Reviewerly/exHarmony huggingface dataset:
Hand Gesture Recognition using Double CNN and Transfer Learning

Hand gesture recognition forms a very integral part of human-computer interaction (HCI). With the advent of augmented reality, it becomes even more important for a computer system to understand visual cues obtained from the environment. While easier to design the system with the usage of physical sensors placed on the user hands, it is not an economically feasible. The aim of this work is to understand and implement various hand gesture recognition models to work on webcams and mobile cameras. The implementations will target both static gestures and dynamic gestures. For the former, an image or a single frame can be used to determine the gesture. For the latter, a series of images, or a video clip is used to determine a gesture. For the detection of the gesture, we employ Double-channel CNN over a smaller part of the dataset to detect the presence of the gesture. Once a gesture is detected we use the skeletal based approach along with transfer learning to classify the gesture.
ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception

Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems would lead to costly data re-collection and human annotation processes to re-train models. Existing solutions such as semi-supervised and few-shot methods either rely on numerous human annotations or suffer low performance. In this work, we explore a novel object detector based on interactive perception (ODIP), which can be adapted to novel domains in an automated manner. By interacting with a grasping system, ODIP accumulates visual observations of novel objects, learning to identify previously unseen instances without humanannotated data. Extensive experiments show ODIP outperforms both the generic object detector and state-of-the-art few-shot object detector fine-tuned in traditional manners. A demo video is provided to further illustrate the idea [1].
UPAYA MENINGKATKAN MINAT BELAJAR ANAK MELALUI PEMBELAJARAN SAINS

This study examines the low learning interest in children in teaching and learning in the classroom. The purpose of his research is to investigate efforts to increase children's interest in learning through science learning. This research uses classroom action research methods. The subject of the research was the group of B5 kindergarten YLPI Marpoyan, Pekanbaru totaling 23 children carried out in the 2017/2018 school year. Data collection techniques through observation, interviews, and documentation. The research component uses four stages, namely planning, action, observation, and reflection contained in two cycles which are analyzed by qualitative research methods. The results showed an increase in interest in learning based on pre-research results, cycle I, and cycle II. Seen in the increase in student interest in learning with a very good category from none of the students to 6 students (21.7%) and the good category also increased interest in learning from 6 students (26.1%) in the first cycle and there were 15 students (65,2%) in the second cycle. This study was stated to have met the success criteria above 80% on good and very good criteria.
An Author-based Citation Summary Toolbox for Google Scholar

This work presents an Author-based Citation Summary Toolbox which is a program designed exclusively for the Google Scholar website that extracts all article's information (e.g., author's name, article, year, total citation), forms them into a data frame, and allows users to make changes to each data frame, such as sorting and filtering. This toolbox requires three obligated elements: profile path, WebDriver path, and Google Scholar link as inputs; then the post-process will operate automatically and quickly on the opened web browser without any need for manual actions. Additionally, in the developed program, users have the ability to work on the retrieved data with several options such as sorting, finding information, calculating cumulated citations, exporting to output file with CSV format, etc.
Decentralized Application for managing the Disaster with Block chain, Cloud &amp;IOT

The paper presents a single integrated platform to connect all the organizations at the time of disaster to manage the disaster more efficiently and quickly. At the time of any disaster, the whole system is thrown into chaos and is disorganized. This leads to various mistakes and loss of communication between the parties who are responsible for managing the disaster. By providing a common platform for everyone to communicate, we can avert many mishaps and provide an effective relief to the disaster, allows us to reduce the cost of damage, helps us to save more lives and provide better support. We propose the idea of using cutting edge technologies to make this happen.
Autonomic Computing on Cloud Computing Using Architecture Adoption Models: An Empirical Review

The combination of a few self-capabilities including self-configuration, self-healing, self-optimization, self-protection, self-awareness, and so on, is called autonomic computing. Thus, autonomous computing methodology was then used to build autonomous software systems. Autonomic computing is the ability of a computer to automatically handle itself through adaptive technologies that add computing capabilities and minimize the time taken by computer professionals to solve device problems and other maintenance, such as software updates. This approach makes support systems for different tasks self-adaptable and self-decision-making computing systems. It also helps to decrease human involvement in the process of software management. Cloud protection has become one of the most critical concerns in cloud computing with the continual growth of cloud computing. Data stored on a cloud network, for example, can be targeted, and its security is difficult to ensure. We must therefore apply weight to the question of how the data stored in the cloud should be secured. This paper presents a brief review on autonomic self-management attributes and capabilities of machine additives, describes autonomic computing architectures, autonomic adoption modes and requirements, and examines its properties over cloud computing.
Towards introducing and verifying heterogeneous data integration model for cloud

