Reviewerly/exHarmony huggingface dataset:
Image Captioning Using Machine/Deep Learning

The System that automatically write descriptions or write captions for an image according to the contents of that image is known as Auto Image Captioning. It’s literacy problem of the machine that combines computer vision (for understanding image contents) with Natural processing (for textbook product) Auto picture captioning is a fairly new and fleetly adding academic content. Colourful new styles are being launched on a diurnal base in order to attain satisfying outcomes in this discipline. Still, to gain mortal- suchlike results, a lot of attention is still necessary. The thing of this exploration is to discover, in a methodical manner, what different and recent methodologies and models are employed for picture captioning using deep learning. What approach and new ideas are used to make these models better and more accurate. what’s further, ideas and strategy are more likely to give suitable result or outcome. To get so, we did a methodical literature analysis of recent exploration published between 2017 and 2019 from well- known datasource (Scopus, Web of Lores, IEEEXplore). We plant an aggregate of over 60+ primary studies that were related to this study. We found out that CNN understands the content in the picture and detect objects in an image, whereas LSTM/RNN is used to induce language. MS COCO, flicker 8k and flicker 30k, which are used in almost every studies, are the most extensively used datasets. The BLEU (1 – 4) standing rating matrix is the most extensively utilised in all projects or studies. The analysis also show that CNN with LSTM is more preferable than CNN with RNN. We found out that the Encoder Decoder and the attention medium are one of the most suitable and best ways for enforcing this model, and combining them can ameliorate outcomes to more accurate and good level. Experimenters or other people that want to work or contribute to auto image captioning will surely find this exploration very useful and can use it as a guidance.
Real-time Face Mask Detection and Contactless Body Temperature Measurement on Edge Device Using Deep Learning

The world is severely affected by COVID-19 disease and it has became a threat to everyone. One of the effective methods to prevent infection of this disease is to wear a face mask in public places. The body temperature of a person is an important indicator of COVID-19 infection. Many public places or services give entry to the people only if they wear a mask and have body temperature in a normal range. In areas like college labs, internet cafes and malls, they keep a daily log of visiting persons with details such as name, date, body temperature etc. In this work, a system is proposed that can be utilized to remind people to wear a face mask and monitor them. It can also measure body temperature using an IR temperature sensor and alert respective authorities if it is high. In the proposed system, convolutional neural network MobileNetV2 is used for face mask detection deployed on NVIDIA Jetson Nano.
A Secure E-Learning Platform An Approach of Blockchain Technology

The E-learning platform is secured using a blockchain framework, according to this article. The proposed frameworks will be utilised to create a scalable and secure data delivery system that will connect existing educational data. Without requesting a copy from central systems, institutions can just astound themselves with their data history. The proposed blockchain technology enhances data security and resolves problems with user-third-party trust while accessing applications and services. To guarantee that businesses keep control over their data entry, are aware of the sources of acquired data sources, and are kept up to date when their data is accessible by others, data distribution can be utilised in conjunction with smart contracts. Immutable audit logs are created from data sources to keep institutions safe, and contact histories are informed of the time it takes to recover their data. Blockchain has the potential to provide online safe databases and a system with broad access to e-learning platforms. Based on the institutions' information security policies, the blockchain can be applied on various learning platforms.
Semantic Knowledge Base Ontology Framework for Discovery Stage of the Design Thinking Using Web Ontology Language

The design thinking process involves the development of concepts and designs for products, software, buildings, machines, communication systems, and many other areas that can be used to solve practical and creative problems. Research in natural and laboratory contexts has identified numerous vital concepts and aspects of design thinking. Various fields have adopted design thinking methods and processes, including architecture, engineering, and business. However, the methods and techniques are primarily influenced by design thinking. Design thinking can be used in any field; it does not necessarily have to be design-oriented. Innovative products and services are also developed through a design thinking approach in business and society. The core of design thinking is user-centricity. Identifying and meeting people's needs is a critical component of the approach. Empathy depends on communication. In this paper, Ontology Web Language (OWL) is used in the protege editor to implement the discovery phase of Design Thinking. "Through the use of nomenclature and OWL properties, a variety of entities, classes, and objects are identified individually during the discovery phase.
Unified Image Restoration with Convolutional Autoencoder

