Reviewerly/exHarmony huggingface dataset:
Secure image encryption using high throughput architectures of PRINT cipher for radio frequency identification applications

With the enhancement of technology, data security has become incredibly significant. Nowadays, images and vital information are used in real-time, and high throughput architecture is required for internet of things and real-time applications. We mainly focus on the high throughput architecture of PRINT cipher. PRINT cipher is a lightweight block cipher with a block size of 48-bit and 96-bit. Loop-unrolled, 48-bit pipelined, and 96-bit pipelined architectures are designed and implemented on different field programmable gate array and application-specific integrated circuit platforms. The maximum operating frequency obtained for 48-bit pipelined is 259.67 MHz on Virtex-4 and 310.67 MHz for 96-bit pipelined on Virtex-5. Finally, with the help of a controller, 48-bit high throughput pipelined architecture was utilized for image encryption of both grayscale and color images. The security analysis of encrypted images for color and grayscale images using PRINT cipher shows better performance and more robust protection against statistical, entropy, and differential attacks.
An automatic measurement system for the wall thickness of corrugated plate based on laser triangulation method

Wall thickness is an important parameter used in the characterization of the surface quality of chevron corrugated plates. However, current measurement techniques such as contact mechanical or destructive modes are timing-consuming and are limited by the complex morphology of the measured object. In this study, an optical measurement system is established for experimental evaluation of the wall thickness. Its optical design and working characteristic are described based on the triangulation principle. By performing normal vector estimation, and combining with the search results about the best possible reconstructed mesh surface, the wall thickness at each measurement point can be evaluated. A series of comparison experiments with the maximum deviation 0.043 mm show that our proposed method can provide effective and automatic technical support for dimensional detection of complex components.
Locality preserving triplet discriminative projections for dimensionality reduction

The graph embedding framework is widely used for developing dimensionality-reduction algorithms. Many such supervised algorithms construct an intrinsic graph to compact intra-class samples or describe their local structures and build a penalty graph to increase the separability of inter-class samples. However, in the available intrinsic graph construction, manually selecting a set of appropriate neighbors associated with each sample is challenging. In addition, the construction of a penalty graph may seriously impair the intrinsic structures of the samples. Thus, in an attempt to address these issues, this study proposes an algorithm referred to as locality-preserving triplet discriminative projections. The proposed algorithm comprises locality-preserving and discriminative graphs. First, a weighted least-square function is used to calculate the edge weights of the locality-preserving graph. An improvement of the locality-preserving graph constructed in this study is that suitable neighbors are soft-selected. Meanwhile, the triplets of the samples are exploited for discriminative graph construction. Through the separation of the marginal samples and using less focus on samples with good discriminability, this graph enhances the discriminability with less damage to the intrinsic structure. In addition, to further improve the performance of the discriminative graph, a class-relevant margin is added to separate similar classes. The experimental results on four public datasets show that the proposed algorithm outperforms several other dimensionality reduction methods.
Improving Security in Cloud Data Using Paillier Homomorphic Encryption System

Data has regularly circulated between data holders and data consumers in the digital era. One of the difficulties that should be handled when adopting the cloud is data security and privacy. The practice of cryptography is security conscious and confidential, as well as the truthfulness of data transferred, despite of the technique used to establish a secure link between individuals or organizations establish the link. In order for the results to be encrypted and computations performed on it, the secret key of the data holder needs to be sent to the server. To secure data transport over the cloud, we propose using partially homomorphic encryption approaches such as the Paillier algorithm in the study. In the cloud, data loss and leakage, malware attacks, and susceptibility are still common. We use a security algorithm and a proposed technique to protect data from unauthorized access. The Paillier homomorphic encryption technique was employed to make the system secure. To prevent the data from illegal access, a Paillier encryption is used. In recent research on the topic of cloud security and privacy, preserving compute, Paillier has become increasingly popular. Using Python, we describe a new implementation of Paillier homomorphic encryption with bit shifting. These approaches allow you to conduct arithmetic computations on encrypted files without decrypting them.
REET: Region-Enhanced Transformer for Person Re-Identification

