Reviewerly/exHarmony huggingface dataset:
A Variational Model with Second-Order Laplacian for Underwater Image Restoration

Images captured underwater are usually degraded by color distortion, blur and low contrast because the light is absorbed and scattered when traveling through water, which limits their applications. To address these problems, based on the underwater image formation model, we establish a Laplacian variation model that includes novel data and regularization terms. Technically, we design a fidelity term to constrain the radiance scene, and a regularization item for divergence to strengthen the structure and texture details. Moreover, a bright-aware blending algorithm and quad-tree subdivision scheme are also integrated into our variational framework to estimate the transmission map and underwater background light, respectively. Accordingly, we provide a fast-iterative algorithm based on alternating direction method of multipliers to solve the optimization problem and accelerate its convergence speed. Experimental results demonstrate that the proposed method achieves outstanding performances on dehazing, detail preserving, and texture enhancement for underwater images. Moreover, extensive qualitative and quantitative comparisons with several state-of-the-art methods also validate the superiority of our proposed method.
Imperfections in music

Abstract Music is a universal pleasure, but totally individual in terms of our hearing response, cultural background, and the pleasure that different performances and genres of music generate. Our hearing is also individual and varies with age and lifestyle. There are immense differences between instruments, musical scales (around 80), concert halls, audio electronics and broadcasting, plus each of us has a unique hearing response. This provides both originality and pleasure (or the reverse). All these various differences may be viewed as imperfections, or as the source that is essential to our enjoyment of music. For example, there are tangible compromises with instrument construction, such as the use of an equal temperament scale on keyboard instruments to allow one to play with different key signatures (in fact it is a highly imperfect, and significant, detuning imposed on a piano).
IoT and Blockchain for Secured Supply Chain Management

As supply chains become more dynamic, incorporate a scope of partners, and intensely depend on an assortment of outside counterparties, blockchain has arisen as a feasible possibility to de-tangle all information, archives, correspondence exchanges that exist inside the production network organization. Each production network will have enormous measure of information being traded between different stages in a supply chain network. To deal with colossal of measure of information and guarantee its security, supply chain can consolidate IoT and blockchain. This will help in further developing security, usefulness, proficiency, and benefit of the production network. This chapter examines the foundation of blockchain, IoT, and a portion of the issues confronting present day supply chain. The significant advantages for supply chains utilizing IoT and blockchain are analyzed, and future examination heading for Integration of IoT and blockchain for supply chain management are discussed.
Meta Transfer Learning-Based Super-Resolution Infrared Imaging

We propose an infrared image super-resolution method with meta transfer learning and a lightweight network. We design a lightweight network to learn the map between the low-resolution and high-resolution infrared images. We train the network with an external dataset and use meta transfer learning with internal dataset that makes the network drop to a sensitive and transferable point. We build an infrared imaging system with an infrared module. The designed network is implemented on a personal computer and the SR image is reconstructed by the trained network. Both numerical and experimental results show that the proposed method achieves the infrared image super-resolution, and the performance of the proposed method is superior to four state-of-art image super-resolution methods. The proposed method has practical application in the image super-resolution of mobile infrared devices.
Аналіз методів застосування матриць Ляпунова для оптимізації стаціонарних динамічних систем

In this article there has been conducted analysis of Lyapunov matrix application in order to form control inputs under different dynamic systems' optimization methods oriented by quadratic integral criterion.For this purpose, the methods of finding the Lyapunov matrix and optimization based on the Bellman functional equation with subsequent application of the Riccati equation, optimization taking into account the initial values of state variables, optimization based on the Bellman equation using linear matrix inequalities and Lyapunov equation are considered.Despite the complexity of solving the Riccati equation, the problem of finding the Lyapunov matrix is unambiguous only in the case of application of optimization methods based on dynamic Bellman programming and representation of the Bellman function by the Lyapunov function.Optimization based on the application of the linear matrix inequality condition is not unambiguous, as it requires the choice of the inequality solution.The optimization of the system by the integral quadratic criterion and the initial values of the state variables is also ambiguous because there is a problem of solving nonlinear interconnected optimization equations.
Designing an Interactive Game for Preventing Online Abuse in Namibia

