Reviewerly/exHarmony huggingface dataset:
ACBN: Approximate Calculated Batch Normalization for Efficient DNN On-Device Training Processor

Batch normalization (BN) has been established as a very effective component in deep learning, largely helping accelerate the convergence of deep neural network (DNN) training. Nevertheless, its hardware architecture has not received much attention in the field of DNN on-device training processors. Several previous designs incur either high off-chip memory traffic or high circuit complexity, and hence have deficiencies in terms of hardware efficiency and performance. This article proposes approximately calculated BN (ACBN) to achieve a much better tradeoff between hardware efficiency and performance for DNN on-device training processors. The accuracy and convergence rate of the proposed ACBN have been extensively evaluated using four typical DNN models. Compared with the state-of-the-art reference design, the hardware simulation results show the proposed ACBN can at least reduce floating point operations by 22.2% and save external memory access by 33.3% on average. Moreover, the proposed ACBN introduces 63.6% data sparsity for the backward propagation of BN layers of VGG16 on average. To the best of our knowledge, we are the first to introduce data sparsity for the backward propagation of BN layers. The ACBN module is implemented on Zynq UltraScale+ ZCU102 system-on-chip (SoC) field-programmable gate array (FPGA), and the results show that the implementation of ACBN hardware module saves 33.9% look-up table (LUT), 49.4% flip-flop (FF), 75% digital signal processor (DSP), and reduces the power by 12.4% compared with the reference design while achieving better performance.
CBP: Coalitional-Game-Based Broadcast Proxy Re-Encryption in IoT

This article proposes coalitional game-based broadcast proxy re-encryption (P-RE) in IoT—a broadcast P-RE for adding new IoT devices. The P-RE is extended to broadcast P-RE to prevent recalculation of the re-encryption key (ReKey). However, the group of recipients needs to be predetermined before the calculation of the ReKey. If any new IoT device requires the same data, an individual ReKey is generated for him/her. Hence, generating individual ReKey is an overhead for the organization. We propose a ReKey updation for the broadcast P-RE method. If excessive IoT devices want to join the group of the existing recipient, then updation needs to be done learnedly as an excessive number of recipients in a group increases the computation cost of the decryption extremely for all the members of the group. Therefore, we use the coalitional game theory to estimate the optimal number of new recipients from all new recipients. We update the ReKey for the optimal number of members and a separate ReKey is calculated for other recipients. We prove the correctness of the CBP. We prove that if any recipient behaves maliciously, s/he cannot get the secret key of the organization.
A Deep Learning Approach for Hand Gestures Recognition

Hand gestures are part of communication tools that allows people to express their ideas and feelings. Those gestures can be used to insure a communication not only between people but also to replace traditional devices in human-machine interaction (HCI). This last leads us to use this technology in the E-learning domain. COVID’19 pandemic has attest the importance of E-learning. However, the Practical Activities (PA), as an important part of the learning process, are absent in the majority of E-learning plateforms. Therefore, this paper proposes a convolution neural network (CNN) method to ensure the detection of the hand gestures so the user can control and manipulate the virtual objects in the PA environment using a simple camera. To achieve this goal two datasets have been merged. Also the skin model and background subtraction were applied to obtain a performed training and testing datasets for the CNN. Experimental evaluation shows an accuracy rate of 97,2.%.
A Solution Based on Faster R-CNN for Augmented Reality Markers’ Detection: Drawing Courses Case Study

This work aims to prove the relevance of Augmented Reality (AR) in distant Practical Activities. To do so, several practical activities have been proposed to two groups (AR-Grp and N-Grp). The participants of N-Grp use only the 2D printed models during the period of the experimentation while the participants of AR-Grp use the proposed solution where the models are presented in three dimensions using AR. In this work, customs markers have been proposed and was detected in the scene using the Faster R-CNN so the 3D object will be placed in the appropriate position. Finally, a quantitative and qualitative mertrics were used to explore the impact the integration of AR on the drawing courses.
Heuristic Algorithm for Extracting Frequent Patterns in Transactional Databases