Person re-identification (ReID) plays a significant role in intelligent surveillance systems. However, it is challenging due to large variations in the intra-class, where the same person is captured in different scenes or cameras. The current person ReID research focuses on creating robust features for class distinction and generalizing neural networks for covering various target domains to address the issue. Recently, after the achievement of vision transformers, the application of transformers has also begun to person ReID studies. The transformer-based methods have improved quantitative performance of person ReID; however, they still suffer from class distinction. Therefore, this paper proposes a novel region-enhanced transformer (REET) to create robust ReID features. Unlike conventional transformer-based approaches, the REET emphasizes the tokens generated by region-level. Our method achieves state-of-the-art results on three public datasets; Market1501, DukeMTMC, and CUHK-03.
A Data-Driven Approach for the Localization of Interacting Agents via a Multi-Modal Dynamic Bayesian Network Framework

This paper proposes a multi-modal situational inter-action model for collaborative agents by fusing multi-sensorial information in a Multi-Agent Hierarchical Dynamic Bayesian Network (MAH-DBN) framework. The proposed model is learned in a data-driven methodology to estimate the states of interacting agents only from video sequences. This can be regarded as a two-fold methodology for improving visual-based localization and interaction between autonomous agents. In the learning stage, the odometry model is used to drive the video learning model for a robust localization and interaction modeling. During the testing phase, the learned Multi-Agent Hierarchical DBN (MAH-DBN) model is used for the localization of collaborative agents only from video sequences by proposing an inference method called Multi-Agent Coupled Markov Jump Particle Filter (MAC-MJPF).
3D Object Detection of Cars and Pedestrians by Deep Neural Networks from Unit-Sharing One-Shot NAS

This work adopts camera, lidar and radar sensors to realize 3D object detection of cars and humans in varied and complicated road scenes. Based on the Radar Voxel Fusion Net (RVF-Net) consisting of voxel feature extraction module, feature aggregation module, and detection head module, we extend layers of the voxel feature extraction module with dimensions at a consistent scaling ratio, and increase a prediction head in the detection head module. According to this modified RVF-Net, the depth of the voxel feature extraction module is searched by the proposed unit-sharing one-shot Neural Architecture Search (NAS) scheme where there are five choices of bypass, and one to four copy units for each layer. Only four copy units with conditional links yield five choices for structure searches. Such an approach can minimize the search cost and parameter amount. By using the forward-looking sensing data of nuScenes, the network from the proposed NAS, and the modified RVF-Net can yield superior Average Precisions (AP), more than 2.3% AP increase, than the conventional ones in detecting cars and pedestrians simultaneously. Therefore, the proposed network can effectively accomplish multi-object recognition tasks for self-driving applications.
Study and analysis of various link predictions in knowledge graph: A challenging overview

Knowledge Graph (KG) is the network which contains some topic-based entities, called nodes, and the associated information among the entities. Here, the concept in the knowledge graph is denoted by the tuple relationship, such that the ⟨entity1, predicate, entity2⟩. The entity in the knowledge graph is the abstract concepts based on the particular object, namely the organization, dataset, people, and some associated documentation. The big issue in the KG is that it consists of some incomplete information. The missing details can be identified by employing the knowledge graph completion (KGC) solution. KGC is the same as the link prediction concepts in the knowledge graphs. However, this concept is more complex that it does not predict the link relationship among the nodes but also the diversified information from the link relations. Hence this survey analyzes different methods of link prediction techniques, and this review provides a detailed review of 50 research papers concentrating on various methods, like embedding-based methods, deep learning methods, knowledge acquisition methods, ranking methods, and representation learning methods. The analysis is carried out with respect to the survey based on the publication year, research techniques, performance measures, dataset, toolset and achievement of the research methodologies. Also, the problems in the methods are explained in the research gaps and issues. Furthermore, the future extent of these research works is done based on the limitations identified from the existing research methods.
Online and offline hybrid teaching mode of piano education under the background of big data and Internet of Things

Big data and the idea of the Internet of Everything have become popular technologies at the moment. The application of these two technologies has also promoted the development of various industries. The education and training industry is the focus of the country and the public. Piano education is the cradle of artists. The application of Internet of Things technology to the piano education industry will help to upgrade the industry teaching model. In order to investigate the current situation of online and offline hybrid teaching in piano education, this paper uses the method of questionnaire distribution to conduct a survey on teachers and students in 50 colleges of music and music training institutions. It was found that after using big data and Internet of Things technology to innovate the online and offline hybrid teaching mode of piano education, so the teaching efficiency of teachers and the learning effect of students have been greatly improved.
Reconfigurable PM2.5 Sensor Green Deployment Mechanism Based on Blockchain Technology

