Reviewerly/exHarmony huggingface dataset:
Performance of Flexible and Low complexity Decoding RS-SPC Product Codes for Short-Packet Communications

In this paper, we propose a product code scheme based on a Reed-Solomon (RS) code concatenated with the single parity check code (SPC). We introduce a novel soft-in-soft-out (SISO) algorithm with very low decoding complexity for the SPC code. We show that this new coding scheme has similar performance to the RS turbo product code (TPCs) for BER values as low as $10^{-5}$ but with significant gain in both decoding complexity and packet size flexibility.
FindLoc: A Sentiment Analysis-Based Recommender System

A recommendation system (RS) is a tool for filtering information that attempts to recommend interesting items. Globally, RS is based on analyzing users’ preferences and their implicit or explicit evaluations of items. Searching for a good doctor, restaurant, school, dentist, cafeteria, etc., in real-time from the user's current location remains challenging in Algeria. In order to facilitate the finding of interesting locations for Algerian users, we propose in this work a collaborative recommendation system called FindLoc that learns to classify the different places according to their distances and previous users’ evaluations. First, we gathered users' evaluations (ratings and comments) on various locations in Guelma city. Then, a sentiment analysis method is applied to analyze the dataset and distinguish positive from negative opinions. Then, a recommendation algorithm is proposed to recommend interesting locations based on four parameters: the location (longitude and latitude), previous item ratings, users' comments, and users' ages. Real users evaluated the system, and we obtained very encouraging results in terms of precision, which was 0.91.
Dehazing of Multispectral Remote Sensing Images Using CNN with ResNet

Satellite image utilization for data interpretation has significantly increased in the current scenario. Deep learning techniques have gained widespread popularity for analyzing satellite image data. However, a major challenge in this field is the presence of haze, which hampers accurate interpretation. Haze occurs when fine dust, smoke, or light vapors reduce the air's transparency, resulting in a lack of contrast in affected regions of satellite images. To tackle this issue, atmospheric correction is commonly employed, but it is a complex and time-consuming process that requires knowledge of various geophysical factors to achieve precise results. Hence, the objective of this research is to develop and implement image processing algorithms that can effectively restore pixels affected by haze, thereby improving the interpretability of satellite images. The proposed work consists of two main phases. Firstly, noise reduction techniques will be applied to the images to enhance their quality. In the second phase, a Convolutional Neural Network with a Residual structure will be utilized to dehaze the images. The evaluation of the proposed system will focus on the Kondapalli region and its surrounding areas in the Kondapalli dataset. This research aims to develop a robust solution that can enhance satellite images affected by haze, providing clearer and more interpretable data for various applications. The effectiveness of the proposed system will be compared with the Dark Object Subtraction method.
K-means and Principal Components Analysis Approach For Clustering Malicious URLs

Security flaws in the world of cyber security are a major concern with the advancement of internet technologies. Malicious URLs are increasing with the number of web links in the world. Developing techniques to classify blacklisted malicious URLs is one of the most focused solutions in the security community. In this paper, we developed a clustering approach to find the similarity into different malicious URLs.The effectiveness of our technique was evaluated using the ISCX-URL-2016 data set, which contains over 110,000 URLs. The collection of necessary features group data is included in the ISCX-URL-2016 data set. Therefore, the data were prepossessed and scaled, then two most commonly used lightweight algorithms are implemented: k-means and principal component analysis. Experimental results exhibit by combining the two algorithms has produced several evaluation results of identifying new potential attack categorizes.
Distributed Multi-Agent Deep Reinforcement Learning based Navigation and Control of UAV Swarm for Wildfire Monitoring

Decentralized deep reinforcement learning is an emerging and most effective approach to solve the problem of resource allocation and coordination among the swarm of UAVs. Nowadays, the use of autonomous aerial vehicles in wildfire monitoring is increasing and considered as the reasonably feasible option as surveillance in calamity-hit areas can benefit from this kind of automation. The flocks of UAVs can generate maps of affected areas which could improve the process of relief planning so that necessary aid can be reached the burnt areas quickly. This paper presents the Multi-agent Deep Q network-based technique for planning optimized trajectories for the UAV swarm which can sense the wildfire in the forests and nearby regions. In this work, the UAV agents are trained over simulated wildfires in virtually generated forests with two reward schemes. The simulation results verify the effectiveness of the proposed strategy for leveraging it in real-world scenarios.
Concatenating Extended CSS Codes for Communication Efficient Quantum Secret Sharing

