Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking False Positives with Class Activation Maps: Can we define false positives without ground-truths?

In this study, we have used Class Activation Mapping (CAM) technique to improve the accuracy of our Gloves classification model. By extracting the CAM and computing its areas, we have created a mechanism that effectively reduces false positives. We have trained the ResNet18 model on 2000 images of hands with and without gloves, achieving 98.65% accuracy. We have used the last convolutional layer of the model to extract the CAM outputs using the Gradient-CAM technique. Then, we have taken the extracted areas into account to decide whether a hand exists in the given image. Our results show that this approach is effective in reducing false positive detections.
Intelligent Recognition of Traffic Safety Facilities Using DINO Algorithm in Deep Learning

In an era of rapid technological advancement, the deep integration of computer technology with various fields, including traffic engineering, has become increasingly profound. Traditional methods of facility inspection and management suffer from inefficiencies and incomplete information, challenges that are effectively addressed by the intelligent and automated capabilities of computer technology. This pioneering study employs the DINO deep learning algorithm, focusing on comprehensive facility inspection and proposing an efficient intelligent recognition model. The model achieves an outstanding mean Average Precision (mAP) of 80.6%. Simultaneously, the article compares the training results of DINO with its predecessors, DN-DETR and Deformable DETR, highlighting the significant performance advantage of DINO. In comparison to previous research, this study places greater emphasis on the identification of various types of traffic safety facilities, further significantly enhancing the efficiency of traffic facility management and road safety.
Anomalous Sound Detection System in Manufacturing Industry Using Unsupervised Learning

In the manufacturing industry, skilled employees have conventionally judged abnormalities in products and machines by listening to the operating sounds of the machines. However, there are problems with the human ear, including human error such as missing sounds and the need for skill in distinguishing between sounds. By automating these human tasks with anomalous sound detection technology, there is a need for stable inspections and inspections that do not rely on employees. In the manufacturing industry, the occurrence of abnormal samples is limited to a small number. Therefore, it is difficult to construct an AI that learns from normal and abnormal samples. Therefore, unsupervised learning for anomalous sound detection is being considered. In this study, an abnormal sound detection system that detects abnormalities by using Multi-Layer Feature Sparse Cording (MLF-SC), which is an abnormality detection method, is investigated. The discrimination accuracy exceeds 80%. We investigated the change in accuracy depending on the number of training data, and found that good results were obtained with a small amount of training data. In addition, the discrimination accuracy of VGGNet used in MLF-SC was investigated, and it was confirmed that VGG19BN produced the highest accuracy.
Gender Recognition using Central Difference Convolution in AlexNet with Haar Cascades

Gender classification is a crucial aspect of face recognition systems in computer vision today. However, accurately determining gender from facial images can be challenging, particularly when dealing with diverse and uncontrolled image datasets. The paper introduces a modified AlexNet Network [1] which has Central Difference Convolution (CDC) [2] layers instead of convolution layers. The approach trains these networks using the CASIA-WebFace [3] dataset and evaluates their performance on labeled faces in the wild (LFW) [4] dataset and CasiaWebface dataset. The dataset is also cascaded using Haar cascading method and the neural model is trained and tested on it. Cascaded CasiaWebface(5000) dataset got the highest testing accuracy of 96.2%.
Enhancing Anomaly Classification Over Log Files through Topic Modeling and Ensemble Methods

Log files play a vital role in monitoring system changes, and their usage is rapidly increasing in cloud computing. To effectively address this challenge, text processing is an essential step in extracting information from unstructured text data, specifically log files, and transforming it into identifiable patterns using specific methods. Given the brevity of most log file text, this study focuses on the application of short text topic modeling and ensemble methods to classify log files andextract meaningful insights. The findings demonstrate that anomaly detection using short-text topic modeling and ensemble methods surpasses the benchmark method of Latent Dirichlet Allocation (LDA) topic modeling. Notably, the classification approach utilizing GSDMM,in combination with experiments involving XGBoost, achieves the highest performance when compared to other ensemble methods such as Random Forest, Gradient Boosting, andAdaBoost. To further optimize the performance of the XGBoost method in anomaly detection classification, hyperparameter tuning is conducted using Optuna. This approach effectively identifies the most optimal hyperparameters for XGBoost, leading to enhanced performance. Overall, this research illustrates that the utilization of short text topic modeling and ensemble methods, along with hyperparameter optimization, significantly improves the accuracy and effectiveness of anomaly detection in log file classification.
AI Based Fish Farm Management System Using Internet of Under Water Things (IoUT)

