Reviewerly/exHarmony huggingface dataset:
Hybrid L1 Adaptive Controller-II: Fuzzy Parallel Distributed Compensation Based Approach

In this chapter, fuzzy PDC logic is augmented with fuzzy L1 adaptive controller to tackle nonlinearities along with uncertainties and disturbances. The nonlinear system is considered as the combination of linear consequence rule based fuzzy systems. For each linearized zone, fuzzy L1 adaptive controller with fuzzy PDC controller is designed. The fuzzy PDC-L1 adaptive controller is employed on four simulation case studies. In this method, the fuzzy L1 adaptive controller provides better rejection of uncertainties and disturbances as well as fuzzy PDC controller efficiently handles nonlinearities, delays, unmodelled dynamics to provide en bloc satisfactory transient and steady state tracking performance.
Grid based blockchain framework for information management in surgical process proceedings

Numerous academic and industrial research studies have focused on the feasibility of implementing blockchain-based healthcare systems data management that can transform the way data systems functioning, usage, and development can be effective. In this manuscript, the focus is on the development of a Grid-based blockchain structure formanaging the surgical process-related information in integrated systems. Considering the implications, scope of human error in the surgical processes, and the quantum of records integral to such process, the need for having robust records of surgical data is imperative. Aiming at improving the security of the information, in this manuscript, the Grid-block structure constituting multiple grids framework is proposed, which can be optimal for transaction query processing, responsiveness for triggers, and security inheritance. The proposed system, if chosen for real-time implementation, can support interoperability of data among the distinct healthcare systems and support in minimizing the issues of human error inspections, prevention scope for errors whilst improving the quality of healthcare solutions.
Sentiment Analysis Model Based on Multi-head Attention in Multimodality

To solve the problem that different in emotion analysis have different degrees of influence on the target discourse, which leads to the unsatisfactory effect of emotion classification, This paper proposes a sentiment analysis model based on multi-head attention mechanism. Firstly, the text, visual and acoustic features are extracted from the preprocessed utterance information; Then, the multi-head attention mechanism is used to label the weight of the data, and the data information of different positions is extracted from multiple subspaces to get high-level semantic features; Next, The data fusion of context-independent feature extraction is input into LSTM network to generate context-dependent feature extraction; finally, the data is input to the softmax layer to get the result of sentiment classification. The experimental results show that compared to other existing models on the CMU-MOSI data set and IEMOCAP data set, it is increased respectively to 84.48% and 79.61%. Therefore, the model in this paper has a better sentiment analysis effect.
Utilizing Blockchain Technologies with IoT: Review Paper

The core aim of the internet of things is providing communication capability between huge numbers of different entities “things”. The Abundance feature of connected devices to the emergence of many security challenges for IoT data, which in turn stimulate researchers to search for appropriate solutions. The Blockchain is one of the security technologies which consider as a game changer in safeguarding the internet of things data. Through its impact, it has made the addition of more devices into the Blockchain ecosystem. Boosting the security of devices used in IoT to accelerate the rate of adoption of Blockchain technology. The main aim of this paper is to investigate the current literature to determine how the management of IoT has become independent and widespread doing away with the need of having to rely on one entity in the storage of data in the new technology, Blockchain assumes the role of a database making it different from the storage methods used in the past. In addition to, the authors have organized the utilization of Blockchain in a well-defined structure. Finally, this paper concluded that by utilization of Blockchain results in improved security and distribution using devices such as sensors which are connected efficiently with the aim of coming up with a secure database which gives rise to lots of business opportunities in the future.
Local directional coding-based scene change detection for frame rate up-conversion

This paper proposes a new scene change detection method that uses local directional coding for frame rate up-conversion. The directional coding technique converts the luminance value of the edge direction response for given frames to bit codes and calculates the bit code difference between the previous and current frames. Existing scene change detection methods usually calculate the difference between the histogram shapes or the luminance values between the previous and current frames to detect scene changes. Thus, they erroneously detect scene changes in a scene with moving objects or camera motion. The existing automatic thresholding-based method uses iterative operations to calculate the optimal threshold and determine the scene change using the difference between the calculated threshold values, and it therefore has a high computational cost. To solve these problems, the proposed method uses the difference between the bit codes from the eight edge response values between the previous and current frames to detect the initial scene change regions. In addition, the proposed method uses a refinement process on the detected initial scene change regions to enhance the scene change detection accuracy. The experimental results showed that the proposed method enhanced average F1 score to 0.5235 (a 126.36 % improvement) as compared with the benchmark methods. The average computation time per pixel of the proposed method also reduced to 13.3668 μs (an 87.39 % reduction) compared with the benchmark methods.
A Method of Circle Center Fitting for Water Wall Cladding