Frequent Itemsets Mining (FIM), the extraction of frequent patterns from a transactional database, is regarded as one of the most effective data mining strategies. The FIM issue may often be treated using either exact or metaheuristic-based approaches. For small to medium datasets, exact approaches, such as the Apriori algorithm, are very successful. In the case of huge datasets, however, these approaches suffer from temporal complexity. The must of metaheuristic-based approaches are still inefficient, despite the fact that their speed is increasing. Several experiments were done to enhance metaheuristics-based techniques by integrating the Apriori algorithm with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bees Swarm Optimization (BSO). This combination produced three distinct methods: GA-GD, PSO-GD, and BSO-GD. This research compares the performance of different algorithms in terms of two metrics, including execution time and the number of frequently occurring itemsets created. In terms of execution time, the findings indicate that the techniques outperform the Apriori algorithm. In addition, the found solutions converge to 100%.
Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.
A novel H and E color augmentation for domain invariance classification of unannotated histopathology prostate cancer images

Most current deep learning models for hematoxylin and eosin (H&E) histopathology image analysis lack the power of generalization to datasets collected from other institutes due to the domain shift in the data. In this research, we study the domain shift problem on two prostate cancer (PCa) datasets collected from the Vancouver Prostate Centre (source dataset) and the University of Colorado (target dataset) and develop a novel center-based H&E color augmentation for cross-center model generalization. While previous work used methods such as random augmentation, color normalization, or learning domain-independent features to improve the robustness of the model to changes in H&E stains, our method first augments the H&E color space of the source dataset to color space of both datasets and then adds random color augmentation. Our method covers the larger range of the color distribution of both institutions resulting in a better generalization. We compared our method with two different State-Of-The-Art (SOTA) un-annotated domain adaptation methods: color normalization and unsupervised domain adversarial neural network (DANN) training, with an ablation study. Our proposed method improves the model performance on both the source and target datasets, and has the best performance on the unlabeled target dataset, showing promise as an approach to learning more generalizable features for histopathology image analysis.
Generation of RGB EVG stained image from hyperspectral H and E stained image using generative adversarial network (GAN)

Quantifying elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. In conventional pathology, special staining technique such as EVG (Verhoeff’s Van Gieson) is applied physically for this purpose which is expensive and time-consuming procedure. Though H&E (Hematoxylin and Eosin) staining is routinely used, less expensive and most common tissue staining technique, elastic and collagen fibers cannot be differentiated using it. This study proposes a modified CycleGAN based unsupervised method for the computerized generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Our proposed method is designed to utilize the sufficient spectral information provided by the H&E hyperspectral image (HSI) without reducing the spectral dimension. For doing so, we have faced challenges to calculate one of the training losses (identity loss) of CycleGAN that requires reducing the channel dimension of H&E HSI to be the same as RGB EVG stained image. We have addressed the issue by adopting intentionally designed three basis functions that can reduce the channel dimension of HSI into three without losing the essential color of elastic fibers. The set of this function includes Linear Discriminant Function (LDF) and the transmittance spectrum of Eosin and Hematoxylin which has proved to best preserve the underlying important features of EVG stained image while reducing the dimensionality of hyperspectral H&E. The experimental result proves the feasibility of our proposed method to generate realistic EVG stained image from its corresponding H&E stained one.
Preliminary Feasibility Study of Quantum Key Distribution for Future Air Traffic Management Systems

The authors conducted a preliminary feasibility study on applying quantum key delivery (QKD) to future air traffic management systems. We evaluated the number of bits required to exchange the information needed for air traffic management systems to ensure air traffic safety and found that the currently feasible QKD key generation rate is sufficient for the daily operations of airlines.
Chinese named entity recognition based on Heterogeneous Graph and Dynamic Attention Network

Abstract Lexicon have been proved to enhance character representation to help Chinese named entity recognition (NER) model distinguish entities. Although lexicon information includes both semantic and boundary information of words, existing studies usually use only part of them and has low utilization of lexicon information. To efficiently extract dictionary features and integrate character representation, we propose a Heterogeneous Graph and Dynamic Attention Network (HGDAN), aiming at fusing contextual information and capturing dynamic associations between characters and words, thus improving the performance of Chinese NER. PGDNA uses the boundary information of the dictionary to construct a heterogeneous graph and uses the graph attention method to extract semantic information, as well as suppressing lexical noise through the gating unit. In addition, we found the traditional attention model has a non-zero attention problem that will distract the attention of the model, and proposed a simple and effective method to solve it. Experiments on the performance and inference speed of HGDAN on four Chinese datasets have proved its superiority.
Why Decentralize Deep Learning?

