Reviewerly/exHarmony huggingface dataset:
Universal quantum obfuscation for quantum non-linear functions

Research on quantum cryptography has burgeoned in the recent decades and combined quantum mechanics and cryptography theory. Among the existing quantum cryptographic primitives, quantum obfuscation is an emergent force to be reckoned with. Quantum obfuscation means obfuscating a circuit by quantum mechanics to improve security. It is used to hide functionality and prevent the reverse engineering of quantum circuits. However, research studies on the construction of quantum obfuscation are relatively immature due to its difficulty in implementation and application. Also, the obfuscation for quantum non-linear functions has not been suggested yet, although quantum non-linear functions cover a wide range of quantum functions that can be obfuscated. In this paper, we initiate a universal definition of quantum obfuscation which utilizes quantum teleportation to construct an obfuscator and interpreter for quantum non-linear functions. Furthermore, we demonstrate the validity of applying the obfuscation to the quantum asymmetric encryption scheme and rigorously prove that the encryption realized by quantum obfuscation satisfies IND (indistinguishability)-security. This work provides a positive possibility of quantum obfuscation for quantum non-linear functions and will complement the theory of both quantum obfuscation and quantum asymmetric encryption.
Materiality, Corporeality, and Relationality in Older Human–Robot Interaction (OHRI)

This article presents interdisciplinary research on the social and technological aspects of interactions between older adults and the humanoid robot Pepper (SoftBank Robotics). Our case study is based on the regular meetings that are a part of an experimental intervention taking place at the Active Ageing Centre for older adults in Prague, run by the NGO Life 90. Through the methods of participant observation, unstructured interviews, analyses of video recordings from interventions with Pepper, and subsequent reflections on the “user” experience with the robot, we have unpacked the complexity of materiality and corporeality in older human–robot interactions (OHRI) in the context of age and gender. The project brings new applied knowledge, exploring OHRI using concepts relevant to gerotechnologies, informed by studies of materiality and ageing studies.
Enhancing UML Connectors with Behavioral ALF Specifications for Exogenous Coordination of Software Components

Connectors are powerful architectural elements that allow the specification of interactions between software components. Since the connectors do not include behavior in UML, the components include the behavior for coordinating the components, complicating the designs of components and decreasing their reusability. In this study, we propose the enrichment of UML connectors with behavioral specifications. The goal is to provide separation of concerns for the components so that they are freed from coordination duties. The reusability of the components will increase as a result of such exogenous coordination. Additionally, using the associated behaviors, we aim to resolve the ambiguities that arise when n-ary connectors are used. We use a series of QVTo transformations to transform UML models that include connector behaviors in ALF specifications into UML models which include fUML activities as connector behavior specifications. We present a set of example connectors specified using the proposed method. We execute the QVTo transformations on the example connectors to produce models that represent platform-independent definitions of the coordination behaviors. We also present and discuss cases from real-life large-scale avionics software projects in which using the proposed approach results in simpler and more flexible designs and increases component reusability.
Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching

Feature detection and correct matching are the basis of the image stitching process. Whether the matching is correct and the number of matches directly affect the quality of the final stitching results. At present, almost all image stitching methods use SIFT+RANSAC pattern to extract and match feature points. However, it is difficult to obtain sufficient correct matching points in low-textured or repetitively-textured regions, resulting in insufficient matching points in the overlapping region, and this further leads to the warping model being estimated erroneously. In this paper, we propose a novel and flexible approach by increasing feature correspondences and optimizing hybrid terms. It can obtain sufficient correct feature correspondences in the overlapping region with low-textured or repetitively-textured areas to eliminate misalignment. When a weak texture and large parallax coexist in the overlapping region, the alignment and distortion often restrict each other and are difficult to balance. Accurate alignment is often accompanied by projection distortion and perspective distortion. Regarding this, we propose hybrid terms optimization warp, which combines global similarity transformations on the basis of initial global homography and estimates the optimal warping by adjusting various term parameters. By doing this, we can mitigate projection distortion and perspective distortion, while effectively balancing alignment and distortion. The experimental results demonstrate that the proposed method outperforms the state-of-the-art in accurate alignment on images with low-textured areas in the overlapping region, and the stitching results have less perspective and projection distortion.
InfoMax Classification-Enhanced Learnable Network for Few-Shot Node Classification

