Reviewerly/exHarmony huggingface dataset:
An optimized Yolov5s approach and its application to image detection problems

Traditional object detection algorithms cannot cope with motion blur and have poor classification accuracy for small cluster targets, which poses challenges to the development of image classification technology. In order to address these issues, this paper adopts a multi-strategy integrated optimization framework and optimizes the training speed and classification accuracy of the model based on the YOLOv5s network model. Firstly, the CBAM attention mechanism is introduced to mitigate motion blur. Then, the Focal-EIoU loss function is used to enhance the classification of low-accuracy objects. Next, the SiLU activation function is employed to enhance the model's representation capability, and the SGD optimizer is replaced with the Adam optimizer to improve training efficiency. Finally, the improved model underwent 500 rounds of training on a subset of the BDD100K dataset. The results show that the classification accuracy of the model, after multiple strategy optimizations, increased from 46.5% to 74.7%. The YOLOv5s model, optimized by multiple strategies, can effectively solve problems such as motion blur, thereby improving the accuracy and stability of the model.
Voice Analysis for Detecting Artificial Speech and Predicting Gender and Age: A Literature Survey

This article surveys the literature on voice analysis. The present invention relates to a method and system for analyzing voice data to detect artificial speech then demodulate it to original speech and predict the gender and age of the speaker if the input speech is not fake. The system includes a voice analysis module that processes the voice data and extracts features from the voice signals. The extracted features are then compared with pre-stored reference features to determine the authenticity of the voice signal and the gender and age of the speaker.
Adaptive Path Planning for Autonomous Vehicles in Complex Traffic Scenarios

Over the past decades, an Autonomous Vehicles (AVs) driving functions have obtained the more interests from both the industry and academia. The AVs needed a basic planner for all the practicable scenarios and the recent research develops named a planner through the integrated scenario interpretation. Nevertheless, it may remarkably enhance the planner difficulty indeed in some simple operations. Examining the safety of AVs is the significant challenge in relation to their distribution on the roads because of large number of achievable conditions happened in traffic. In this research, the adaptive path planning approach of AV established multi-light trained Reinforcement Learning (RL) is proposed, aimed to enhance the fuel cost as well as AV comfort. By utilizing a logical Deep Q-learning function, the training approach obtains the key setting data collected through the vehicles as inputs, eventually outcomes an acceleration that enhances a cumulative reward. The proposed method attains better results by using the performance metrices of average acceleration, distance and computational time values about 0.149, 0.98 and 0.49 respectively compared with Pertinent Boundary-based Unified Decision (PBUD), Adaptive Mode Predictive Control (MPC) and Dynamic Decoupling Lane-Changing (DDLC).
Comparative Study of Map Reduce Task Scheduling Optimization Techniques

As data processing capabilities improved, the flow of data increased significantly, necessitating the use of distributed systems and a new programming framework, MapReduce, to manage this massive amount of information. Any job requiring a large amount of data must be divided into subtasks for parallel execution. In order for all subtasks to be executed concurrently need to be scheduled parallelly on available set of virtual machines. This is not an easy task; it requires consideration of a number of critical parameters such as make-span, Execution cost, risk of execution, energy consumption and so on. The optimization of these parameters is critical for effective scheduling. The most effective hybrid scheduling techniques have been developed and implemented on a single platform with a diverse set of virtual machines. On a single platform, it compares all well-known and popular scheduling techniques. All experimental values for various parameters of interest for all best-ever scheduling techniques will be recorded for analysis, and results representing parameter-wise best-available scheduling techniques will be presented. This comparative study aids in the direction of future research and serves as a guide for progress from the current state of the research.
Seagrass Blurred Image Enhancement and Detection using YOLO and CLAHE Algorithms Performance Comparison

Underwater ecosystems, comprising seagrass, is one of the integral components of marine biodiversity, requiring meticulous observation and analysis to ensure their preservation and ecological balance. However, the complex and challenging visual conditions in underwater environments demand innovative approaches to object recognition and detection. This research investigates the efficacy of Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the You Only Look Once (YOLO) algorithm in enhancing object recognition within blurred fluorescence images of underwater ecosystems. The study specifically focuses on seagrass as this organism offers vital insights into marine habitat health. By conducting a comparative analysis of YOLO models and YOLO with CLAHE, this research aimed to identify the most effective and accurate methodology for detecting and recognizing underwater organisms in blurred fluorescence images. These insights contributed significantly to marine ecology research and facilitated practical applications in environmental monitoring and conservation efforts. The comparative analysis of these results sheds light on the impact of using blurred sample images versus authentic samples for training the YOLO-V3 model. In this analysis, YOLO-V3 was found to be the preferred algorithm for the task at hand, although it occasionally fell short in detecting all the required objects in test images. In contrast, YOLO-V5 exhibited impressive proficiency in identifying nearly all blurred images. Additionally, applying the CLAHE algorithm to CLAHE-equalized images had the potential to substantially boost the accuracy of YOLO-V5, leading to improvements of 8% to 10% in the results.
Research on VR Virtual Intelligent English Teaching System Based on Computer Big Data Technology

