Reviewerly/exHarmony huggingface dataset:
Image Enhancement for Breast Cancer Detection on Screening Mammography Using Deep Learning

Mammography offers the most efficient approach for detecting breast illnesses early. Nevertheless, Image enhancement to improve breast cancer detection is required since mammograms are low-contrast and noisy images, and typical diagnostic markers such as microcalcifications and masses are challenging to identify. Due to this issue, this paper evaluates the impact of image enhancement on the performance of the deep learning approach and conducts qualitative and quantitative testing of various deep learning models in breast cancer classification. This study uses Mini Digital Database for Screening Mammography (Mini-DDSM) breast dataset containing cancer and normal images. The mammography images are then improved using morphological erosion and enhanced using two image enhancement algorithms which are Unsharp Masking (UM) and High-Frequency Emphasis Filtering (HEF). Deep learning classification algorithms such as ResNet, DenseNet, and EfficientNet are employed to classify breast cancer. Each architecture is compared and analyzed regarding the effect of the image enhancement techniques and achieves the highest 76.08% accuracy score on breast cancer classification in the mammography dataset using the HEF technique.
Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers’ online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions.
Airport Runway Foreign Object Debris (FOD) Detection Based on YOLOX Architecture

Today, every airport manager in various countries has tightened runway security to avoid the entry of foreign objects that can endanger passengers and aircraft both when landing and taking off. Inspection and supervision of the runway must be carried out regularly. However, there are still many airports that carry out inspections and supervision by human labor without any technological support. Whereas inspection and supervision using human labor takes a relatively long time and is prone to errors, especially in bad weather and limited visibility. Technological developments in runway security using radar are one of the solutions. However, radar technology is relatively expensive, so many airport managers use computer vision because it is considered cheaper and more accurate. The use of computer vision has grown rapidly in monitoring FOD on aircraft runways. Our method is an impovement of the YOLOX architecture by moving output objects to branch classes. Our method got a MAP score of 0.832 which has an increase in score of 0.021 from the previous method in detecting FOD in classes of people, vehicles, birds, cats and dogs.
Sensing Performance Analysis of Mobile Robot Navigation Based on Depth Camera and Lidar

One of the potential implementations of mobile robots in mitigating the risk of communicable diseases is for delivering items and handling materials with no-human contact. In that scenario, mobile robots require the capability of performing simultaneous multi-object detection and distance estimation for dynamic exploration in a real-world environment. Some existing research papers have contributed to the development of object detection or distance estimation, separately. However, there is still a lack of studies on simultaneous multi-object detection and distance estimation for robot navigation, especially in terms of performance analysis of the integrated techniques. One of the most challenging issues of integrating multi-object detection and distance estimation is the validation and evaluation of the type of objects with their relevant estimated distance to the robot. For that reason, this research aims to analyze the mobile robot's sensing performance in integrated simultaneous multi-object detection and distance estimation during navigation scenarios. A depth camera of Intel RealSense D455 and a rotating LIDAR of Slamtec RPLidar A1M8 are utilized in the experiments. The result of experiments shows that the object detection algorithm using Mobilenet SSD V2 can perform an accuracy of 74%, with precision, recall, and F1-Score consecutively achieving 74,54%, 74,00%, and 74,22%. The multi-object distance estimator also performs satisfactory results with a low measurement average error of 10.3 cm for indoor experiments. The integrated multiobject detector and distance estimator system can work in a real-time mode of 30 FPS, thence significantly improving the navigation performance of the mobile robot.
Feature Selection using Grey Wolf Optimization Algorithm on Light Gradient Boosting Machine

high dimensional data provide a major problem to supervised learning. In identifying high dimensional data, the learning models usually exhibit overfitting and become less understandable. One way to find the ideal features on high-dimensional data implemented feature selection on dataset Feature selection is one of the crucial aspects on data preprocessing step. Several algorithms for feature selection were proposed over the decades such as wrapper method, filter, and embedded method. In this research, we implemented wrapper method with Grey Wolf Optimization. We implemented Grey Wolf Optimization on wrapper method because the algorithm is efficient, simple and had lower computational time. We are also compared Grey Wolf Optimization to other meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithms. The result showed the GWO provide better computational time with the average time from four different dataset was 6.1125s. The accuracy result showed the GWO performed better on Ionosphere dataset.
A Combination of the Haversine Formula Algorithm and the Sequential Searching Algorithm in Web based Online Attendance

