Reviewerly/exHarmony huggingface dataset:
METHOD FOR STEGANOGRAPHIC HIDE OF INFORMATION BASED ON CONTINUOUS WAVELET TRANSFORM

This article discusses continuous wavelet transform as a method of steganographic embedding of confidential information into an image. The main purpose of steganography is to hide information so that the possibility of data detection is minimized. This is done by hiding the message inside the container so that outsiders are not aware of the secret’s existence. Thus, the main principle of steganography is the principle of invisibility, which is also the basis of security when using these systems to transfer information. Steganography methods are divided into two large groups: spatial and frequency. The former visually degrade the image quality by directly changing the components (pixels). The latter interact with frequency characteristics, which has the best effect on the quality of the converted image. At this point in time, one of the most common frequency methods (discrete-cosine transform) is increasingly being replaced by a wavelet transform. This method of embedding confidential information is visually less noticeable to human vision, relative to those existing at a given time.The paper presents brief theoretical information on the wavelet transform, gives the characteristics of the Haar transform, presents images demonstrating the principle of the wavelet transform. The developed algorithm for steganography based on the wavelet transform is shown. The algorithm was implemented in the Matlab environment, as well as the analysis of the results obtained using the example of a test image. Conclusions, advantages of this method, as well as recommendations for further research in this area are given.
Factors Affecting the Implementation of Smart Education in Indonesia: A Systematic Review

The educational progress of an area is determined by the way the government manages its education system. Smart education is a learning concept that utilizes 21st-century skills by following the latest technological innovations. Not all regions are able and ready to implement the concept. This study utilizes the Systematic Review method to identify what factors need to be considered in implementing smart education based on previous studies. The research resulted in five main interrelated aspects of all the factors found in previous studies, namely technological aspects, data aspects, H.R. aspects, organizational aspects, and cost aspects. By paying attention to all the factors from these five aspects, it will increase the level of success of the government in implementing smart education.
Upper Bound Computation of Information Leakages for Unbounded Recursion

Confidentiality is an important security goal that is ensured by the absence of information flow between secrets and observable outputs. Quantitative information flow (QIF) analyses quantify the amount of knowledge an attacker can gain on the secrets by observing the outputs. This paper presents a novel approach for calculating an upper bound for the leakage of confidential information in a program regarding min-entropy. The approach uses a data flow analysis that represents dependencies between program variables as a bit dependency graph. The bit dependency graph is interpreted as a flow network and used to compute an upper bound for the leakage using a maximum flow computation. We introduce two novelties to improve the precision and soundness: We strengthen the precision of the data flow representation by using the path conditions. We add sound support of unbounded loops and recursion by using summary graphs, an extension of a common technique from compiler engineering. Our approach computes a valid upper bound of the leakage for all programs regardless of the number of loop iterations and recursion depth. We evaluate our tool against a state-of-the-art analysis on 13 example programs.
A Novel Fault Diagnosis Method Based on Ensemble Feature Selection in The Industrial IoT Scenario

Fault diagnosis as a research hotspot in the field of prognostics and health management (PHM) has attracted the attention of academia and industry. Deep learning is widely used in academia due to its strong self-learning ability. However, as a "black-box" model, the features extracted by deep learning have poor interpretability which can’t be understood by IoT devices. In this paper, we propose a novel fault diagnosis method based on ensemble feature selection. We separately evaluate our method on the four-fault hydraulic components, which are derived from the real-world hydraulic time series data sets. The results show that compared with the deep learning method the proposed method can greatly reduce the complexity of the model without much reduction accuracy. Meanwhile, the balanced ensemble feature selection method we proposed is better than traditional ensemble methods such as union, intersection, and weighted linear aggregation. After testing, the method we proposed can be applied to the industrial IoT fault diagnosis.
An Hybrid Approach to Insure Data Integrity on Outsourced Data using Symmetric Key Cryptography