This paper proposes a method of English reading distance education using virtual reality technology. The hardware of the system is composed of HTC Vive head-mounted VR equipment and high-performance computer architecture. A multimedia video processing method based on multi-level network coding is proposed. A virtual experiment platform based on error expression is constructed. An interactive English education method based on conflict detection is proposed. Finally, this paper tests on 14 English corpora. The average response time for reading resources is 25.12s. The system in this article has been proven to have excellent performance through simulation.
mTADS: Multivariate Time Series Anomaly Detection Benchmark Suites

Detecting anomalous events in time series data, ranging from manufacturing processes to health care monitoring, is important. The problem of uncertainty in real-world datasets and its anomalies makes it challenging to validate and compare results between algorithms. With this in mind, we present two benchmark suites to fill gaps in today’s landscape of datasets for anomaly detection in multivariate time series data. Here, one suite focuses only on fully synthetic sequences to provide a playground for testing algorithms with complete knowledge of the sequences. The second suite bridges between fully synthetic and real-world sequences. It provides a few extensive sequences with close-to-reality complexity but synthetic injected anomalies. The paper provides a detailed overview of the suites content and complexities. It further includes a concise overview and showcases the strengths and weaknesses of 34 algorithms in the evaluation. The benchmark suites highlight issues regarding algorithms and metrics and should support new research directions.
Optimization of Dangerous Goods Stowage: A Hybrid Approach between Faster R-CNN and Coronavirus Metaheuristic

Efficient stowage planning is essential to enhance operational performance in container terminals. Our research focuses on the stowage planning of dangerous goods containers (SPDGC), a variant of the problem incorporating various real-world constraints inspired by the literature. Given the complexity of this problem, we propose a hybrid approach that combines the use of the advanced detection algorithm Faster R-CNN to classify and group containers based on their types. This classification is then utilized as the initial state for the Coronavirus metaheuristic algorithm, aiming to reorganize bays inside the container ship and determine the optimal location for each container. Our paper presents the computational results and comparative studies, demonstrating the effectiveness of our approach in addressing the complex container stowage problem.
VHGN: Cooperative Multiagent Reinforcement Learning with Historical Information Graph

In many real-world settings, an agent needs to collaborate with other agents to learn the optimal strategy. Recently, the combination of graph attention mechanism and multiagent reinforcement learning algorithms has attracted widespread attention. However, many available pieces of information were ignored in previous efforts. In general, historical information locally stored by agents can be used to learn from past experiences, and feedback state information from environment also helps agents learn the optimal strategy more quickly. Therefore, we propose a new value decomposition with historical information graph attention networks (VHGN), which uses local historical information of agents as node features of graph and utilizes additional state information during learning process. Furthermore, it is theoretically proven that the proposed value decomposition equation satisfies the decomposable condition during centralized training. Finally, we evaluate the performance of our method on the StarCraft Multiagent Challenge benchmark (SMAC). The experimental results show that our method outperforms state-of-the-art value-based multiagent reinforcement learning algorithms and exhibits better convergence.
Lunar Lander Using Reinforcement Learning Algorithm

The harsh and dynamic lunar surface environment, developing autonomous lunar landing has proven to be a difficult problem for space agencies. Reinforcement learning (RL) has recently come to light as a viable solution to this issue. In this research, In this paper we proposed that an RL-based lunar lander control system that enables the lunar lander to travel and land on the moon on its own. The proposed paper comprises of a deep neural network that has been taught the best control strategy using RL algorithms. The lander may learn from its mistakes and gradually improve through the RL algorithm. The results of the simulations demonstrate that, even in the presence of uncertainties and disturbances, the proposed system can effectively lead the lunar lander to a secure landing on the lunar surface. The proposed method offers fresh options for space research and shows the potential of RL in creating autonomous control systems for lunar landers.
Automatic meeting minutes generation using Natural Language processing