This paper presents the design of reconfigurable green PM2.5 sensor module deployment scheme based on blockchain technology. The proposed scheme proposes classes with hierarchical layers implemented by name registry smart contract that could be reconfigured for solving the green deployment problem among PM2.5 sensor modules. This proposed scheme design three functions made by smart contracts including Registration smart contract, Hierarchical Class Mapping Based on Name Registry Smart Contract Function, and Reconfigurable Working Grouping Smart Contract Function. The results shown that the modules which are measured the similar data will be distributed into the candidate group instead of the current working group, where the total corresponding energy consumption will be reduced for the BC transaction operations in this blockchain network. Moreover, the relevant data storages will be also avoided to store the duplication data as well as redundant data.
Application of Pathfinder, Honey Badger, Red Fox and Horse Herd algorithms to phase equilibria and stability problems

Thermodynamic problems are usually formulated as optimization problems but are often nonlinear and nonconvex and require robust optimization techniques to find the global solution. This work studied four newly developed swarm-based stochastic optimization algorithms, which are Honey badger algorithms (HBA), Pathfinder algorithms (PFA), Horse herd optimization algorithms (HOA) and Red fox optimization (RFO). They were applied to solving several phase stability and equilibrium problems with different degrees of complexity. The strengths and weaknesses of all the algorithms were compared. The stochastic algorithms solved the problems and obtained the global minimum value with a high success rate. HBA and HOA performed very well in all the problems considered, though the performance of HBA was slightly better than that of HOA. The performance of PFA closely followed while RFO was poor and had the worst performance among the four algorithms, but with the inclusion of a local optimizer, there was a significant improvement. Therefore, HBA and HOA are robust and reliable for solving phase stability and equilibrium problems.
Progressive integration network for single‐image rain removal

Abstract Due to the inherent overlap between the background and rain streaks, most deraining methods have suffered greatly from the over‐smoothing effect, which restrains the deraining models’ performance to a great extent. To address this issue, a progressive integration network (PIN) is presented to reduce this effect. Specifically, in our network, the high‐level representations will be used to enhance the low‐level feature maps through our proposed global integration and local integration blocks. Moreover, based on the difficulty of prediction, a novel curriculum‐learning mechanism is explored to ease the procedure of training. Extensive experimental results demonstrate that the proposed method achieves considerable performance in contrast to other deraining methods.
Knowledge‐aware representation learning for diagnosis prediction

Abstract Diagnosis prediction exploits electronic health records (EHRs) to predict the future diagnoses of patients, further supporting clinical decision making and personalized treatments. However, a patient's EHR is an irregular sequence of visits that contains a large number of medical concepts. The disease progression patterns are closely related to the visits, as well as the contextual knowledge of each visit. The existing diagnosis prediction methods ignore the complex relationships between the visits and the contextual knowledge, and thus cannot achieve satisfactory performance. Therefore, we develop a knowledge‐aware representation learning method to comprehensively model these complex relationships. Specifically, we first construct a medical knowledge graph to model the correlations between medical concepts in EHRs, and project the contextual knowledge into the pre‐learned vectors. We then devise an enhanced gated recurrent unit (GRU) neural network to extract the long‐term intra‐relationships between visits, and design a novel knowledge attention module to capture the complex inter‐relationships between the visits and the contextual knowledge. Armed with these, we provide a powerful and flexible framework to capture the long‐term discriminative disease progression patterns for diagnosis prediction. Intensive experiments are conducted on two real‐world EHR datasets. And the model achieves a competitive performance with Code‐level Accuracy@20 of 0.7465 and Visit‐level Precision@20 of 0.7547 on MIMIC‐III, and Code‐level Accuracy@20 of 0.9337 and Visit‐level Precision@20 of 0.9427 on MIMIC‐IV.
Strategic value of Online Social Networks (OSNs) in supply chain networks during COVID-19

