Reviewerly/exHarmony huggingface dataset:
Disaster Preparedness Using Augmented Reality/Virtual Reality

Disaster preparedness is an important factor that plays a major role in diminishing the casualties in case of any disasters. Training people in society has an important role in facing situations of crisis. Information on procedures to be followed, rules to be obeyed, and actions to be taken and avoided in critical situations are available, but most people are unaware of these procedures and have no prior knowledge to navigate through this safely. Now, to be able to act properly in these situations, society as a whole have to be trained and equipped with advanced technological equipment, and to do so, a huge investment in training equipment development is required. So, in this project, we are planning to use augmented reality (AR) technology to help people get the necessary information and training to navigate the situation. We plan on doing so by creating an application that includes scenarios regarding the evacuation in a disastrous event. The general task of this application is to provide appropriate views based on the underlying scenario and use artifacts that would be used at such times.
A note on the intersection of two distinct constacyclic codes

This note gives a counterexample of a fact applied to the proof of two results in the papers of Dinh and Liu et al. Under suitable conditions, we give a sufficient condition for this fact to be correct. By means of the sufficient condition, we give the correct proof of the two results.
Accelerating Polynomial-Based Image Secret Sharing Using Hadoop

Polynomial-based secret sharing techniques are most preferred in many applications. Image secret sharing techniques based on polynomial functions become computationally heavy if image size is large. Sequential operations are not that effective for construction and reconstruction algorithms on large size images. Parallel computing works efficiently when load and time reduction on a single machine is required. The concurrent execution of polynomial operations on image pixel values while applying steps of construction of shares and reconstruction of the secret makes the scheme more efficient. Hadoop-based concurrent approach is proposed for polynomial-based image secret sharing scheme. The experimental results show that Hadoop-based cluster speeds up the process up to 18% by one slave and up to 14% by adding two slave nodes. The addition in number of slave nodes accelerates the performance up to 50% for very large size images in the polynomial-based secret sharing algorithms.
Healthcare Chatbot System: A Survey

A chatbot is a software that produces a computer model of conversation through auditory or textual method between the user and the computer. Such bots use AI and NLP to understand user&#x2019;s questions and automate responses to them, simulating human conversation. In this modern technology almost, every person utilize smartphones in their day to day lives. Simply because, it has made life of a person simple by the touch of the screen, from extracting information from the internet, to provide personal assistance to entertainment everything happens just by the click of a button. Today Chatbots are used in various domains like education, customer service, medical, website help etc for seeking information, FAQ answering and guidance. Due to the advancements in AI and techniques used in data mining, healthcare chatbots are often used for medical screening. The aim of this paper is to do detailed study of various research papers related to chatbots and analyze the different tools, algorithms, software and platforms that are utilized.
A Secured Storage Scheme for Cloud Environment using ECC-IRNS based Deduplication Approach

Cloud computing implementation offers a wide range of web-based service paths for providing various needs. Moreover, the security of data and the privacy comes out to be the most critical issue which may restrict several applications of cloud. Cloud operators are having the chances of reaching sensitive data. This remains as the major concern on increasing the anxiety of user’s thereby reducing the cloud computing adaptability in various field like governmental organizations and commercial industries. Thus, to sort out these issues, this paper aims at providing secured storage system in cloud environment with the use of deduplication and a novel cryptographic approach. The Elliptical Curve Cryptographic is employed for encryption and for key generation the Improvised residue number system (ECC-IRNS) based deduplication scheme is proposed. The technique of deleting duplicate data from cloud storage is known as data deduplication. The encrypted data is delivered to the cloud service provider, who checks for redundancy. Cosine similarity checking is used to deduplicate data. The method’s advantage is that it prevents malicious uploads and downloads from using up store space. Thus, the proposed methodology provides secured storage of data backup in an effective manner. The performance analysis is carried out and compared with existing techniques in terms of storage space, execution time, similarity analysis, memory consumption, encrypting and decrypting time, and computational time. The analysis shows that the proposed approach is far better than the existing methodologies.
Transform Based Lightweight CNN Models for Image Classification

