Reviewerly/exHarmony huggingface dataset:
Vehicle To Vehicle and Vehicle To Ground Communication - Speech Encryption Algorithm

Vehicle to vehicle (V2V) communication has many applications in intelligent transportation system (ITS) like smart grid systems, fleet management and traffic light control. It aims to interconnect several components like road infrastructures, wireless networks, sensors, and vehicles. Therefore, it can be utilized to reduce congestion, accidents and fuel consumption. This paper propose a speech encryption system based on a one-dimensional logistic map for vehicle to vehicle communication (V2V) communication in intelligent transportation systems (ITS) to improve road safety and efficiency, where encryption and security play a major role. This study employs a symmetric key cryptography technique for chaotic ciphering. In this approach, a random key table lookup criterion was paired with a one-dimensional chaotic map in order to encrypt speech samples with a high level of security and acceptable speed. XOR encryption and speech row shuffling are used in the proposed technique. Using the difference between the old and new row locations, a combination of variable rotation and inversion was performed to each shuffled row in order to increase the level of security. As a consequence of the testing, the proposed approach was shown to be both efficient and usable. Combining key table and logistic map illustrates the advantages of both high-level security and large random key space. The encrypted speech produced is suitable for practical usage especially in vehicle to vehicle and vehicle to base transmission.
RAMEN: A Ratio-Weighted Majority Entropy- Based Decision Tree Algorithm for Classifying Imbalanced Datasets

Dealing with imbalanced datasets is an essential part of most analytic lifecycles. However, an inadequate classification of imbalance datasets may result in the loss of information and insights, as well as in the reduced repurposability of the data. While most well-known classification algorithms, including the decision tree algorithm, can efficiently make predictions from balanced datasets, these algorithms are inefficient when classifying imbalanced datasets. To address this concern, the present study, building on a traditional decision tree classification algorithm – namely, the ID3 algorithm –proposes a ratio-weighted majority entropy decision tree algorithm (RAMEN). RAMEN removes bias toward the majority class in imbalanced datasets. The RAMEN algorithm is then tested on two imbalanced datasets: a cancer case dataset and a banknote verification dataset. Comparing the performances of the RAMEN technique with those of the traditional ID3 decision tree algorithm and the minority entropy-based decision tree algorithm, we find that RAMEN outperforms both aforementioned algorithms for the two datasets.
Capsule Network based 3D Object Orientation Estimation

Convolutional neural networks have proven to be one of the most efficient methods for processing visual data. Due to the popularity of the field, there is a growing interest in the reliability of intelligent systems. It has been shown that convolutional neural networks can be fooled by extreme inputs or noisy inputs. To overcome the current problems of convolutional neural networks, the theory of capsule networks was introduced by Geoffrey Hinton and his research team. In this work we want to investigate the theory of capsule networks for orientation recognition of 3-dimensional objects. We consider the case when the data are noise loaded by various adversarial attacking methods. We compare our results with the efficiency of convolutional neural network based solutions, highlighting the difference between the two theories. We investigate the efficiency reduction that can be observed using different adversarial attacking methods. Our results will show how much more efficient the capsule network is compared to the neural networks.
Energy Estimation of Sleep Monitoring Algorithms for Application Specific Integrated Circuit Implementation

Sleep monitoring is crucial for a variety of health issues. The emergence of wearable technologies allows access to valuable sleep data to a wide range of users. For such wearable technologies to be unobtrusive and practical for daily living, their size and weight need to be minimized. Consequently, the work presented in this paper is with the ultimate goal of the implementation of a wireless wearable sleep monitoring device using Application Specific Integrated Circuits (ASICs). Sophisticated machine learning algorithms are commonly used for sleep stage classification, however, the high computational cost of some of these algorithms leads to high electrical power consumption and the need for large and heavy batteries. This paper investigated the energy consumption of four of the commonly used classification algorithms used for sleep monitoring, i.e. k-nearest neighbor, neural network, logistic regression, and random forest, based on the low-level counts of arithmetic and relational operations and simulated energy consumed by required operations for an ASIC hardware implementation. The method used in this paper ensures software-implementation independence of the estimated results by investigating low-level operations. Based on the assessments performed on a publicly available sleep dataset, results showed that the logistic regression and neural network classification algorithms were able to perform with considerably lower energy consumption when ASIC implementation is considered.
Deep Learning Models for Opinion Mining Analysis

