Reviewerly/exHarmony huggingface dataset:
Improvement of Cloud Computing Medical Data Protection Technology Based on Symmetric Encryption Algorithm

The traditional data-protection method has the advantages of a long average secrecy time and low encryption efficiency in healthcare. This paper proposes an improved method of cloud-computing data-protection technology based on a symmetric encryption algorithm. According to the characteristics of cloud computing, a trusted communication channel for virtual machine monitoring is established on users and servers. The cloud-computing data-protection technology with four functional modules of “multi-copy protection,” “file access,” “integrity verification,” and “accountability” is deployed in the channel by using the improved symmetrical encryption differential-algebraic equations algorithm combined with the symmetrical encryption algorithms data encryption standard and advanced encryption standard to do the following: bind the identity of cloud-service providers; realize mixed encryption of plaintext segmentation; and add random interference information. These clusters are built on Hadoop to simulate the cloud-computing environment. The experimental results show that the average encryption and decryption times of this method are the best under different data amounts. At the same time, the encryption efficiency and throughput are over 95 %. The protection effect of this method on cloud-computing data is verified.
Dual-bottleneck feature pyramid network for multiscale object detection

Multiscale object detection is a challenging task due to the multiscale and multiclassification nature of different objects. Convolutional neural networks are commonly used to extract the features. However, continuous downsampling operations and spatial position quantization result in the loss of detailed information. Subsequently, the high-level feature maps cannot distinguish the features of adjacent objects, and in particular, small objects are ignored. To solve the above problems, we propose a model for multiscale object detection. First, we design a dual-bottleneck subconvolutional network to extract shallow features from the multiscale image pyramid. The subnetwork provides more spatial information for feature maps of the backbone network. Then, we introduce a new downsampling method to avoid losing a large amount of detailed information during the downsampling operations. Finally, we use a loss function to alleviate the foreground–background class imbalance. In the ablation study, we research the matching between the image pyramid module and the backbone network, and we compare different feature fusion modules. We evaluate our proposed model on two publicly available large-scale datasets and observe that our model achieves an 8.5% improvement compared with the classical feature pyramid network and exhibits acceptable detection performance for the occluded and diverse objects.
An Uncertainty-Aware Encoder for Aspect Detection

Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
The Role of Instructional Design in Promoting Digital Pedagogy. Review of the book: Beetham H., Sharpe R. (2020) Rethinking Pedagogy for a Digital Age 

According to the authors of the joint monograph Rethinking Pedagogy for a Digital Age: Principles and Practices of Design, not only does the digital society shape the demand for new skills and methods of learning how to learn but it also generates challenges in economy, digital security, privacy, and ethics. From this standpoint, the authors show how instructional design responds to the challenges of digitalization, what should be taught to modern students, what learning outcomes students need, what kinds of resources, technologies and learning environments are required, and how the teacher’s role is changing. Part One, Theories and Principles, looks for foundations for instructional design decisions, suggesting three perspectives: associationist, cognitive and situative. Learning activities are shown as the focus of instructional design, and learning outcomes are understood as clearly defined changes expected from the learner. Ideas for collaborative online and blended learning designs for learning communities are put forward as a response to the growing demand for methods of social learning in online environments. Part Two, Practices, analyzes the results of over ten years’ work with teachers. A number of cases from Australian and British universities are used to demonstrate how pedagogical design projects helped solve the strategic problems of introducing a new framework of graduate attributes, promoting teacher research and its deeper integration into the learning process, and organizing a mass transition to blended learning. Part Three, Influences and Futures, addresses the challenges and prospects of instructional design for mobile and professional learning, providing a critical analysis of the datafication of education and learning analytics. This monograph is an important step toward rethinking the role of pedagogical design in the coming age of digital reality. The principles of instructional design are discussed and adjusted by the authors with due regard to the ongoing transformation of the student and teacher roles, learning environment, and expected learning outcomes. The ideas expressed in the book become especially relevant amidst the emergency digitalization of education during the COVID‑19 pandemic and the high demand for a quality redesign of educational products.
Machine learning methods for generating high dimensional discrete datasets