in the wake of digitalization, technology has changed the world, connecting people with information and services at the touch of a finger. Regular Internet access, smartphones, and other technological advances have made the world a global village by bringing people from various geographical locations together. Despite the positive changes, the pace and scale of this technological revolution have created space for nefarious actors. Namibia has been grappling with issues centring on cyber security education, policy, and lack of online safety awareness. In recent years, Namibians have experienced identity theft, data breaches, fake news, phishing, hacking, money laundering, and child abuse initiated on online platforms. As such, this study developed an interactive video game to teach online safety in Namibia to combat online child abuse. An online survey, game design challenge workshops, and focus groups were used to gather information. A total of 84 relevant stakeholders (students, parents, teachers, and game developers) participated in two game design challenge workshops and the alpha and beta testing sessions. These workshops followed a solid co-design practice. We designed and developed two Namibian-inspired game prototypes that were combined into one game, which was implemented and tested during this study. All the game prototypes followed an iterative process of information gathering and sharing stories of online inappropriate behaviours, drawing storyboards, designing wireframes, and testing with cognitive walkthroughs and a think-aloud protocol, alpha and beta testing. The final game is called Onlinicus- Share your Story versioned in 2D and 3D. This game took an approach of user instructiveness and interaction in form of story-telling. The use of predefined personas and utilising participants’ own stories contributed to the richness of the stories and games developed.
Noise-Resilient Ensemble Learning Using Evidence Accumulation

Ensemble Learning methods combine multiple algorithms performing the same task to build a group with superior quality. These systems are well adapted to the distributed setup, where each peer or machine of the network hosts one algorithm and communicate its results to its peers. Ensemble learning methods are naturally resilient to the absence of several peers thanks to the ensemble redundancy. However, the network can be corrupted, altering the prediction accuracy of a peer, which has a deleterious effect on the ensemble quality. In this paper, we propose a noise-resilient ensemble classification method, which helps to improve accuracy and correct random errors. The approach is inspired by Evidence Accumulation Clustering, adapted to classification ensembles. We compared it to the naive voter model over four multi-class datasets. Our model showed a greater resilience, allowing us to recover prediction under a very high noise level. In addition as the method is based on the evidence accumulation clustering, our method is highly flexible as it can combines classifiers with different label definitions.
Performance Evaluation of Extreme Learning Machines Classification Algorithm for Medical Datasets

The choice of efficient algorithms is a critical issue in the classification of medical datasets. This requires the consideration of a number of measures to ensure reliable results. In this study, the robustness of Extreme Learning Machine (ELM) and some state-of-arts classifiers were investigated on six (6) different (complete and incomplete) medical datasets. Multiple imputation technique with 5-fold-iteration was used to address the issue of missing data points in datasets with holes. The technique regenerated the missing values 100% in all the datasets. The performance of ELM was compared with Support Vector Machine (SVM), k-Nearest Neighbour (KNN) and Classification and Regression Tree (CART) on the complete and imputed datasets. The evaluations were based on classification accuracy, computational time and stability of the algorithms. ELM has 83.33% overall best accuracy, and 100% best computational time of the simulations. However, the stability of ELM is subject to further improvement, which is an area of further research.
IoT-GAN: Anomaly Detection for Time Series in IoT Based on Generative Adversarial Networks

In order to monitor the behaviors of IoT devices, a large amount of time series data are collected by sensors embedded in them. To take timely action further to resolve the underlying issues of IoT devices, it is critical to detect anomalies among the time series. However, anomaly detection for time series in IoT is particularly challenging due to its complex temporal dependence and dynamics. In this paper, we propose an unsupervised anomaly detection method for time series based on generative adversarial networks (GANs), which can learn the normal patterns of time series data, and then use the reconstruction errors to recognize anomalies. To the best of our knowledge, we are the first to incorporate Gated Recurrent Unit with bi-directional generative adversarial network architecture to capture the complex temporal dependence and dynamics of time series in IoT. We also introduce cycle-consistent loss as a deterministic control to further improve the performance of anomaly detection and stabilize GANs training. Based on the trained model, any newly arrived observation can immediately be determined as anomalous or not without requiring an additional inference time. Extensive empirical studies on three real-world datasets demonstrate that the proposed IoT-GAN is effective and efficient in detecting anomalies of time series in IoT.
Specific manifestation of innovative university initiatives in different regimes of the "triple helix" organization

Subject. The article considers the innovative interaction of universities with the government and the business. Objectives. The purpose is to define the role of universities in shaping innovation. Methods. The study employs the systems, graph and analytical methods of research. Results. The study shows that in conditions of innovation-driven economy, the "Triple Helix" is one of promising organizational systems of interaction between universities, the business and the State. I performed a comparative analysis of "Triple Helix" organization modes, defined the role functions of initiator subjects of innovative activity, and executor subjects of innovative activity that provide the innovative development of established ecosystems. Conclusions. The balanced regime of the "Triple Helix" organization is the most effective from the standpoint of innovation activity of universities in their interaction with the State and the business, since it contributes to the synergistic effect through the integration of roles of the initiator and the executor of various functions.
Attendance System of Computer Laboratory Assistant in PT.XYZ Using Wireless Fidelity