Convolutional autoencoders are widely used in image processing, and have achieved remarkable success. This paper proposes a unified framework for image restoration problems based on convolutional autoencoder. Our framework can deal with denoising, dehazing, and text elimination effectively. For each problem, we train a restoration model by establishing a mapping between a set of corrupted images and their corresponding clean images. These models are obtained in a data-driven manner, which are simple and practical for these restoration problems. A variety of experiments show that the proposed framework can generate satisfactory results even under high corruptions.
An Energy-Efficient Task Scheduling Method for CPU-GPU Heterogeneous Cloud

Given the problems of high energy consumption and unreasonable resource utilization in current heterogeneous cloud computing platforms, this paper proposes an energy-efficient task scheduling strategy. First, by analyzing the relationship between the energy consumption of physical machines and the utilization rate of CPU-GPU resources, we can establish the task scheduling model and energy consumption model. Second, a task scheduling method HCGTS (Heterogeneous CPU-GPU Task Scheduling) for energy consumption optimization of CPU-GPU heterogeneous cloud platform is proposed. Different from traditional subtasks, we divide the multi-task data flows into CPU tasks, GPU tasks and pending tasks, which are scheduled and executed by different processors to implement multi-task parallel scheduling. In addition, the execution order of subtasks is dynamically adjusted to realize the load balance of CPU and GPU, thereby reducing the task completion time and overall energy consumption. Finally, the simulation experiment is completed on the cloud simulation software. Through comparing with the other three algorithms, the HCGTS algorithm has better performance in terms of execution time, resource utilization and energy consumption optimization.
TBFL: A Trusted Blockchain-based Federated Learning System

Federated learning (FL) is a promising distributed machine learning architecture that allows participants to cooperatively train a global model without sharing local data. However, both the trust of a central FL server and well-designed attacks against FL have significantly restricted the development of FL. In this work, we propose TBFL, a trusted blockchain-based federated learning system. It replaces the traditional central server with the functionalities provided by a decentralized blockchain. To prevent malicious participants from attacking the global model, TBFL employs a novel model verification and a scoring mechanism to keep detecting malicious participants. In addition, TBFL leverages a scalable incentive mechanism to enhance its reliability and fairness. We demonstrate the efficacy and attack-resilience of the proposed TBFL through experimental evaluation. The results validate the great performance and security properties of TBFL in training global models.
F3FAD: Fast 3D Facial Avatar Digitization With XR Cloud Service

As 3D Facial Avatar Digitization has become an increasingly important and popular feature for Extended Reality (XR), a high-fidelity and specially customized face avatar can greatly improve immersive experience for users who have been engaged in the virtual space. In this paper, we propose Fast 3D Facial Avatar Digitization (F3FAD), a novel end-to-end deep learning method for digitizing highly detailed 3D faces from a single image. There are four advantages of the F3F <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> D. Unlike previous works, F3F <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> D can infer both 3D mesh surface and color texture for overall human head model from a single view. There would be no need for sophisticated 3D scanning devices, multi-view stereo algorithms, or tedious capture procedures of hand-hold devices. It merely takes a single picture to digitize an entire face in the 3D world. Next, face attributes of the avatar can be freely manipulated according to user's preference. Thirdly, the avatar can be physically driven by multiple modalities of message inputs. At last, we have designed a novel AI computing framework to intelligently arrange the workload of F3FAD pipeline on a server-client system. In addition, we demonstrate the F3FAD technology with a speech-driven talking avatar, to showcase the advantages of our technology and system design in the real-world scenario.
Research and Improvement of Chinese Sign Language Detection Algorithm Based on YOLOv5s