Abstract The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real‐life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two‐step approach: first, a real dataset is analyzed to derive relevant patterns and, then, to use such patterns for reconstructing a new dataset that preserves the main characteristics of . This survey explores two possible approaches: (1) Constraint‐based generation and (2) probabilistic generative modeling. The former is devised using inverse mining () techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling () are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Machine Learning Algorithmic Development &gt; Structure Discovery
Universal Framework for Joint Image Restoration and 3D Body Reconstruction

Recent works have demonstrated excellent state-of-the-art achievements in image restoration and 3D body reconstruction from an input image. The 3D body reconstruction task, however, relies heavily on the input image’s quality. A straightforward way to solve this issue is by generating vast degraded datasets and using them in a re-finetuned or newly-crafted body reconstruction network. However, in future usage, these datasets may become obsolete, leaving the newly-crafted network outdated. Unlike this approach, we design a universal framework that is able to utilize prior state-of-the-art restoration works and then self-boosts their performances during test-time while jointly carrying out the 3D body reconstruction. The self-boosting mechanism is adopted via test-time parameter adaptation capable of handling various types of degradation. To accommodate, we also propose a strategy that generates pseudo-data on the fly during test-time, allowing both restoration and reconstruction modules to be learned in a self-supervised manner. With this advantage, the universal framework intelligently enhances the performance without any new dataset or new neural network model involvement. Our experimental results show that using the proposed framework and pseudo-data strategies significantly improves the performances of both scenarios.
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling

The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the negative sampling strategy to alleviate the time-consuming burden of pairwise computation. However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error. Specifically, we show that the sampling-based CML would introduce a bias term in the generalization bound, which is quantified by the per-user Total Variance (TV) between the distribution induced by negative sampling and the ground truth distribution. This suggests that optimizing the sampling-based CML loss function does not ensure a small generalization error even with sufficiently large training data. Moreover, we show that the bias term will vanish without the negative sampling strategy. Motivated by this, we propose an efficient alternative without negative sampling for CML named Sampling-Free Collaborative Metric Learning (SFCML), to get rid of the sampling bias in a practical sense. Finally, comprehensive experiments over seven benchmark datasets speak to the supriority of the proposed algorithm.
Development of an Automatic Parking Algorithm Based on Fuzzy Logic

Intelligent Transportation System (ITS) has generated a lot of research interest in the last few decades. Many problems have been identified in this area, among which autonomous parking has attracted more attention. Automatically parking vehicles in a confined space - an area where there are moving and stationary obstacles - is a challenge. Therefore, the development of parking systems based on new technologies, the placement of a more optimal number of cars in a limited space has become an urgent problem at a time when there is a large number of ground vehicles. The article has developed an automatic parking algorithm based on fuzzy logic. A new model of the parking lot was proposed, as well as the development of an automatic parking control system for passenger cars in the proposed parking lot. To solve this problem, it is proposed to divide the parking space by methods of the theory of fuzzy sets and fuzzy logic.
Selfie Segmentation in Video Using N-Frames Ensemble

Many camera apps and online video conference solutions support instant selfie segmentation or virtual background function for entertainment, aesthetic, privacy, and security reasons. A good number of studies show that Deep-Learning based segmentation model (DSM) is a reasonable choice for selfie segmentation, and the ensemble of multiple DSMs can improve the precision of the segmentation result. However, it is not fit well when we apply these approaches directly to the image segmentation in a video. This paper proposes an N-Frames (NF) ensemble approach for a selfie segmentation in a video using an ensemble of multiple DSMs to achieve a high-performance automatic segmentation. The proposed NF ensemble approach executes only one segmentation model upon a current video frame and combines segmentation results of previous frames to produce the final result. For the experiment, we use four state-of-the-art image segmentation models and 81 videos dataset with a single-person view from publicly available websites. This paper calculates Intersection over Union (IoU), IoU standard deviation, false prediction rate, Memory Efficiency Rate and Computing power Efficiency Rate to measure the performance of segmentation models. The average IoU value of the Two-Frames NF ensemble was 95.1253%, and the Three-Frames NF ensemble was 95.1734%, whereas the average IoU value of single models was 92.9653%. The result shows that the proposed approach improves the accuracy of selfie segmentation by more than 2% on average. The result of cost efficiency measurement shows that the proposed method consumes less computing power like single models. To sum up the overall work, the proposed approach is as fast as single selfie segmentation models. At the same time, it produces optimized and improved segmentation results like the ensemble of multiple segmentation models at once.
Mirror, Mirror on the Wall, Tell Me… Finding the Positum Through the Missing and the Reflections