The use of Android smartphones is becoming increasingly widespread in all walks of life in people's daily lives. Supported by the development of mobile applications that are so innovative and varied, is one of the reasons. Mobile application development aims to support almost all human activities, especially in the world of work, therefore the programmer creates a presence system using Android-based wireless fidelity (WIFI) at laboratory of PT.XYZ. At the laboratory of PT.XYZ, they still use manual attendance system for presence of computer laboratory assistant, so there are several problem like productivity decreases due to fraud in attendance, namely the help of friends to be absent without having to be present, to data errors. Through wireless fidelity programmers solve problems with prototype research models and Object Oriented Programming (OOP) methods. The software development tool is the Unified Modeling Language (UML). The Applications has made using hash function of encryption on admin passwords and the user passwords. Both of the encryptions work on private passwords, even the admin and programmers don't know the contents of the user's password. The main result of this research is we can provide that there’s many way to make presence system one of them uses WIFI.
Towards a Cost-Effective Predictive Mammogram Classification Model for Breast Cancer Diagnosis

Breast cancer is the deadliest common cancer in women and slightly in men worldwide. Routine mammography is the standard technique for preventive care, detection and classification of breast cancer before a biopsy. It has come to our attention that, routine mammography is still a manual process, prone to human errors which result in unnecessary costs on both the patient and medical institute which may lead to loss of life. In this paper, we developed a prototype cost-effective predictive mammogram classification model for breast cancer diagnosis using Deep Learning Studio performing data augmentation, transfer learning and careful data preprocessing. The resulting prototype model was trained on a publicly available In-breast dataset and achieve above human-level performance on the classification of mammograms. Finally, it is worth noting that the experiments we performed showed some degree of confidence that our prototype could improve the currently used methods for predictive mammogram classification.
Exploring Cell-Based Neural Architectures for Embedded Systems

Neural Architectures Search (NAS) methodologies, which automatically discover state-of-the-art neural networks, have seen a growing interest in recent years. One particular group of NAS methodologies searches for small sub-networks called cells, which are then linearly connected to form the complete neural network. The composition of the final neural network, established through the width of the cells and the depth of the connections, is manually designed while being influenced by the available GPU memory. Typically, the hardware architectures targeted in NAS research are powerful, high-end GPUs. Hence, the attention is on creation of a large neural network that will still fit in the GPU, in turn leading to a very high accuracy for the given task. In direct contrast, we exploit the inherent flexibility of cells to create smaller neural networks, with the intention to study their behaviour on resource-constrained embedded systems. We use the cells discovered from Stochastic Neural Architecture Search (SNAS), to explore the effect that the composition of the cell has on various metrics, namely, the number of parameters, accuracy, latency and power usage. The last two metrics are measured on NVIDIA Jetson Nano, an embedded AI computing platform with a small GPU with mere 4GB on-chip memory. When comparing results of our exploration to the original SNAS architecture’s with 97.02% accuracy for the CIFAR-10 dataset, one particular architecture, with only a tenth of original parameters, achieved an accuracy of 96.14%, notably with 15% lower power consumption and \(\approx \)3x faster inference time. Furthermore, this model outperforms other architectures, which are designed for edge devices, specifically to reduce the model size. Thus demonstrating that cell-based architectures, with adequate composition, provide efficient models to be deployed on resource-constrained edge devices.
Analysis of Market Determinants Impacting the Blockchain Technology in the Healthcare Sector

Blockchain technology is paving its way from novel technology to leveraging its exclusive proficiencies. This technology refers to a platform that chronologically accounts and tracks the transactions and assets via distributed ledgers in a network. In today's scenario, the blockchain technology is gaining traction to completely revolutionize the healthcare services. This chapter discusses different competitive advantages offered by the healthcare sector on inclusion of blockchain technology in their strategic decisions and models. One of the key focus areas of the chapter includes market determinants impacting blockchain technology in the healthcare industry along with the market sizing and forecast analysis. Further, this chapter emphasizes how the blockchain concepts help in simplifying healthcare businesses amidst different challenges being faced by these industries in today's competitive scenario.
A cloud-based tool for federated segmentation of whole slide images