Convolutional Neural Networks (CNN) are playing a big role in image classification, object detection, and segmentation tasks. The integration of several CNN models at the appropriate place can address the majority of computer vision problems. CNN model consists of millions of parameters that have to be trained on high-performance computational devices. Model compression is inevitable as high number of model parameters needs to be reduced without losing the model performance. This research study employ Discrete Wavelet Transforms (DWT) to decompose input images before running them through a set of fused convolutional layers in an existing model. Further, this study also attempts to minimize the accuracy drop due to the downsampling by replacing the conventional pooling operations with the integration of DWT. DWT also helps to minimize the information loss due to the downsampling. Along with DWT, Further, the models are compressed by using quantization techniques to make lightweight models, which can be easily deployed on mobile and edge devices. A Quantization Aware Training (QAT) is applied followed by the application of wavelets. Thus the modified models are referred as ’WaveQuant’ models. The commonly used CNN models such as AlexNet, GoogLeNet, and ResNets are tested on the CIFAR10 dataset, and accuracy results are comparable with their vanilla models. The modified LeNet model (WaveQuant LeNet) has only 13k parameters and it has a comparable accuracy on testing as well. Our all modified ’WaveQuant’ models have significantly lesser parameters than their original models and it makes models more lightweight.
<scp>ECAAP‐SG</scp>: Efficient certificateless anonymous authentication protocol for <scp>SG</scp>

Abstract A smart meter (SM) is an essential device within a smart grid (SG). It collects electrical power data from the customer and reports it to the service provider at regular intervals. Unfortunately, these regular reports may disclose confidential customer information without their knowledge. Moreover, SM is a device with limited resources, which means it cannot manage heavy security protocols. To mitigate these problems, an efficient certificateless anonymous authentication protocol for SG (ECAAP‐SG) protocol was designed. It cannot allow an adversary to tap into the communication channel and access exchanged information. In addition, the designed scheme fits SM's resource capacity. Furthermore, the authentication security provided by ECAAP‐SG guarantees that legal entities are communicating. Moreover, ECAAP‐SG provides key agreement security that establishes the session key. The established session key will be used to secure future communication. Besides, a random oracle model is used for the security analysis of the proposed protocol, and we prove that it is secure under the discrete logarithm problem and the computational Diffie–Hellman problem.
Novelty detection approach for the monitoring of structural vibrations using vision-based mean frequency maps

Damage detection is an important part of modern-day engineering. Early damage detection is facilitated by Structural Health Monitoring methods, which may be employed using numerous modalities, such as vibration, guided waves, thermography, or computer vision. These methods produce information that can then be interpreted to detect and localize damage or quantify its extent. Novelty Detection (ND) is a data interpretation approach that enables damage detection without prior knowledge of damage-related influences on gathered data. ND can be conveniently performed using computer vision methods, which allow continuous, non-contact monitoring of large structures with the possibility of relying on such quantifiers as deflection, vibration, or strains. In this work, we present an ND method for monitoring the structural changes in rotary machinery equipment using vision-based data. The proposed technique detects changes in the characteristic frequencies of vibrations due to damage. Using the optical flow calculated for the videos acquired using a high-speed camera, the maps of mean frequency can be estimated and used for evaluating the differences between the reference data set and the data obtained during the monitoring. The Optimal Baseline Selection is used to compensate for the varying operational conditions under which the structure is monitored. The approach was tested on the air compressor working under variable pressure, and the damage introduced to the structure was successfully detected. • Full-field vibration is estimated using optical flow of the vision-based data. • The shifts of characteristic frequencies can be tracked using frequency maps. • The novelty detection approach compares frequency maps from different states. • Optimal baseline selection is used for compensating varying operational conditions. • Analysis of the experimental data detected defects in rotary machinery.
An adaptive read/write optimized algorithm for Ceph heterogeneous systems via performance prediction and multi-attribute decision making

Abstract The advent of the Big Data era has brought considerable challenges to storing and managing massive data. Moreover, distributed storage systems are critical to the pressure and storage capacity costs. The Ceph cloud storage system only selects data storage nodes based on node storage capacity. This node selection method results in load imbalance and limited storage scenarios in heterogeneous storage systems. Therefore, we add node heterogeneity, network state, and node load as performance weights to the CRUSH algorithm and optimize the performance of the Ceph system by improving load balancing. We designed a cloud storage system model based on Software Defined Network (SDN) technology. This system model can avoid the tedious configuration and significant measurement overhead required to obtain network status in traditional network architecture. Then we propose adaptive read and write optimization algorithms based on SDN technology. The Object Storage Device (OSD) is initially classified based on the Node Heterogeneous Resource Classification Strategy. Then the SDN technology is used to obtain network and load conditions in real-time and an OSD performance prediction model is built to obtain weights for performance impact factors. Finally, a mathematical model is proposed for multi-attribute decision making in conjunction with the OSD state and its prediction model. Furthermore, this model is addressed to optimize read and write performance adaptively. Compared with the original Ceph system, TOPSIS_PA improves the performance of reading operations by 36%; TOPSIS_CW and TOPSIS_PACW algorithms improve the elastic read performance by 23 to 60% and 36 to 85%, and the elastic write performance by 180 to 468% and 188 to 611%, respectively.
Common Factor Analysis of Information Security Incidents Based on Psychological Factors