This paper explores the process of reflection and its influence on personality formation. The importance of this issue for people throughout the ages is evidenced by fairy tales, parables, and also their predecessors – myths. The article discusses some of them, in which the symbol of the mirror and reflection makes it possible to know and assign qualities and abilities. Keywords: Positive psychotherapy, positum, reflection, contact, differentiation
Distributed Agile Patterns-Using Agile Practices to Solve Offshore Development Issues

As offshoring is becoming mainstream, companies are moving towards using agile methods. Offshoring has many advantages such as reduced development cost, proximity to market and round the clock development, it has created new challenges for the application of agile practices as the teams are now distributed. Companies are adapting and modifying agile practices in order to overcome these challenges. However, little effort has been put into identifying common practices, which are being repeatedly, used to solve frequent problems in offshore development. In this research, we have studied from literature over 200 cases and have interviewed professionals participating in distributed offshore teams. Based on the observations, we have designed a solution known as Distributed Agile Patterns, which will address the common issues in offshoring scenario. Fifteen distributed agile patterns have been identified and classified into four categories based on the type of problem they solve. A reflection workshop was conducted where professionals were invited to review the pattern catalogue and help us verify and validate the catalogue. Based on their feedback, the catalogue was finalised. The purpose of the catalogue is to serve as a guideline for practitioners to use to aid them in adoption of agile practices in offshoring.
Quality Evaluation Model for Smart City Social Sports Information Cloud Service

With the continuous development of social economy, social sport is more and more valued and favored by the people as a universal and nationwide sport, but it should be noted that social sport involves a wide range of aspects, but due to its particularity, it is also constrained by economic development. In view of these needs and limitations, three technical methods of entropy method, RSR (rank-sum ratio), and TOPSIS are introduced, to sort out the development of social sports, national physical development, social sports guidance, and the number of people to be measured in various places based on the relevant technologies of smart cities in this paper, realize the application analysis of social sports in the public service level. This paper aims to provide a scientific evaluation and evaluation method for social sports information cloud services. The results of simulation experiments show that the evaluation of social sports information cloud service quality based on smart cities is effective, and the comprehensive application of methods can be implemented perfectly, and the further promotion and popularization of social sports services can be realized.
Hardware Acceleration of Large-Scale CMOS Invertible Logic Based on Sparse Hamiltonian Matrices

Invertible logic has been recently presented that can realize bidirectional computing based on Hamiltonians for solving several critical issues, such as integer factorization and training neural networks. However, a hardware architecture for supporting large-scale general-purpose invertible logic has not been studied. In this paper, we introduce a scalable hardware architecture based on sparse Hamiltonian matrices. In order to store and compute the Hamiltonians efficiently in hardware, a sparse matrix representation of PTELL (partitioned and transposed ELLPACK) is proposed. A memory size of PTELL can be smaller than that of a conventional ELL by reducing the number of paddings while parallel reading of non-zero values are realized for high-throughput operations. As a result, PTELL achieves around 1% and 10% memory usages of a conventional dense and ELL matrices, respectively, in case of invertible multipliers. In addition, the proposed hardware accelerator of invertible logic for supporting arbitrary Hamiltonians is implemented on Xilinx VU9P FPGA, which achieves around two orders of magnitude faster than a 16-core Intel Xeon implementation.
Astronomi Öğrenme Ortamlarını Zenginleştirmeye Yönelik Bir Uygulama: Star Walk 2

