Reviewerly/exHarmony huggingface dataset:
Visible–Thermal Person Reidentification in Visual Internet of Things With Random Gray Data Augmentation and a New Pooling Mechanism

Visible–thermal person reidentification (VT-ReID) is an emerging cross-modality matching problem, which aims to identify the same person across the daytime visible modality and nighttime thermal modality in the Internet of Things. Existing cutting-edge approaches consistently attempt to exploit image generation technique to generate cross-modality images or design various feature-level constraints to align feature distribution of heterogeneous data. However, color variations originating from the different imaging processes of spectrum cameras remain unsolved, which leads to suboptimal feature representations. In this article, we present a simple but very effective data augmentation method named Random Gray for the cross-modality matching task. Given a training sample, Random Gray randomly selects a rectangular region and translates it to grayscale. In this process, training images with fusing various levels of visible and grayscale information are generated, thereby reducing the risk of overfitting and making the model robust to color variations. Besides, we introduce a novel pooling method called softpooling to retain more information in the reduced activation maps. With softpooling layer, the network can learn more discriminative person features and further boost its retrieval performance. We conduct extensive experiments on publicly available cross-modality Re-ID data sets (SYSU-MM01 and RegDB) to demonstrate the effectiveness of our proposed method. Experimental results show that Random Gray and softpooling strategies yield significant accuracy improvement, and they can be utilized as training tricks for further VT-ReID research.
Blockchain-based Decentralized KYC Verification Framework for Banks

Know Your Customer (KYC) process plays a vital role for all banks in authenticating the identity of their customers. KYC verification is crucial to prevent banks from being used by illegal elements for money laundering activities such as drug trafficking, terrorism and other crimes. Manual KYC process in mainstream at present is less secure, time consuming and outdated. Since Blockchain offers features like decentralization, immutability and security, these limitations can be eliminated by using Blockchain based KYC verification. In this paper, we have proposed decentralized KYC verification process using Ethereum Blockchain platform. It would allow all the banks in the Blockchain network to verify and vote for legitimacy of the data provided by the customer. Depending on number of votes KYC status of customer gets stored on the Blockchain. Major enhancement in our proposed method is, banks can also vote for other banks if any bank is tampering with the KYC data and it will get removed from the network based on this voting. In this way, Blockchain can be used to improve the efficiency of KYC process with its distinctive features.
Cybersecurity vs. Information Security

Protection of data assets is a hot trend topic that is attracting considerable interest worldwide. There are different concepts revolving security of data including cybersecurity and information security. Cybersecurity and information security terms are both related to the security of data aiming to defend data against different types of threats. Despite cybersecurity and information security are often used interchangeably, there are differences in each concept, and they should not be used interchangeably. Various definitions of cybersecurity and information security provided by scholars and standards are discussed in this article and differences are explained.
Ensemble Deep Learning Models for Lung Cancer Diagnosis in Histopathological Images

Computed Tomography, widely known as CT scan, is used for the detection of lung cancers. However, sometimes doctors cannot detect such harmful diseases based on the CT scan images; hence they recommend a manual biopsy. A manual biopsy is very tedious and exhausting and can also consume much time, and there is a chance of medical error in it. So, it is high time for researchers to develop a Computer-Aided Diagnosis (CAD) System that can help detect lung cancer in minimal time and effort with the highest accuracy. Lung biopsy reports are collected, and histopathological images are generated out of it. Image processing and data augmentation techniques are applied upon a collected histopathological image to prepare a dataset. Three different models are trained by using this dataset, Convolutional Neural Networks and its variants- Inception-V3 and ResNet50. These developed models suffer from high variance on unseen data as they are measured on training and validation datasets. As there is no obvious indication of which model is better than another toward the conclusion of the training period, picking the final model is dangerous based on the results. Ensemble learning techniques help to reduce high variance and give better accuracy than a single model. Ensemble learning provides many strategies to overcome such problems. This research demonstrates approaches to ensemble deep learning models to reduce high variance and improve predictive accuracy on unseen data.
The Role of Smart Contract Blockchain in 6G Wireless Communication System