With the rapid expansion of the Internet alongside the adoption of Digital Transformation (DX), the number of information security incidents has increased and diversified. Incidents of information leakage and loss have particularly increased, and internal fraud and inattention (i.e., human psychological risk) are attracting attention as causes of these incidents. In this paper, we focus on the psychological aspects of internal fraud and propose the appropriate risk countermeasures. We first extracted 50 security incidents involving internal fraud over the past ten years and then identified the "factors behind" them using "five whys". We then classified these factors on the basis of the fraud triangle theory and other factors and came up with 12 common factors. Finally, we proposed various risk countermeasures such as "mutual inter-checking" and investigated their effectiveness through qualitative evaluation. Our findings contribute to the reduction of information security incidents caused by psychological factors.
Blockchain traceability for agroindustry - a literature review and future agenda

Abstract The development of technology is increasingly fast, making the demands of life also continue to increase. Until this blockchain technology is present, interest in research on the blockchain has risen in recent times. This literature study was conducted to discover the current development of blockchain technology and research trends focusing on contributions to traceability systems. After knowing the latest research developments and trends, it is hoped that other researchers can take the gap to develop the research further and utilise this technology in a variety of industrial applications. The method approach used in this article uses the literature review method. Scopus is used as a source of data to determine the development of blockchain research from year to year. Search results data from the web Scopus with the keyword ‘Blockchain Traceability’, filtered and sorted based on research methods, study subjects and content analysis. The development of current research trends that are still limited by conceptual and descriptive methods on issues of computer science studies can be seen. With content analysis in the financial or financial sector, recommendations are given for further research (future research) for researchers based on current research trends.
An Approach to Real-Time Fall Detection based on OpenPose and LSTM

Falls are consistently the top cause of death among seniors. At a time when the global population is getting older and fewer births. The shortage of nursing staff seriously affects the health care of the elderly. If information and communication technology can be used, automatic detection and identification the elderly fall, we believe it can reduce the injury of the elderly due to falls. This paper proposes a method different from the previous wearable sensing device, which is based on the displacement of human relative positional parameters in the image to identify the occurrence of human fall. We implemented a system based on OpenPose and combined with the deep learning neural network model LSTM with time series, the image recognition is carried out, the human joint parameters of human posture falling and falling in the image are captured, and the identified parameters are simply filtered, and then the filtered parameters are used for model training.
Analysis of Twitter Spam Detection Using Machine Learning Approach

Now a days there are many online social networks (OSN) which are very popular among Internet users and use this platform for finding new connections, sharing their activities and thoughts. Twitter is such social media platforms which is very popular among this users. Survey says, it has more than 310 million monthly users who are very active and post around 500+ million tweets in a day and this attracts, the spammer or cyber-criminal to misuse this platform for their malicious benefits. Product advertisement, phishing true users, pornography propagation, stealing the trending news, sharing malicious link to get the victims for making money are the common example of the activities of spammers. In Aug-2014, Twitter made public that 8.5% of its active Twitter users (monthly) that is approx. 23+ million users, who have automatically contacted their servers for regular updates. Thus for a spam free environment in twitter, it is greatly required to detect and filter these spammer from the legitimate users. Here in our research paper, effectiveness &#x0026; features of twitter spam detection, various methods are summarized with their benefits and limitations are presented. [1]
HybEx: A Hybrid Tool for Template Extraction

HybEx is a site-level web template extractor that combines two algorithms for template and content extraction: (i) TemEx, a site-level template detection technique, and (ii) Page-level ConEx, a content extraction technique. The key idea is to add a preprocess to TemEx that removes the main content inferred by Page-level ConEx. It is a fact that adding this new phase to the TemEx algorithm involves an increase of its runtime, however, this increase is very small compared to the TemEx runtime because Page-level ConEx is a page-level technique. On the other hand, HybEx improves the precision, recall, and F1 of TemEx. This paper describes the new template extractor and its internal architecture. Furthermore, the paper also presents the results of its empirical evaluation.
Signature-Aware RF Exploitation (SNARE) Fingerprinting using Deep Learning to identify UAVs