It is commonly known that diverse datasets of WSIs are beneficial when training convolutional neural networks, however sharing medical data between institutions is often hindered by regulatory concerns. We have developed a cloud-based tool for federated WSI segmentation, allowing collaboration between institutions without the need to directly share data. To show the feasibility of federated learning on pathology data in the real world, We demonstrate this tool by segmenting IFTA from three institutions and show that keeping the three datasets separate does not hinder segmentation performance. This pipeline is deployed in the cloud for easy access for data viewing and annotation by each site's respective constituents.
Realization of testable D Flip-Flop based on reversible logic

In low-power Very Large Scale Integration Design, reversible logic-based computation is a prominent research topic. As applications evolve from IoT to AI, the number of devices on a single chip grows, resulting in increased power dissipation. As a result, reversible logics have received a lot of attention in terms of power reduction in digital circuit design. The major objective of this work is on utilising reversible logic gates to construct Shift Registers, Jhonson Counters (Twisted Ring Counters), and Ring Counters (Fredkin gate, Feynman gate). Because reversible logic gates feature inputs that are distinctively mapped to outputs, they utilise less power and take up less space as compared to conventional logic gates. Garbage outputs, latency, quantum cost, and transistor count are all considered in the analysis of these reversible logic-based circuits. Various Boolean logic based circuits are developed and simulated using the Cadence tool in this work. The SCL 180nm library is used to efforts on specific.
Online Multiple Object Tracking Algorithm Based on Heat Map Propagation

Online multiple object tracking is an important computer vision task with a wide range of application scenarios which integrates target detection, re-ID (Re-identification) and feature association matching. Most of the existing methods have two problems: ignoring the spatio-temporal information, resulting in poor tracking effect, and using two-stage detector makes the tracking speed slow. In this paper, we design an online multiple object tracking algorithm by generating a heat map based on the spatio-temporal information of the tracker, and propagate it to a one-stage detector to improve tracking speed and stabilize detection quality. Without introducing additional regressors, the generation of the propagation heat map are both simply and efficiently. Consequently, the proposed algorithm can achieve a balance between the speed and accuracy, and provides a paradigm for the utilization of the spatio-temporal characteristic information of the one-stage detector. Experiments are carried out on MOT-17 and 2DMOT-15 which verifies that 43.27% and 63.7% improvement in tracking speed is obtained with a small accuracy compromise.
Analysis of Semi-Supervised Algorithms in Natural Language Processing

With the rapid development of modern science and technology, the application space of natural language processing technology is constantly expanded, and good application results are achieved in various industry fields. In particular, the use of semi-supervised algorithms can not only reduce the cost of natural language processing technology, but also improve the processing efficiency. Based on this, the article focuses on the practical application of semi-supervised algorithms in natural language processing, firstly, it elaborates the connotation and important value of semi-supervised algorithms, secondly, it introduces the main application techniques of semi-supervised algorithms, and finally, it proposes the practical application effects of such techniques in natural language processing, hoping to provide reference and reference for relevant researchers.
Real-time Video Transmission Optimization Based on Edge Computing in IIoT

In the Industrial Internet of Things (IIoT) scenario, the increase of surveillance equipment brings challenges to the transmission of real-time video. It needs more efficient approaches to finish video transmission with more stability and accuracy. Therefore, we propose a self-adaptive transmission scheme of videos for multi-capture terminals under IIoT in this paper. To fit for the constant variation of network environment, we compress the videos that wait for transmitting from multi-capture terminals by reducing the non-key frames with Graph Convolutional Network (GCN). Moreover, a self-adaptive strategy of transmission is implemented on the Mobile Edge Computing (MEC) server to adjust the transmission volume of processed videos, and a multi-objective optimization algorithm is utilized to optimize the strategy of transmission during the video transmission. The relative experiments are conducted to validate the performance of the proposed scheme.
Towards a Dynamic Visualization of Complex Reverse-Engineered Object Collaboration

UML is useful to model a higher abstraction level concepts of the software in a forward engineering context, but it is still challenging to reverse engineer more complex behavior of realistic object-oriented programs (OOPs) based on such visualization techniques. For example in a sequence diagram, an object appears in quite different ways when it serves as a sender or receiver of some message and as a parameter or return value of another message, and thus compound method invocations such as invocation chains and callbacks cannot be represented directly. In this paper, first, we define a dynamic metrics named alternation complexity that indicates the number of alternations of object roles between sender/receiver and parameter/return value within collaboration. Through experiments with 12 professional programmers, we confirmed that the metrics captures a certain aspect of difficulty in comprehending features. Furthermore, we present a dynamic visualization model to directly represent collaboration where the types of object roles frequently change.