Reviewerly/exHarmony huggingface dataset:
A Novel Density-Based KNN in Pattern Recognition

The k-nearest neighbor learning algorithm is indeed one of the most commonly used supervised algorithms in machine learning. KNN is a lazy learner, and one of the most important similarity features used in this algorithm is the Euclidean distance. Although, in this paper, to achieve high accuracy, other criteria besides the Euclidean distance are considered for classification as well. In the proposed algorithm (DBKNN), first, a Gaussian function (GF) is applied to each training data in the dimensional space of the dataset. After adding all the applied Gaussians, a weight is assigned to each training data. This weight shows that the data belongs to the related class, and is used in calculating and influencing the Euclidean distance. Several simulations have been performed on the standard datasets, and the results of the simulation have confirmed proposed KNN accuracy related to the conventional KNN. It also performs very well in noisy conditions and outliers. The mathematical analysis of this algorithm is furnished to validate our assertion.
Link Prediction for Recommendation based on Complex Representation of Items Similarities

Recommender systems have different goals, including coverage and hit rate. A graph-based recommender system that includes users' ratings of items can be represented as a bipartite graph, where users and items are nodes and ratings are treated as edges. The recommendation task in bipartite recommender systems can be modeled as a subproblem of link prediction. Modified link prediction methods are used in related research to distinguish between fundamental relational dichotomies like versus dislike, and similar versus dissimilar. However, similarities between users and items should be noticed. In this paper, we present a novel method that focuses on developing an enhanced adjacency matrix designed to bolster link prediction and recommendation accuracy. This innovative approach strengthens our ability to capture intricate user behaviors. The results over a real-world dataset indicate the effectiveness of the proposed approach.
SGFL: A Federated Learning Approach for Non-IID Data Using Semi-Supervised DCGAN

Federated learning (FL) has been introduced as a method of cooperative machine learning models on decentralized machines. However, the assumption of independently and identically distributed data (IID) is often not true in real scenarios because the data distribution can vary significantly from device to device. This paper introduces SGFL, a novel Federated Learning approach designed specifically to address the problems posed by non-IID data distributions. SGFL employs the power of semi-supervised learning and deep convolutional generative adversarial networks (DCGANs) to enhance the federated learning process. By leveraging the redundant data received from each device, SGFL utilizes a semi-supervised DCGAN to fine-tune a global model. This approach enables better modeling of non-IID data while preserving privacy and security through local training. We evaluate the performance of SGFL on non-IID data, and the results show its effectiveness in achieving higher accuracy compared to traditional federated learning methods. The source codes of the methods presented in the paper is available at https://github.com/apaliray03/SGFL.
Explainable Error Detection Method for Structured Data using HoloDetect framework

In the past few years, researchers have made significant progress in achieving precise outcomes using error detection tools based on deep learning models. However, many of these models are considered black boxes as they offer limited insights into the reasons and context behind data errors. If we can present the results of these models in a more understandable manner, professionals could utilize them to verify and adjust the model, comprehend the causes of errors, and rectify them accordingly. In this research, we propose a method to generate explanations for the results of HoloDetect framework, which is a few-shot learning framework designed for error detection. We subsequently evaluate the effectiveness of our proposed method using computer-centeric and human-centeric methods.
Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm

Neural Architecture Search (NAS) methods autonomously discover high-accuracy neural network architectures, outperforming manually crafted ones. However, The NAS methods require high computational costs due to the high dimension search space and the need to train multiple candidate solutions. This paper introduces LCoDeepNEAT, an instantiation of Lamarckian genetic algorithms, which extends the foundational principles of the CoDeepNEAT framework. LCoDeepNEAT co-evolves CNN architectures and their respective final layer weights. The evaluation process of LCoDeepNEAT entails a single epoch of SGD, followed by the transference of the acquired final layer weights to the genetic representation of the network. In addition, it expedites the process of evolving by imposing restrictions on the architecture search space, specifically targeting architectures comprising just two fully connected layers for classification. Our method yields a notable improvement in the classification accuracy of candidate solutions throughout the evolutionary process, ranging from 2% to 5.6%. This outcome underscores the efficacy and effectiveness of integrating gradient information and evolving the last layer of candidate solutions within LCoDeepNEAT. LCoDeepNEAT is assessed across six standard image classification datasets and benchmarked against eight leading NAS methods. Results demonstrate LCoDeepNEAT’s ability to swiftly discover competitive CNN architectures with fewer parameters, conserving computational resources, and achieving superior classification accuracy compared to other approaches.
FarCQA: A Farsi Community Dataset for Question Classification and Answer Selection