Currently, there are emerging challenges with non-cooperative unmanned aerial vehicle (UAV) operating in urban airspaces. Likewise, law-enforcement groups need UAV detection capabilities to respond for safety and security (e.g., as defined for situation awareness at airports and forest fires where protocols restrict UAVs from operating within a few nautical miles from the event). This paper presents a novel physical layer authentication solution to identify UAVs that have identical visual signatures such as the same drone type and manufacturer. Within each UAV, the radio frequency (RF) signals transmitted from UAVs have a unique signature, called RF fingerprint, that can be used to distinguish among UAVs. The proposed Signal-to-Noise Ratio (SNR) of the transmitted signal in the wireless domain knowledge signifies the equipment onboard the UAV. The SNR-Aware RF Exploitation (SNARE) method solution improves the overall performance of conventional machine learning neural network models applied to imagery. This paper compares the performance metrics of different deep learning techniques including convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), and the effect of related hyper parameters such as size of sliding window, learning rate, and SNR range. Experimental RF data collected from multiple identical UAVs hovering in different ranges from the receiver node are employed in this study. Compared to the traditional models that do not consider the received RF signal related SNR information, our proposed SNARE improves UAV classification of the CNN, DNN, and RNN models from 84&#x0025; to 96&#x0025;, 91&#x0025; to 96&#x0025;, and 80&#x0025; to 86&#x0025;, respectively.
Mechanism to Capture Learners’ Interactions in Virtual Reality Learning Environment

Virtual Reality (VR) has the potential to improve learning in the education domain due to its characteristics such as multi-sensory stimuli, immersion, interaction, first-person perspectives, etc. In spite of these advantages, the literature analysis carried out has revealed that there is no literature that explored the learning processes happening in a VR environment. The learning processes can be analyzed using the data collected from the interaction behavior happening in a VR learning environment (VRLE). The existing data collection mechanisms in VR such as questionnaires, surveys, physiological sensors, and human observers do not provide data related to the interaction behavior of the learners happening in VR head-mounted displays (HMD). Hence, in this paper, we discuss a mechanism developed to log the interaction data happening in VRLE automatically in real-time and store them in a locally accessible location. The logged interaction data can be further processed to extract features and study the learning processes in VRLE.
MPO: MQTT-Based Privacy Orchestrator for Smart Home Users

Security and privacy concerns present the most significant obstacles to consumer adoption of Internet-of- Things (IoT) devices. A lack of transparency and control complicates user trust in IoT. Additionally, a growing history of misuse and abuse exists in IoT. Notably, smart TVs have periodically scanned and collected users' private information without consent. Due to a hybrid of distributed ecosystems within IoT, users cannot easily implement traditional access control over their devices as data flows within different nodes for storage and processing. We propose MQTT-Based Privacy Orchestrator (MPO) to implement traditional access control on IoT devices. MPO enforces privacy preferences by implementing access control at an MQTT broker. We open-source and provide MPO as an add-on to the popular Home Assistant open-source home automation framework to support widespread adoption. We conducted experimental evalu-ations to validate the functionality and examine the performance of MPO. Our performance evaluation generated more than 16,686 messages, which MPO delivered in under a second. Our work demonstrates a practical solution to facilitate users' privacy preferences and enforce access control for MQTT-based devices.
Mean-Shift and Local Outlier Factor-Based Ensemble Machine Learning Approach for Anomaly Detection in IoT Devices

IoT devices are rapidly being used in everyday life. However, many of these devices are susceptible as a result of insecure design, implementation, and setup. As a consequence, many networks already include susceptible IoT devices that are simple to infiltrate. This paper presents a Mean-Shift and Local Outlier Factor (LOF) based ensemble machine learning (ML) approach for anomaly detection in IoT devices. The UNSW-NB15 IoT-based network dataset is used for the evaluation of the proposed model. LOF is used with the Mean-Shift clustering approach for clustering and the ensemble of ML technique is used for the classification of usual or unusual activities. The proposed model is tested and evaluated with different performance measures like Precision (P), Recall (R), Accuracy (ACC), and F1-score. This research study has comprehensively evaluated the proposed model and compared it with different ML methods. The proposed model reported the best results for the detection of anomalies.
An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit Placement

Mixed-Polarity Multiple-Control Toffoli (MPMCT) gates are generally used to implement large control logic functions for quantum computation. A logic circuit consisting of MPMCT gates needs to be mapped to a quantum computing device that invariably has a physical limitation, which means we need to (1) decompose the MPMCT gates into one- or two-qubit gates, and then (2) insert SWAP gates so that all the gates can be performed on Nearest Neighbor Architectures (NNAs). Up to date, the above two processes have only been studied independently. In this work, we investigate that the total number of gates in a circuit can be decreased if the above two processes are considered simultaneously as a single step. We developed a method that inserts SWAP gates while decomposing MPMCT gates unlike most of the existing methods. Also, we consider the effect on the latter part of a circuit carefully by considering the qubit placement when decomposing an MPMCT gate. Experimental results demonstrate the effectiveness of our method.