The combination of 6G and blockchain can revolutionize innovative applications in future wireless networks. Various applications in 6G require a high level of security and privacy that blockchain can award them. In addition, the complex infrastructure of 6G and the different terms and conditions of different applications need to be guaranteed, where a smart contract can be a good solution. In this paper, we studied the role of smart contract blockchain in 6G network services and applications. Smart contracts have a disruptive effect on 6G network infrastructure and applications. The integration and interoperation of blockchain and 6G is an important and foreseeable development in computational wireless communication networks.
Blockchain in Food Supply Chain

Blockchain as we all know has captured interest all over the world. Because of its features like decentralization, enhanced security, and distributed ledgers its application is increasing day by day. Ever since the Vegan and PETA movements have taken strong grounds for questioning the production of food and the conditions they are kept in, the customer has gotten deeply involved and wants to know every step that takes place to make the food they eat. This has led agribusiness to seek supply chain management software to improve food safety, food quality, and traceability throughout the agricultural supply chain. Blockchain is changing the concept of trust here. Using blockchain for the supply chain makes the agricultural business easier and more transparent to monitor products from farmland to store, as well as decreasing financial risks and promoting inclusive trade. The suggested framework method eliminates the requirement for a trusted centralized authority, intermediates, and transaction records, allowing for a more transparent and direct interaction between peers. We are taking data/transactions from every step in the production cycle and storing it gradually on blocks as we go while using the features of blockchain to make everything more secure. We are also making sure that the peers involved have all the information regarding the transactions making it decentralized enabling a peak level of transparency and traceability in the supply chain ecosystem in a solid, reliable, and well-organized manner.
Enhancing the Security of WPA2/PSK Authentication Protocol in Wi-Fi Networks

In today's world of wireless communication, the prime point of concentration lies on data security. This depends mainly on proper authentication of users who are intended to connect to the network. with modern day technology, the credentials (password), which the legitimate user send to the access points for his/her authorization, can be sniffed and used to enter the system, compromising the system security. WPA2/PSK (Wireless Protected Access/Pre-Shared Key) has a vulnerability which allows an attacker to perform Man-in-the-middle attack by capturing the handshake sent by the legitimate user for authentication. The captured handshake can be used to recover the password using dictionary attack or can be brute forced. To overcome the short comings of the current system, the proposed method uses Frequency Hopping Technique to secure the MIC(Message Integrity Code) before transmitting it. This method reduces the possibility of brute forcing the password, even if the handshake is captured.
A Modified PRBS: Vertical Stacked LFSR Primitive Polynomial for Secure Data Communication

Linear Feedback Shift Register (LFSR) generates the fault coverage test patterns to compute the target fault of data compression methods. Future Generation wireless communication covers irrespective of complex situation and dense environments marginable throughput to everyone. LFSR widely applied to generate Pseudo Random Noise Sequence, Digital Random Number Generator commonly employed in Cryptography, encoding techniques, Built in Self-Test (BIST) etc. Sequence generated by the LFSR are easily predictable as periodic nature of the sequence. A Modified Pseudo Random Sequence Generator (PRBS) with vertical stacked LFSR (VS-LFSR) is proposed to improve security against predictability nature of PRBS. The primitive polynomial of the nbit LFSR is vertically stacked with top most polynomial and ends with the least polynomial with this flexible of the primitive polynomial are processed and Random sequences are generated. The proposed method generates the random numbers of flexible ranges by appropriately selecting necessity primitive polynomial code in the vertical stack. Compare to conventional LFSR, all zero (0) states also generated in the VS-LFSR, enables the test pattern to have all 2npossible states of n-bit LFSR. With little hardware complexity, periodic pattern of LFSR is further improved with the maximum number of states and result attained maximum number of random outputs as Πn-202n-m - 1.
Cooperative Control of Fixed-wing Drones Using Relative Jacobian with Obstacle Avoidance

This paper implements a new strategy to perform cooperative control for fixed-wing drones by the use of a relative Jacobian, with a subsequent method of avoiding obstacles while flying cooperatively. We present a derivation of the corresponding relative Jacobian for two fixed-wing drones that is expressed in modular form. We perform two experiments: using absolute pitch movement and another using absolute yaw movement in order to avoid obstacles, while simultaneously controlling the relative position with respect to each other. Experimental results are shown and analysed. Simulation experiments were perform in Gazebo and ROSflight to show relative position and orientation with respect to each other, absolute orientation with respect to the world, and obstacle avoidance.
Secured information sharing in SCM: Parametric Analysis on Improved Beetle Swarm Optimization