Question Answering Systems (QASs) have become increasingly important due to the need for accurate and concise answers that traditional search engines often struggle to provide. However, the development of QASs for the Persian language has been limited due to its complexity, fewer available resources, and tools compared to other languages. One crucial component of a QAS is question classification, which plays an effective role in retrieving correct answers. In this paper, we introduce FarCQA, the first open domain Persian community dataset for question classification and answer selection tasks, collected from an online forum. This dataset is tagged with 9 types of questions and includes both formal and informal language. In addition, we propose question classification and answer selection models using transformer based models and combining word embedding and deep learning techniques. Our approach demonstrates a notable accuracy on the test set, surpassing state-of-the-art methods.
Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach

The primary objective of medicine and technology is to provide services capable of identifying and treating patients based on their particular conditions. The accuracy of disease diagnosis is of paramount importance to this endeavor. Cancer is a major cause of death on a global scale, with prognosis, prevention, and prompt intervention offering the possibility of complete patient remission. In this Python-based experiment, we investigate the viability of transfer learning for lymph node diagnosis in histopathology images, which are used to fine-tune a DenseNet121-based pre-trained model. The results indicate that fine-tuning the DenseNet121 model is more effective than training the model from start. Due to variations in the network’s initial weight distribution, the network’s average accuracy is 96%. Following the implementation and completion of ten training epochs, the average overall accuracy reaches a maximum of 98%, with less than 10% error accuracy.
Machine Learning Hardware Implementation of Handwritten Digit Inference using Arduino and Ternary Output Binary Neural Network

In this paper, we analyze binary neural network (BNN) and ternary output BNN (ToBNN) from a software perspective, and introduce tiny machine learning (TinyML) hardware implementation of handwritten digit inference. Both BNN and ToBNN achieve a reduction of approximately 70% in memory usage for weight storage by using binary values. Inference energy consumption was also reduced by approximately 32%. ToBNN achieves 90.24% inference accuracy for MNIST dataset, while conventional BNN shows 50.25% accuracy. To implement TinyML hardware, Arduino Uno, Portenta H7, TFT touch LCD shield, and 16×2 LCD were used. The hardware implementation results show successful inference for handwritten digits.
Automatic Segmentation Labeling for Autonomous Driving Datasets

In this paper, we propose an algorithm that automatically labels datasets for the development and testing of autonomous vehicles. Traditionally, it is commonly used hand-labeled datasets, which is costly and time-consuming. In this work, we address these problems by automatically labeling based on YOLOPv2 model and validate it on an autonomous driving algorithm.
Zero Trust Security Strategy for Collaboration Systems

Nowadays, society is characterized by a highly mobile population and growing needs in terms of sharing large volumes of sensitive data within companies and collaborating with partner or competitor organizations. As a result, the computer systems of these organizations systems are exposed to various types of increasingly sophisticated threats and cyber-attacks. Traditional perimeter-based infrastructure security strategies are no longer sufficient. The Zero Trust security model is a cybersecurity approach that considers all entities in an infrastructure as potentially vulnerable at any time and in any place. This strategy is a response to the problem of securing heterogeneous, complex and decentralized systems. However, its implementation varies according to the context of the system, and requires organizational and cultural changes. In this article, we propose a Zero Trust security strategy in the context of collaboration between organizations. The model presents a hierarchical architecture to secure exchanges within and between organizations. It provides a decentralized management system for user and organization identities through decentralized identifiers and a blockchain registry. In addition, the approach suggests a trust inference technique to assess trust between organizations. Furthermore, the proposed framework includes a mechanism for specifying and monitoring collaboration contracts and defining access policy rules. A case study on the deployment of our strategy in a community cloud is also presented.
Automatic First Arrival Picking for Seismic Data using Kalman Filter