Graph neural networks have a wide range of applications, such as citation networks, social networks, and knowledge graphs. Among various graph analyses, node classification has garnered much attention. While many of the recent network embedding models achieve promising performance, they usually require sufficient labeled nodes for training, which does not meet the reality that only a few labeled nodes are available in novel classes. While few-shot learning is commonly employed in the vision and language domains to address the problem of insufficient training samples, there are still two characteristics of the few-shot node classification problem in the non-Euclidean domain that require investigation: (1) how to extract the most informative knowledge for a class and use it on testing data and (2) how to thoroughly explore the limited number of support sets and maximize the amount of information transferred to the query set. We propose an InfoMax Classification-Enhanced Learnable Network (ICELN) to address these issues, motivated by Deep Graph InfoMax (DGI), which adapts the InfoMax principle to the summary representation of a graph and the patch representation of a node. By increasing the amount of information that is shared between the query nodes and the class representation, an ICELN can transfer the maximum amount of information to unlabeled data and enhance the graph representation potential. The whole model is trained using an episodic method, which simulates the actual testing environment to ensure the meta-knowledge learned from previous experience may be used for entirely new classes that have not been studied before. Extensive experiments are conducted on five real-world datasets to demonstrate the advantages of an ICELN over the existing few-shot node classification methods.
3D Facial Plastic Surgery Simulation: Based on the Structured Light

The 3D quantitative analysis of facial morphology is of importance in plastic surgery (PS), which could help surgeons design appropriate procedures before conducting the surgery. We propose a system to simulate and guide the shaping effect analysis, which could produce a similar but more harmonious face simulation. To this end, first, the depth camera based on structured light coding is employed for facial 3D data acquisition, from which the point cloud data of multiple facial perspectives could be obtained. Next, the cascade regression tree algorithm is used to extract the esthetic key points of the face model and to calculate the facial features composed of the key points, such as the nose, chin, and eyes. Quantitative facial esthetic indexes are offered to doctors to simulate PS. Afterward, we exploit a face mesh metamorphosis based on finite elements. We design several morphing operators, including augmentation, cutting, and lacerating. Finally, the regional deformation is detected, and the operation effect is quantitatively evaluated by registering the 3D scanning model before and after the operation. The test of our proposed system and the simulation of PS operations find that the measurement error of facial geometric features is 0.458 mm, and the area is 0.65 mm2. The ratings of the simulation outcomes provided by panels of PS prove that the system is effective. The manipulated 3D faces are deemed more beautiful compared to the original faces respecting the beauty canons such as facial symmetry and the golden ratio. The proposed algorithm could generate realistic visual effects of PS simulation. It could thus assist the preoperative planning of facial PS.
A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users.Therefore, studying faulttolerance mechanisms in cloud computing attracts intense attention in industry and academia.The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems.A task-scheduling mechanism distributes tasks to a group of instances to be executed.Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption.However, little work on task scheduling has studied the problem of lost tasks from the broker's perspective.Task loss can happen due to virtual machine failures, server crashes, connection interruption, etc.The broker-based concept means that the backup task can be allocated by the broker on the same cloud service provider (CSP) or a different CSP to reduce costs, for example.This paper proposes a novel fault-tolerant mechanism that employs the primary backup (PB) model of task scheduling to address this issue.The proposed mechanism minimizes the impact of failure events by reducing the number of lost tasks.The mechanism is further improved to shorten the makespan time of submitted tasks in cloud systems.The experiments demonstrated that the proposed mechanism decreased the number of lost tasks by about 13%-15% compared with other mechanisms in the literature.
Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval, which uses movie review datasets.Here the document indexing based on controlled vocabulary, adjective, word sense disambiguation, generating hierarchical categorization of web pages, spam detection, topic labeling, web search, document summarization, etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search optimization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining, feature analysis and classification.By comparing with the previous work, proposed work designed to achieve the efficient results.Overall design is performed with MATLAB 2020a tool.
Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment.When finding a suspicious person, the tracked object cannot be locked in time for tracking.This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article, we proposed a TIMT (The Intelligent Monitoring and Tracking) algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent (AI) face recognition technology and the OpenPose which is able to monitor the physical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote control interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore, the system could find abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community, and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone system has an excellent performance, which not only dramatically reduces the number of monitors and the required equipment but also achieves perfect monitoring and tracking.
Enhanced Rsa (Ersa): An Advanced Mechanism for Improving the Security