Today, with the progression in ICT, information sharing (IS) has turned outto be more possible. IS in supply chains (SC) has grown up more proficient by the global preamble of long-term coordination and cooperation that leads to enhancement of aggressive advantages of companies. However, lack of IS in companies results in incompetence of coordinating activities in organizations. Here, this work aims to enhance the security and privacy of the proposed block-chain-aided SCM, wherein the modified “Data Sanitization and Data Restoration” is done with an optimal key creation procedure to protect the receptive data in every block. Here, the optimal key is chosen via Sine Map estimation-based BSO (SME-BSO) algorithm. Finally, parametric analysis varies ra2 from 0-1, 0.2, 0.4, 0.6 and 0.8.
An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic Grey Wolf Optimization algorithm using combination of Grey wolf optimization (GWO) algorithm and Genetic algorithm.
Insights into NoSQL databases using financial data: A comparative analysis

The massive increase of data and the existence of several distinct methods in which data is generated, accumulated, stored, and utilized has significantly changed with time. Additionally, the nature of data has changed throughout the years transforming from structured to more unstructured data. This brings about a need for efficient storage and management of such data which cannot be handled by traditional RDBMS methods. Hence, NoSQL databases have gained popularity and have become pivotal in database management. Finance is one domain where dynamic and large amounts of data is produced on a daily basis, thereby making NoSQL databases an ideal choice for data management. This paper compares these types of NoSQL databases based on certain metrics like data model, indexing methods, atomicity, integrity and several more and demonstrate implementation of three NoSQL databases namely, MongoDB, Cassandra and Redis, using financial data. Experiments were performed to compare the performance of the aforementioned databases when using fundamental READ queries to retrieve the complete dataset and complex READ queries to retrieve a specific section. Aggregation operations were also implemented on the data. Fundamental WRITE queries to load the entire dataset and complex WRITE queries to update particular parts of it were also performed.
An efficient and enhancement of recent approaches to build an automated essay scoring system

In this paper, we present an efficient system (models) to automate the task of essay grading through a comprehensive study and enhancement of recent approaches namely multiple linear regression (MLR) and gradient boosted regression (Xg-boost) which can positively impact the operational efficiency, time consumption, labour dependency, Unbiased grading and consistency. We also illustrate the efficiency of the proposed systems(models) through a fair comparison of recent models with respect to the evaluation metric, features applied and limitations.
A novel approach for context-aware sensor optimization in a smart home

Ambient Intelligence is an emerging technology that allows for reasoning of activities that are carried out in a smart environment. Smart sensors play a vital role in activity recognition tasks. Though a great number of sensors provide more pinpointed information about where and when the interaction in the environment occurred, addition of sensors also results in higher power consumption and greater cost constraints. The number of training samples used for activity recognition tasks also increases and becomes computationally expensive. Thus, to overcome these constraints sensor optimization in smart environment is carried out. Mapping of sensors to a user's activity becomes tedious when more than one activity is carried out at the same timestamp. Unambiguous sensor-activity relationship is derived with the help of spatial and temporal context represented by ontology model that captures real time sensor data. Since there exits spatial noise during activity annotation process, it is eliminated with help of pointwise mutual information score. Spatial and temporal data of an activity helps sensor optimization in multi resident living with concurrent activity occurrence. Inactive sensors are removed using mutual information score and redundant sensors are contextually paired based on where an activity happens. By doing so, only unique set of sensors are paired every time even when concurrent activity occurs. The quality of features derived is tested by testing the accuracy of the model by activity recognition task using Random Forest algorithm. Nearly 24% of sensors were identified as inactive in the smart environment. The accuracy of Human Activity Recognition (HAR) task remained constant even after removing the inactive sensors. Addition of spatial context helped to improve HAR with increased accuracy. Contextual pairing of redundant sensors resulted in minimal drop in the model's accuracy.
Hybrid Neural Network Architecture for Multi-Label Object Recognition using Feature Fusion

Multi-label image recognition has gained significance in recent years due to its increase in employment in numerous domains. In multi-label object recognition, objects are recognized by creating a bounding box across every object present in the image and classifying the objects into specific labels. In this project, we propose a hybrid method implementing a transfer learning-based approach using four different Convolutional Neural Network (CNN) models namely Single-shot Detector (SSD), You Only Look Once (YOLO), RetinaNet, and, Faster R-CNN. Further, feature fusion is applied to learn all the image features from different layers. Since the transfer learning approach is utilized, we can train the pre-trained model precisely even with small amounts of data. Through this hybrid approach, we can achieve better accuracy in object recognition in the case of multi-labeled images compared to traditional methods
Neural Network Quantization Based on Model Equivalence

