Reviewerly/exHarmony huggingface dataset:
Workflow Scheduling With Guaranteed Responsiveness and Minimal Cost

Workflow scheduling is the process of optimally assigning virtual machines to workflow tasks subject to response time and cost consideration. Since such an optimization problem is NP-compete, providing an effective heuristic approach is essential. In this article, we consider the workflow scheduling problem with the least cost subject to a bound on the response time. We show that existing solutions fundamentally care for the longest execution path within the workflow without appropriately handling non-critical paths. To overcome such a shortcoming, we propose a novel heuristic algorithm based on discrete mathematics. We first demonstrate that a workflow has a bijective relation with a partially ordered set and then introduce two operations on the workflow to show that it is an algebraic structure. We then form a totally ordered set, <inline-formula><tex-math notation="LaTeX">$(T^{exe}({\mathcal P}), \preccurlyeq)$</tex-math></inline-formula> , of workflow paths where <inline-formula><tex-math notation="LaTeX">$T^{exe}({\mathcal P})$</tex-math></inline-formula> is the set of path execution times. Based on <inline-formula><tex-math notation="LaTeX">$(T^{exe}({\mathcal P}), \preccurlyeq),$</tex-math></inline-formula> we identify the path with the maximum cumulative execution time and allocate a virtual machine to each task of the path based on the workflow deadline. We then delete the scheduled path and run our algorithm for each resulting sub-workflow in parallel. The results indicate that the proposed algorithm outperforms the best-known approaches in the literature.
Features of information and digital competence of students of agricultural and environmental institutions of higher education

The article reveals the features of the information and digital competence of students of agricultural and environmental institutions of higher education. These features include: 1) abilities formed through the content of professional training, which should be aimed at developing skills: a) search, analysis and use of information sources in the specialty; b) use modern software and equipment for the implementation of production activities; c) professional (educational, advertising videos about the features of the structure, setting up agricultural machinery, seeds, organic and mineral fertilizers, plant protection products); 2) the ability to carry out future professional activities based on digital agriculture (aerial photography, GPS tracking equipment and control over fuel use, precision farming systems, artificial intelligence technologies, the use of geographic information systems (GIS); 3) the relationship to modern digital technologies that has certain features depending on the area of residence, it should be noted that the composition of the student contingents of agrarian and environmental institutions of higher education is mostly young people who come from rural areas; 4) the ability to implement professional activities in the context of the need to increase the level of environmental culture, which implies the need for agricultural activities on the basis of sustainable development, using precision farming, innovative environmental technologies for the production and processing of agricultural products; 5) the ability and motivation of young people to work in rural areas, since it is young people who are a dynamic group of society that is able to quickly learn, master and apply new information technologies in their professional activities. The purpose of the article is to highlight and substantiate the features of the information and digital competence of students of agricultural and environmental institutions of higher education.
Improving Big Data Analytics With Interactive Augmented Reality

Since, data is generated every minute by everyone including consumers and/or business worldwide, there is an enormous worth for big data analytics. Big data analytics is a technique for extracting important information from large amounts of a data. Visualization is the best medium to analyze and share information. Visual images help to transmit bid data to the human brain within a few seconds. Visual interpretations help in visualizing data from different angles. Visualization helps to outline problems and understand current trends. Augmented reality enables the user to experience the real world, which is digitally augmented in a way. The main objective of this research work is to find the solution to visualize the analyzed data and show it to the users in a 3D view and to improve the angle of visualization with the help of augmented reality techniques.
Radar Signal Classification Based on Bispectrum Feature and Convolutional Neural Network

Radar signal classification is the key link in electronic information warfare, but as radar modulation becomes more sophisticated and the electromagnetic environment of the battlefield becomes complex, it is increasingly difficult to classify the radar signal. Aiming at the problem of low accuracy of radar signal classification in a low signal-to-noise ratio environment, a classification method based on bispectrum feature and convolutional neural network is proposed, it increases the accuracy of signal classification by taking advantage of bispectrum, which suppresses the Gaussian noise and retains the phase information. Therefore, the images of the signal bispectrum after pre-processing and data enhancement can train convolutional neural networks to obtain deeper signal features. Experimental results show that radar signal classification based on bispectrum features and convolutional neural networks can effectively improve the effect of radar signal classification.
Psychological content and factors of intrapersonal competence of a woman in “woman–man” dyad