In recent years, one of the most active areas of research in the fields of statistics, linguistics, and natural language processing has been opinion analysis. The aim of this paper is to develop a recurrent deep learning model for analyzing people’s opinions. The model is all-encompassing and may be applied to many different natural language processing (NLP) purposes., such as relation categorization, answer selection, textual entailment, and part-of-speech tagging. The suggested recurrent neural network design outperforms both convolutional and long short-term memory recurrent neural networks, according to experimental results.
BenCo: First Step Towards Coreference Resolution in Bengali

Coreference resolution is well-studied in NLP; however, Bengali coreference resolution research has not been as well investigated as it has been for English and other rich languages. Bengali has a richer morphology than English despite having fewer resources. This research introduce a tiny new dataset of coreference annotations over Bengali texts from four domains in this article called BenCo. In this dataset, there are 48,610 tokens and 5488 mention annotations structured into 470 mention clusters. The article outline the procedure used to generate this dataset and use it to build an end-to-end neural network-based system. This study will help to clarify how Bengali coreference phenomena differ depending on the domain and inspires others to produce more materials in the language. Also, insufficient information transfer in the zero-shot multilingual situations, which may indicate the need for language-specific resources for this job.
Comparing Traditional and Deep Learning Approaches in Developing Chess AI Engines

Artificial intelligence (AI) has a long and storied history with chess, dating back to the early days of AI research. Chess engines have served as a crucial benchmark to evaluate the progress of AI algorithms, as chess is a complex game with many possible moves and strategies. In this paper, we contribute to chess AI’s ongoing advancements by presenting two chess engines. The first engine leverages conventional AI methodologies, including Alpha-Beta Pruning, Quiescence Search, and sophisticated Move Ordering techniques, to construct a formidable and competitive chess engine possessing an efficacious evaluation function. In contrast, the second engine employs a simplified Alpha-Beta Pruning algorithm with a deep learning model, specifically an artificial neural network, which functions as a feature extraction mechanism for identifying optimal moves. We evaluate the performance of both engines against the renowned Stockfish engine, with the traditional engine attaining an ELO rating of 2200. At the same time, the deep learning model secures an ELO rating of 1900.
Artificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Test

This paper applies computer vision and artificial intelligence algorithms to the HTP (House-Tree-Person) test, a projective test intended to measure different aspects of personality using drawings. The drawn pictures are assumed to represent the subject’s attitudes and feelings regarding themselves, other and their family. The House-Tree-Person evaluation uses "Qualitative Scoring," which is a subjective analysis influenced by the therapists that can be used to infer aggressive, depressive or anxious characteristics in the drawings. This paper is part of a larger project that aims to use artificial intelligence and image-processing techniques to support this process, hence reducing the bias factor from the equation. With the collaboration of the Department of Psychology at Istanbul Bilgi University, the project investigates on possible approaches to extract discriminative features out of HTP sketch images and it searches for a meaningful combination, which will support therapists in their diagnostic assessments. After data pre-processing, image classification of clinical HTP data was conducted using the ResNet152 model and achieving a test accuracy of 66%. Furthermore, the experiment of the detection of the "pen pressure" feature was performed using Skeletonization and morphological image processing; however, due to a lack of ground truth, the performance of the proposed algorithm is not determined yet.
Robust position-based visual servoing of industrial robots using feedforward kinematic approach based on integral quasi-super twisting algorithm

This paper presents a novel robust kinematic control approach based on feedforward inverse kinematic compensation for real-time pose correction of vision-based industrial robots to enhance the trajectory tracking accuracy. The conventional methods entail the robust design of the dynamic controller to handle the uncertainties in the dynamical model, which is not applicable to most industrial robots since they operate based on their built-in controller designed by the manufacturer and usually are not accessible. The proposed method, however, is a robust kinematic controller capable of handling the uncertainties in both dynamic and kinematic models. To this end, first, the robot's pose is estimated by a nonphysical contact sensor (in this research, a photogrammetry sensor). Then it is fed to the kinematic controller for the real-time pose control task. The feedforward part of the proposed controller, which is the inverse kinematic function of the robot, is considered in the control system design to reach the highest possible accuracy, especially for trajectory tracking purposes. The proposed method utilizes a novel integral quasi-super twisting algorithm (IQSTA) as the compensator within the control loop to reach a finite-time convergence with very high precision and robust performance with minimal chattering. The stability analysis of the proposed method is presented. The experimental results on an industrial robot (Denso VP-6242) equipped with an ATEMEK photogrammetry sensor show the superiority of the proposed method over other state-of-the-art approaches.
Cloud systems with its security, privacy and trust claims to a sustainable solution