Manual attendance systems are generally inefficient and can waste time calling individually. The lecture attendance system is one of the most important elements in education. Attendance is part of the evaluation process between lecturers and students. This affects the final grades received by students. Problems that arise are often found in class, namely false attendance and often cheating by students related to absence so that they can achieve a minimum level of attendance in teaching and learning activities. Then an application is made using two algorithms that can produce solutions to reduce problems such as cheating with the method used, namely the haversine formula algorithm to measure the distance between students and campus buildings and the sequential search algorithm to search data. The results of the calculation of the haversine formula algorithm get an accuracy of 99.5969% from 100 student data to campus buildings and the data search process in sequential search gets an average run time of 19.0634 seconds.
A Deep Reinforcement Learning Visual Servoing Control Strategy for Target Tracking Using a Multirotor UAV

In this work, a deep Reinforcement Learning control scheme is developed in order to execute autonomous tracking of an unmanned ground vehicle (UGV) with a multirotor unmanned aerial vehicle (UAV). The UAV is equipped with a downward looking camera and the detection of the target UGV is achieved through a convolutional neural network (CNN). The deep RL control policy is deployed as a cascade position controller, which commands the inner attitude controller of the autopilot, and achieves the following of the UGV despite the aggressive maneuvers of the target. The efficacy of the proposed framework is demonstrated via a set of outdoor experiments using an octocopter flying above the ground vehicle.
Photogrammetry-based Dynamic Path Tracking of Industrial Robots Using Adaptive Neuro-PID Control Method and Robust Kalman Filter

This paper proposes a novel accurate dynamic path tracking (DPT) method for industrial robots based on photogrammetry sensors and an adaptive neuro-PID (ANPID) control method. First, the pose of the robot's end-effector is detected by the photogrammetry sensor (C-Track stereo camera). It passes through a robust Kalman filter to reduce the noise in the signals. Then, the filtered signals are fed to the ANPID, whose gains are tuned online using an adaptive multi-layer perceptron neural network (AMLPNN). The steepest descent optimization method is adopted online. The cost function is the least mean square of the system states errors. Experimental results on FANUC M-20iA robot show the tracking accuracy reaches ±0.08mm and ±0.04deg, which exhibits the superiority of the proposed method over the conventional methods such as PID (tracking error±0.2mm and ±0.1deg) [4].
A Systematic Literature Review of Generative Adversarial Network Potential In AI Artwork

Humans have studied calligraphy and calculated programs to foster creativity for years. Image generation technology using artificial intelligence and Generative Adversarial Networks is currently reaching the peak of its performance. While there are newer and newer algorithms to improve the image generation system, the output of the images is still suitable at best and only excels in their category. While it is true that some of the images generated are good enough to be used, it is still unclear whether the capabilities of AI image generation can outperform their creative human counterparts. Therefore, this literature study aims to explore the basics of AI image generation, how they work, and what factors contribute to creating art such as simple pictures. Previous studies from several years ago show that most generated images are not good enough for creative usage because they only replicate traces of their dataset. The most significant factor contributing to this is the algorithm used and how it is used to create new images. In general, the concluded that while current AI-generated images are improving, they are still not creative enough to replace human creativity.
Modified Bug Algorithm with Proximity Sensors to Reduce Human-Cobot Collisions

This paper presents an adaptation of the mobile robot bug algorithm to control a cobot to reduce collisions with a human worker in the same work cell. Collaborative robots (cobots) are popular in industry because they enable close collaboration between a human and a robot. Although the cobot can stop when a collision happens, it is not a pleasant or safe working environment for the human to be hit by the cobot possibly multiple times a day. The proposed approach uses inexpensive proximity sensors on the cobot and a simple algorithm. An adjustable yaw angle is introduced to the algorithm to further reduce collisions. Results from pick-and-place experiments with UR 10 cobot show significant reduction of collisions.
Correlation Analysis of Factors Influencing the Motion Planning Accuracy of Articulated Robots

The motion planning accuracy of articulated robots can be increased by augmenting them with an objective function. Parameters that influence the absolute accuracy of 6-axis articulated robots can be integrated into such objective functions. However, it is still unclear how to prioritize the various influencing factors so that they can be utilized together instead of separately. Here, it is shown how to rate these factors by analyzing and comparing their impact on the accuracy of the robot. The factors manipulability, smoothness, gear backlash caused by direction change of axis rotation, joint space distance and robot layout are studied in this regard. The evaluation with a 6DoF laser tracker reveals that manipulability and smoothness have the biggest impact. With these results it is possible to combine multiple factors into a comprehensive function and achieve a higher accuracy without the necessity of applying any hardware upgrades.
Cyber Security for Surgical Remote Intelligent Robotic Systems