Recently, a class of quantum secret sharing schemes called communication efficient quantum threshold secret sharing schemes (CE-QTS) was introduced. These schemes reduced the quantum communication cost during secret recovery in quantum threshold secret sharing schemes. However, non-threshold schemes are known to have less storage cost compared to threshold schemes. This motivates us to extend the idea of communication efficiency to non-threshold schemes as well. In this paper, we introduce a general class of communication efficient quantum secret sharing schemes (CE-QSS) which include both threshold and non-threshold schemes. Specifically, we provide a framework for constructing CE-QSS schemes by concatenating a pair of CSS codes. The main component in this framework is a new class of Calderbank-Shor-Steane (CSS) codes called the extended CSS codes.
Advancements in Cancer Disease Prediction Using Modified Multi Model Deep Neural Networks

Cancer disease prediction is a critical task in healthcare that can significantly impact patient outcomes. This abstract presents a multimodal deep neural network (DNN) approach for cancer disease prediction, implemented in MATLAB. The proposed model leverages the power of DNNs to handle diverse types of data, such as medical images, genomic profiles, clinical records, and patient demographics. By integrating these multimodal data sources, the model aims to capture a comprehensive view of the disease, incorporating both genetic and phenotypic information. The MATLAB implementation allows for efficient data processing, model training, and evaluation. The performance of the proposed multimodal DNN model is assessed using benchmark datasets, and the results demonstrate its potential in accurate cancer disease prediction. Further research is needed to explore the interpretability of the model's predictions and expand its application to real-world clinical settings. Overall, this work contributes to advancing the field of cancer diagnostics by utilizing a multimodal DNN approach implemented in MATLAB for improved disease prediction.
Automated Landslide Event Archive Generation from News Articles

Landslides are a type of natural disaster characterised by ground movements in the form of rockfalls, debris falls, mud-flow and slope failures. It is one of the primary natural disaster and has been studied since as early as 1807 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://shorturl.at/nvxAJ. Disaster management is a stream of research in Information Technology, Political Science, Legal and Ethical Studies, Geology and so on which has garnered a lot of attention in the recent past. A primary dependency of such research is on archival data related to landslides. As a part of this work, a novel framework for generating landslide archive is proposed. The source of this data archive are online news articles from dependable news publishers. The data archive contains comprehensive information about the date, location, severity, and meteorological causes for landslides. The end-to-end framework contains a user-friendly web interface for running easy, concise, and customisable queries with near real-time responses.
Oral Expression Evaluation Algorithm Based on Multi-Feature Fusion

The role of oral expression in oral learning is very important, but there is a problem that language expression is not clear. The public's oral expression information is easily distorted, poorly recorded, and the transmission distance is short. Therefore, this paper proposes a multi-feature fusion oral expression scheme for oral expression evaluation and analysis. Firstly, the teaching theory is used to evaluate the oral expression of learners, and the oral expression is divided according to the fusion of multiple characteristics, so as to reduce the defects in general oral language Factor. Then, the pedagogical theory teaches learners ordinary oral English, forms a contrasting scheme, and synthesizes the results of the two Analyse. MATLAB simulation shows that under certain evaluation criteria, the oral expression evaluation algorithm with multi-feature fusion has higher accuracy, more complete expression, and long propagation distance.
Decentralized Framework for Cultural Heritage Conservation using Blockchain

The preservation and management of cultural heritage sites and artifacts is an important task that requires efficient and secure methods for data management and access control. Blockchain technology, with its decentralized architecture and immutability features, has the potential to provide innovative solutions for these challenges. This research aims to explore the use of blockchain technology for the preservation and management of cultural heritage sites and artifacts. The study will focus on the design and implementation of a decentralized framework that allows for the collaborative conservation and restoration of cultural heritage, using blockchain-based smart contracts and digital tokens. The framework will also provide secure and transparent access control mechanisms for the management and sharing of cultural heritage data among stakeholders, including researchers, conservationists, and the general public. The research will also evaluate the potential benefits and limitations of the proposed framework and its impact on the preservation and management of cultural heritage.
Surface Anomaly Detection Based on Multi-Scale Feature Propagation and Normalized Flow

