Reviewerly/exHarmony huggingface dataset:
ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement

A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential shortcomings that must be addressed. In this work, we focus on three of these shortcomings: (i) the lack of uncertainty estimation, (ii) the lack of a guiding mechanism to help locate edges and encourage spatial consistency in the segmentation maps, and (iii) the models' inability to do one-step multi-class segmentation. Without modifying or requiring a specific backbone architecture, we propose a modified prototype extraction module that facilitates the computation of uncertainty maps in prototypical FSS models, and show that the resulting maps are useful indicators of the model uncertainty. To improve the segmentation around boundaries and to encourage spatial consistency, we propose a novel feature refinement module that leverages structural information in the input space to help guide the segmentation in the feature space. Furthermore, we demonstrate how uncertainty maps can be used to automatically guide this feature refinement. Finally, to avoid ambiguous voxel predictions that occur when images are segmented class-by-class, we propose a procedure to perform one-step multi-class FSS. The efficiency of our proposed methodology is evaluated on two representative datasets for abdominal organ segmentation (CHAOS dataset and BTCV dataset) and one dataset for cardiac segmentation (MS-CMRSeg dataset). The results show that our proposed methodology significantly (one-sided Wilcoxon signed rank test, p<0.05) improves the baseline, increasing the overall dice score with +5.2, +5.1, and +2.8 percentage points for the CHAOS dataset, the BTCV dataset, and the MS-CMRSeg dataset, respectively.
Blockchain integration with the internet of things for the employee performance management

As time went on, technological progress inevitably altered our daily routines. Many new technologies, such as the Internet of Things (IoT) and cryptocurrency, offer revolutionary possibilities. To put it simply, the blockchain is a distributed, public, and auditable database that can be used to record financial transactions. The IoT, or "Internet of Things," is a system of interconnected electronic devices that can communicate with one another and be remotely monitored and handled. This paper reviews the most recent findings in the field of blockchain and Internet of Things with the goal of examining blockchain as a possible answer to secure IoT data management within supply networks. There is a dearth of literature in the early stages of both blockchain and IoT study because they are such novel topics. The study's findings suggest that in order to improve their leadership quality to intentionally impact employee performance, industry managers should pay attention to human resource management indicators like collaboration, involvement, actualization, perception, and teamwork. This is primarily because of the inherent limitations of IoT devices and the distributed ledger architecture of the blockchain technology. There is potential for IoT to provide many advantages if blockchain capabilities can be optimized for it.
Divisibility on point counting over finite Witt rings

Let Fq denote the finite field of q elements with characteristic p. Let Zq denote the unramified extension of the p-adic integers Zp with residue field Fq. In this paper, we investigate the q-divisibility for the number of solutions of a polynomial system in n variables over the finite Witt ring Zq/pmZq, where the n variables of the polynomials are restricted to run through a box lifting Fqn. It turns out that in general the answers do depend upon the box chosen. Based on the addition operation of Witt vectors, we prove a q-divisibility theorem for any box of low algebraic complexity, including the simplest Teichmüller box. This extends the classical Ax-Katz theorem over finite field Fq (the case m=1). Taking q=p to be a prime, our result extends and improves a recent theorem of Grynkiewicz for the unweighted case.
A quantum algorithm for solving weapon target assignment problem

Quantum computers, known to have the potential for exponential speedup in solving some problems due to their superposition property, are expected to facilitate the solution of NP-hard optimisation problems. This study proposes a quantum algorithm to solve the weapon target assignment problem (WTAP), one of the NP-hard optimisation problems. The proposed quantum algorithm is a gate-based approach, and scenario examples are executed on Qiskit quantum computing platform and IBM Lima quantum computer with Falcon r4T processor type. The results manifest that the proposed quantum algorithm has low space and time complexity, demonstrating its memory and computational resources efficiency.
Seg-DGDNet: Segmentation Based Disguise Guided Dropout Network for Low Resolution Face Recognition