Deep learning, big data, IoT and blockchain are individually very important research topics of today’s technology, and their combination has the potential to generate additional synergy. Such synergy could enable decentralized and intelligent automated applications to achieve safety, security and optimize performance and economy. Deep learning, big data, IoT and blockchain all rely on infrastructure capabilities in computing and communications that are increasingly decentralized. Edge computing deployments and architectures are commencing with 5G and expected to accelerate in 6G. Existing application domains like healthcare and finance are starting to explore the integration of these technologies. Newly emerging application areas such as the metaverse may well require native support of decentralized deep learning to achieve their potential. But the path of new technology development is never smooth. New challenges have been identified and additional architectural frameworks have been developed to overcome some of these issues. Decentralizing deep learning enables increased scale for AI implementations, but also enables improvements in privacy and trustworthiness. The plethora of literature emerging on decentralized deep learning prompts the need for rationale criteria to support design decisions for implementation to utilize decentralized deep learning
Implementation of Smart Contract on Autonomous Decentralized Voting Blockchain

Now there are variable type of digital data, contents and real time signals are coming from multiple IoT sensors which is called dynamic big data and these trading are operating on the decentralized system in our life. One of unique transaction model, Non Fungible Token (NFT) become popular now, and it has a high financial value in areas such as arts and intellectual property right, sometime. But, NFT trading system requires a secured system policy. NFT exchange marketplace system should have a high availability and security for P to P node transfer and confirmation policy. This paper proposes smart contract mechanism and its implementation to confirm for blockchain transaction on the autonomous decentralized layer one voting blockchain system. The voting blockchain system architecture is shown and its smart contract implementation using Geth code is also shown in this paper.
A pipeline to collaborative AI models creation between Brazilian governmental institutions

The government has worked to improve technolo-gies to advance criminal investigations. It is very common for Brazilian public institutions to spend resources on systems to improve population security or investigations through artificial intelligence. A central point in this context is the data used by the institutions classified as highly sensitive. This sensitiveness creates a complex barrier to cooperation between governmental institutions from different areas. In this context, this study proposes a federated learning pipeline to encourage artificial intelligence model sharing between government institutions, taking advantage of high-security networks and computational resources from governmental institutions. We leveraged consolidated frameworks such as Docker and TensorFlow to ease the model sharing and training process without working with sensitive data risks. In this work, the performance of 5 different Federated Learning algorithms was tested using three different AI algorithms. In our experiments, the use of Federated Learning in the context of Brazilian governmental institutions proved to create models with performance similar to the standard Centralized Learning techniques in three different federated learning algorithms.
Blockchain Based Security and Privacy Scheme for Smart Meter Communication

Smart grid is considered as next generation power system which performs automated control and monitoring operations to make it self-resilience. Advanced Metering Infrastructure is an essential part of smart grid which enables two-way communication thereby providing real-time communication link between consumer and grid operators. Emergence of Distributed Energy Resources (DERs) at end user make AMI's operation more challenging. Smart Meters (SMs) are key components in AMI that sends Power Consumption Data (PCD) of home appliances to Distributed System Operator (DSO). PCD received at DSO can be used for real time pricing applications, real time energy consumption, automatic diagnostic, daily energy metering and tasks such as billing, monitoring, planning and predicting of energy usage. In the context of selling of excess energy generated at DER's to the grid, SMs participates in Demand Response (DR) applications to perform energy trading. Revealing PCD of a home leads to privacy breach, hence it is necessary to achieve secure communication between SM and DSO. At the same time, it is essential to make energy trading transparent. To address this issue, this paper proposes a cloud based private blockchain framework to accomplish secure communication between SM and DSO without compromising privacy. Furthermore, trust between SM and DSO is achieved through smart contracts. Performance evaluation of the proposed blockchain shows its applicability to the said environment.
Attention-based Deep Neural Network for Wind Power and Solar Radiation Prediction