Cloud technology is advancing rapidly because of it’s capability to replace the traditional computing techniques. Cloud offers various kinds of services for the user that are being used. In this research paper, storage as a service provided by cloud is examined as the data of the owner is being shared to the cloud so we have to ensure that data integrity is being maintained. In order to have a robust mechanism that offers a secure pathway for sharing data different encryption algorithms have been utilized. We investigate all the suitable algorithms with various combinations because any single algorithm is prone to some kind of attack. Testing of these algorithms is done by analyzing the parameters such as time required for execution, use of computational resources, key management, etc. Finally the best one that stands and fulfill all the criteria in a reasonable manner is selected for the purpose of storage.
Lightweight Secure Search Protocol for EPC Systems

With the rapid development of Internet of things technology, EPC system plays an increasingly important role in the information age. Because EPC systems generally work in a heterogeneous and complex Internet of things environment, electronic tags with low cost and limited resources are vulnerable to malicious attacks from adversaries. At present, security factors have become the biggest problem restricting the large-scale application of the system. The purpose of this article is to provide technical advice on how to better apply EPC systems by studying lightweight secure search protocols for EPC systems. In this paper, by using the principle of EPC system realizes adjustable intensity of privacy and search efficiency, proved in UC framework agreement meet common composable security, makes the agreement can be run in a concurrent environment, proved that the agreement under the standard security model meet resistance playback, to synchronization, tracking and anonymity attacks, such as the search deal with strong security and privacy, and the results show that the protocol can effectively resist more than 86% of service attack, which is beneficial to the development of the safe search deal under the EPC system.
Class-based Core Feature Extraction Network for Few-shot Classification

Few-shot classification classifies unlabeled samples into correct classes when only few training samples are available for each class. Current researches focus on extracting better features and learning similarities between support data and query data based on the extracted features. However, the location of the main objects and the background in an image are interfering factors for few-shot classification, as they contain little useful information. In this paper, we propose a class-based core feature extraction network (CCFEN) which highlights common object of images in the same class and reduces the interference of background by using local feature descriptors and core feature extraction network to learn the common object. Experiments on two classical few-shot learning datasets show that our method achieves better results than state-of-the-art few-shot learning methods.
Augmentation of Printed Content with Web-based Technologies

This paper examines approaches to the development of augmented reality applications. It presents the current state of the art of mobile and web-based AR applications, as well as different versions of marker tracking. A case study provides best-practices for web-based AR, with the main focus point on an implementation of natural feature tracking. Finally, advantages and disadvantages of both mobile and web-based AR applications are discussed and suggestions for future directions are made.
Model-Assisted Reinforcement Learning with Adaptive Ensemble Value Expansion

Integrated with model-based approaches, reinforcement learning can achieve high performance with low sample complexity. However, the inaccurate learned dynamics model will degrade the performance, and the cumulative bias increases with the length of imaginary rollout. A key challenge is to improve sample efficiency without introducing significant errors. In this paper, Model-assisted Adaptive Ensemble Value Expansion (MAEVE) is proposed, which augments value expansion with imaginary training. By explicitly estimating the uncertainty of the dynamics and the value fucntion based on stochastic ensemble method, MAEVE adjusts the length of rollouts adaptively to maintain a dynamic balance between sample complexity and computational complexity. Considering the impact of the cumulative model bias on different rollout-length, MAEVE adjusts the sampling probabilities of samples at different imagination-depths instead of treating them equally. Therefore, MAEVE ensures that the learned dynamics model is only utilized if it does not introduce serious errors. Altogether, our approach significantly increases the sample efficiency compared to model-free and model-based baselines on challenging continuous control benchmarks without performance degradation.
Answer-Centric Local and Global Information Fusion for Conversational Question Generation

Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results.
Benchmarking and Performance Modelling of Dataflow with Cycles

Over the years, the popularity of iterative data-intensive applications such as machine learning applications has grown immensely. Unlike batch applications, iterative applications such as k-means, regression or classification algorithms require multiple access to the input data to train it sufficiently for convergence. In the context of big data, these applications are executed on distributed computing frameworks such as Apache Spark. These frameworks are simple to deploy and use, however, under the hood they are complex and highly configurable. To perform an exhaustive study of the impact of these ubiquitous parameters on application performance would be cumbersome due to the exponential amount of their combinations.
Should Technology Be [just] Delightful?

