on the Level of Consumption in L Region : ——An Empirical Study Based on Regression Analysis Model

Around 2022, China's population over the age of 65 will account for 14% of the total population, realizing the transition to an aging society. The issue of the impact of aging on the consumption level of residents has received more and more attention. L Region is one of the regions with the highest degree of aging in China, so this study chooses it as the research object to explore the impact of aging in this region on residents' consumption through empirical research based on regression analysis models. This study compiled relevant data on the aging and consumption levels of urban and rural populations in L Region over the past fifteen years, and conducted a study based on the least squares regression analysis model on the impact of the aging of urban and rural populations on residents' consumption levels in this area. Finally, it is concluded that the aging of the urban population in this area has a negative impact on the consumption level of urban residents in this area, while the aging of the rural population in this area has no obvious impact on the consumption level of rural residents.
A Data Mining Framework for Response Modelling in Direct Marketing

The main objective of response modelling is to identify customers most likely to respond to a direct advertisement. This paper introduces a comprehensive methodology for response modelling that addresses two practical difficulties: large training data and class imbalance. For that, first several pre-processing techniques are applied in order to remove non-significant attributes. Next, two wrappers filter methods (Ensemble Random Forest and Relief algorithm) are combined with two balancing methods (the random under-sampling and the SMOTE method) and validated with several models. The experimental results demonstrate that the random under-sampling outperforms SMOTE and the wrapper filter Relief algorithm gives better results than the Ensemble Random Forest when combined with all classification algorithms considered.
Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Abstract Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.
Autonomous Multi-agent Systems Using SVGS Camera Sensor for Lunar Surface Mobility Applications

There are significant research challenges for design of a reliable mechanism of operation in autonomous multi-agent systems (MAS). This demands several fundamental advances in distributed localization, guidance, navigation, and state estimation. Control and coordination of a team of mobile robots for cooperative functions in harsh and remote environments in space is an even greater challenge than the MAS systems on earth's surface. In particular, as opposed to dealing with single system dynamics, here the problem is the dynamics and consensus of the entire swarm state. Our goal is to study the Smartphone Video Guidance System (SVGS) camera system as a potential navigation camera sensor and create a framework to obtain relative and global states of multi-agent groups. The goal is for rovers to obtain a reliable estimation of the other agents through a decentralized state estimation method, targeted at robots searching for water-ice in the challenging domain of the lunar south pole.
From A desk to Lecture Hall: A Tutorial on Virtual e-Gov and e-Part Event Delivery

In our Tutorial, we are going to explore the full socio-technical pipeline required to delivering an online, Immersive VR e-Government & E-Participation event using state of the art Immersive Virtual Reality Technologies. In particular, we are going to elaborate upon the use of specific mainstream software and hardware solutions for serious events in the context of e-Government. We are going to provide a practical step by step guidelines and explore options that organizers can choose from to deliver an efficient and desired VR experience whether for research purposes or simply for delivering a highly engaging, innovative e-Participation experience.