Renewable energy is essential in day-to-day human life where we focus on two renewable energies such as solar, and wind energies. Solar and Wind power changes dynamically, depends on location, and it is important to predict the energy in the future to manage the load requirement in various applications. Predicting energy accurately in the presence of noisy or missing values is difficult. To accurately predict the energy, we proposed a BiLSTM with masked attention where the masked values are predicted by the model and learnt better vector representations for the data. We evaluated the proposed model on two datasets Hi-SEAS and California-ISO. From the results obtained, we can conclude that the proposed model outperforms the state-of-the-art models with the lowest MSE value of 0.002863 for Hi-SEAS solar radiation prediction and 0.001141 for California-ISO wind power energy prediction.
Optimal Size and Orientation of Solar Tree with Hybrid Crow Search and Particle Swarm Algorithm

Due to increasing demand in urban areas, solar PV installation has become popular for harvesting renewable energy. PV installation has a few drawbacks in a particular location, including a limited installation area and difficulty tracking irradiance due to its variable nature throughout the day. To overcome the challenges in an urban area, a solar tree (ST) can be installed because it has several advantages over a solar panel. Therefore, ST can be installed at a fixed orientation with consideration of the clearness index (K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> ), which tracks the diffused radiations as well. This study describes the ideal ST size and orientation for a specific location in India in order to deliver the maximum amount of energy in the smallest amount of PV area possible. Also, the maximum energy is extracted by optimal orientation. It is achieved by the annual variation of the solar azimuth angle (ß) and latitude tilt angle (ϒ). The GHI and DNI are validated in the MATENORM software for a location of 24.7577° N and 92.7923° E at their optimal angles. A recent metaheuristic algorithm, hybrid crow search and particle swarm optimization (HCSA-PSO), has been used to find optimal values for ST design parameters. A comparative study shows the proposed meta-heuristics approach gives the maximum output power for the tree configuration as compared to other techniques, such as GA, PSO, and CSA. Further, the two 2 kW ST model has been compared with the horizontal tilt surface PV panels that result in a 4.72 % improvement in output energy with the tree configuration.
An MCDM optimization based dynamic workflow scheduling used to handle priority tasks for fault tolerance in IIOT

Cloud computing's prominence can be mainly attributed to its convenient on-demand service delivery via the internet. The difficulty in managing these requests and allocating them optimally is exacerbated by the growing number of users making them and the variety of criteria used to apply cloud resources. Some of these difficulties include scheduling work while considering the user's Quality of Service (QoS) preferences, ensuring adequate load balancing, and adapting to the ever-changing conditions of the cloud environment. As server capabilities evolve, static load balancers, which distribute requests using fixed ratios of available resources, fail to provide optimum throughput. It has been shown that no other scheduling algorithms offer an adaptive method that considers both load balancing and maximizing the QoS criteria. The authors of this study present a novel adaptive strategy for dealing with this issue by fusing the best-worst multi-criteria decision-making method (BWM) with the compromise ranking method (VIKOR). Task priorities are determined by using the VIKOR method as a decision-maker. Numerical experiments are used to verify the validity of the suggested method, and the methodology is compared to other scheduling methods in terms of a variety of performance criteria. The simulation findings show that the presented method outperforms its rivals regarding throughput, makespan, waiting time, virtual machine (VM) utilization, and VM usage cost across the board of simulated experimental settings.
Data hiding steganography model based on hyper chaos 2D compressive sensing inhabited with manchester encoder/decoder using circular queue exploiting modification direction

Reversible data hiding (RDH) based on Steganography is considered as one of the future related aspects in the field of security for the information hiding paradigm. Existing research work has been carried out based on secure data transmission as well as reducing the dataloss from one user to other users. But due to encryption data expansion over non-linear transformation, complexity in attacking caused due to keyspace, ineffective image compression, poor embedding ratio, poor quality, overflow/underflow problems, data loss etc., leads to inefficient data transmission causing a security risk. This paper proposes a novel method named Triple Secured Data Hiding Steganography Model which provides solutions to the above challenges. This work is initiated with Hyper Chaos 2D Compressive Sensing that performs image compression and encryption simultaneously. It provides control over low dimension chaos system bearing secure risks with suffering from data encrypted expansion while adopt non-linear transformation. In addition to reduce the error rate and providing signal synchronization as well as system reliability over the transmission channel, Manchester Encoder/Decoder is initiated. To cope up with data embedding and extraction our work has proposed Circular Queue Exploiting Modification Direction(CQEMD). Thus, overall proposed model enhances effective secure data transmission under RDH by inhabiting a triple secured system.