Reviewerly/exHarmony huggingface dataset:
GAM-YOLOv3: A Lightweight Object Detection Network based on GhostBottleneck and CBAM

In traditional convolutional neural networks, there is a contradiction that complex network detection is slow and basic network detection accuracy is low. We propose a lightweight object detection network GAM-YOLOv3. Firstly, GhostBottleneck replaces Residual block in Darknet-53 backbone network. It obviously diminishes parameters of the network model and effectively improves the detection speed. Secondly, by adopting the CBAM attention module to substitute the ordinary convolution layer in the original neck network. The network parameters continuously decrease. The accuracy decrease caused by simplified networks is effectively improved. Conclusively, Hard-Swish activation function enhances network model detection stability. Compared with YOLOv3, GAM-YOLOv3 network parameters are reduced by 50% overall. It greatly speeds up object detection and perfectly satisfies real-time detection tasks.
Assessment of Network Security Situation of Industrial Control System Based on CG-ELM

This paper proposes a network security situation assessment model of the industrial control system, and the model divides the industrial control network security situation assessment into two subtasks: threat traffic detection and determination of hierarchical weight. The purpose of threat traffic detection is to detect possible network attacks in network data traffic. In order to identify network attacks quickly and accurately, this paper uses the extreme learning machine model to classify network attacks, and uses the conjugate gradient method to improve the training efficiency of the extreme learning machine model. For the purpose of assessing the impact of different network attacks on the security situation of industrial control systems, this paper proposes a hierarchical industrial control network model for situation assessment based on the Analytic Hierarchy Process. The experimental verification of the situation assessment is carried out on the simulation platform of the sewage treatment industrial control system.
Exploring Trust and Explainability of Unmanned Systems

Explainability (often referred to as interpretability) refers to the concept of providing context to an AI/ML model and its output, thereby assisting a human user in understanding the system’s decision-making process. The concept of explainability is especially helpful when we consider the high cognitive load and intense data management strategies that are required for current human-in-the-loop operations in use today. The work presented here aims to provide an explainability framework for autonomous systems, to provide system transparency, and enhance operator awareness. This work served to develop a novel method of sorting and evaluating data streams taken from an operational system, to filter and transmit data packages based on mission conditions. Post mission analysis yielded apparent trends in messaging hierarchy, indicating that certain health and status data streams were consistently prioritized, regardless of the pre-defined metrics. Additional data analysis was performed to evaluate sensor outputs with respect to health and status messaging. This process included conducting data correlation and data characterization, to evaluate relationships between data streams, identify data associated with nominal behavior, and perform anomaly detection. Key functional categories were developed, in which the system’s behavior is mapped to a corresponding component (and its respective data stream). Monitoring subsystem performance assists with cross-referencing sensor outputs, to confirm data projections and/or aid in identifying faulty readings. Furthermore, the application of anomaly detection algorithms is coupled with data correlation and/or pattern recognition to extract the most important and salient information.
Body Structure Constraint For 3D Human Pose Estimation

2D to 3D pose lifting is a promising method in 3D human pose estimation, and exciting improvement has been achieved after Graph Convolutional Networks (GCNs) is introduced into this task. In this paper, we propose a training method that achieved symmetry constraints of the skeleton, and it works well combined with different lifting methods. To make full use of the connection relationship of the joints, we propose a novel GL-Net, which views the human skeleton as a graph, for lifting 2D pose to 3D. Then, we add a Body factor Net to extract features from the 2D human pose estimation networks for correcting the scale of the 3D skeleton. We validate our method on public datasets. Experiments show that our model makes great progress. The proposed lifting method should be a promising tool for accurate 3D human pose estimation.
A Boundary-Guided and Lightweight Object Detector for Sonar Images

The complex underwater environment results in low resolution, poor contrast, and serious noise interference of sonar images. Although many denoising methods are used in sonar images, edge information blur has always been an important problem in sonar image denoising. In this paper, we propose a sonar image processing module based on edge guidance to transform sonar images into binary images with targets highlighting and clear boundaries, thereby proving that this module is helpful to improve the accuracy of sonar image detection tasks. In addition, we introduce the Transition Block module to decrease the parameters of the YOLOv7 detection model and improve the detection efficiency of the model. A comparative test on a public data set shows that our detector has better performance in accuracy and detection efficiency under four widely used evaluation indexes.
Entangled coherent states and violations of Bell-CHSH inequalities