A large population of word dependent on fisheries product to fulfill need of animal protein. The increasing population will require more fish food to fulfill their requirements. In this paper a Robotic fish is presented for fish farm management to increase fish production in fish farms and to provide ease of life to fish farmers. Most of the studies suggested the fish production can be increased in a fish farm or pond if characteristics of water are managed. This robotic fish will be the combination of artificial intelligent system and Internet of underwater things (IoUT). Multiple sensors will be used to measure water quality chemical properties and other parameters of use for fish farmers. Robotic fish will collect these parameters with the help of IoUT system and will send them to cloud database where they will be analyzed and required action will be taken by fish farmers or administrator of system. Robotic fish will also be able to detect health of other fishes, oxygen level in water, food requirements of fishes and size of fishes. Robotic fish will have the feature to monitor tail frequency of other fishes and can change own tail frequency to move with different groups of fishes. This AI based Robotic Fish will help the fish farmers in intelligent control of fish farms.
A Comparative Study of Inrush Current On Star, Delta, and Star-Delta Starters for a 1,5kW Three Phase Induction Motor with Power Quality Analyzer and Powertag

Implementing three-phase induction motors in the industrial sector is very diverse, such as an exhaust fan, a conveyor drive, Etc. A starter with a function as an initial control is needed to operate the motor. This research presents a condition of inrush current based on Direct On-Line (D.O.L) Star, Direct On-Line (D.O.L) Delta and the Star-Delta starter windings. The initial stage is creating a “Starter Simulator” module, which a 1.5 kW induction motor will later test. During that test, there will be an identification process using Power Quality Analyzer METREL related to the condition of the inrush current from every three starter windings, and its steady state current will be monitored by HMI-based PowerTag. The result shows that Star-Delta starter winding performs better than the Star and Delta starters.
Thought Scope: Human Cognitive Thinking Classification from Eye Position using Deep Learning and Digital Image Processing

Human eye serves as a unique gateway to understand thoughts and cognitive activities. As the eyes always convey the truth, throughout history, we’ve been able to perceive a person’s emotions and sentiments to a certain extent by observing their eyes. With the dynamic technological advancement in the world, we want to level up from just reading emotions from the eyes to deciphering what that person might be thinking. Understanding the potential thoughts of individuals based on subtle change of eye positions, can help us immensely in several sectors. In this paper, we are proposing an approach to decode human cognitive processes by analyzing eye images captured from distinct eye positions. Our system uses captured images of eyes, then it utilizes digital image processing and Convolutional Neural Network (CNN) to detect the eye position. It compares the position to seven predefined classes and determines the closest match. Based on the similarity of the eye positions, our system will provide textual feedback about what the person might be thinking. To classify cognitive thinking based on eye position, CNN was used with transfer learning from two pretrained models- MobileNetV2 and NasNetLarge, where MobileNetV2 obtained the highest accuracy of 98.65% and is capable of delivering accurate textual representations of the potential thoughts remarkably.
Introduction to New Hybrid Approach in Sustainable PV MPPT Architecture

Immense-scale solar photovoltaic (PVC) systems are susceptible to partial shadowing circumstances (PSCS). By generating repetitive peaks in the power-voltage (P-V) feature, PSCS has potential to decrease the performance of the Photovoltaic system. To use the PV system as its utmost possible limit, it should be operated at global maximum power point (GMPP). In this work, a hybrid methodology depending upon the Cuckoo Search algorithm (CUSA) and Flying Squirrel Search Optimization (FSSO) is developed for tracking of GMPP. In order to take advantage of both methods' strengths, this research suggests a novel MPPT algorithm that combines the CUSA algorithm and FSSO. The suggested approach is tested with various partial shading instances and the uniform irradiance condition in order to validate it. Comparing the simulation and experimental findings to the original CUSA method and FSSO algorithm, there is a discernible performance gain.
Beyond Language Boundaries: Analysis and Ensemble Approach of Hate Speech Detection in South Asian Social Media

Nowadays social media has become the common platform to be connected with many people all over the world. Due to the increased use of social media, sometimes users are exposed to great risk. The anonymity of user in social media is the sole facilitator of spreading hate in a community. Several studies have been published recently regarding detection of such offensive speech. The majority of these studies deal with English language data due to its availability. The primary goal of this paper is to present a comprehensive approach to hate speech detection, focusing specifically in South Asian languages. In this paper, a comparative analysis of various machine learning classifiers has been conducted on four South Asian languages: Bangla, Indonesian, Urdu and Sinhala. Also an ensemble approach consisting of XGBoost, CatBoost and LightGBM has been used to improve the overall performance resulting in an accuracy of 92% for Bangla, 85% for Indonesian, 90% for Urdu and 85% for Sinhala. Finally, a comparative analysis shows the most effectiveness of MLP (Multi Layer Perceptron) among the 9 classifiers, confirming the potential of our approach for efficient hate detection in mentioned four languages.
Revolutionizing Last-Mile Delivery: Unleashing Unprecedented Efficiency in Package Logistics