We present the issue of the relationship between a man and a woman, analyze the concept of intrapersonal competence of a woman in a relationship with a man. This concept is considered from the point of view of different researchers. Structural components of intrapersonal competence are given: motivational, cognitive, executive, evaluative, relational and regulatory. The motivational component reflects the woman’s need for self-realization. The cognitive component reveals the reflection and reproduction of images of communication partners and relationships in dyadic interaction. The executive component reflects the strategies of a woman, applied by her in resolving a conflict with a man. The evaluation component reveals the level of satisfaction with relationships, which, in turn, is determined by the psychological maturity of the woman and her choice of adaptive coping strategies in dyadic interaction. The relational component reveals compatibility in the dyadic interaction “woman–man”. The regulatory component is determined by the forms of personal behavior of partners, their complementarity and congruence of assessments in dyadic interaction. Psychological factors are the fundamental reasons for the development of a woman’s intrapersonal competence, which are the source for starting the entire process of developing relations between partners, which determined its result. To optimize the factor solution we use the Varimax rotation method, which maximizes high loads on the factors and minimizes small loads. As a result, 5 factors were identified that explain most of the variability in the answers of the subjects: social emotional competence, competence in interaction with a partner, personal competence (understanding oneself and managing oneself), idealization of the image of a desired partner, tolerance and acceptance of others.
SOME IDEAS FOR TEACHER, S SUCCESSFUL WORK IN SOLVING GRAPHIC PROBLEMS IN PHYSICS EDUCATION IN SECONDARY SCHOOL

Graphic skills are defined as part of the necessary key competences that form „life skills“. They play an important role in learning as a tool of knowledge, as a factor in development of graphic culture of students and a positive motivation to learn. Our focus is on solving graphic problems as an effective didactic tool for the formation of graphic knowledge and graphic skills in students in Physics education. We emphasize the need to solve graphical problems based on experimental results. In this way, with concrete examples, students form the skill of constructing physics graphics. We offer a methodology for plotting based on experimental results. In order to successfully solve tasks for extracting information from the graphic, it is necessary for students to be trained in the activities they have to perform. In this regard we offer a methodology for extracting information from the constructed graphics. An important methodological technique that should be applied by the physics teacher when solving graphic problems is the variation of the graphics. We also offer a methodological approach, which includes composing graphic tasks by students. It plays an essential role in activating the mental activity of students, consolidating knowledge and applying it in practice.
Cross-Rolling Attention Network for Fashion Landmark Detection

The variety and deformable characteristics of fashions are the challenges in improving the accuracy of fashion landmark detection. Considering the delicate characteristics, this paper proposes a new method for fashion landmark detection based on the cross-rolling attention module and the feature pyramid network. It imports the cross-rolling attention mechanism into the multi-scale structure to extract and fuse different features with rich context, which is beneficial to capture fine-grained fashion keypoints more accurately and improve detection performance. While constructing the network, we also focus on controlling the network scale to avoid a large increase in parameters and longtime training. Finally, we perform some experiments suggesting that the proposed method achieves superior performance on both DeepFashion and FLD datasets. It demonstrates the effectiveness of the cross-rolling attention module in the proposed method.
Multi-user Verifiable Database with Efficient Keyword Search

The notion of Verifiable database (VDB) allows data owners with limited resources to outsource large databases to powerful but untrusted cloud servers, ensuring the integrity of the database when performing index-based query operations. In recent years, researchers have extended the index-based query method to propose VDB scheme supporting keyword search. However, the existing VDB schemes supporting keyword search only allow the data owner to generate the search token based on the private key to perform search. In order to extend this single-user setting to the multi-user setting, we first propose a new multi-user VDB scheme which allows the data owner to authorize keyword search ability to other users. Specifically, different users have different search permissions, that is, they can only perform search operations under the keywords authorized by the data owner. In addition, we also give a new construction which supports conjunctive keyword search. Finally, efficiency analysis and security proof ensure the feasibility and security of our VDB scheme.
Analysis of Workloads for Cloud Services

Capturing best quality datasets for a study is the first evidence for better outcomes of research. If the analysis are based on such datasets, then the metrics, the characteristics and few factors determines proof point for well proven theories. Hence it is obvious that we rely on the best possible ways to arrive at such data acquiring sources. It can be either based on historical techniques or from the innovations in application of it to industry. This paper introduces a mapping framework for analyzing, and characterizing data previously used by research community and how they are made to fit for Cloud systems, i.e. using “workloads” and “datasets” as the “refined definitions”. It was contributed in the past two decades within the scientific community setting their own workflow analysis mechanisms. The framework thus is validated by acquiring a sample workload per layer of cloud. The sources are form the literature that are available from existing scientific theories. These workloads are then experimented against the three tiers of the cloud computing ie., IaaS(Infrastructure as a Service), PaaS(Platform as a Service), & SaaS(Software as a Service). The selected data is analyzed by the authors for an offline model presented here based on the Machine Learning tool-kits. There are future studies planned for and to be experimented in a cloud auto scaled environment with online model as well.
RFID based secure voting system with biometric authentication