The first arrival time of seismic waves is a crucial parameter for seismic data analysis, which is used to determine the depth and location of subsurface structures. However, the estimation of first arrival time is often challenging due to the presence of noise and uncertainties in the data. In this study, we propose a novel approach that utilizes the Kalman filter with generalized likelihood ratio (GLR) to estimate the first arrival time in seismic data. First, a discrete time linear system with unknown amplitudes changes occurring at unknown time instants is used to reformulate the convolutional model. Then, to provide residual signals, we apply a Kalman filter based on the no change hypothesis to the linear system. Finally, to estimate the first arrival time, a generalized likelihood ratio (GLR)-based change detection technique is utilized. Using the simulated data, we verified the performance of the proposed approach. Overall, this study presents a promising approach for improving the accuracy of first arrival picking in the presence of different noise levels.
End-of-Sentence Token Modeling for Streaming Conformer-Based Korean Children’s Speech Recognition Applied to a Social Robot

This paper proposes an end-of-sentence (EOS) token modeling approach for streaming conformer-based Korean children’s speech recognition applied to a social robot. The baseline system for speech recognition is a streaming conformer-based speech recognition model with the joint connectionist temporal classification attention-based encoder-decoder architecture, and it is trained with the AIHub Child•Infant Free-Talk dataset. However, it is difficult to determine sentence boundaries in children’s speech because the speaking style of children is somewhat different from that of adults. Thus, the proposed approach is to insert EOS tokens into each sentence of a large children’s dataset to detect sentence boundaries. Finally, we evaluated the performance of the proposed approach on a dataset collected under scenarios of a voice interaction between children and a social robot. Consequently, the proposed EOS token modeling decreased character error rate by 1.7%, compared to the baseline.
Deep Reinforcement Learning for Autonomous Navigation on Duckietown Platform: Evaluation of Adversarial Robustness

Self-driving cars have gained widespread attention in recent years due to their potential to revolutionize the transportation industry. However, their success critically depends on the ability of reinforcement learning (RL) algorithms to navigate complex environments safely. In this paper, we investigate the potential security risks associated with end-to-end deep RL (DRL) systems in autonomous driving environments that rely on visual input for vehicle control, using the open-source Duckietown platform for robotics and self-driving vehicles. We demonstrate that current DRL algorithms are inherently susceptible to attacks by designing a general state adversarial perturbation and a reward tampering approach. Our strategy involves evaluating how attacks can manipulate the agent's decision-making process and using this understanding to create a corrupted environment that can lead the agent towards low-performing policies. We introduce our state perturbation method, accompanied by empirical analysis and extensive evaluation, and then demonstrate a targeted attack using reward tampering that leads the agent to catastrophic situations. Our experiments show that our attacks are effective in poisoning the learning of the agent when using the gradient-based Proximal Policy Optimization algorithm within the Duckietown environment. The results of this study are of interest to researchers and practitioners working in the field of autonomous driving, DRL, and computer security, and they can help inform the development of safer and more reliable autonomous driving systems.
Victim-related Assumptions Underlying Plea-based Sentence Reductions: A Communicative and Experiential Framework

Common law sentencing regimes rely on a series of assumptions around guilty pleas to justify sentence reduction. Several of these assumptions relate to the beneficial effects on victims suggesting that guilty pleas have a beneficial impact on victims, by either reducing process-related harms or contributing to their welfare. Others also focus on these effects on victims but are framed within a desert-based logic, suggesting that the guilty plea diminishes the offender’s level of blame since it is tied to remorse and to a desire to diminish the harms suffered by and increasing their welfare.Following an analysis of case law, this chapter argues that victim-related assumptions are often readily made without careful consideration or engagement with the underlying sentencing rationales and individualised contexts. This risks affecting the process of censure and its dialectical value by blurring the underlying rationales and communicating inaccurate statements to the polity.Building on a communicative and relational theory of punishment, the chapter proposes a dynamic and principled understanding of guilty pleas by ensuring that the assumed grounds for mitigation are (1) rooted within clear and principled sentencing rationales and (2) understood and weighed in relation to contextual grounds that justify pleas rather than formulated in abstract and pre-determined assumptions.
Performance Analysis of Credit Card Fraud Analysis and Detection Machine Learning Algorithms

