Reviewerly/exHarmony huggingface dataset:
Self-Improved Learning for Salient Object Detection

Salient Object Detection (SOD) aims at identifying the most visually distinctive objects in a scene. However, learning a mapping directly from a raw image to its corresponding saliency map is still challenging. First, the binary annotations of SOD impede the model from learning the mapping smoothly. Second, the annotator’s preference introduces noisy labeling in the SOD datasets. Motivated by these, we propose a novel learning framework which consists of the Self-Improvement Training (SIT) strategy and the Augmentation-based Consistent Learning (ACL) scheme. SIT aims at reducing the learning difficulty, which provides smooth labels and improves the SOD model in a momentum-updating manner. Meanwhile, ACL focuses on improving the robustness of models by regularizing the consistency between raw images and their corresponding augmented images. Extensive experiments on five challenging benchmark datasets demonstrate that the proposed framework can play a plug-and-play role in various existing state-of-the-art SOD methods and improve their performances on multiple benchmarks without any architecture modification.
Mitigating the Adverse Effects of Long-Tailed Data on Deep Learning Models

When the data distribution in a dataset is highly imbalanced or long-tailed, it can severely affect the effectiveness of a deep network model. This drop in performance is caused due to the biased classifier, which favours the head-class samples because these samples have more dominant features compared to the tail-class samples. Addressing this challenge requires not only capturing subtle inter-class differences and intra-class similarities but also effectively utilising limited data for the minority classes. Supervised contrastive learning (SCL) and transfer of angle information from head classes to tail classes have recently been proposed to address the problem of long-tail classification. For a well-balanced dataset, SCL demonstrates effectiveness by pulling together samples from the same classes while pushing away samples from different classes. However, when applied to long-tailed datasets, SCL could become biased towards the head-class samples. On the other hand, the method of transfer of angle information aims to address the challenges posed by long-tailed image classification; however, it lacks in achieving both intra-class compactness and inter-class separability. To address the shortcomings and exploit the strengths of both of these approaches, we propose a unique hybrid method that seamlessly integrates supervised contrastive learning and angular variance to mitigate the adverse effects of long-tailed data on deep learning models for image classification. We name our method as Supervised Angular Contrastive Learning (SACL). In our experiments on long-tailed datasets with different class imbalance ratios, we demonstrate that our method outperforms most of the existing baseline approaches.
Proposal of Principles of DeFi Disclosure and Regulation

This paper proposes eight principles for DeFi disclosure and regulation based on the implications from the key challenges, risks, and questions. Such implications include Innovation Trilemma, whether a DeFi constitutes the “financial service” which participants are subject to consumer/investor protection needs to be considered, regulations from the perspective of systemic risk and anti-money laundering, establishment of a disclosure system and enforcement framework suitable for the characteristics of DeFi and crypto-assets. Creating a common disclosure platform in which national authorities and international standard-setting bodies could participate and give authority would be desirable. Such a framework would provide basic information regarding a DeFi, such as disclosure of data, governance mechanisms, token information, as well as information on audits, etc., and it would be a convenient way to check for compliance as well. Designing an appropriate common disclosure platform, that also considers participants’ incentive structure, is very challenging and will be the subject of further research.
The Impact of Professionalism Theory Constructs on the Applicability of Forensic Accounting Services: Evidence from Jordan

This study aims to provide an understanding of the impact of professionalism theory constructs on the applicability of forensic accounting services, as well as reviewing the obstacles to implementing the profession of forensic accounting through a literature review. The accounting side of the study explores the profession of forensic accounting and the sociological side of the study will focus on professionalism theory, more specifically Pavalko’s 1988 model. A quantitative method was used in this study; 154 questionnaires from Certified Public Accountants (CPAs) were validated and used in the data analysis. Data analysis was conducted using SmartPLS, which was used for testing the study’s hypotheses. The results demonstrated that the professionalism theory constructs have a significant impact on the applicability of forensic accounting services. There was an indication that six out of eight constructs had a positive impact, which are, code of ethics, commitment, intellectual technique, motivation, training, and relevance to social values, leaving only autonomy and sense of community with no impact shown. This study recommends that forensic accountants and practitioners contribute to developing the profession of forensic accounting and to provide services with high quality. The study’s significant constructs can inform the development of training programs for aspiring forensic accountants in Jordan, emphasizing ethics, commitment, technical expertise, motivation, training, and social values. These findings may also apply to other countries.
Random Padding Data Augmentation