In recent years, the science of astronomy has gone beyond the lessons covered in schools and become a learning domain that people promote with new experiences in line with their individual curiosity, which has paved the way for astronomy learning methods to diversify and attract attention with the contribution of technology.Augmented Reality (AR) applications, which have been encountered in many fields including education in recent years, are also actively used in the astronomy learning process.Star Walk 2 is one of these AR applications, which enables individuals to acquire real-time images and information flow while observing the sky, and includes features to promote astronomy learning for individuals of all ages.There are some elements with significant effects on individuals in preferring these applications, and these are language preferences, user-friendliness and accessibility.Star Walk 2 application can meet users' needs in terms of accessibility and user-friendliness.However, it lacks a detailed guide and language options in Turkish.This situation is likely to prevent users, whose primary language preference is Turkish, from making effective use of the application.In this context, the purpose of this study is to provide a detailed Turkish guide on the use of Star Walk 2 application for students, teachers and other individual learners.Within the scope of the study, the researchers have used all the features of the application and analyzed the observations enabled.They have prepared a detailed guide by examining the paid and free versions of the application separately.With the visual-aided guide prepared, it is aimed to enable users' effective use of the application features and to facilitate their astronomy learning process.In addition, it is envisaged to provide Turkish user guide support to users who cannot use the application due to language options.It is believed that the guide and observation details about the application presented in the study will be a useful resource for both students and teachers, and individual users.
Not So Digital After All? A Look at the Nature of Digital Nudging through the Prism of the Digital Object Concept

Digital nudging is an effective way to influence individuals' behavior when they interact with digital computers.However, scholars only partially discussed how digital technology transforms nudging mechanisms in digital choice environments.Considering the recently proposed research agenda on digital objects, studying the 'digital' component of digital nudging can help to understand how the 'digital' transforms the phenomenon of nudging and creates new, digital-only methods of influencing individuals' behavior.This paper investigates the current state of the literature on the context of digital nudging and discusses the role of digital objects in nudging with examples of how digital properties can transform the mechanisms of nudging.
THERAPY OF UPPER LIMBS BY MEANS OF VIRTUAL REALITY TECHNOLOGIES

This paper deals with an approach to upper limbs therapy that uses virtual reality technologies. The previous methods and subsequent improvements of these procedures by means of a skeletal model of the upper limb in a virtual environment are presented here. So, main focus of the paper is on the description of calculation related to the bone rotation system within appropriate skeletal model. The therapist can add either more virtual upper limb objects or more virtual training objects to the virtual environment and thus expand/change the scene or the therapy complexity. The functions used in the limb movement calculations are useful for creating additional animations with various objects. With this system, the patient can be stimulated under the supervision of a therapist to practice certain rehabilitation procedures. Due to the use of collaborative web-based virtual reality, the therapy can be also applied in a remote form. The way in which the underlying idea of rehabilitation process is implemented and it is also described. In the conclusion are the some notes about system testing and evaluation including description of a therapist interface.
Land Holding Using Blockchain

Blockchain has been found of great use in various sectors and this technology promises much more because of the high level of security it provides and blockchain provides us trade without the need of any mediators so Land holding system seems to be a very promising area of interest. All the countries around the world are now implementing blockchain in the area of land registrations in India, we should also consider it as a viable option. The blockchain has provided major security benefits in diverse fields. Therefore, this land holding system is implemented using a blockchain architecture. In India main problems in traditional land holding system include space constraints, fraud in land registry, lack of uniformity and poor maintenance of land records, lack of single window title verification and investigation system, fear of destruction of records by force majeure events. Therefore, a blockchain is prepared to aid these problems in order to solve these problems we have analyzed, identified and developed an application with the help of blockchain smart contracts. In this paper we are developing a land holding System to encounter the problems in traditional systems.