The voting system around the world has been plagued by numerous fundamental flaws, resulting in a corrupt candidate winning an election. For elections in democratic countries like India, the voting mechanism is extremely important. Generally, India's electoral commission has relied on electronic voting machines, which require more staff, take longer to process, and are less reliable. Approaches to addressing the issues with voting methods are required. So, In this project we came up with an idea to eradicate the existing system problem by incorporating various technologies that are being used these days especially for confidential purposes. This proposed system explicates a vulnerable part of the existing system to be terminated by proceeding with IOT being the power source for this project, followed by that we have included RFID to be used for authentication along with the biometrics for enhancing our system as a functional, safe and secure system. Being a Multi-tier authentication system, our system is handled by two individuals to start the process of voting. One, with the initialization of IOT and another who commences the casting process with the authentication of RFID. With this, we made use of GSM that acts as a communicator between the voter and the system for sending the confirmation information to the candidate’s mobile. Our primary goal is to eliminate fraud while also improving the feasibility of the system.
A Concept of Unbiased Deep Deterministic Policy Gradient for Better Convergence in Bipedal Walker

After a quick overview of convergence issues in the Deep Deterministic Policy Gradient (DDPG) which is based on the Deterministic Policy Gradient (DPG), we put forward a peculiar non-obvious hypothesis that 1) DDPG can be type of on-policy learning and acting algorithm if we consider rewards from mini-batch sample as a relatively stable average reward during a limited time period and a fixed Target Network as fixed actor and critic for the limited time period, and 2) an overestimation in DDPG with the fixed Target Network within specified time may not be an out-of-boundary behavior for low dimensional tasks but a process of reaching regions close to the real Q value's average before converging to better Q values. To empirically show that DDPG with a fixed or stable Target may not exceed Q value limits during training in the OpenAI's Pendulum-v1 Environment, we simplified ideas of Backward Q-learning which combined on-policy and off-policy learning, calling this concept as a unbiased Deep Deterministic Policy Gradient (uDDPG) algorithm. In uDDPG we separately train the Target Network on actual Q values or discounted rewards between episodes (hence “unbiased” in the abbreviation). uDDPG is an anchored version of DDPG. We also use simplified Advantage or difference between current Q Network gradient over actions and current simple moving average of this gradient in updating Action Network. Our purpose is to eventually introduce a less biased, more stable version of DDPG. uDDPG version (DDPG-II) with a function “supernaturally” obtained during experiments that damps weaker fluctuations during policy updates showed promising convergence results.
Research on Data Processing and Visualization of Simulation System

The ELK (Elasticsearch, Logstash, Kibana) log analysis system based on the big data platform is a representative solution for data collection and analysis. This paper analyzes and visualizes the simulation data based on the ELK log analysis system. Through a typical case of router data processing results in the simulation system, it is shown that the ELK log analysis system is simple to deploy, provides a variety of APIs for users to query and operate, and can also present the data in a variety of dynamic charts or visualizations through Kibana. After searching, summarizing and analyzing important information, it can realize real-time monitoring and retrieval analysis of data.
Biophilic Design Approach in Yogyakarta Digital Art Design

The creative industry based on digital technology is the current strength of the creative industry in improving the national economy. In creating a national creative industry, every region in Indonesia is required to create space for creative industry players. The Digital Art Center is a building typology that can accommodate digital creative industry players to carry out all their activities. In the midst of the significant development of the creative industry, a healthy psychological and physical condition is needed to increase the creativity and productivity of industry players. Biophilic Design can help humans achieve prosperity and comfort, as well as increase creativity and productivity of creative industry players by shaping the built environment by creating human interaction with the surrounding natural elements. In addition, creating a space for interaction between humans can increase the sense of kinship and shape the user's psychology to be more positive. Thus, the design of the Yogyakarta Digital Art Center is expected to create human interaction with nature and human interaction that can increase the productivity and creativity of its users.
A Research for Segmentation of Brain Tumors Based on GAN Model