This paper highlights the progress toward securing teleoperating devices over the past ten years of active technology development. The relevance of this issue lies in the widespread development of teleoperating systems with a small number of systems allowed for operations. Anomalous behavior of the operating device, caused by a disruption in the normal functioning of the system modules, can be associated with remote attacks and exploitation of vulnerabilities, which can lead to fatal consequences. There are regulations and mandates from licensing agencies such as the US Food and Drug Administration (FDA) that place restrictions on the architecture and components of teleoperating systems. These requirements are also evolving to meet new cybersecurity threats. In particular, consumers and safety regulatory agencies are attracted by the threat of compromising hardware modules along with software insecurity. Recently, detailed security frameworks and protocols for teleoperating devices have appeared. However, a matter of intelligent autonomous controllers for analyzing anomalous and suspicious actions in the system remain unattended, as well as emergency protocols from the point of cybersecurity view. This work provides a new approach for the intraoperative cybersecurity of intelligent teleoperative surgical systems, taking into account modern requirements for implementing into the Surgical Remote Intelligent Robotic System LevshAI. The proposed principal security model allows a surgeon or autonomous agent to manage the operation process during various attacks.
Cooperative Collision Avoidance in Mobile Robots using Dynamic Vortex Potential Fields

In this paper, the collision avoidance problem for non-holonomic robots moving at constant linear speeds in the 2-D plane is considered. The maneuvers to avoid collisions are designed using dynamic vortex potential fields (PFs) that are functions of relative velocities in polar coordinates. Introduction of vorticity in the calculation of the gradients leads to a cooperative collision avoidance behaviour between the robots and also ensures the absence of local minima. Such a repulsive field is activated by a robot only when it is on a collision path with other mobile robots or stationary obstacles. By analysing the kinematics-based engagement dynamics in polar coordinates, the PF parameters are identified that ensure collision avoidance with stationary and moving robots, as well as those actively seeking to collide with it. Experimental results acquired using a mobile robot platform that support the theoretical contributions are presented.
Fused Swish-ReLU Efficient-Net Model for Deepfakes Detection

With the rapid development of sophisticated deepfakes generation methods, the realism of fake content has reached the level where it becomes difficult for human eyes to identify such high-quality fake images/videos, thus increasing the demand for developing deepfakes detection methods. The diversity in deepfakes images/videos in terms of ethnicity, illumination condition, skin tone, age, background setting, and generation algorithms makes the detection task quite difficult. To better address the aforementioned challenges, we present a novel Swish-ReLU Efficient-Net (SRE-Net) that is robust to the identification of deepfakes generated using different face-swap and face-reenactment techniques. More precisely, we fused two EfficienNet-b0 models, one with the ReLU and the other with the Swish activation function along with layer freezing to achieve better detection results. Our SRE-Net attained the average accuracy and precision of 96.5% and 97.07% on the FaceForensics++ dataset, and 88.41% and 91.28% on the DFDC-preview dataset. The high detection results demonstrate the effectiveness of SRE-Net while detecting the deepfakes generated using different manipulation algorithms.
How Information Manipulation on Social Media Influences the NFT Investors’ Behavior: A Case Study of Goblintown.Wtf

People favor nonfungible token (NFT) because of the attribute to prove digital assets’ ownership and promote interactions. Investors are keen to buy and use NFT pictures as social media avatars and participate in online communities around NFT collections. However, information manipulation in the NFT market has led to investors significant losses. Our work explored a way to correspond social media accounts with Ethereum addresses and studied the microstructure of NFT market. Taking Goblintown.wtf as an example, we analyzed the participants, mechanism, and impact of Twitter information manipulation in the market. We found five categories of investors in the NFT market under information manipulation: primary investors, amateur investors, fanatic investors, short-term rational investors, and long-term rational investors. We argue that investors will consume their limited attention more likely when joining NFT online communities. This will lead to more complicated for them to make investment decisions rationally.
Abnormal Signal Recognition Method of Wearable Sensor Based on Machine Learning