The convolutional neural network (CNN) learns the same object in different positions in images, which can improve the model recognition accuracy. An implication of this is that CNN may know where the object is. The usefulness of the features' spatial information in CNNs has not been well investigated. In this paper, we found that the model's learning of features' position information hindered the learning of the features' relationship. Therefore, we introduced Random Padding, a new type of padding method for training CNNs that impairs the architecture's capacity to learn position information by adding zero-padding randomly to half of the border of feature maps. Random Padding is parameter-free, simple to construct, and compatible with the majority of CNN-based recognition models. This technique is also complementary to data augmentation such as random cropping, rotation, flipping, and erasing and consistently improves the performance of image classification over strong baselines.
The Construction of the Measurement Model of Balanced Development of Compulsory Education Resources based on the Hierarchical Analysis Method

The balanced development of compulsory education is an important measure to fully implement the equalization of basic education public services, promote education equity, and maintain social justice. Among the paths to the realization of balanced development of compulsory education, the most crucial is to ensure the balanced development of compulsory education resources, so it should attract high attention. The purpose of this paper is to construct a measurement model of the balanced development of compulsory education resources, to explore the key factors affecting the balanced development of compulsory education resources, and to find a scientific path for the balanced development of compulsory education resources. Based on this, this paper uses the method of hierarchical analysis to construct a measurement model of the balanced development of compulsory education resources, analyzes the measurement model of the balanced development of compulsory education resources in a certain region, and concludes that the main factors include school conditions, teacher quality, and education funding. Accordingly, this paper further proposes effective strategies for the balanced development of compulsory education resources from the aspects of increasing education funding, refining the assessment indicators of relevant staff, improving the personnel system of teachers, implementing the mobility policy of teachers, and strengthening the construction of basic resources. The research of this paper, on the one hand, can provide a guarantee for the development of balanced compulsory education resources in a certain region, and on the other hand, it can also provide certain references for the solution of the problem of balanced development of compulsory education resources in other regions with similar situations, and it has strong practical significance for the effective realization of balanced development of compulsory education.
A Deep Reinforcement Learning Algorithm for Robotic Manipulation Tasks in Simulated Environments

Industrial robots are used in a variety of industrial process tasks, and due to the complexity of the environment in which these systems are deployed, more robust and accurate control methods are required. Deep reinforcement learning emerges as a comprehensive approach that directly allows for the mapping of sensor data and the setting of motion actions to the robot. In this work, we propose a robotic system implemented in a semi-photorealistic simulator whose motion control is based on the A2C algorithm in a DRL agent; the task to be performed is to reach a goal within a work area. The evaluation is executed in a simulation scenario where a fixed position of a target is maintained while the agent (robotic manipulator) tries to reach it with the end-effector from an initial position. Finally, the trained agent fulfills the established task; this is demonstrated by the results obtained in the training and evaluation processes, and the reward value increases when the measured distance decreases between the end-effector and the target.
StairWave Transformer: For Fast Utilization of Recognition Function in Various Unmanned Vehicles

Newly introduced vehicles come with various added functions, each time utilizing data from different sensors. One prominent related function is autonomous driving, which is performed in cooperation with multiple sensors. These sensors mainly include image sensors, depth sensors, and infrared detection technology for nighttime use, and they mostly generate data based on image processing methods. In this paper, we propose a model that utilizes a parallel transformer design to gradually reduce the size of input data in a manner similar to a stairway, allowing for the effective use of such data and efficient learning. In contrast to the conventional DETR, this model demonstrates its capability to be trained effectively with smaller datasets and achieves rapid convergence. When it comes to classification, it notably diminishes computational demands, scaling down by approximately 6.75 times in comparison to ViT-Base, all the while maintaining an accuracy margin of within ±3%. Additionally, even in cases where sensor positions may exhibit slight misalignment due to variations in data input for object detection, it manages to yield consistent results, unfazed by the differences in the field of view taken into consideration. The proposed model is named Stairwave and is characterized by a parallel structure that retains a staircase-like form.
HiEve Challenge on VOT

Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A complex event relates to dense crowds, anomalous individuals, or collective behaviors.
Experimental study of modified Tavis-Cummingsmodel with directly-coupled superconductingartificial atoms