Three classes of entangled coherent states are employed to study the Bell-CHSH inequality. By using pseudospin operators in infinite dimensional Hilbert spaces, four dichotomic operators (A,A′,B,B′) entering the inequality are constructed. For each class of coherent states, we compute the correlator 〈ψ|AB+A′B+AB′−A′B′|ψ〉, analyzing the set of parameters that leads to a Bell-CHSH inequality violation and, particularly, to the saturation of Tsirelson's bound.
Project-Based Software Engineering Curriculum for Secondary Students

Background. Software Engineering (SE) is a new and emerging topic in secondary computer science classrooms. However, a review of the recent literature has identified an overall lack of reporting on the development of SE secondary curriculum. Previous studies also report low student engagement when teaching these concepts. Objectives. In this experience report, we discuss the development of a 9-week, project-based learning (PBL) SE curriculum for secondary students. During this curriculum, students create a socially relevant project in groups of two to three. We discuss displays of participant engagement with CS concepts through the PBL pedagogy and the SE curriculum. Method. We examine participant engagement through group artifact interviews about student experiences during a week-long, virtual summer camp that piloted activities from our curriculum. During this camp, students followed a modified SE life cycle created by the authors of the paper. Findings. Participants showed engagement with the curriculum through various aspects of PBL, such as autonomy, creativity, and personal interest in their project topic. Implications. The lessons learned from this experience report suggest that PBL pedagogy can increase student engagement when teaching CS concepts, and this pedagogy provides detail and structure for future secondary SE curriculum implementations to support educators in the classroom.
Design and Develop A COVID-19 Vaccine Level Detection System For Access Public Services Users Using Face Recognition by Raspberry-pi

The COVID-19 pandemic has transformed many aspects of society, including widespread social restrictions. The COVID-19 vaccination has been actively advocated throughout Indonesia and is now a requirement for accessing public services. Facial recognition technology has emerged as one of the solutions used during this pandemic. In this research, we propose a system that can detect individuals vaccinated against COVID-19 by leveraging facial recognition technology based on the Haar Cascade algorithm, using data stored in a database. The facial recognition process underwent several tests, including dataset acquisition distance, attributeless face recognition, and face recognition with glasses. Dataset acquisition at a distance of 150cm was deemed safe and produced accurate results. Furthermore, the system recognized attributeless faces and correctly identified individuals wearing glasses. This facial recognition system is integrated with a database, ensuring user data is accurately displayed based on the detected facial recognition results. The results of these tests demonstrate the system’s ability to present data following the dataset and user ID stored in the database.
Investigating the Role of Computing Education for Informed Usage Decision-Making

Every person is confronted with multiple usage decisions, especially in the context of digital systems. The challenge for computing education is to enable students to make these decisions in an “informed” manner, which is considered a key competence needed to become a responsible citizen. As a first step within our research project, we report an attempt, inspired by healthcare research, to measure informed usage decisions by calculating a score indicating the “informedness” of a decision. We evaluate and discuss our method based on a first findings from a field test with 28 students.
Knowledge Management Readiness In The Airport Industry: The Case of Juanda International Airport, Indonesia

The airport is one of the country’s vital objects. The airport is a driving and supporting factor for industrial, trade, and tourism activities in encouraging national economic growth. The airport must consider the 3S+1C elements in its management: Safety, Security and Services, and Compliance. Every airport person must understand this as a reference in organizing the airport. A knowledge Management System (KMS) is an information system used to manage knowledge in an organization to support and improve organizational processes that include creating, storing, retrieving, transferring, and applying knowledge. In implementing the Knowledge Management System, organizations need to make comprehensive arrangements such as organizing the organizational structure and involving the human resources in this system. In this study, a knowledge management readiness measurement was carried out on the implementation process of the Juanda International Airport knowledge management system. Several aspects of assessment are carried out, namely management & strategy, organization, culture, and technology. It is a part or component of the knowledge management critical success factor (KMCSF). The knowledge management system’s readiness level is measured on a Rao Scale. Data was collected using a Likert scale questionnaire. It involves 40 respondents who are company employees. This research successfully presents that from four aspects of the assessment, the management & strategy aspect gets a result of 94%, the organizational element receives a result of 89%, and the culture aspect gets a result of 90%. The technology aspect receives a result of 87%. It indicates that the organization can adapt and has the flexibility to realize KM Readiness.
Multi-Hypothesis Task Planning: integrating temporal AI planning and semantic world modeling for AUV inspections in unknown environments