Cloud computing has become ubiquitous in our daily lives in recent years.Data are the source of technology that is generated hugely by various sources.Big data is dealing with huge data volumes or complex data.The major concern in big data is security threats.Security concerns create a negative impact on the user on the aspect of trust.In big data still, security threats exist as commonly known as DDOS (Distributed-Denial-of-Service) attacks, data loss, Inadequate Data Backups, System Vulnerabilities, and Phishing as well as Social Engineering Attacks.In our work, we have taken the data loss and Inadequate Data Backups issues into consideration.We analyze that RSA (Rivest, Shamir, & Adleman) is the most secure cryptography mechanism.In cloud computing, user authentication is the weaker section to be secured.Generally, the cryptography mechanism is done in the authentication section only.We implemented our new idea of registration with selected images and pins for processing RSA.By valid authentication approval earned by the proposed mechanism, the user is allowed to use the cloud database, encryption, decryption, etc.To prove the efficiency level of our proposed system, a comparison work is conducted between DSSE (Digital Signature Standard Encryption) and EFSSA (Efficient framework for securely sharing a file using asymmetric key distribution management).The experimental work is carried out and the performance evaluation is done using encryption time and decryption time analysis, throughput, and processing time.On this observation, the security level attained by ERSA is far better in comparison to DSSE and EFSSA with the maximum throughput attained by the proposed E-RSA being 500 Mb/Min and encryption time of 3.2 s, thus ensuring the user trust in using the cloud environment.
Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

Women from middle age to old age are mostly screened positive for Breast cancer which leads to death.Times over the past decades, the overall survival rate in breast cancer has improved due to advancements in early-stage diagnosis and tailored therapy.Today all hospital brings high awareness and early detection technologies for breast cancer.This increases the survival rate of women.Though traditional breast cancer treatment takes so long, early cancer techniques require an automation system.This research provides a new methodology for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best characteristics.Initially, after successful learning of Convolutional Neural Network (CNN) algorithms, data augmentation is used to enhance the representation of the feature dataset.Then it uses BreastNet18 with fine-tuned VGG-16 model for pre-training the augmented dataset.For feature classification, Entropy controlled Whale Optimization Algorithm (EWOA) is used.The features that have been optimized using the EWOA were utilized to fuse and optimize the data.To identify the breast cancer pictures, training classifiers are used.By using the novel probability-based serial technique, the best-chosen characteristics are fused and categorized by machine learning techniques.The main objective behind the research is to increase tumor prediction accuracy for saving human life.The testing was performed using a dataset of enhanced Breast Ultrasound Images (BUSI).The proposed method improves the accuracy compared with the existing methods.
Classification of Nonlinear Confusion Component Using Hybrid Multi-Criteria Decision Making