In order to facilitate the daily communication of hearing-impaired people, a model based on target detection for real-time recognition of sign language is studied to allow ordinary people who do not know sign language to communicate fluently with hearing-impaired people. YOLOv5 target detection network model has a series of advantages such as fast and efficient detection and easy deployment. This paper improves yolov5s network model applied to sign language recognition detection by adding an attention mechanism module to spatially and channel to extract effective information. And improve the loss function to improve the regression progress. The model is trained with a homemade sign language dataset, and the trained model is used to detect and recognize sign language and convert between sign language, speech and text. In tests in real-world environments, the improved yolov5s-based sign language network detection model can quickly and effectively recognize more than forty basic everyday sign languages with a detection accuracy of 96.50% and an average detection accuracy mAP of 98.92%. The results show that the improved yolov5s-based sign language detection model can accurately and effectively recognize sign language gestures in different environments, and its easy deployment in mobile devices can help hearing impaired people in their daily communication.
Aspect-Level Sentiment Analysis Based on Graph Attention Fusion Networks

The goal of aspect-based sentiment analysis is to predict the sentiment polarity of various aspect terms. In aspect-based sentiment classification, the most advanced approach uses the graph convolutional network (GCN) based on integrating the syntactic and semantic structures of sentences. When dealing with word relations, however, GCN only apply the same weight to the edges between words. In contrast, graph attention network (GAT) can assign different weights to different edges according to the importance of the words through an attention mechanism. Therefore, we propose a Graph Attention Fusion Network: in order to establish word dependencies, a Syntactic Graph Attention Network (SyGAT) module with rich syntactic knowledge is designed. A Semantic Graph Attention Network (SeGAT) module with Self-attention is developed to capture the semantic associations of words. Syntactic and semantic features are exchanged through Transitional Graph Attention Network (TraGAT). SyGAT, SeGAT and TraGAT are fused into a new GAT. A convolutional layer is also added to increase the learning ability of n-gram words features. Finally, the fused GAT is merged with the convolutional layer features for sentiment prediction. Experimental results on three benchmark datasets demonstrate the effectiveness of the model in this paper.
Block-based copy-move forgery detection in digital images: a survey

Digital photographs are employed in a variety of settings, including crime scene investigation, forensic analysis, and courts. With the introduction of image editing tools, modifying and manipulating image content has become a breeze. Nowadays, anyone with minimal computer skills may readily edit or delete significant image data without leaving apparent evidence. Image forgeries can be done in several methods, including copy-move and splicing. Given the seriousness of these alterations, it is critical to detect any picture manipulation properly. Several approaches for detecting copy-move forging (CMFD) have been developed. These approaches are categorised as block-based (BLK) or keypoint-based (KP). This paper provides an overview of recent research papers on CMFD published in prestigious conferences and journals.
EgpuIP: An Embedded GPU Accelerated Library for Image Processing

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm; 2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm; 3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm; 4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{19}\times,\ \mathbf{88}\times$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3}\times$</tex> speedup respectively.
Improvement of An Ascending Algorithm by Using Break Statement

Ascending algorithm enjoys important applications in fields such as automatic control, artificial intelligence. As an important ascending algorithm, the simple selection sorting method (ascending algorithm) aims to arrange a group of known disordered data in ascending order. Essentially, the simple selection sorting algorithm is comparison, subscript assignment and data exchange. By analyzing two different ascending algorithms which only use the main function for simple selection sorting and call function for simple selection sorting, it is found that ascending algorithm actually uses a double loop, with the outer loop controlling the number of ascending trips, and the inner loop controlling the number of comparison, assignment and data exchange. If no value is assigned in a certain inner loop, it indicates that the data has been ascended in order. In this study, while loop statement is used to replace the outer loop, and break statement is used to control the number of comparisons used in the ascending order. The improved algorithm uses fewer comparisons in the ascending order. Numerical experiments prove that the improved algorithm is effective, which not only supports the ascending order, but also effectively reduces the number of comparisons used in the ascending order, thus saving time.
Hybrid Parameter Update: Alleviating Imbalance Impacts for Distributed Deep Learning