Cloud computing is a technology that enables the delivery of computing services over the internet. This service model has been rapidly adopted as it offers many advantages such as cost savings, scalability and accessibility, allowing users to access and manage information and applications through a network of remote servers. According to a report by International Data Corporation (IDC), the worldwide cloud computing market is forecasted to reach $732 billion by 2023. This paper will provide an overview of cloud computing technology, its architecture like NIST, security, privacy and trust (SPT) concerns, firsthand survey amongst about Sixty (60) cloud companies. Cloud Computing has various issues like user privacy, data theft, unauthenticated access and hackers attacks. These unsolved security issues of authentication privacy, data protection and data verification are the main hindrance to cloud adaptation for future developments, which is getting addressed to identify the adequacy and effectiveness of cloud security through qualitative retrospective casual comparative methods.
Filipino and Bisaya Speech Corpus and Baseline Acoustic Models for Healthcare Chatbot ASR

Philippine public schools often need an efficient and systematic way of assessing children’s physical wellness. With the lack of medical personnel in public schools compounded with the limited financial resources of children’s families, children are often barred from routine checkups. These problems motivated the authors to develop a healthcare chatbot for Filipino children. The chatbot employs an automatic speech recognition (ASR) for its speech-to-text module that offers an alternative way for children to interact with the healthcare chatbot. Currently, there are no readily available ASR systems for conversational Filipino speech of children specifically in the healthcare domain. In this paper, we describe the collection of a Filipino and Bisaya speech corpus for the purpose of developing the ASR module of a chatbot that can monitor the general physical wellness of children. We also present the results of initial experiments on baseline acoustic models for two different phoneme sets for Filipino and Bisaya speech, namely PS27 and PS35. The best-performing acoustic model for Filipino was obtained from PS35 which gave an average word error rate of 4.37%. The best-performing acoustic model for Bisaya was obtained from PS35 which gave an average word error rate of 7.16%.
A Novel Martingale Model for Human Activity Recognition using Robust Optimisation Techniques

The subject of human activity recognition is becoming eminent in the study of human movement patterns and disabilities. Many existing approaches are effective in identifying change points in physical activities; however, algorithm optimisation could potentially be applied to improve some of these models. In this paper, we proposed a method known as mean of the geometric moving average of the Martingale sequence that can detect changes in human activity recognition. Furthermore, the suggested method is optimised for enhanced performance using meta-heuristic optimisation techniques based on the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithms. Experimentation shows that the suggested method improves the performance of the previous Martingale method.
The Negative Effects of Recommender Systems on the Unsuspecting User

The average everyday technology user is often unaware of how recommender systems influence their actions and behavioural patterns. Companies operating through or with the help of these systems inject their agenda into their recommendations giving a biased opinion – usually for economic gain – instead of suggestions based solely on the user's behaviour. These 'nudges' are unethical practices. This paper will look at recommender systems, their various types, and the proven negative effects they can have on their users. The paper will also seek to examine three of the most popular companies in the world that incorporate recommender systems into their services: Netflix, YouTube, and Amazon to see if their systems also perpetuate biases. Lastly, trust-based recommender systems are suggested as a feasible countermeasure to all the negatives of recommender systems.
Preference Learning in Food Recommendation: the "Myfood" Case Study

This paper introduces MyFood, a software platform that utilizes semantic technologies and artificial intelligence to represent knowledge in the food domain and to provide smart services to its users. The platform leverages big data for large volume analysis, providing innovative, high-value services for all players in the food chain. MyFood’s design began with creating a comprehensive ontology covering food components, enabling restaurateurs and customers to consult a complete, detailed menu in compliance with the law. The platform also supports people searching for specific food services based on ingredients, verifying compatibility with ethical, religious, or food-related allergies or intolerances. MyFood’s knowledge graph allows for "intelligent" operations that increase its value for various users. This paper presents a sub-component of the MyFood platform that utilizes preference neural nets (PNN) to provide recommendations to end users based on their profile of needs related to the ingredients of the dishes. The PNN suggests the most suitable restaurant(s) for the user based on their needs. This paper provides an overview of the PNN-based recommendation system and its implementation within the MyFood platform. Overall, the MyFood platform’s preference learning component has the potential to enhance the user experience by providing tailored recommendations that match their preferences and dietary restrictions. We present insights into the early implementation and design of MyFood, highlighting its potential to transform the food industry through its cutting-edge technology and innovative services.
Shot Boundary Detection and Convolutional Neural Network for video Classification