Unsupervised learning is widely used for surface anomaly detection, due to the sparse and difficult description of anomaly samples. The goal of this paper is to improve the problems of low accuracy and slow speed in unsupervised anomaly detection algorithms. Starting off, the pre-trained convolutional neural network with large dataset is used for feature coarse extraction. Then, a multiscale feature propagation architecture is constructed to perform feature fusion for features at different spatial locations to enhance features coarse extraction of anomaly. Finally, the extracted high-dimensional feature embeddings are mapped to the latent space using normalized flow for distribution fitting, and the likelihood of anomaly existence is calculated using the latent distribution. As a result, our empirical study on the MVTec_AD dataset show that the detection accuracy of our method exceeds baselines, and our method has a faster inference speed. The ablation experiments also demonstrate the effectiveness of the multiscale feature propagation architecture, especially on enhancing the model's ability to detect tiny anomalies. In addition, our approach includes a faster inference speed and less space consumption.
Multimodal Sentimental Analysis Using Hierarchical Fusion Technique

Multimodal sentiment analysis encompasses modalities beyond text, including visual and audio data, enabling combinations of two or three modalities. It has diverse applications in domains like virtual assistants, YouTube movie reviews, news videos, and emotion recognition, including depression monitoring. Sentiment analysis involves extracting opinions from public discourse to enhance various operations. In this paper, using the Multimodal Opinion-level Sentiment Intensity (MOSI) dataset, which consists of video, audio, and text data, for the sentimental analysis of multimodal data is proposed. One of the significant challenges in this analysis is accurately determining the sentiment (optimistic or pessimistic) when the data is presented in the form of a tone. The proposed technique analyzes sentiments by focusing on audio and text data. Feature extraction is performed using Mel-Frequency Cepstral Coefficient (MFCC) for audio and Word2vec for text. These extracted characteristics are combined using hierarchical fusion and a Convolutional Autoencoder (CAE) is employed to obtain bottleneck features. Experimental results demonstrate an accuracy of 62.45% for audio data, 73% for text data, and 76.09% for the fusion of audio and text using the Long Short-Term Memory (LSTM) classifier.
Recent Trends in Deep Learning Based Omnidirectional Image SuperResolution

Omnidirectional imaging is extensively used in fields such as virtual reality, surveillance, and remote sensing. Super-resolution aims at enhancing the detail and resolution of low-resolution images, including omnidirectional ones. This article provides an overview of deep learning methods for achieving super-resolution in omnidirectional images. Using techniques like CNNs-convolutional neural networks and GANs-generative adversarial networks, deep learning has proven highly effective in improving the quality and resolution of omnidirectional images. The study explores various techniques, including models for single-image super-resolution, methods for multi-frame fusion, and the challenges faced in applying deep learning to omnidirectional image super-resolution. The article also highlights the method used in each technique, discusses potential applications, and identifies future research directions. It lays the groundwork for further advancements in this exciting field of deep learning-based methods to apply super-resolution in omnidirectional images.
Solving Tracking Challenges: Lightweight Algorithm for Single Pedestrian Tracking on Nano-UAVs

With its pocket size and flexible flight characteristics, the nano-UAV can achieve more stealthy flight. If the full on-board deployment of the single pedestrian tracking algorithm can be realized on the nano-UAV, It will completely unmanned tasks such as personnel monitoring and security inspections, and has a broad application prospects. However, it is very challenging to design single pedestrian tracking algorithms for nano-UAV platforms. On the one hand, nano-UAVs are not able to deploy the algorithms fully on-board due to their strictly limited computing and storage resources; on the other hand, there are various tracking challenges (multi-scale changing target tracking, small-scale target tracking) for single pedestrian tracking tasks. In this paper, we design a lightweight single pedestrian tracking algorithm that ensures its resource footprint meets the limitations of nano-UAVs. The algorithm proposed in this paper consists of a multi-task learning front-end and a fused ReID tracking back-end, and we design a complete algorithm deployment process, including dataset construction, model training quantification and model deployment, which can effectively solve the challenges of current tracking tasks. A high efficiency (computational efficiency reaches 4.8 MACs/Cycle, and energy efficiency reaches 4.7 mJ/frame) has been achieved on the nano-UAV, achieving an accuracy score of 0.988 and a success score of 0.578 with good robustness in flight. The full-board deployment of nano-UAVs can achieve real-time tracking efficiency (28fps), which has application value and greatly promotes the research work of nano-UAVs.
Work in Progress: The Effectiveness of Knowledge Management System on Companies Performance