Our world has been steadily populated by a growing number of artificially “a-live” objects and smart devices, over the last decades, with varying degrees of autonomy, and seeming to become better at guessing and responding to our needs, offering pleasurable or delightful experiences, and making us more willing to trust them. Nevertheless, such development and understanding of ubiquitous smart technologies are not without criticism and challenges. The more users regard technology in magical terms, the more they ignore the complexities of the underlying infrastructure enabling their devices. However, these come with vulnerabilities that expose users to various undesirable situations, threatening their privacy and forcing them to deal with complications, with potential unforeseen social and economic consequences. This is an important reason why Human-Centered Design needs more than being empathic towards the user; it needs to assume a clear ethical stance. Designers should learn to say “no” and ask “why” while examining the role technology plays in our lives and asking what types of new relations we want to establish with it, and ultimately be equipped to ask about our human project for the future and the role a humanistic design approach should play in it. So, Should technology be [just] Delightful? This introductory chapter addresses this fundamental question while commenting on the contributions of the other chapters selected for this Design for Interaction section of the book.
An Expectation-Maximization Algorithm for Including Oncological COVID-19 Deaths in Survival Analysis

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can beconsistently taken into account in typical survival estimates. We refer to oncological patientssince there is empirical evidence of strong correlation between Covid and cancer deaths, whichimplies that Covid deaths cannot be treated simply as non-informative censorings, a propertyusually required by the classical survival estimators. We consider the problem in the frameworkof the widely used Kaplan-Meier (KM) estimator. Through a counterfactual approach,an algorithmic method is developed allowing to include Covid deaths in the observed data bymean-imputation. The procedure can be seen in the class of the Expectation-Maximization(EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm.We discuss the CoDMI underlying assumptions and the convergence issue. Thealgorithm provides a completed lifetime data set, where each Covid-death time is replacedby a point estimate of the corresponding virtual lifetime. This complete data set is naturallyequipped with the corresponding KM survival function estimate and all available statisticaltools can be applied to this data. However, mean-imputation requires an increased varianceof the estimates. We then propose a natural extension of the classical Greenwood's formula,thus obtaining expanded confidence intervals for the survival function estimate. To illustratehow the algorithm works, CoDMI is applied to real medical data extended by the additionof artificial Covid death observations. The results are compared with the estimates providedby the two naïve approaches which count Covid deaths as censorings or as deaths by thedisease under study. In order to evaluate the predictive performances of CoDMI an extensivesimulation study is carried out. The results indicate that in the simulated scenarios CoDMIis roughly unbiased and outperforms the estimates obtained by the naïve approaches. Auser-friendly version of CoDMI programmed in R is freely available.
End-to-End Person Re-identification including Camera Zooming based on Meta-Analysis for Images

This paper proposes an end-to-end person re-identification method including pan-tilt-zoom camera control in a real-world environment containing many moving people. Person re-identification, which means consistently identifying the same person from images from multiple cameras, brings great benefits to many services such as real-time person search. However, re-identification in the real world has challenges such as uniqueness of appearance, image quality, lighting, and poses of person. We address these challenges in two ways. First, we use the face images for re-identification because the face is the most unique feature of a person. Second, to acquire high-resolution images for facial recognition, our approach utilizes camera zooming. On the other hand, the reduction of field of view by zooming decreases the number of probed people. This paper proposes a method that resolves this trade-off by selecting the probed person from many people in the environment. Although many prior works have focused on data analysis by assuming data are pre-given, the proposed method solves the joint problem of facial recognition and image acquisition. The method selects the probed person who is likely to generate effective images for improving the accuracy of re-identification on the basis of the recognition results and the resolution, quantity, and variation of collected images. Experiments using a video dataset showed that the proposed method increased the success rate of re-identification by 25% compared with random selection.
МУЗИЧНО-ВИКОНАВСЬКА КОМПЕТЕНТНІСТЬ ВЧИТЕЛЯ МУЗИЧНОГО МИСТЕЦТВА