In today's scenario when more and more enterprises are shifting towards cloud computing, the variety volume and velocity of the cloud is expected to cope up to the expectations. Although big data centers and latest technologies have handled huge volumes of data but still the problem of integrating heterogeneous data on the cloud persists. This paper proposes a model for integrating heterogeneous data on the cloud focusing on the security of data at rest. The model is based on service oriented computing, as services are based on fundamental features of interoperability and loose coupling. The diﬀerent layers of the proposed model deal with various aspects of data interpretation on cloud. The client layer provides interface to the client and works on basic request and response parameters. The service inventory layer comprises of diﬀerent services working on integration and encryption of data and the cloud data layer holds the actual data in uniform format. The paper aims to provide a solution to the heterogeneous data integration problem on cloud and data security at rest.
Cognitive IoT enabled by Layered Architecture and Neural Networks in a Smart Home Environment

Internet of Things (IoT) is playing an important role in a Smart Home Environment in the last few years. An IoT architecture is proposed to manage a heterogeneous set of smart devices in the home environment. The network infrastructure is improved using IoT protocols like Message Queuing Telemetry Transport (MQTT). A Cognitive Smart Object is used to support the management of thermal comfort in the Smart Home context. Neural Networks are used for suggested action prediction and anomaly detection operations based on the user's habits. The Continuous Learning mechanism is described, which considers user feedback to shape the neural network and obtain a neural network that follows user behaviors that deviate from standard behavior.
Transformer-based channel noise reduction deep convolution residual network

In order to overcome the problems of interference caused by noise to wireless communication systems, the paper proposes a deep convolution residual network based on Transformer. The network first uses convolutional layers with different receptive fields to fuse and extract features, so that the channel achieves the effect of noise reduction; then, it uses the multi-head attention mechanism that comes with Transformer. The advantage of this mechanism is that the network can be The focus of attention is on more effective features and information. This advantage can be better applied to channel noise reduction; and in order to avoid the problems of gradient disappearance and weight matrix degradation, the residual connection mode is used to perform the network Refactored. The experimental results show that the network proposed in this paper has achieved better results than ordinary deep convolutional networks in terms of noise reduction.
Introduction to Cloud Resource Management

Cloud computing provides users with access to system resources on demand. Before the advent of Cloud computing, users could run applications or programs from software downloaded to their computer or server. Cloud computing allows access to the same applications through the Internet. cloud computing has brought down the cost involved in computing. Users can avail resources and services according to their need. This ability to access information anywhere, anyhow, and at any time has positively impacted migration to the Cloud by organizations. With the advancement of cloud computing, there is a paradigm shift is in data storage and usage of remote applications. Adding mobility to computing, cloud has made data and applications to move out of physical buildings. So, data are no more confined. It is available on the go. By adding these features, the cloud also facilitates reliability, efficiency, and scalability. The demand of the cloud is exponentially growing with invention of the high-end and sophisticated devices. The cloud services are transparent and easy. Hence, it addresses the increasing demand easily. Cloud computing architecture is responsible for the distribution of cloud computing services that involve numerous cloud computing constituents, which communicate between them using a technique such as messaging line. The problem of managing the resources in the cloud still needs more innovations as the problems still persist. The cloud computing architecture of a cloud solution is the structure of the system, which involves premise and cloud resources, services, middleware, and software. In cloud computing, resource management comprises of provisioning, allocation, and monitoring. Cloud resources consist of the servers, memory, storage, network, CPU, application servers, and cybernetic systems otherwise called virtual machines. These machines are the processing units in cloud. Virtualization provides solutions for managing the cloud resources. The performance of any system depends on the effective management of resources. The resource management in cloud computing systems encompasses to manage the large number of virtual machines and physical machines (Vashistha, A., Kumar, S., Verma, P., Porwal, R. A self-adaptive view on resource management in cloud data center. IEEE Computer Society, 2018). Cloud architectures are constructed as software applications that use Internet accessible on-demand services. The applications in cloud architectures make use of the computing infrastructure when it is needed. It requests the necessary resources on demand, accomplishes a prescribed job, then releases the unneeded resources, and often disposes them after the job is done.
THE USE OF TRIZ TECHNOLOGY IN THE DEVELOPMENT OF CREATIVE ABILITIES OF OLDER PRESCHOOL CHILDREN IN VISUAL ACTIVITIES