The Tavis-Cummings model is intensively investigated in quantum optics and has important applications in generation of multi-atom entanglement. Here, we employ a superconducting circuit quantum electrodynamic system to study a modified Tavis-Cummings model with directly-coupled atoms. In our device, three superconducting artificial atoms are arranged in a chain with direct coupling through fixed capacitors and strongly coupled to a transmission line resonator. By performing transmission spectrum measurements, we observe different anticrossing structures when one or two qubits are resonantly coupled to the resonator. In the case of the two-qubit Tavis-Cummings model without qubit-qubit interaction, we observe two dips at the resonance point of the anticrossing. The splitting of these dips is determined by Δλ=2g12+g32, where g1 and g3 are the coupling strengths between Qubit 1 and the resonator, and Qubit 3 and the resonator, respectively. The direct coupling J12 between the two qubits results in three dressed states in the two-qubit Tavis-Cummings model at the frequency resonance point, leading to three dips in the transmission spectrum. In this case, the distance between the two farthest and asymmetrical dips, arising from the energy level splitting, is larger than in the previous case. The frequency interval between these two dips is determined by the difference in eigenvalues (Δλ=ε1+-ε1-), obtained through numerical calculations. What we believe as novel and intriguing experimental results may potentially advance quantum optics experiments, providing valuable insights for future research.
An Efficient Bit-Based Approach for Mining Skyline Periodic Itemset Patterns

Periodic itemset patterns (PIPs) are widely used in predicting the occurrence of periodic events. However, extensive redundancy arises due to a large number of patterns. Mining skyline periodic itemset patterns (SPIPs) can reduce the number of PIPs and guarantee the accuracy of prediction. The existing SPIP mining algorithm uses FP-Growth to generate frequent patterns (FPs), and then identify SPIPs from FPs. Such separate steps lead to a massive time consumption, so we propose an efficient bit-based approach named BitSPIM to mine SPIPs. The proposed method introduces efficient bitwise representations and makes full use of the data obtained in the previous steps to accelerate the identification of SPIPs. A novel cutting mechanism is applied to eliminate unnecessary steps. A series of comparative experiments were conducted on various datasets with different attributes to verify the efficiency of BitSPIM. The experiment results demonstrate that our algorithm significantly outperforms the latest SPIP mining approach.
Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms

In recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects and locating them on a map in real time. Detection and positioning are achieved solely through the utilization of the drone’s camera and sensors. To accomplish this goal, several deep learning algorithms are assessed and adopted because of their suitability with the system. Object detection is based upon a single-shot detector architecture chosen for maximum computation speed, and the tracking is based upon the combination of deep neural-network-based features combined with an efficient sorting strategy. Subsequently, the developed system is evaluated using diverse metrics to determine its performance for detection and tracking. To further validate the approach, the system is employed in the real world to show its possible deployment. For this, two distinct scenarios were chosen to adjust the algorithms and system setup: a search and rescue scenario with user interaction and precise geolocalization of missing objects, and a livestock control scenario, showing the capability of surveying individual members and keeping track of number and area. The results demonstrate that the system is capable of operating in real time, and the evaluation verifies that the implemented system enables precise and reliable determination of detected object positions. The ablation studies prove that object identification through small variations in phenotypes is feasible with our approach.
Learning Hierarchical Task Structures for Few-shot Graph Classification

The problem of few-shot graph classification targets at assigning class labels for graph samples, where only limited labeled graphs are provided for each class. To solve the problem brought by label scarcity, recent studies have proposed to adopt the prevalent few-shot learning framework to achieve fast adaptations to graph classes with limited labeled graphs. In particular, these studies typically propose to accumulate meta-knowledge across a large number of meta-training tasks, and then generalize such meta-knowledge to meta-test tasks sampled from a disjoint class set. Nevertheless, existing studies generally ignore the crucial task correlations among meta-training tasks and treat them independently. In fact, such task correlations can help promote the model generalization to meta-test tasks and result in better classification performance. On the other hand, it remains challenging to capture and utilize task correlations due to the complex components and interactions in meta-training tasks. To deal with this, we propose a novel few-shot graph classification framework FAITH to capture task correlations via learning a hierarchical task structure at different granularities. We further propose a task-specific classifier to incorporate the learned task correlations into the few-shot graph classification process. Moreover, we derive FAITH+, a variant of FAITH that can improve the sampling process for the hierarchical task structure. The extensive experiments on four prevalent graph datasets further demonstrate the superiority of FAITH and FAITH+ over other state-of-the-art baselines.
Numerical Investigation on β = 3/2 in the Generalized Multiquadric Neural Networks for Classification Challenge