In the current challenging environment with the highly speed development that the companies’ world faced, finding and keeping up with the auxiliary tools and systems has become a requirement and a necessity to deal with the complexity and uncertainty changes. One of the efficient ways to reach the previous goal is to implement knowledge management system (KMS). KMS is a systematic way that stores and retrieves knowledge to improve the process of understanding and collaboration [1]. The major purpose of this research is to examine the effectiveness of the knowledge management system on performance and a case study of Petroleum Development Oman (PDO) company is selected. This research will adopt mixed methodology consisting of quantitative and qualitative approaches through conducting questionnaire and interviews primarily. In addition to evaluate literature review as additional source. After that, the collected data will be analyzed with the appropriate data analysis technique.
Research on Template Matching Algorithm Based on an Improved Continuous Circular Projection

Rotation invariance is one of the important research contents of template matching algorithm. This paper mainly discusses the matching problem of target image under the condition of rotation, and proposes a template matching method based on continuous circular projection transformation. In this method, the continuous circular projection transformation model is used as the feature mapping method, and the feature vector with rotation invariance is obtained by combining the gray feature extraction method of the local gradient extremum, and the template matching is completed based on the vector. The experimental results show that the matching rate of the algorithm is 95.8% in the original data set; the occlusion matching rate of the algorithm is 98.3%_in the rotated image set and_the matching rate is 85.4% in the interference image set.
ELMo Layer Embedding Comparision with Short Text Classification

ELMo was the first context based embedded generated neural model which mainly solved the problem of polesmy issue. Word embedding generated with ELMo was used with downstream tasks such as text classification to improve the classification performances. The architecture of ELMo consists of 3 layers such as convolution layer, and two LSTM layers. These layers can be either concatenate or used separately to generate different embedding representations. with the existing work, ELMo embedding layer performances was not properly evaluated for short text classification tasks. Moreover, it is hard to identify the best traditional classification algorithms with ELMo embeddings. Therefore, this study mainly focuses on identifying the best layer embedding weighting scheme as well as the best traditional machine learning algorithm which performs well with short text classification tasks. Seven short text type datasets were selected for the experiment. According to the experiment, concatenation of three layers was identified as the best layer embedding representation over the others. There was a around 2–4% performance improvement over the other layer combination. Moreover, the support vector machine algorithm showed the best classification performances among other Machine learning algorithms.
Analysis of Computer Application Software Progress Technology Based on Artificial Intelligence Background

With the complexity and diversification of computer systems and network technology, the whole world is more closely connected with the computer, application software. Due to computer system application scope is more and more widely, more and more in-depth, computer software system is becoming more and more complex. Therefore, in order to realize the automation, convenience and intelligence of computer application software progress Tech, AI technology is applied in it. Therefore, on the basis of the advantages of AI technology, this article first analyzes the role of AI neural network in improving the efficiency of user work and accurately identifying the threat factors that invade computer software, and then builds an automatic progress platform for computer application software. Users can input CNN parameters and hardware resource parameters through the graphical interface of software progress environment. Then the progress environment can automatically integrate the data structure of different types of parameters by analyzing the input parameters, and automatically construct processor-level network structure configuration information and instruction program. The software performance test shows that the computer application software designed by AI neural network has good performance, the accuracy rate is 97.6%, the loss function value is only 0.12, and the training time is only 6 minutes. Compared with the traditional artificial programming computer application software, the convergence speed is faster. It can be proved that the method of designing computer application software based on AI is effective and has high performance.
Enhancement of Data Transfer Rate by Blockchain based Transmission for Internet of Things

The emergence of the Internet of Things (IoT) brings great opportunities to both society and the economy. Among various emerging technologies, blockchain has great potential in ensuring the reliability and security of the transmission of IoT data. The distributed ledger technology on which blockchain is founded can protect against data prone to tampering, counterfeiting, and manipulation, ultimately increasing customer confidence and trust in the information they rely upon. Blockchain based transmission systems can enable secure, near-real-time, and distributed data exchange between different types of nodes. This will allow for a safer exchange of coins and tokens, enable decentralized access control, improve traceability and transparency, enable the monetization of data and services, and create a single source of truth for IoT devices. By deploying a blockchain to discern itself from other devices and initiate trust relationships, bring the blockchain to IoT devices and specific use cases will enable a seamless end-to-end experience to the user. Additionally, the scalability of blockchain technology for the large-scale deployment of devices and services, like automated parking systems and smart cities, is another advantage that can help create a decentralized alternative to commonly used centralized systems. In this way, blockchain-enabled IoT systems are able to offer enhanced security and trust measures, improve transaction latency, ensure privacy, and reduce costs.