Analysis of medical image is a useful method that can support doctors in medical diagnosis. The development of deep learning models is essential and widely applied in image processing and computer vision. Application of machine learning and artificial intelligent in brain tumor diagnosis brings an accuracy and efficiency in medical treatment field. In this research paper, we present a method for determining and segmenting the brain tumor region in the medical image dataset based on 3D Generative Adversarial Network (3D-GAN) model. We first explore the state-of-the-art methods and recent approaches in such field. Our proposed 3D-GAN model consist of three steps: (i) pre-processing data, (ii) building an architecture of multi-scaled GAN model, and (iii) modifying loss function. The last our contribution is creating an application to visualize 3D models that representation of medical resonance brain images with the incorporation of the chosen models to determine exactly the region containing brain tumors. Comparing to the existing methods, our proposed model obtained better performance and accuracy.
Prognostication of Unseen Objects using Zero-Shot Learning with a Complete Case Analysis

Generally, for a machine learning model to perform well, the data instances on which the model is being trained have to be relevant to the use case.In the case of relevant samples not being available, Zero-shot learning can be used to perform classification tasks.Zero-shot learning is the process of solving a problem when there are no examples of that problem in the phase of training.It lets us classify target classes on which the deep learning model has not been trained.In this article, Zero-shot learning is used to classify food dish classes through an object recognition model.First, the data is collected from Google Images and Kaggle.The image attributes are then extracted using a VGG16 model.The image attributes belonging to the training categories are then used to train a custom-built deep learning model.Various hypermeters of the model are tuned and the results are analyzed in order to get the best possible performance.The image attributes extracted from the zero-shot learning categories are used to test the model after the training process is completed.The predictions are made by comparing the vectors of the target class with the training classes in the Word2Vec space.The metric used to evaluate the model is Top-5 accuracy which indicates whether the expected result is present in the predictions.A Top-5 accuracy of 92% is achieved by implementing zero-shot learning for the classification of unseen food dish images.
Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement

Video super resolution (VSR) aims to construct the high resolution (HR) frames from the low resolution ones. In this paper, we propose a new deep VSR network based on the flow-guided deformable alignment (FGDA) module and the sparsity-based temporal-spatial enhancement (STSE) module. More specifically, the FGDA module is designed to generate temporally-aligned features with the bidirectional propagation. Meanwhile, the STSE module is constructed to eliminate the alignment error for the features and strengthen the sparsity of them to enhance the spatial details to construct the high-quality HR result. In addition, we design a sparsity-based loss function to guarantee the germinated HR frames with sharp details. The experimental results demonstrate that our proposed method achieves superior performance compared with the existing popular methods.
Developments in Data Science and Artificial Intelligence in Learning Technology and CCI Research

Abstract This book is focusing on experimental studies in learning technology and CCI research. During the last years, the areas of data science and AI have influenced different aspects of human-factors IT-related research in general and learning technology and CCI research in particular. Therefore, although this book does not provide a deep discussion on how data science and AI have influenced contemporary learning technology and CCI research; in this chapter, we provide a brief presentation of the developments in data science and AI, and the role of those developments in learning technology and CCI research.
On Probabilistic Extension of the Interaction Theory

In this paper, we propose a uniform interaction theory that covers both classical and randomized models. Under this framework, two basic relations, the equality relation within a model and the expressiveness relation between different models, are restudied. We also give an interesting comparison between the world of classical models and the world of randomized models.
Convolution Optimized Density Clustering Algorithm for Image Segmentation

In order to improve the usability of DBSCAN in the field of high-resolution image segmentation, according to the characteristics of digital images, an efficient DBSCAN based on three-dimensional convolution is proposed, and on this basis, a color image segmentation method is implemented. First, convolution is introduced into DBSCAN, three-dimensional convolution is used to obtain the core object, avoiding distance calculation and region retrieval, and reducing the time complexity of this step from the second order to a constant level. Then, the three-dimensional convolution is optimized based on dynamic programming, so that the convolution time is independent of the size of the convolution kernel. Finally, the neighborhood search range of the sample points is simplified according to the ordered core object set. Compared with the existing algorithms, the experimental results on the Berkeley BSD300 show that the algorithm is effective, the CH index and the time efficiency increase by 17% and 49% on average. The results of experiments using the remote sensing image set Mts-WH show that the computational efficiency of the algorithm is significantly improved. Compared with the DBSCAN optimized by the <italic>k</italic>D tree, the efficiency is increased by an average of 19 times, and the improvement effect is more obvious as the number of sample points increases. When the number of points exceeds 500 000, the efficiency is increased by 169 times.