Nowadays, data parallelism has been widely applied to train large datasets on distributed deep learning clusters, but it has suffered from costly global parameter updates at batch barriers. Performance imbalance among worker instances, introduced by uneven workload partitioning or biased resource allocation, can cause straggly workers, which can lead to severe impacts on both training speed and result accuracy. This paper studies the issue focusing on the tradeoff between training speed and result accuracy. We propose Cooperate Grouping Parallel (CGP), a hybrid parameter update solution that allows the flexibility of both synchronous and asynchronous update schemes. We introduce a novel Cooperate Worker Grouping Problem (CWGP) that seeks a task grouping configuration that leads to maximum model accuracy and holds customized training speed guarantees. We propose an evolution-based Pareto local searching algorithm to compute efficient worker grouping configurations. Comprehensive evaluation results are presented to demonstrate the effectiveness of CGP under both persistent and fluctuant imbalances. The proposed method alleviates the imbalance impacts without introducing extra adjustment over-heads.
Blockchain Privacy Disclosure Risk Assessment Scheme Based on Improved Paillier Algorithm

Statistical analysis of medical and health data is an important task. However, due to privacy limitations, existing privacy protection systems cannot balance data availability and privacy, that is, ensure data availability while preventing patient privacy leakage. As a distributed ledger shared among nodes of a computer network, blockchain unique feature can provide a powerful security and privacy protection for user data, and homomorphic encryption can be directly to manipulate the characteristics of the ciphertext to ensure the availability of data and privacy, combining both this study proposes a blockchain node classification scheme based on homomorphic encryption. To evaluate the data privacy security on the blockchain, specifically, we store transaction data according to the node classification through the improved Paillier algorithm and node classification, evaluate the risks of the blockchain, and compare the data privacy and security before and after homomorphic encryption. Finally, the feasibility and effectiveness of the algorithm are verified by simulation experiments.
Secure Multidimensional Data Aggregation in IoT-Fog Environments using ECIES

Recently, in IoT -Fog environments, securing the aggregated data in its multidimensional form has attracted much of researchers' attention due to the security challenges that arise when considering multidimensional data. Existing security schemes for multidimensional data incur a high overhead in terms of communication and computation. In IoT -Fog architectures, the relay fog node acts as an aggregator gateway and remains a resource-constrained device, and thus cannot support heavy security protocols. In this article, we propose an efficient and secure mechanism for multidimensional data for IoT -Fog environments. The scheme employs the Elliptic Curve Integrated Encryption Scheme (ECIES) which is a hybrid encryption based on Elliptic Curve Cryptography (ECC). In our proposal, we modify the ECIES by involving symmetric encryption which enables IoT devices to perform encryption of data in its multidimensional form and allows the recovery of all of the data at the decrypting device. Our security and performance analysis demonstrates that the proposed scheme can achieve the required security services (e.g., privacy-preserving, data integrity, source authentication, etc.) with a cost-efficiency in terms of communication and computation.
Recognition of Tifinagh characters using Extreme Learning Machine

The main objective of this paper is to propose a robust architecture that recognizes Amazigh language manuscript characters written in Tifinagh. To do so, we proposed first an Extreme Learning Machine (ELM) model carried on our Berber-MNIST dataset. Lack of data was a big issue in the case of our model. Hence, extending the existing database using data augmentation methods was necessary. For that, we have adopted three techniques such as duplication, brightening, and zooming. The simulation results showed that the new ELM model achieves a satisfactory results in terms of recognition rate reaching 91.42%, thanks to the use of the data augmentation techniques.
Modi Handwritten Characters Recognition Using Deep Learning Algorithm

The “MODI lipi” script was used to record religious works in Maharashtra, Western India, and was also the official script of the Maratha administration from the 17th century to the mid-twentieth century. The “MODI-manuscript,” a cultural treasure, tells the tale of its time period. Few people are aware of this “lipi,” despite its value as a source of inspiration and knowledge for today's youth. A recognition system for MODI might be developed in the domain of handwritten character recognition to make it easier to learn. Deep learning-based algorithms are used in a variety of pattern identification applications, including character recognition. Now a days transfer learning algorithms are used to improve the results of the recognition task. In the proposed CNN and Vgg16 algorithms are used for classification of MODI characters.