The coronavirus (COVID-19) epidemic has transmuted the business environment and disordered supply chains worldwide. Over the last decade, online social networks (OSNs) have impacted every element of human life and corporate organization. Deploying OSNs services in supply chain networks is currently a pressing need. This study developed a model based on the IS success model, resource-based view, and absorptive capacity to explore the strategic value of OSNs in the supply chain network during COVID-19. Structural Equation Modeling (SEM) analyzes 220 data collected using online questionnaires. Information, system, and service quality emerged as influential features of online social networks that enhance the absorptive capacity and supply chain visibility and agility. The performance of the supply chain is positively impacted by each of OSN's strategic values, including visibility, agility, and absorptive capacity during COVID-19. In addition, absorptive capacity and supply chain visibility and agility mediate the association of information, system, and service quality features with supply chain performance. Supply chain managers and policymakers will better grasp OSNs adoption necessities and features during pandemic situations. It will also contribute to operation management, information systems, and social media by presenting OSNs in a supply chain environment.
SAR Image Denoising in High Dynamic Range with Speckle and Thermal Noise Refinement Modeling

Synthetic Aperture Radar (SAR) images inevitably contain speckle noise. Despeckling SAR images are typically represented as linear forms due to the consistency of denoising network training/inference settings on a linear scale. However, this leads to controversial problems when the linear SAR images are seen with the naked eye: i) restriction of representation for dark areas and ii) excessive expression for bright areas due to the high-intensity range. To overcome these problems, we propose a denoising framework that simultaneously eliminates speckle and thermal noise in the decibel (dB) domain through novel noise modeling. Our noise modeling allows the network to learn in the dB domain of the desired dynamic range, enabling stable end-to-end learning without separate spatial transformations. Our modeling is the first attempt to consider thermal noise. Experimental results show that our method is superior in quantitative and visual performance compared to the existing methods.
Design and Evaluation of an Agile Framework for Continuous Education in Software Engineering

This Research-To-Practice Full Paper presents an agile framework that was created and evaluated in the context of a continuous professional training and development program in the IT industry. It was designed with the values and principles of Agile Software Development in mind and provides a learning concept to become acquainted with DevOps and Cloud Computing practices. While DevOps promises to build, test and release software faster and more reliably through methods like continuous integration, continuous deployment/delivery, automated testing and infrastructure-as-code, the integration of Cloud Computing services empowers each step of the agile life cycle through ubiquitous access to computing resources that can be provisioned with minimal effort. The paper describes the experiences of creating and applying the didactic concept as well as observations of conducted work, learning progress, motivation and achievements.
Operationalizing team commitment in a project-based learning environment

Commitment is a multi-dimensional construct that has been extensively researched in the context of organizations. Organizational and professional commitment have been positively associated with technical performance, client service, attention to detail, and degree of involvement with one’s job. However, there is a relative dearth of research in terms of team commitment, especially in educational settings. Teamwork is considered a 21stcentury skill and higher education institutions are focusing on helping students to develop teamwork skills by applied projects in the coursework. But studies have demonstrated that creating a team is not enough to help students build teamwork skills. Literature supports the use of team contracts to bolster commitment, among team members. However, the relationship between team contracts and team commitment has not been formally operationalized.This research category study presents a mixed-methods approach towards characterizing and operationalizing team commitment exhibited by students enrolled in a sophomore-level systems analysis and design course by analyzing team contracts and team retrospective reflections. The course covers concepts pertaining to information systems development and includes a semester-long team project where the students work together in four or five member teams to develop the project deliverables. The students have prior software development experiences through an introductory systems development course as well as multiple programming courses. The data for this study was collected through the team contracts signed by students belonging to one of the 23 teams of this course. The study aims to answer the following research question: How can team commitment be characterized in a sophomore-level system analysis and design course among the student teams?A rubric was developed to quantify the team commitment levels of students based on their responses on the team contracts. Students were classified as high or low commitment based on the rubric scores. The emergent themes of high and low commitment teams were also presented. The results indicated that the high commitment teams were focused on setting goals, effective communication, and having mechanisms in place for timely feedback and improvement. On the other hand, low commitment teams did not articulate the goals of the project, they demonstrated a lack of dedication for attending team meetings regularly, working as a team, and had a lack of proper coordination while working together.