In today's digital world, the most inevitable challenge is the protection of digital information.Due to the weak confidentiality preserving techniques, the existing world is facing several digital information breaches.To make our digital data indecipherable to the unauthorized person, a technique for finding a cryptographically strong Substitution box (S-box) have presented.An S-box with sound cryptographic assets such as nonlinearity (NL), strict avalanche criterion (SAC), bit independence criteria (BIC), bit independence criteria of nonlinearity (BIC-NL), Bit independence criteria of Strict avalanche criteria (BIC-SAC), and Input/output XOR is considered as the robust S-box.The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach of multi-criteria decision making (MCDM) is proposed for finding the interrelation among cryptographic properties.A combination of two MCDM methods namely Entropy and multiobjective optimization based on ratio analysis (MOORA) is applied for the best S-box selection.A robust substitution box is selected for secure communications in cryptography by using the combination of DEMETAL selection criteria, entropy weight assigning, and MOORA ranking scheme.The combination of these three methods provides a fast selection procedure for the secure confusion component.The offered selection method can also be utilized for the choice of the best cryptosystem with highly secure properties and resistive against all possible linear and differential attacks in the cryptanalysis.
Leveraging explainability for understanding object descriptions in ambiguous 3D environments

For effective human-robot collaboration, it is crucial for robots to understand requests from users perceiving the three-dimensional space and ask reasonable follow-up questions when there are ambiguities. While comprehending the users' object descriptions in the requests, existing studies have focused on this challenge for limited object categories that can be detected or localized with existing object detection and localization modules. Further, they have mostly focused on comprehending the object descriptions using flat RGB images without considering the depth dimension. On the other hand, in the wild, it is impossible to limit the object categories that can be encountered during the interaction, and 3-dimensional space perception that includes depth information is fundamental in successful task completion. To understand described objects and resolve ambiguities in the wild, for the first time, we suggest a method leveraging explainability. Our method focuses on the active areas of an RGB scene to find the described objects without putting the previous constraints on object categories and natural language instructions. We further improve our method to identify the described objects considering depth dimension. We evaluate our method in varied real-world images and observe that the regions suggested by our method can help resolve ambiguities. When we compare our method with a state-of-the-art baseline, we show that our method performs better in scenes with ambiguous objects which cannot be recognized by existing object detectors. We also show that using depth features significantly improves performance in scenes where depth data is critical to disambiguate the objects and across our evaluation dataset that contains objects that can be specified with and without the depth dimension.
VoiceJava: A Syntax-Directed Voice Programming Language for Java

About 5–10% of software engineers suffer from repetitive strain injury, and it would be better to provide an alternative way to write code instead of using a mouse and keyboard and sitting on a chair the whole day. Coding by voice is an attractive approach, and quite a bit of work has been done in that direction. At the same time, dictating plain Java text with low accuracy through the existing voice recognition engines or providing complex panels controlled by the voice makes the coding process even more complex. We argue that current programming languages are suitable for programming by hand, not by mouth. We try to solve this problem by designing a new programming language, VoiceJava, suitable for dictating. A Java program is constructed in a syntax-directed way through a sequence of VoiceJava commands. As a result, users do not need to dictate spaces, parentheses, and commas, reducing the vocal load.
(Retracted) Super-resolution generative adversarial networks using autoencoder reduce dimension

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.QX, GZ, and HC agree with the retraction.
Semi-Blindly Enhancing Extremely Noisy Videos With Recurrent Spatio-Temporal Large-Span Network

Capturing videos under the extremely dark environment is quite challenging for the extremely large and complex noise. To accurately represent the complex noise distribution, the physics-based noise modeling and learning-based blind noise modeling methods are proposed. However, these methods suffer from either the requirement of complex calibration procedure or performance degradation in practice. In this paper, we propose a semi-blind noise modeling and enhancing method, which incorporates the physics-based noise model with a learning-based Noise Analysis Module (NAM). With NAM, self-calibration of model parameters can be realized, which enables the denoising process to be adaptive to various noise distributions of either different cameras or camera settings. Besides, we develop a recurrent Spatio-Temporal Large-span Network (STLNet), constructed with a Slow-Fast Dual-branch (SFDB) architecture and an Interframe Non-local Correlation Guidance (INCG) mechanism, to fully investigate the spatio-temporal correlation in a large span. The effectiveness and superiority of the proposed method are demonstrated with extensive experiments, both qualitatively and quantitatively.