The recognition of abnormal signal of wearable sensor is of great significance to the application value of the device. In order to improve the accuracy of abnormal signal recognition of wearable sensors and indirectly ensure the safety of wearable sensor devices, a method of abnormal signal recognition of wearable sensors based on machine learning was proposed. According to the different abnormal types and principles of wearable sensors, the signal abnormal judgment criteria are set. The wearable sensor signal is collected, and the initial signal is preprocessed by Kalman filtering, normalization and weighted fusion. The machine learning algorithm is used to extract the features of sensor signals, and the recognition results of the abnormal type, abnormal semaphore and abnormal location of sensor signals are obtained through feature matching. Through the identification performance test experiment, it is obtained that the average abnormal type error detection rate of the optimization design identification method is 0.86%, and the average statistical error of abnormal semaphore is 0.22 db, lower than the preset value.
Domain Knowledge Informed Multitask Learning for Landslide-Induced Seismic Classification

Automatic seismic signal classification methods are extensively investigated to reduce or replace manual interpretation, with great potential in previous research.Discriminative seismic wave propagation physical characteristics, such as velocities and accelerations, are rarely considered for classification.A multitask learning scheme is proposed that utilises the seismic wave equation and three-dimensional (3D) P-wave velocity Vp model for signal representation learning.The classifier uses the obtained latent feature maps on a convolutional neural network architecture for classification of rockfall, slide quake, earthquake, and natural/anthropogenic noise events, recorded at an ongoing landslide.Our experimental results show that our approach outperforms state-of-the-art methods.
Generative Data Free Model Quantization With Knowledge Matching for Classification

Neural network quantization aims to reduce the model size, computational complexity, and memory consumption by mapping weights and activations from full-precision to low-precision. However, many existing quantization methods, either post-training with calibration or quantization-aware training with fine-tuning, require original data for better performance, which may not be available due to confidentiality or privacy constraints. This lack of data can lead to a significant decline in performance. In this paper, we propose a universal and effective method called Generative Data Free Model Quantization with Knowledge Matching for Classification (KMDFQ) that removes the dependence on data for neural network quantization. To achieve this, we propose a knowledge matching generator that produces meaningful fake data based on the latent knowledge in the pre-trained model, including classification boundary knowledge and data distribution information. Based on this generator, we propose a fake-data driven data free quantization method that uses the generated data to take advantage of the latent knowledge for quantization. Furthermore, we introduce Mean Square Error alignment during the fine-tuning of the quantized model to more strictly and directly learn knowledge, making it more suitable for data free quantization. Extensive experiments on image classification demonstrate the effectiveness of our method, achieving higher accuracy than existing data free quantization methods, particularly as the quantization bit decreases. For example, on ImageNet, the 4-bit data free quantized ResNet-18 has less than a 1.2% accuracy decline compared to quantization with real data. The source code is available at https://github.com/ZSHsh98/KMDFQ.
Coarse-Refined Consistency Learning Using Pixel-Level Features for Semi-Supervised Medical Image Segmentation

Pixel-level annotations are extremely expensive for medical image segmentation tasks as both expertise and time are needed to generate accurate annotations. Semi-supervised learning (SSL) for medical image segmentation has recently attracted growing attention because it can alleviate the exhausting manual annotations for clinicians by leveraging unlabeled data. However, most of the existing SSL methods do not take pixel-level information (e.g., pixel-level features) of labeled data into account, i.e., the labeled data are underutilized. Hence, in this work, an innovative Coarse-Refined Network with pixel-wise Intra-patch ranked loss and patch-wise Inter-patch ranked loss (CRII-Net) is proposed. It provides three advantages: i) it can produce stable targets for unlabeled data, as a simple yet effective coarse-refined consistency constraint is designed; ii) it is very effective for the extreme case where very scarce labeled data are available, as the pixel-level and patch-level features are extracted by our CRII-Net; and iii) it can output fine-grained segmentation results for hard regions (e.g., blurred object boundaries and low-contrast lesions), as the proposed Intra-Patch Ranked Loss (Intra-PRL) focuses on object boundaries and Inter-Patch Ranked loss (Inter-PRL) mitigates the adverse impact of low-contrast lesions. Experimental results on two common SSL tasks for medical image segmentation demonstrate the superiority of our CRII-Net. Specifically, when there are only 4% labeled data, our CRII-Net improves the Dice similarity coefficient (DSC) score by at least 7.49% when compared to five classical or state-of-the-art (SOTA) SSL methods. For hard samples/regions, our CRII-Net also significantly outperforms other compared methods in both quantitative and visualization results.