The pivotal role of efficient delivery processes in the success of e-commerce enterprises necessitates a comprehensive examination of optimized delivery routes. This paper introduces an innovative software solution to revolutionize delivery agents' performance by implementing streamlined routing strategies. This research study endeavors to redefine and optimize the landscape of delivery processes for e-commerce enterprises by introducing an advanced routing optimization solution. This study uses the renowned traveling salesman's algorithm to develop a sophisticated software application that empowers delivery agents to maximize operational efficiency. The findings of this study underscore the substantial advantages offered by the software solution. The optimized routes generated by the algorithm demonstrate significant reductions in delivery time and distance, thereby enhancing operational efficiency for e-commerce enterprises.
Host-Based Flow Table Size Inference in Multi-Hop SDN

As a novel network paradigm, Software Defined Networking (SDN) has greatly simplified network management, but also introduced new vulnerabilities. One vulnerability of particular interest is the flow table, a data structure in every SDN-enabled switch that caches flow rules from the controller to bridge the speed gap between the data plane and the control plane. Prior works have shown that an adversary-controlled host can accurately infer parameters of the flow table at its directly-connected edge switch, which can then be used to launch intelligent attacks. However, those solutions do not work for flow tables at internal switches. In this work, we develop an algorithm that can infer the different flow table sizes at internal switches by measuring the Round Trip Times (RTTs) of a path traversing these switches from one of its endpoints. A major challenge in this problem is the lack of an inferable relationship between the RTTs and the flow table hits/misses at the traversed switches. Our solution addresses this challenge by experimentally identifying the inferable information and designing an inference algorithm that combines carefully designed probing sequences and statistical tools to mitigate measurement noise and interference. The efficacy of our solution is validated through experiments in Mininet.
Orbit-Grid-Based Dynamic Routing for Software Defined Mega-Constellation Network

Low Earth Orbit (LEO) Mega-Constellation Net-works (MCNs) have become increasingly popular in recent years due to their ability to offer global coverage, high-throughput data transmission, and low latency for users across the world. While MCNs significantly improve continuous service coverage worldwide, they also pose a challenge in terms of managing a large number of satellites in the system, which increases the complexity of routing and the number of hops required for inter-satellite links (ISLs). In this paper, we propose a software-defined mega-constellation network (SDMCN) architecture that provides flexible network management by enabling dynamic global network monitoring and collecting satellite information. In order to optimize routing in Walker Delta MCNs, we propose an orbit-grid-based dynamic routing (OGDR) algorithm, which begins by developing a path model that selects access satellites based on both the minimum hop metric and the ISLs distribution. Then, leveraging the periodicity of the satellite and the characteristics of the path model, inter-plane and intra-plane links are selected on an orbit grid directly based on the latitude of the satellites to ensure the minimum delay. Finally, we introduce a path failure recovery mechanism to ensure the reliability of the path. Experimental results demonstrate that the algorithm not only achieves performance similar to the shortest path but also minimizes the number of ISL hops and has lower computational complexity.
Performance Evaluation of Monolithic and Microservice Architecture for an E-commerce Startup

Today's businesses must be flexible enough to develop scalable applications that can influence emerging models of production and management. The prevailing imperative for organizations to adopt new technologies and business strategies stems from the need to enhance the efficiency and optimization of both human and technology resources. The concept of "software architecture" has been employed to define the overall blueprint of a software system. Software architecture has been proposed as a strategy to decrease complexity and enhance reusability by facilitating system decomposition into its high-level components and their interrelationships. Within the technology sector, the phrase "e-commerce" is frequently employed to denote the activities encompassing acquiring, vending, and promoting goods and services. The necessity for an e-commerce enterprise lies in reusable code that pays minimal costs and possesses a streamlined infrastructure. Implementing a suitable architectural framework to meet the existing demands of e-commerce is the most optimal strategy presently. This study undertakes a comparative analysis of monolithic and microservice architectures in the context of an e-commerce startup. Two separate software applications are developed, each following one of the mentioned architectures while utilizing the same technology platform. The findings of the tests suggest that monolithic architectures have the potential to decrease infrastructure expenses and enhance performance when compared to traditional microservice architectures. In conclusion, we clarify the challenges encountered during the deployment and implementation phases of microservices and monolithic web applications.
15-ch Wearable Body Surface Potential Sensor for Wireless Electrocardiogram Monitoring