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A hybrid framework for brain tissue segmentation in magnetic resonance images

Abstract Having a robust image segmentation strategy is very important in magnetic resonance image (MRI) processing for an effective and early disease detection and diagnosis. Since MRI can present tissues of interest in both morphological and functional images, various segmentation techniques have been employed for this. The algorithms based on Markov random field (MRF) have shown strong abilities in dealing with noisy image segmentation compared to other methods. In this article, inspired by the random drift particle swarm optimization (RDPSO) algorithm, we propose a novel hybrid framework based on a combination of the RDPSO with the hidden MRF model and the expectation–maximization algorithm (HMRF‐EM), to be used for MRI segmentation in real‐time environments. The proposed hybrid framework is compared with the standalone HMRF‐EM method, two other MRF‐based stochastic relaxation algorithms, and two widely used brain tissue segmentation toolboxes on both simulated and real MRI datasets. The experimental results prove that the proposed hybrid framework can obtain better segmentation results than most of its competitors and has faster convergence speed than the compared stochastic optimization algorithms.
Weighted Gaussian Process Regression for Single Image Super-resolution Based on Randomized Sample Clustering and Augmentation

Gaussian process regression (GPR) model is widely used in the field of image processing due to its simplicity and strong generalization ability. However, when the size of the training data set is large in some vision tasks such as image super-resolution (SR), the training of GPR model needs extensive calculation. In this paper, we propose a fast clustering method and a weighted GPR model to solve this problem. In the proposed framework, we first employ a randomized sample clustering and augmentation method to reduce the size of training data set. And then we train GPR model on each training subset by parallelization method. Finally, we propose a weighted GPR model to combine multiple GPR models, which can effectively utilize the data of each subset and get higher reconstruction quality. Extensive experimental results show that the proposed method can reduce the computational cost efficiently and performs well in image super-resolution.
Crisis in the domestic and world book market: causes and ways to eliminate its consequences

Based on the study of world experience in the development of book trade, the author has developed a number of anti-crisis measures to improve the situation and increase the analysis of the structure of sales, growth in prices for book products. Particular attention in the article is paid to the consideration of modern technologies, such as print on demand, self publishing, ensuring the implementation of the foundations of a lean manufacturing system, reducing costs and inventory, as well as the use of digital prints on Internet platforms as a means of effectively combating the distributors of pirated content on the network.
Jump and Wobble: A Defense Against Hidden Terminal Emulation Attack in Dense IoT Networks

The unprecedented growth in Internet of Things (IoT) deployment is making it difficult to safeguard IoT infrastructures against novel security threats. Recently, a new attack, hidden terminal emulation (HTE), has shed light on a vulnerability in the co-located and dense IoT networks, where the attacker emulates a hidden node from an external co-located network. HTE attack exploits the heterogeneity among different IoT networks, the shared nature of spectrum access, and the proximity to the victim IoT device in a dense IoT scenario to interrupt the victim’s communication. Prior work on HTE attack, however, considers an omniscient attack model, which has strong assumptions. In contrast, we propose a constrained attack model, which considers the sensing constraints of an attacker. Afterward, we propose a novel safeguard approach based on the Markov decision process to counteract the proposed attack model, namely Jump and Wobble. This work is among the very few to highlight the lower-layer vulnerabilities of spectrum coexistence in dense co-located IoT networks and, to the best of our knowledge, it is the first to propose a defense mechanism against HTE attacks.
Unattended Object Detection and Tracking

This paper proposes a design of smart surveillance system that can detect unattended objects and tracking owner of object upon the successful implementation, it can decrease the workload of security staff. Developing system is implemented by using image processing techniques alone with machine learning. In the event of an unattended object being detected, the system will alert the responsible person and display tracking the owner of the object. The system consists of four major steps: 1.) Image Acquisition 2.) Image Detection 3.) Event Recognition 4.) Result Visualization. The experiment is conducted in order to measure the accuracy of each process in the system. This system uses two datasets which is PETS 2006 and self generated datasets. Finally, the correctness of the background subtraction process is 89.55%, the correctness of the tracking process is 87.76%, and overall correctness of the system is 78.54%.
Beyond Industry 4.0: Leveraging AI-powered Anomalous Sound Detection for Smart Maintenance