In the process of automatic laser cladding of the water wall, the determination of the cladding path is a vital step. As the cladding progresses, the central angle of the measurable pipe gradually decreases, and the pipe will be slightly deformed due to thermal stress. A method for obtaining the center of the pipe is proposed to guarantee a high fitting accuracy under the condition of small arc. The principle of the method is to introduce Lagrangian constraint parameters λx, λy to the circle center obtained by a fitting curve. Then the gradient descent method is performed on the parameters to find the optimal solution for the center of the section circle. The simulation and experimental results show that when the central angle is 10°, the center of the fitting circle's error is 0.2326mm. It possesses a higher fitting circle center accuracy for water wall cladding.
Small Object Detection Based on Super-Resolution Enhanced Detection Network

Object detection is one of the most important fields in computer vision and plays a key role in various practical application scenarios. Although most of the current mainstream object detectors have achieved outstanding results in the detection of large and medium-sized objects, the detection accuracy of small objects lacking sufficient detailed information is still unsatisfactory. To solve this problem, we propose a super-resolution enhanced detection network (SREDet). In the SREDet, we improve and integrate ESRGAN and YOLOv3 to make the joint loss of the entire detection network can assist in optimizing the generator, so that the generated super-resolution images are more conducive to the task of small object detection. At the same time, the method of edge extraction and enhancement is adopted in our network to reduce the impact of background noise on small object detection, while its loss function is also improved by us. In addition, we propose an image segmentation and mapping module to adapt the input size of super-resolution images and detectors. Finally, we completed the whole object detection network with an improvement of NMS algorithm. Our experiments are based on the COCO dataset, and the results demonstrate the effectiveness of each module. Compared with some classic object detectors, our network has a significant improvement in the detection performance of small objects.
Research on Person Re-identification Based on Pose-Attention-GAN

Person re-identification (reID) is a challenging task, with the purpose of matching pedestrian images with the same identity across multiple cameras. With the wide usage of deep learning methods, reID performances by different algorithms increase rapidly. However, in the presence of large pose variations, it faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features. To solve the above challenges, this paper proposes an image generation and classification framework for pedestrian arbitrary pose based on the combination of Pose-attention-Generative Adversarial Network(PAGAN) and Resnet50 network. Firstly, the human pose network is used to extract the pose corresponding to the pedestrian image, and the generator and discriminator of the PAGAN network are trained with the pedestrian image and its pose. The generator of the network comprises 9 pose attention transfer blocks that each transfers certain regions it attends to, generating arbitrary pose and high-quality fake images progressively, and discriminator distinguishes whether the input image is real or not. Secondly, the fake images of arbitrary pose are increased as training samples. Finally, the expanded training set is used to train the classification and recognition network to learn features related to identity but not pose, effectively improving the generalization ability of the model. The Rank-1/mAP on the Market1501 and DukeMTMC-reID datasets reach 94.1%/85.2% and 84.7%/72.2% respectively. Experimental results show that the proposed algorithm has certain advantages.
Meta Actor-Critic Framework for Multi-Agent Reinforcement Learning

In recent years, multi-agent reinforcement learning has received sustained attention in the last few years. The typical Actor-Critic methods learn mappings directly from observation to action without understanding the tasks themselves. In this paper, we present a meta actor-critic framework for meta-actor critique based on observational learning processes and the additional loss of meta-learning actors to accelerate and improve multi-agent learning across agents' experiences. Within our framework, all agents are deliberately designed to share the same meta-critic loss to achieve the optimum actor learning progress. Meanwhile, by minimizing the loss of meta-actors, the meta actor learns the features of the meta-observation, leading to better actions. We implemented the MADDPG and MATD3 algorithms in our proposed framework and empirically demonstrated the superiority of our framework on two kinds of multi-agent tasks. In addition, the framework can be flexibly incorporated into various contemporary multi-agent Actor-Critic methods.
On Algorithms of Digital Simulators in Education