The early detection and treatment of cardiovascular diseases hold significant importance; an electrocardiogram (ECG) is the most widely employed tool for assessing the functional status of the heart. Traditional standard 12-lead and Holter electrocardiographs used in medical facilities are characterized by their substantial size and possess drawbacks, including the necessity for professional assistance during application and removal, as well as limitations on patient mobility during monitoring. Consumer-oriented devices employed within the home environment such as wristwatch and patch electrocardiographs encounter issues related to measurement accuracy. Consequently, a demand arises for a system capable of conveniently and accurately measuring and monitoring cardiac activity within a home setting. This study presents a 15-channel wearable body surface potential sensor using a flexible electrode sheet. In addition, we introduce a machine-learning approach for estimating the waveform of a standard 12-lead ECG based on measurements obtained from the proposed sensor device that integrates a wireless measurement circuit.
PresCount: Effective Register Allocation for Bank Conflict Reduction

Modern processors with large multi-banked register files often rely on hardware solutions to resolve bank conflicts efficiently. However, these hardware-based methods, while flexible, can incur runtime penalties and restrict the exploration of optimized hardware designs. In contrast, compiler-based methods for register bank assignments avoid runtime overhead. However, incorporating bank assignment into the complex register allocation process presents significant challenges, leading existing methods to adopt conservative approaches to avoid potential side effects. This paper introduces the novel register allocation method PresCount, which enhances the coloring strategy for the Register Conflict Graph (RCG) and incorporates a bank pressure tracking mechanism to improve performance. The integrated register bank assigner in PresCount effectively reduces bank conflicts, achieving remarkable reductions of 43.28% and 27.76%, respectively, compared to existing methods on platforms with rich register banks and limited register budgets, as demonstrated by SPECfp and CNN-KERNEL benchmarks. Furthermore, a subgroup splitting technique is introduced to facilitate register allocation under the bank-subgroup register file design, specifically our Domain-Specific Architecture (DSA) for AI computing. This technique demonstrates an impressive 99.85% reduction in bank conflicts for domain-specific kernel functions. By addressing the challenges of bank conflicts in register allocation, the proposed PresCount method showcases significant improvements in performance and efficiency for platforms with different register configurations and domain-specific workloads, allowing for more flexible exploration of optimized hardware designs.
Low to High Dimensional Projection of Seizure Electroencephalography Using Recurrent Neural Network

This article describes solutions for an electroencephalographic (EEG) signal-based seizure detection problem, where the class imbalance in the dataset is a severe problem. The 'IEEE ICASSP 2023 Signal Processing Grand Challenge (SPGC): Seizure Detection' posed the diagnostic problem using wearable behind-the-ear (bhe) EEG electrodes. The challenge required using machine learning models to predict seizure events from bhe-EEG signals. We present a data-minimal training strategy to improve the class imbalance in the data, and proposes a projection layer that can map a low dimensional input to a high dimensional space. A fully connected recurrent neural network (RNN) based on Long Short-Term Memory cells (LSTMs) is proposed as the projection layer that finds this optimal projection. We additionally discuss interpretations of the latent representations learned by the classifier RNN network to evaluate if the domain adaptation has facilitated the generalizability of the network. These results suggest that projection-RNN can facilitate robust mappings of the target domain input signal on to the source domain. To illustrate the effects of data engineering and class imbalance, an existing deep–learning model (Chrononet) was optimized using the ’data-minimal’ strategy. Data engineering improved Chrononet's performance modestly (100% sensitivity and 3 false alarms per hour) over baseline models. On the other hand, the domain adaptation scheme resulted in a sensitivity of 89.37% with 94 false alarms per hour in the source domain. Additionally, the network achieved a sensitivity of 42% and a false alarm rate of 19 per hour in the target domain. These solutions were among the best-performing methods presented in the competition.
EAFNet: Feature Enhancement and Self-Adaptive Guided Feature Fusion for Object Detection in Haze Conditions

Recently, there is a significant success in the current object detection task. However, existing research has demonstrated that detectors trained on regular images experience a significant decrease when facing to wild scences such as haze scence. Although enhancing detection performance is possible through multi-stage adaptive parameter-guided image enhancement, this method fails to consistently enhance downstream object detection performance. To address this issue, we propose an end-to-end object detection framework that utilizes weakly supervised parameter-guided feature fusion to improve detection performance. Specifically, we introduce the Self-Adaptive Feature Fusion Module (SAFFM) that dynamically guides feature fusion in the detection network through the haze image information encoded by the Prior Knowledge (PK) components. By the joint learning of the PK components and the detection network in an end-to-end manner, we ensure that the PK components weakly supervise the feature fusion process to enhance detection performance. Additionally, we propose a Downsampling Feature Enhancement Module (DFEM) that utilizes multibranch down-sampling operators and reparameterization to enhance the feature extraction capability of the network, allowing it to capture more latent information. Comprehensive experiments on synthetic datasets and real datasets, demonstrated that the detection performance of the proposed network significantly outperforms other state-of-the-art detection methods.