The classification of videos is a crucial computer vision task. Convolutional Neural Networks (CNN) have become a popular choice for video classification due to their ability to learn hierarchical representations of the input data. Shot boundary detection is another technique that is often used in video classification to segment the video into shots, which are temporal segments that contain a continuous action or scene. In the long-term video of a television broadcast there are continuous scene changes, some of which better define the beginning or end of a program, an advertisement, a type of human action and much more, supporting information for classification made only on specific video segments. Shot boundary detection can be incorporated into CNN models to segment the video into shots, and then feeding each shot into the CNN-based model for classification of television programs and their content. In this paper the implementation of a shot boundary technique will be described together with a pre-trained CNN on ImageNet for the video classification of some types of programs and television contents according to a schedule provided by the Italian national communications agency, the presented framework succeeds in achieving accuracy levels of 96%.
A Deep Reinforcement Learning Framework for Training SARSA Agents (DQSARSA Agents) to Improve Consensus in Blockchain Networks

Peer-to-peer (P2P) network applications are experiencing rapid growth due to their many appealing features, such as decentralization, security, and efficiency. Blockchain technology is a specific type of P2P network that is gaining popularity due to its ability to create secure and temper-proof records of transactions. However, blockchain faces a significant challenge in terms of collective decision-making. In order to function effectively, blockchain networks require consensus among the distributed nodes within the network. This can be a difficult task, as it is often difficult to reach agreement among a large number of independent nodes. This paper proposes a framework aimed at facilitating the process of collective decision-making among distributed nodes in a blockchain network. The framework employs distributed smart agents called deep Q SARSA (DQSARSA) agents, which are trained using deep reinforcement learning (DRL) with State Action Reward State Action (SARSA) technique. Employing this effective method, the agents are able to learn how to interact with each other in order to reach consensus on a given decision. In order to thoroughly examine the effectiveness of the proposed framework, three models—DQSARSA-1, DQSARSA-2, and DQSARSA-3—were designed and subjected to rigorous evaluation. These models were tested using a diverse range of randomly generated datasets, ensuring a comprehensive assessment of their performance across various scenarios and conditions. DQSARSA-3 emerged as the most successful, achieving an overall accuracy of 85.14% in reaching consensus among the blockchain nodes. The proposed framework has the potential to significantly improve the efficiency and scalability of the blockchain network. By automating the process of collective decision-making, the framework can help to reduce the time and effort required to reach consensus. This can lead to faster and more efficient decision-making, which can benefit the blockchain network.
Denoising using a Hybrid Filter Comprised of GGIF, WLS, and 2D Bilateral Filtering

Denoising helps improve image quality and recover important information from noisy pictures. This work introduces a novel denoising method. GGIF, WLS, and 2D Bilateral Filtering are used in a hybrid filter. The hybrid filter shown eliminates noise while preserving the picture's characteristics, edges, and textures. Denoising begins with GGIF, which preserves edges and reduces artefacts. Then, WLS reduces amplified noise and smooths the image adaptively according to the local gradient. Last, 2D bilateral filtering reduces noise while keeping structural integrity and edge information. The hybrid filter is tested using simulated and real-world noisy images. The trials reveal that the recommended strategy outperforms current denoising approaches in noise reduction, edge retention, and visual quality. The hybrid filter may solve image processing, computer vision, and remote sensing problems. It uses GGIF, WLS, and 2D Bilateral Filtering to denoise well and improved perceptual fog density.
An Efficient Resampling Technique for Financial Statements Fraud Detection: A Comparative Study

Financial statement fraud detection is the process of identifying falsified financial statements. Traditional auditing methods are time-consuming, expensive, and subject to error. Therefore, adopting an efficient and robust machine learning mechanism is important. Unfortunately, the current data sources suffer from a severe class imbalance. The lack of sufficient fraudulent financial statement records inspires the use of various resampling techniques. This paper a) examines the efficiency of different resampling strategies to detect fraudulent financial statements while employing multi-layer feedforward neural networks, support vector machines, and naïve Bayes machine learning models, and b) investigates the superiority of using Raw Accounting Variables (RAVs) over financial ratios for financial statement fraud detection. A benchmark dataset of numerical financial variables (RAVs and financial ratios) is used as features for model evaluation. The fraud labels correspond to the Accounting and Auditing Enforcement Releases by the U.S. Securities and Exchange Commission (SEC). We analyze the performance of the models on 28 RAVs and 14 financial ratios suggested by accounting experts. Using the area under the receiver operating characteristic curve (AUC) as the performance metric, the synthetic minority oversampling technique (SMOTE), along with a three-layer feedforward neural network (AUC: 0.863), greatly outperformed the RUSBoost (AUC: 0.717) model.