The paper dwells upon examples of algorithmizing digital simulators for school and university classes. Building such simulators became especially relevant last year due to the pandemic, a situation that calls for more extensive opportunities for self-training, distance learning, and remote practicing. The paper presents algorithms for simulators designed for mathematics and related subjects; however, they can be easily generalized for use elsewhere. These simulators could be used, e.g., to train normal university students before they begin their internship at schools, or to help school and university students in their self-studies in the context of distance learning.
WBT-GAN: Wavelet based Generative Adversarial Network for Texture Synthesis

Generative Adversarial Networks (GANs) and their variants have become a state-art-of-the-art method in the field of texture transfer and synthesis. GANs are effective technics in computer graphics and have been widely used for image-to-image translation. Generation of high-resolution and realistic images is the main challenge of these networks as it depends on high-frequency features such as textures and edges. Wavelet Transform (WT) as one of most powerful time-frequency transformations in image processing is able to describe images at multi-level resolution. In this study, a generative model called WBT-GAN is proposed by using the four-level WT and employing objective function for defining of its loss function. In general, WBT-GAN is an extension of the existing network Texture-GAN. Experimental results showed that these changes have improved image resolution and sharpening and have led to better texture spread.
Gallery Defender: Integration of Blockchain Technologies into a Serious Game for Assessment: A Guideline for Further Developments

Thomas, André Pfeiffer, AlexanderBlockchain technologies are already being used in many different applications to secure specific data sets. For the same purpose, blockchain is also used in digital games. This technology is not only discussed, but also actively implemented in the educational sector, for example, in such projects as “Blockcerts”. Typical applications are issuing certificates and their validation by using a one-way hash; more creative uses of blockchain beyond that are still uncharted territory. This article shows how blockchain (Ardor and its childchain Ignis) has been integrated into a serious game for assessment of students’ knowledge. More specific details include encrypted meta-data in the respective transactions, and offer guidelines for future projects in education.
Cloud-Based Biometric-Enhanced ATM Powered by Deep Learning

Automatic teller machines (ATMs) are the most common way to withdraw cash from a user’s account. It offers a quick and convenient way to access one’s funds. However, the technology used to authenticate the user and authorize the transaction has somewhat remained unchanged. The advances in computer vision technologies have made it reliable than ever before. Utilizing these computer vision-based technologies combined with fingerprint-based authentication-based technologies in ATMs makes the system fool-proof and robust. With an additional eye watching over, the user can carry out the transaction in complete confidence.
Recognition of Unknown Radar Emitter Based on USRP and Machine Learning

Aiming at the problem that the existing priori recognition methods basically fail when identifying newly emerging unknown radar emitter, a method for identifying unknown radar emitter based on machine learning is proposed. First, the designed convolutional neural network can be used to identify four known signals. Based on the transfer learning method, the trained and untrained signals are input into the CNN respectively, and 256 neurons in the fully connected layer are extracted as the features of the known signal and the unknown signal. Then, based on signal characteristics and actual application requirements, random forest is used to identify unknown radar emitter signals. Under the condition of -6dB 9dB signal-to-noise ratio, the overall recognition rate of unknown signals can reach 72.6%. By changing the types of unknown signals and increasing the unknown, the method has proved to have good stability further. Finally, signal generators and software radio peripherals are used to build a signal transceiver experimental system, and a signal recognition interactive interface has been designed, the recognition network is optimized and fine-tune by the actual signal received. The recognition method proposed in this article is verified by adding a new unknown signal further.
'I Use YouTube Now in COVID': Understanding Technology Adoption of Indigenous Communities during COVID-19 Pandemic in Bangladesh

Indigenous communities in Bangladesh are comparatively disadvantaged and face several barriers regarding rights. Access to technology as well as ICT can help the indigenous communities by opening new dimensions in economic, political, and social aspects. The recent COVID-19 pandemic necessitated the adoption of technology for routine use which is equally important for indigenous communities, but their technology adoption scenario remains unexplored in ICTD research. Considering the research gap, we interviewed n=36 (Female 26 and Male 10) indigenous people from six different indigenous communities from Chattagarm and Sylhet divisions in Bangladesh. We found their strong interest and eagerness to learn available technologies helped them in their professions, enrich their technical skills, communication, social participation, and expand the business. The study also discussed the community-centric strengths that helped them to fight against the COVID-19 crisis and work for their development.
Experimental Evaluation