Autonomous Underwater Vehicles (AUVs) have become fundamental tools for marine scientists and industries to explore and monitor underwater areas. Nonetheless, the collected data quality is not guaranteed: AUVs passively store the sensor acquisitions that are then analyzed offline after their recovery by human operators in charge of identifying and localizing the so-called Objects of Potential Interest (OPIs). As a consequence of these statements, the marine community has sought robots able to meaningfully perceive and model the surroundings while autonomously selecting and performing the requested tasks, which are the foundations of fully autonomous vehicles. Motivated by such considerations, the novel Multi-Hypothesis Task Planning (MH-TP) architecture has been designed to integrate temporal Artificial Intelligence (AI) based planning with semantic world modeling techniques toward fully autonomous AUV inspections in unknown environments. The proposed architecture extends the standard AI planning methodologies relying on the Problem Domain Definition Language (PDDL) by dynamically varying the PDDL problem with the probabilistic, semantically enriched objects that the World Modeling (WM) itself evaluates. The developed methodology has been validated with realistic simulations made by means of the UUV Simulator, where a dynamic model of FeelHippo AUV was implemented alongside the requested perception and payload devices.
Exploring Effects of Online and Face-to-Face Teaching Formats on Students’ Interest and Engagement

The COVID-19 pandemic has highlighted the need for flexible teaching formats, particularly online education, as an alternative to traditional face-to-face (F2F) education. This study investigates the impact of teaching format (online vs. F2F) on secondary school students’ interest and engagement. We conducted an exploratory analysis of survey data from 16 workshops (F2F: 12, online: 4) held between January and December 2022, with 129 participants completing the survey (F2F: 76, online: 53, age: 10-16). All workshops covered the same learning topics, provided by online courses developed to raise interest in computer science (CS). Our findings show that the teaching format had a negligible effect on interest development. Still, students in the F2F setting showed higher engagement levels than those in the online setting. Additionally, an analysis of the effect of age indicates that younger students are more engaged in online settings than their older peers. We also found indications for longer-running programs having a greater effect on personal interest development compared to one-day programs. This paper discusses the results and identifies implications for teaching practice and future research. Overall, the study highlights the need to balance the advantages and limitations of both teaching formats and suggests potential strategies to improve online engagement.
HFLIC: Human Friendly Perceptual Learned Image Compression with Reinforced Transform

In recent years, there has been rapid development in learned image compression techniques that prioritize rate-distortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving with comparable perceptual quality.
Towards Robust Bayesian Estimation for Linear Dynamical Systems

This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.
Filtering Feature Selection Algorithm based on Fusion Strategy

This paper mainly concerns filtering algorithms for feature selection of high-dimensional data in the field of machine learning. Feature selection implies that some of data is useless or even plays a negative role for machine learning, more specifically, there are redundant and invalid features among them. This paper applies fusion strategy for feature selection by combining different filtenng standards to obtain a new one. The basic architecture of step-by-step filtering is described in order to ensure the accuracy and efficiency of feature selection, upon which the Relief-F-MRMR filtering criterion and specific operation steps are designed. An illustrative example is provided to show the validity and advantage of the proposed approach.
Kalman Filtering with Position-Dependent Noise Modeling for Active Sonar

The Kalman Filter (KF) is commonly applied in problems that require tracking of target like sonar applications for Anti-submarine Warfare (ASW) or harbor surveillance. The conventional KF approach to implementing a 2D tracking problem assumes a nonlinear measurement equation that provides a measurement of the target’s range and bearing, corrupted by additive white Gaussian noise (AWGN). These nonlinearities and the noise make accurate positioning of the target challenging. In this paper, it’s proposed another KF procedure for estimating the target’s state, i.e., the target’s position and speed, where the measurement equation directly gives the target’s position in Cartesian coordinates combined with a position-dependent noise for an active sonar platform. This noise is modeled and used to derive the measurement noise covariance matrix of KF algorithm. The performance of the proposed algorithm was compared with that of classic filtering techniques, and the proposed method achieved a similar relative gain (measured in terms of Relative Mean Squared Error (RMSE)) compared to the best baseline results (Extended KF (EKF) and Unscented KF (UKF)), however its processing time is almost five times smaller than the second- fastest accurate filter (EKF), due to its simplicity.
Identification of Acute Myeloid Leukemia (AML) Subtypes: M1, M2, M3 on White Blood Cells Using Microscopic Images