With the rapid development of deep learning the size of neural networks has become increasingly large and it has become more and more difficult to deploy large neural networks on lightweight devices as well as small devices. Neural network model compression has become a hot research topic as a major approach to solving the above problems. One of the common compression techniques is model quantization but current quantization techniques ignore the equivalence between quantization models and floating-point models and simply use the performance metrics of floating-point models as the performance metrics of quantization models. For this reason this paper proposes a new evaluation criterion of model equivalence and proposes a neural network quantization algorithm based on the model equivalence criterion. According to the experimental findings the algorithm-quantized ResNet50 ensures no decrease in accuracy while improving the equivalence rate on the CIFAR100 dataset by 0.97% as compared to the conventional approach under eight-bit precision. In contrast the accuracy of the algorithm-quantized ResNet50 on the ImageNet dataset only decreases by 0.7%
Urdu Text-to-Speech Conversion Using Deep Learning

The recent advancements in communication technologies have provided new channels of communication for people speaking various languages all over the world. Text-to-Speech (TTS) systems have been proposed by researchers to facilitate language learning and communication among people speaking different languages. There are thousands of languages spoken in different parts of the world, with Urdu being one of the most widely spoken in the subcontinent and many other countries. The existing literature on TTS is mainly focused on languages other than Urdu, and existing approaches for Urdu TTS do not make use of cutting-edge deep learning techniques. Therefore, this study aims to create a system that takes Urdu textual content as input and then produces an audio version of the same textual content by using state-of-the-art techniques. In the proposed deep learning-based technique, Tacotron 2 with WaveGlow is used, which consists of various layers of Long Short-Term Memory (LSTM) and convolutional networks with various hidden layers that help improve the result. The proposed Urdu TTS system is trained on a preprocessed dataset before being tested on a dataset of 100 sentences. The results are evaluated using the Mean Opinion Score (MOS), which is a standard performance evaluation measure in the TTS conversion domain. The evaluation results show that the proposed approach outperformed the existing approaches, achieving a MOS of 3.76.
RBHLT: A High Liquidity Technology for Non-fungible Token

Blockchain-based implementations of non-fungible tokens represent ownership of specific digital objects and are indivisible, irreplaceable and unique. However, there are currently two problems: 1) Due to the low throughput rate of the blockchain system, it cannot meet the needs of large-scale scenarios of non-fungible tokens assets. As the core technology of blockchain scaling, Layer2 has become an important factor affecting the performance of blockchain. 2) The current liquidity mechanism of non-fungible tokens greatly limits its liquidity. Therefore, this paper combines cryptographic zero-knowledge proof technology with off-chain scaling technology to design and implement a system architecture that can significantly improve the liquidity of non-fungible tokens. Firstly, this paper proposes a zk-rollup based off-chain scaling architecture RBHLT, on top of which a high liquidity protocol for non-fungible tokens is implemented. Analysis and experimental results show that RBHLT enables higher liquidity of non-fungible tokens while reducing transaction costs.
Anonymity Preserving Secure Authentication for a Transparent Internet Voting Process

The COVID’19 pandemic of recent times has changed the trends of socializing, that is, from physical to online (virtual) presence. Likewise, remote or work-from-home culture has flourished and resulted in business gains with the least maintenance cost. However, the situation has its downsides, like risking users’ privacy and compromising their anonymity by getting hacked, which has caused distrust in online businesses/activities. i-voting is also not indifferent, where people fear compromising their anonymity by casting votes via digital electoral. In an attempt to rebuild trust in i-voting, this research, as a test case, focuses on the security aspects of the authentication mechanisms used in the i-voting System of Estonia. It is to pledge the significance of voter authentication just before a vote gets cast because admittance to internal applications may expose the network to instant attack. Following that is the suggested remedy for the limitations observed, such as using a static e/m-ID for online transactions and limited time verifiability of their casted vote, through our proposed digital authentication mechanism, which ensures the anonymity of both the ballot and the voter and is a novel contribution to date.