Neural networks are an important tool in medical classification tasks that can help healthcare providers make more accurate diagnoses, develop personalized treatment plans, and improve patient outcomes. This study aimed to investigate the effectiveness of the value of beta = 3/2 in a different data structure under the architecture of a neural network, following its success in a previous data structure. To achieve this, a neural network was constructed using the multiquadric radial basis function (MQ-RBF) and two other popular values of beta were included for comparison. The hepatitis C-virus dataset and the breast cancer dataset were used for classification and the accuracy of the classification was evaluated using various metrics and validation techniques. The study found that selecting beta as 3/2 did not yield the expected results, unlike the other two choices which provided dependable confidence. Moreover, the study identified a problem with the interpolation matrix being non-invertible, which created another challenge for analysis. These findings demonstrate that the selection of beta values requires careful consideration and should not be assumed to perform equally well across different data structures. Further research is needed to explore alternative approaches to improve classification performance in challenging datasets.
Formation‐Aware Planning and Navigation with Corridor Shortest Path Maps

Abstract The need to plan motions for agents with variable shape constraints such as under different formations appears in several virtual and real‐world applications of autonomous agents. In this work, we focus on planning and execution of formation‐aware paths for a group of agents traversing a cluttered environment. The proposed planning framework addresses the trade‐off between being able to enforce a preferable formation when traversing the corridors of the environment, versus accepting to switch to alternative formations requiring less clearance in order to utilize narrower corridors that can lead to a shorter overall path to the final destination. At the planning stage, this trade‐off is addressed with a multi‐layer graph annotated with per‐layer navigation costs and formation transition costs, where each layer represents one formation together with its specific clearance requirement. At the navigation stage, we introduce Corridor Shortest Path Maps (CSPMs), which produce a vector field for guiding agents along the solution corridor, ensuring unobstructed in‐formation navigation in cluttered environments, as well as group motion along lengthwise‐optimal paths in the solution corridor. We also present examples of how our multi‐layer planning framework can be applied to other types of multi‐modal planning problems.
Cooperative Localization of Ground Targets Based on Improved Adaptive Grid Interactive Multiple Model Algorithm

Aiming at the problem of strong maneuverability of ground targets, unpredictable motion patterns, and difficulty in accurate positioning, the application of adaptive grids, interactive multiple models, and iterative extended Kalman filtering in collaborative positioning of ground targets was studied. In order to cover the motion form of the ground target, an interactive multi-model is used to design a multi-model framework. At the same time, the grid jump of the adaptive grid algorithm is used to complete the adaptive adjustment of the model set. To improve the accuracy of nonlinear approximation, iteratively extended Kalman filtering algorithm is designed to filter and improve the adaptive grid interactive multi-model algorithm. The simulation results show that compared with the adaptive grid interactive multi-model algorithm, this method can further improve the accuracy of the coordinated localization of ground targets, and the calculation amount is still better than the interactive multi-model algorithm with increasing models.
Wavelet Pooling in Convolutional Neural Networks for Breast Cancer Detection with Ultrasound Images

Analyzing ultrasound breast cancer images is crucial for the early detection and diagnosis of breast cancer. It helps identify abnormal masses or lesions, especially in women with dense breast tissue. Early detection is important for successful treatment. Careful examination of ultrasound images can help identify size, shape, texture, density, and location of any identified masses. It plays a critical role in saving lives and improving outcomes for women with breast cancer. The primary objective of this study is to introduce a new approach for enhancing the efficiency of a standard convolutional neural network (CNN) for analyzing ultrasound breast cancer images. The idea suggested is to incorporate wavelet transformation as a substitute for the pooling process in CNN and the new architecture is named ‘The MobileNetV2 plus’. The MobileNetV2 plus architecture was developed. Through various measurement tools, its effectiveness was tested and confirmed, showing promising results with a high level of accuracy and satisfactory performance.
Tracking

In this chapter, we will elaborate on the task of video object tracking (VOT), which aims at producing tight bounding boxes around one or multiple target objects in the video.