In the article, based on the analysis of the musical performance activity of a music teacher, the formation of musical performance competence is substantiated. Its structure is determined, consisting of musical-theoretical, mental-operational, communicative-organizational, interpretive-research knowledge and skills
Enhancing the System Model for home Interior Design Using Augmented Reality

Home interior design is often a challenging and time-consuming task due to several mismatches between the selection of interiors in a shop and their composition in a target room. One of these mismatches is a lack of knowledge in relation to the target room and to other interiors when an item is seen in a shop. In this paper, we propose to design and develop a system model by which users can virtually try out various settings of home interiors by using marker-less Augmented Reality (AR). To minimize the latency of the system, we apply the hybrid approach of combining an AR framework with a Simultaneous Localization And Mapping (SLAM) algorithm, where the 3D feature points are updated dynamically, thereby avoiding occlusion when multiple interiors are placed on the real-world view. Dimension scanning is deployed to provide accurate live-size scaling to the real-world environment whereas transformations are handled efficiently by means of a touch gesture recognition. The proposed system model for home interior design can be easily implemented as a mobile application. The performance evaluation through experiments shows that with the SLAM algorithm, the average response time of the proposed system improves by 43%. The proposed system is intended to meet the requirements of virtual and augmented reality services specified by ITU-T Study Group 16.
Imperfect Knowledge Base Self-organization in Robotic Intelligent Cognitive Control: Quantum Supremacy

The smart control design with secured achievement of information-thermodynamic trade-off interrelations is main goal for quantum self-organization algorithm of imperfect KB. Quantum genetic algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed. Sophisticated synergetic quantum information effect in autonomous robot in unpredicted control situations) and swarm robots with imperfect KB exchanging between “master – slaves” introduced. A new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation discussed. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible.
A Novel Algorithm to Withstand Attacks on Blockchain Using Transaction History and Block Publishing Time

Blockchain is an emerging technology with many kinds of cryptocurrencies like bitcoin, Ethereum etc. where one can earn rewards by creating new blocks through the process of mining. But how about only few getting all these rewards and trying to get control over the network? Does it pose a threat to blockchain integrity? Of course, yes which is leading to attacks such as 51% attack, selfish mining attack and double spending. Proof of work (PoW) is a protocol used in blockchain to reduce these problems, but it is not sufficiently secure. So, this paper proposes a technique, using history of miners and the history of their transactions that helps us to reduce the chances of these attacks. We have chosen history of transactions in order to find the genuineness of a miner, thus helping us to reduce double spending. Analysis shows that the risk of these attacks can be decreased using this technique.
Chinese Intention Recognition Algorithm Based on BERT-GRU-Capsule Network

With the continuous development of natural language processing technology and deep learning technology, related technologies in intelligent question answering field have made rapid progress in recent years. In the application of intelligent question answering system, the research of intention recognition algorithm is very important. In fact, intention recognition corresponds to the task of multi-classification of short texts in the field of natural language processing, and the algorithm of intention recognition is directly related to the question answering effect. In this paper, a BERT-GRU-Capsule network is proposed and compared with some classical intent-recognition networks on the user intent-domain classification dataset of SMP2017-ECDT.The experimental results show that the precision, recall and f1 values of the proposed network on the test set reach 0.931, 0.925 and 0.926 respectively, and the experimental results prove that the proposed Chinese intention recognition algorithm is superior to the classical intention recognition algorithm.
Visual Data Design Aesthetics in the Context of Internet

In the context of the rapid development of Internet, if Internet visualization can be combined with artistic expression based on aesthetics, it can quickly increase users’ understanding of Internet visualization. However, the existing visual design in the context of the Internet, the effect is not satisfactory. In this paper, the aesthetics in the Internet context is regarded as an independent medium, and the “Internet visualization of beauty” is discussed. Based on the in-depth analysis of the cognitive goal basis of data visualization, three information aesthetics concerns suitable for Internet context visualization are summarized, and the potential relevance between scientific data and artistic expression is discussed.