Leukemia is a form of blood cancer characterized by an overproduction of abnormal white blood cells that invade the body’s immune system. Leukemias are divided into four main groups based on cell type and growth rate. Acute Myeloid Leukemia (AML) is one of them, namely leukemia that occurs in the myeloid series. AML itself is divided into several subtypes, according to the French-American-British (FAB), namely M0-M7. AML subtypes M1, M2, and M3 are influenced by the same cell type, namely myeloblast cells, so a more detailed analysis is needed to classify them. Therefore we need a system that can identify the presence of AML in the blood, this identification is done by classifying white blood cells on microscopic images. To detect the presence of AML in microscopic images, multiple processes will be used, including preprocessing, segmentation, feature extraction, and classification stages by applying the K-NN method. This classification procedure will result in a diagnosis of M1, M2, or M3 blood cells. At the end of phase 1 of this final project, pre-processing and segmentation processes and graphical user interface (GUI) designs have been carried out. This final project resulted in an accuracy of 80% for the identification of subtype M1, 70% for the identification of subtype M2, and 80% for the identification of subtype M3.
Interactive 3D Software Development with Integrated Bike Data for Virtual Cycling Experience

Cycling is considered one of the thirteen important sports that offer numerous benefits for maintaining a healthy body. However, in Indonesia, cycling has garnered less interest among the public due to the requirement of a large land area for this sport. Additionally, the density of motorized vehicles has contributed to the lack of public interest in cycling due to the resulting high levels of air pollution, making outdoor cycling activities uncomfortable. To foster public interest in cycling, innovative developments are necessary. The cost of indoor static bicycle exercise equipment poses a significant obstacle to exercising. Consequently, an indoor bicycle simulator has been developed as a solution. The objective is to generate public interest in cycling while also providing a means for training and entertainment. In this research, a virtual bicycle simulator has been designed, incorporating various devices such as bicycle platforms, propulsion units, sensing units, and display units. The control system implemented receives signals from the sensing unit to regulate the movement of the actuation unit. This enables real-time calculation and construction of a virtual environment, resulting in a diverse cycling experience across different models.
A Novel Approach to Visual Search in E-commerce Fashion Using Siamese Neural Network and Multi-Scale CNN

The growing number of users on fashion e-retail platforms has highlighted the limitations of the current text-based search system, as users struggle to effectively describe the clothing products they are searching for. To address this challenge, the integration of a visual search system becomes essential. Visual search allows users to find similar products based solely on images. In this research, we propose the utilization of a Siamese Neural Network for implementing visual search in an e-commerce fashion. The Siamese network consists of two identical networks that process different images. By training the network to learn and represent the differences between images as embedding vectors, we enable the generation of similarity scores for pairs of images. To achieve accurate feature extraction, we employ a Multi-Scale CNN approach that captures both high and low-level features. Various experiments are conducted in this study, including the selection of multi-scale CNNs, distance metrics, and diverse data, to determine the network architecture that achieves the highest accuracy. Our findings demonstrate that utilizing the VGG19 model with a shallow layer and fractional distance metrics achieves an impressive accuracy of up to 97%. This indicates the effectiveness of our model in accurately predicting image similarity.
Efficient Dialogue State Tracking based on Dialogue-level State Prediction and Historical Dialogue State Deletion

The dialogue state tracking module is a crucial component of task-oriented dialogue systems. Recently, some Dialogue State Tracking (DST) methods have used the previous dialogue state as auxiliary input, resulting in errors that propagate and subsequently affect predictions. This paper proposes utilizing dialogue-level state as the prediction target and randomly removing historical dialogue state during training. The experiments demonstrate that this approach can effectively enhance the performance of the DST algorithm, alleviate error propagation, and achieve competitive results on both noisy (MultiWOZ 2.1) and clean (MultiWOZ 2.4) datasets.