This chapter presents the results of the experimental evaluation of the developed method and algorithms for detection and measurement of terminological saturation, implemented in the software suite. It presents the set-up of our experiments in therms of objectives, workflow and toolset, data, meqasurable aspects and measures. Several series of experiments are reported and discussed toward achieving the objectives. The experiments to prove the correctness of the method apply the instrumental pipeline to the boundary cases of immediate saturation and the absence of saturation. The experiments for choosing the best appropriate external term extraction software for the baseline methodcross-evaluate NaCTeM Termine and UPM Term Extractor. The experiments on document orderings cross-evaluate five possible orderings with an aim to select the best domain-neutral and balanced ordering with respect to several reasonable measurable aspects used for comparison. The experiments on terms grouping explore and compare the utility of using four string similarity measures for grouping term candidates before measuring saturation. Finally, the validity, domain neutrality and scalability of the optimized processing pipeline is evaluated. Recommendations are given based on the analyses of the results in these series of experiments.
An Effective Anchor-Free model with Transformer for Logo Detection&lt;Subtitle&gt;Efficient Logo Detection via Transformer

Currently, logo detection has driven research into diverse fields and services, such as video advertising, copyright infringement detection, product brand management on social media and so on. Compared to general object detection, logo detection is challenging since the real-world logo images are largely varied in their appearances and have higher complexity in their backgrounds. While many studies focus on Anchor-based solutions for object detection, few focus on Anchor-free for logo detection. In order to achieve good performance for logo detection, this paper proposes an effective Anchor-Free model with Transformer (AFT-Net), a technique that aims at recognizing logos from images. On one hand, DIoU Loss is firstly used for bounding box regression instead of IoU Loss to ensure faster convergence speed and higher accuracy. On the other hand, the Transformer Attention Mechanism is integrated into AFT-Net to enhance the representation ability of the network. The experimental results demonstrate the effectiveness of the proposed AFT-Net, it obtains 2.4% mAP improvement compared with FSAF method on Logo-Det-3K-1000. It is superior in terms of accuracy of small object recognition in logo detection compared with the state-of-the-art detection methods.
Linear construction of a left Lyndon tree

We extend the left-to-right Lyndon factorisation of a word to the left Lyndon tree construction of a Lyndon word. It yields an algorithm to sort the prefixes of a Lyndon word according to the infinite ordering defined by Dolce et al. (2019). A straightforward variant computes the left Lyndon forest of a word. All algorithms run in linear time on a general alphabet, that is, in the letter-comparison model.
Digital Transformation in Restaurants

The COVID-19 pandemic process, which has affected the whole world, has accelerated technological developments by laying the groundwork for digital transformation in many fields. In this context, various technological innovations have been introduced, such as a social distance tracking system that warns when the appropriate social distance rules are violated between employees, robot dogs on patrol, the use of drones for delivery, and robots that can do many jobs that humans can do. The restaurant industry has also tried to integrate these rapid technological developments as much as possible. In this study, which is about how the recent advances in technology have or can be affected restaurants, examples from the world are given for each technological development mentioned in order to better understand the subject. It is considered that the study will contribute to the literature, future studies, sector representatives who want to apply technological developments in their restaurants, and investors who want to open a new restaurant with technological infrastructure.
Idle and Max Power Forecasting for Linear Server Power Consumption Model Using Random Forests

The number of data centers is growing rapidly these years to meet the requirement of the development of internet technology. Data centers operate continuously throughout the year and consume a large amount of electricity every year. Consequently, they bring a great burden to the power departments and the environment. Optimization of workload scheduling in the data centers can reduce the power consumption of both internet technology system and cooling system. The power consumption model of servers is the basis of workload scheduling, and its accuracy and convenience are very essential to the implementation of scheduling. This paper focused on the linear power consumption model which proved to be simple and of high accuracy. Idle power and max power are two main parameters in the linear model and they can be measured through experiment tests. However, the experiment tests take much time and they can not be conducted on the servers already in use. Therefore, this paper aims to build a prediction model of both idle power and max power using readily accessible hardware parameters. A dataset crawled from the SPECpower website was firstly analyzed. And a random forests regression model was built based on it. The data set showed that idle power and max power had a downward and upward trend respectively. The idle power percentage, however, continued to decrease. The random forests regression model performed well. The mean average percentage error of idle power and max power was 16.7% and 10.3% respectively, which were much better than multiple linear regression model results and empirical equation results.