Credit card fraud has become increasingly common despite advancements in credit card technology aimed at enhancing security. Various forms of fraud, such as stolen or lost cards, as well as hacking into digital payment methods like Apple Pay and Google Pay, contribute to this issue. As a result, it is crucial to detect and prevent credit card fraud, particularly from the perspective of credit card issuers. Machine Learning offers a promising approach to address this challenge, specifically through the application of classification algorithms. These algorithms can analyze a set of variables or a single variable and assign it to a particular class, making them suitable for identifying fraudulent and non-fraudulent credit card transactions. This research paper presents the outcomes of implementing multiple machine learning algorithms to classify credit card transactions as either fraudulent or non-fraudulent. The dataset used in this study comprises credit card transactions made by European cardholders during a two-day period in September 2013. We start with analysing the data of the transactions, building the classification models for detecting fraud, comparing the results of the models built, comparing the performance of the best model with the models researched previously and finally choosing the best model for the fraud detection. In this research paper, we used various classification algorithms and to improve the accuracy of the models, we had used three class balancing techniques which are Random Oversampling, SMOTE and ADASYN. We have found that Random Forest model with ADASYN class imbalancing technique is the best model for fraud detection as it has and ROC-AUC score of 0.99 on the test dataset.
Congestion Monitoring and Exposure of 5G Network for XR service

XR services are being gradually introduced and it is expected to be a significant application in the future. XR service is a kind of time critical service and network performance fluctuation may degrade user experience. This paper discusses congestion monitoring and exposure of 5G network for fast codec/rate adaptation of XR service. We employ Explicit Congestion Notification (ECN) in 5G network. Our proposal is direct echo back of congestion information to the sender instead of waiting until ECN mark reaches to the receiver and the receiver echoes congestion information back to the sender. This direct echo back will enable fast adaptation of codec/rate in XR server.
ChatGPT, Let Us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.
Multimedia Fingerprinting Codes Resistant to Linear Attacks and Adversarial Noise

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes.
Digital Forensic Analysis of AGPTEK Smartwatch Application on Android OS

As wearable IoT devices become more popular, the likelihood for digital forensic investigators and analysts to encounter them in the field increases. Smartwatches are one of the most popular wearable IoT devices today, and many brands and devices have yet to be analyzed. Analysis of such devices plays an integral role in criminal investigations, as data collected by these devices may be used as inculpatory or exculpatory evidence. This paper provides a forensic analysis of the AGPTEK smartwatch application (FitCloudPro) on Android OS for both a non-rooted and rooted smartphone. The smartwatch was worn for a data population period while paired with the smartphone. The phone was then imaged using Magnet AXIOM Process and analyzed using Magnet AXIOM Examine. Directories, files, and databases of interest are documented. The smartwatch application creates one major database containing user and activity data (appDatabase). The logs for the application contain an abundant amount of descriptive data but only are retained for approximately three days.
Deep Neural Network Based Automatic Litter Detection in Desert Areas Using Unmanned Aerial Vehicle Imagery

The United Arab Emirates (UAE) values its relationship with the desert, considering it a crucial part of its heritage and culture. However, the desert faces environmental challenges due to the improper disposal of garbage by visitors and the dumping of waste, as some perceive the desert as an empty wasteland. The rise in tourism exacerbates the problem, as litter negatively impacts the desert's ecology, wildlife, and natural habitats. Traditional litter collection methods involving human patrols are inadequate for the vast desert terrain. Drones equipped with high-resolution cameras offer a potential solution by conducting aerial surveys quickly and efficiently. However, the manual review of drone footage to detect litter is time-consuming. This paper explores the use of deep neural network architectures, such as Faster R-CNN, SSD, and YOLO, to develop litter detection models. These models focus on distinguishing litter from other man-made objects. The training dataset consists of thousands of samples, and the models are evaluated based on their performance in detecting and locating litter in drone images captured at different altitudes and environmental conditions. The evaluation includes objective and subjective analyses. The research aims to alleviate the practical challenges of litter detection in the desert by automating the process through computer vision-based object detection methods.
Adaptive Object Detection: Balancing Accuracy and Inference Time

Object recognition time varies based on object recognizability, from milliseconds for easy-to-recognize objects to seconds for challenging ones. Existing object detection models are constrained by fixed time limits, trading off accuracy and speed. This paper introduces novel methods that leverage mean confidence values for variable-time object detection, aiming to balance accuracy and speed. Our proposed method offers fast and accurate modes, allowing efficient initial detection while enabling accurate re-detection for low-confidence objects. The fast mode is used for easily recognizable images to save time, while accurate mode enhances accuracy for less recognizable images at the cost of utilizing more time. Using these modes together, we are able to utilize resources efficiently by saving inference time required for real-time tasks and maintaining the accuracy.