The ongoing global changes, pushing the digital transformation to Industry 4.0, have been reflected in the launch of new services and process innovations tackling the existing pressure on costs and prices. In this context, AI is becoming an integral part of all future smart maintenance endeavors. The new generation of intelligent maintenance systems, driven by big data analysis and advanced diagnostics, are already guiding automated predictive innovation towards the idea of zero-failure activity. Automated detection of failures is crucial for smart maintenance, for building AI-based factory automation. In this context, the paper describes a solution for detecting failures based on sound obtained from the target machines. Abnormal sound data is difficult to collect, as it rarely occurs and is being hard to extract from a noisy environment and could have various patterns. The proposed solution detects anomalous sound after training the machine-learning model only with the normal operating sound of machines in a factory environment.
Authentication and Identity Validation Blockchain Application

Abstract The biggest obstacle to electronic migrations of many systems is the need to protect the details and to check the identity of users. Currently, the use of dual-factor authentication is based occasionally on a password. The problem with these approaches is that passwords are extremely insecure and authentication by two factors usually involves the sending of code via SMS or a third-party provider. The blockchain might be a solution to this problem. A 50 billion dollar currency is managed by blockchain. But authentication could be subject to the same cryptographic principles. Thus, blockchain authentication eliminates someone from maliciously modifying the ledger when they distribute a ledger to all network members. A majority of the network shall check its validity any time a block of data is attached to a chain. That guarantees the completeness of the ledger. You can then make sure to send passwords safely using public-key encryption, such as highly protected RSA encryption. The receiver could then search for an entry in the unchanged blockchain, which would result in a remarkably safe and reliable way of handling identity verification. These rules apply for the transformation into a stable, fast, reliable, and readily available service from the election process, state identifiers to dual-factor authentication.
Enhancing Medical Images Using Altered Differential Evolution Approach

Most of the image datasets collected are contaminated and corrupt. Image enhancement is an approach of refining the value of input image for displaying and for utilising the image for various image processing tasks. Though numerous approaches for image enhancement are available, continuous research is performed to develop more robust technique to improve the quality of image. For such optimisation problems, evolutionary approaches and its variants are continuously applied. Differential Evolution (DE) approach is a robust practice in evolutionary computing. In this work, a modified DE technique named as Altered Differential Evolution (ADE) is presented by changing the mutation policy of traditional DE approach. Outcomes attained from the projected approach was compared with traditional approach. ADE is then anticipated for application in image enhancement by optimising the image features.
EVALUATION OF DIGITAL TECHNOLOGY FROM THE STUDENTS` PERSPECTIVE IN THE CASE OF PROJECT WORK IN GEOGRAPHY

The aim of the research is to evaluate a project on the subject of reliefs, which included the use of modern digital technology from the perspective of secondary school students. Since the formative assessment of students also includes a critical self-evaluation of the final products, anonymous surveys were used to determine how the students evaluated the project work, and the use and usefulness of digital technology in geography lessons. Results of this research showed that digital technology encourages students to become more engaged and interested in learning, to reflect on their work and learning, to follow the evaluation criteria, and to achieve better learning outcomes. Keywords: digital technology, evaluation criteria, (self-) evaluation, project work, geography teaching
Graph matching based point correspondence with alternating direction method of multipliers

Graph matching is a fundamental problem in image processing and computer vision tasks, which can be used to solve the feature correspondence problem. Most graph matching tasks have been proved to be NP-Complete problems which are generally solved by approximate algorithms based on the continuous relaxation scheme. Targeting at these problems, this paper proposes a novel graph matching algorithm based on the alternating direction method of multipliers (ADMM) which typically decomposes a difficult problem into relatively simpler subproblems. Specifically, the matching constraints are decomposed into four continuous constraints and the assignment vector is decomposed into different vectors according to the constraints. Then the transformed objective function is iteratively optimized under the framework of ADMM. The proposed method achieves the state of the art performance, while maintaining a comparable computational complexity. And a discrete solution can be directly obtained without the projection process. Experimental results on both synthetic points and real images demonstrate the effectiveness of the proposed method by comparing it with the state-of-the-art methods.
Cognitive Computing Strengthen the Healthcare Domain

This paper dealt with the research model supported on the concept of the artificial intelligence with the flavour of the cognitive computing in the healthcare domain. It identifies the mechanism for the novelty in the design of the human–computer interface framework to solve the human problems which assists in transforming the research in the model. The framework illustrates the mechanism to support the human in healthcare domain using cognitive agents associated with machine learning and deep learning that facilitate the cognitive model. The paper analyses the cognitive computing impact on the human–computer system with possible ways of the innovation research approach with robust interference in healthcare domain (intelligent respiratory awareness programmes).