In recent years, online meetings have become a significant component of communication in a variety of organizations. To optimize the generation of meeting minutes, we propose a novel approach based on NLP in this work. A model that converts speech to text, summarizes meetings has been proposed in the existing work. The goal of this proposed work is to automate speech recognition, extract key points, summarize the meeting, and extract action items. The approach begins with extracting text from a meeting that is in transcript or audio format. The proposed work utilizes summarization models like BART (Bidirectional Auto-Regressive Transformers), T5 (Text-to-Text Transformer Model), and a Summarization Pipeline. Summarization pipeline model performed well for generating meeting summary. Finally, the proposed work generates concise meeting minutes for the meeting which can be in the form of transcript or Audio. ROUGE Score and Human evaluation will be used to evaluate these models.
A Simple Fall Detection Scheme for Early Detection of Falls in Elderly People

Human fall is considered to be a prominent risk especially among the elderly population and for disabled people. Fall detection systems often rely on video surveillance, capturing frames and utilizing various algorithms to identify instances of fall. The continued development of such systems is one of the goals of AI technology, which aims to improve quality of life and reduce fall risks. The field of computer vision still faces significant difficulties with the estimate of human postures. Recognizing the poses of multiple individuals in real-world scenarios presents greater challenges compared to recognizing the pose of a single person in an image. An automated camera-based system that can recognize abnormal human postures is presented in this work. To identify people in frames and extract their body key points, a pre-trained pose estimation algorithms like PoseNet and OpenPose is used. The human body geometry that was recorded at various frames of the video series is used in this paper's proposal of a fall detection method. To generate the active energy image, the average of the difference frames obtained from the silhouette sequence is calculated. The resulting image is then divided into multiple segments. For each section, affine moment invariants as gait features are computed. This paper will provide a detailed description of the fall detection technique. Results show an accuracy of more than 90%. On the test set, this method had an F1 score of 92.5%, accuracy of 92.5%, precision of 91.2%, recall of 93.8%.
Comparing ML OCR Engines on Texts from 19<sup>th</sup> Century Written in the Romanian Transitional Script

Many 19 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century Romanian texts are written in the Transitional Script (RTS) combining Cyrillic and Latin characters in variable proportions depending on specific factors (period, region, publishing house, literary trends, and personal beliefs). Thousands of heritage documents in numerous libraries across Romania have yet to be digitized. Reading texts written in RTS is challenging for modern scholars, today’s students and OCR engines which due to specific particularities have difficulties in processing them. Transfer learning can be used to train state-of-the-art tools such as Tesseract or Transkribus, but their efficiency varies according to the underlying deep neural network, the training parameters, and the particularities of the scanned images and used scripts. In this paper, we compare the performance of two architectures on 180 OCR models for 5 scenarios (best CER <4.6% for Tesseract) and discuss OCR challenges on RTS texts.
Analysis of Spammer Reporting Techniques on Online Social Networks

Consumers' real-world experiences affected by interactions on social networks play a dominant role. With the help of popular social networks, useless information was spread by spammers, with this information real users' time and patience are lost. Two of the most important problems are spam detection and the identification of fake accounts. In this proposed work an analysis of current methods for detecting and preventing Twitter spam was carried out. Twitter uses several techniques for detecting spam, such as classifying Technological posts as spam, URLs as spam, Hot subjects as spam, and Duplicate users. Many other criteria, such as those related to end-users, construction, schematics, and even fleeting aspects, are used to evaluate and contrast various technologies. In this work the above-mentioned two important problems were implemented in determining fake content by considering different aspects and also analysis performed to provide a complete overview of Twitter spam detection and analysis.
Time and Space Complexity of Deterministic and Nondeterministic Decision Trees: Local Approach

Extensive research has been conducted on rough set theory, specifically focusing on the study of decision trees (DTs) and decision rule systems (DRSs). This theory addresses several important questions, including the relationship between DTs and DRSs, the tradeoff between time and space for DTs, and the tradeoff for DRSs.The objective of this paper is to investigate infinite binary information systems (ISs), each consisting of an infinite set (universe) and an infinite set of two-valued functions (features) defined on the universe. We examine the concept of a task within an IS, which is described by a finite number of features and a mapping that assigns a decision to each combination of feature values. Our analysis focuses on deterministic and nondeterministic DTs (DDTs and NDTs) as algorithms for task-solving, with the restriction that only features from the task description are used.NDTs serve as representations of DRSs and can sometimes have lower space complexity compared to the original DRSs. We study the time and space complexity of DTs, specifically examining the depth and the number of nodes in these trees. The obtained results allow us to classify the set of all ISs into three families, enabling the identification of nontrivial relationships between DDTs and DRSs represented by NDTs. For each family, we investigate the tradeoff between time and space for both DDTs and NDTs.
Learning with Variational Objectives on Measures

The theory of machine learning has predominantly focused on variational objectives expressed on functions. Yet, the advent of big data has exposed limitations in these formulations: functions struggle to capture uncertainty arising from data veracity issues; and the assumption of data independence often conflicts with data variety. In this vision paper, we propose a shift towards measures as the basis for variational objectives, highlighting their advantages to quantify uncertainty, to generalize out of training distribution, and to deal with scarce annotations. We aim to inspire and stimulate further research in adapting conventional machine learning algorithms to optimize these new objectives, without compromising statistical efficiency.
Artificial Intelligence for Spectral Analysis: Challenges and Opportunities

As a well-developed technology, spectral analysis is intensively utilized in enormous application domains. Despite the variety of spectrometry and spectrometers, classic approaches to spectral analysis typically exploit complex physical modeling to solve the tasks. Although such procedures are powerful and have been widely acknowledged in the academic as well as industry world, they still face many limitations. To address these issues, artificial intelligence (AI)-based approaches have been proposed in the last decades and proven to be successful. Previous work in this emerging interdisciplinary topic enables a fast, accurate and efficient spectral analysis compared to the classic approaches. However, in the meantime, new challenges arise with the introduction of AI methods due to its model-free, data-driven and blackbox behavior. Firstly, as spectral analysis is a highly physics-related topic, there are scenarios where the task is by definition unresolvable but the AI models still try to make predictions, which leads to undesired behavior. Besides, since AI approaches lack interpretability and reliability by nature, in real-world (especially commercial and medical) applications, abnormal results are inevitable and will potentially result in huge losses. In addition, data privacy issue also becomes crucial for cloud-based AI solutions. Thus, in this paper, we thoroughly explain these challenges that current state-of-the-art (SOTA) faces and discuss opportunities for possible outlets in future work.
Improved Algorithm for Image Classification Datasets Using Federated Transfer Learning

The advancement of machine learning is significantly influenced by the accessibility of vast data. However, in reality, data is distributed across various entities and is subject to various legal and practical constraints. Therefore, it is unrealistic to merge these dispersed datasets into a single large dataset. In order to address this challenge, a novel framework called Federated Transfer Learning (FTL) has been introduced. FTL allows knowledge sharing without jeopardizing user privacy and enables the transfer of complementary knowledge across different domains. This capability enables the use of rich labels from the source domain to construct an effective model tailored to the target domain’s specific needs. In this paper, we propose an improved algorithm, named FTL-FIC, based on FTL for image classification datasets. It leverages federated learning to aggregate data and utilizes transfer learning to construct an shared learning model, thereby adapting to the characteristics of the target dataset. Experimental results demonstrate the algorithm’s notable improvement in the classification accuracy of the target dataset (increased by 8.17%), the reduced number of convergence epochs, and its ability to maintain good generalization on the source dataset. Furthermore, FTL-FIC exhibits versatility and scalability, indicating its potential application to other types of datasets.
Formal Modeling and Verification of Validator Voluntarily Exit in Ethereum 2.0 Beacon Chain

The Ethereum 2.0 Beacon chain is a big step toward improving the security, scalability, and decentralization. It is based on the Proof of Stake (PoS) consensus protocol that maintains the validator status, attestation details, and many more. The term validator is introduced in the PoS consensus protocol and its work is to propose blocks and vote for them by becoming a committee member to add those blocks to the blockchain. We address the problem related to validator’s voluntary exit to vanish its active status and do its formal verification using the SPIN Model checker. There does not exist much research in this area as it was introduced recently in 2020. Further, this is the first work on formal verification of the beacon chain using the SPIN model checker. In this work, we formally specify the validator exit process of the Ethereum 2.0 Beacon Chain using the formal specification language, i.e., Process or Protocol Meta Language (PROMELA). We create a program graph for this process through the SPIN Model checker and describe safety and liveness properties using Linear temporal logic (LTL) in the form of a formula. The formal verification is performed to ensure correctness by giving the program graph and LTL formulas as input to the SPIN model checker whether the properties are satisfied with the program graph
Securing Flutter Applications: A Comprehensive Study

When using Flutter, creating multi-platform native applications is economical. Because Flutter apps are instantly converted into native code, developers think security is not a major concern. Flutter apps, like many other common mobile apps, are susceptible to security issues. When utilised in hostile environments, applications created using Flutter are vulnerable to manipulation and dynamic attacks. Hackers skilled in reverse engineering binaries can easily take advantage of security holes in Flutter apps. Comprehensive security mechanisms, such as runtime detections to discover changes or manipulation, are needed to protect Flutter apps. Even though Flutter has grown to be a popular framework among developers, creating apps without the right security precautions could have disastrous implications on the company. In this article, we will examine security practices that are currently in use or that may be applied in Flutter, as well as tools and approaches for protecting our apps. We will also study future upgrades to Flutter application security.