Face recognition models often encounter challenges while recognizing partially occluded faces. Disguise can be manifested intentionally to impersonate someone or unintentionally when the subject wears artifacts such as sunglasses, masks, hats, and caps. To identify a subject accurately, it is essential to discard the occluded regions of the subject's face and use the features extracted from the visible regions. The problem is further exacerbated when the input image is low resolution or captured at a distance. This article proposes a novel Segmentation based Disguise Guided Dropout Network (Seg-DGDNet) to identify the occluded facial features and recognize a person by non-occluded biometric features. The proposed Seg-DGDNet has two primary tasks: 1) identifying the non-occluded pixels in the subject's face using segmentation models and 2) guiding the recognition model to concentrate on visible facial features with the help of the proposed guided dropout. The performance of the proposed model is evaluated on three disguised face datasets with artifacts such as facial masks and sunglasses. The proposed model outperforms existing state-of-the-art face recognition models by a significant margin on different datasets with various levels of disguise and resolutions.
A New Safe-Level Enabled Borderline-SMOTE for Condition Recognition of Imbalanced Dataset

Machine learning-based classification strategy has been successfully applied in actual industrial monitoring but it is often hindered when the data set is imbalanced. Technically, the misclassification phenomenon, as a serious performance degradation of generalisation ability, often occurs in minority class. For this problem, Borderline-Synthetic Minority Over-sampling Technique (B-SMOTE), which aims to enrich the quantity of minority samples around decision boundaries, has received considerable attention. However, most imbalanced classification techniques under the framework of B-SMOTE generate instances by a random weight number from 0 to 1, which may result in an authentic reduction of newly-born samples. Herein, a novel over-sampling strategy, which aims to provide new safety criteria and reassign the threshold of weight coefficient, is proposed to boost the authenticity of generated samples and classification accuracy. In addition, light gradient boosting machine (LightGBM) is adopted to build the classification model. Related experiments show the effectiveness and superiority of the proposed method in handling imbalanced classification tasks.
Moving Object Detection Based on Enhanced Yolo-V2 Model

Object detection is a crucial aspect of computer vision, and deep learning has led to significant advancements in this area. Object categorization, segmentation, and localization have been improved with the use of deep models. Two-stage detectors have higher identification precision, while single-stage detectors have better inference times. You Only Look Once (YOLO) and its successors have shown improved detection accuracy and speed, making them popular in a wide range of applications. This paper proposes an improved YOLO-v2 for detecting tiny objects. The proposed detector is evaluated using the VOC 2012 benchmark dataset, and the experimental results show that it outperforms state-of-the-art detectors in terms of detection accuracy, precision, recall, and IOU. The proposed detector achieved 95.8% detection accuracy, 96.1% precision, 95.5% recall, and 95% IOU.
Gesture Controlled Mobile Robot

In this paper, a 3D printed remote-gesture-controlled maneuverable robot is presented. The manipulator system is based on two hand-worn glove controllers, represented by robotic manipulator arm and the robot vehicle. The developed approach of Human-Computer Interaction (HCI) employs the Arduino- platform, where both hands are used simultaneously to control the robot, expanding the human-robot interaction to a more humanly accessible design that can remotely control both an industrial style 5-axis robotic manipulator and a robot vehicle using hand gestures. The results prove the viability of using glove controllers for this developed design and the possibility of industrial implementations, such as gesture-controlled robotics that can benefit from further advancement. Moreover, the presented dynamic gesture control methods are a crucial medium for human-robot interaction (HRI).
DARMF-UNet: A dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation

Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland segmentation methods currently suffer from challenges such as less accurate edge segmentation, easy mis-segmentation, and incomplete segmentation. To solve these problems, this paper proposes a novel gland segmentation network Dual-branch Attention-guided Refinement and Multi-scale Features Fusion U-Net (DARMF-UNet), which fuses multi-scale features using deep supervision. At the first three layers of feature concatenation, a Coordinate Parallel Attention (CPA) is proposed to guide the network to focus on the key regions. A Dense Atrous Convolution (DAC) block is used in the fourth layer of feature concatenation to perform multi-scale features extraction and obtain global information. A hybrid loss function is adopted to calculate the loss of each segmentation result of the network to achieve deep supervision and improve the accuracy of segmentation. Finally, the segmentation results at different scales in each part of the network are fused to obtain the final gland segmentation result. The experimental results on the gland datasets Warwick-QU and Crag show that the network improves in terms of the evaluation metrics of F1 Score, Object Dice, Object Hausdorff, and the segmentation effect is better than the state-of-the-art network models.
Data-free quantization via mixed-precision compensation without fine-tuning

Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires the original data. This not only brings heavy computation and time costs but also is not conducive to privacy and sensitive information protection. Therefore, a few recent works are starting to focus on data-free quantization. However, data-free quantization does not perform well while dealing with ultra-low precision quantization. Although researchers utilize generative methods of synthetic data to address this problem partially, data synthesis needs to take a lot of computation and time. In this paper, we propose a data-free mixed-precision compensation (DF-MPC) method to recover the performance of an ultra-low precision quantized model without any data and fine-tuning process. By assuming the quantized error caused by a low-precision quantized layer can be restored via the reconstruction of a high-precision quantized layer, we mathematically formulate the reconstruction loss between the pre-trained full-precision model and its layer-wise mixed-precision quantized model. Based on our formulation, we theoretically deduce the closed-form solution by minimizing the reconstruction loss of the feature maps. Since DF-MPC does not require any original/synthetic data, it is a more efficient method to approximate the full-precision model. Experimentally, our DF-MPC is able to achieve higher accuracy for an ultra-low precision quantized model compared to the recent methods without any data and fine-tuning process.
Unsupervised domain adaptation via style adaptation and boundary enhancement for medical semantic segmentation

The objective of semantic segmentation in cross-modal medicine is to align the distribution among different domains. The images from different domains contain various styles and boundary information, which the previous method ignores. Inspired by this observation, we considered employing the style and boundary information from features. We proposed a simple but effective way containing a style adaptation module and boundary enhancement module to facilitate the medical semantic segmentation on an unlabeled domain. In particular, the style adaptation module highlights the style information from the deep features through Fast Fourier Transform and low pass filter to assist in aligning the domain distributions. The boundary enhancement module utilizes the phase spectrum of features to obtain the boundary information and improve the segmentation ability by aggregating the boundary information. Results from experiments on two public datasets show that our proposed method effectively improves the segmentation performance of unlabeled target images and outperforms the most advanced domain adaptation approaches. Both qualitative and quantitative results evaluate the effectiveness of style features and boundary information in domain alignment.
The Research of AutoML Methods in the Task of Wave Data Classification

This paper discusses automatic machine learning methods for solving the problem of objects’ classification. Automated machine learning is the process of automatically creating and optimizing machine learning models, capable of performing automatic feature selection, hyperparameter optimization, model selection, and model evaluation based on given quality metrics. In the task of classifying several signals, automating this process can be particularly useful, since this type of task can be complex to decompose and requires a lot of work to isolate meaningful features. In addition, AutoML has high applicability in the task of building neural networks, such as forward propagation neural networks, and optimizing hyperparameters such as the number of layers, the number of neurons. In this paper different approaches to AutoML have been analyzed. Methods based on MLJAR AutoML, AutoKeras and TPOT showed the best results.
Face Recognition Attendance System Using Local Binary Pattern Algorithm

In the digital age of today, face recognition technology is essential and is utilized by almost all businesses. one of the technologies that made the most use of biometrics. It can be used for authentication, identification, and security and offers numerous benefits. Despite the fact that the method is non-invasive and contactless, it is still frequently used because it is less accurate than iris and unique mark ID. Organizations, universities, and other educational establishments can also make use of the facial recognition technology to monitor students' attendance. The current manual method is cumbersome and difficult to maintain, so this system aims to create a facial recognition-based system for tracking class attendance. Proxy attendance may also be an option. As a result, demand for this system has increased. Database creation, face detection, face recognition, and attendance update are the four stages of this system. Databases are created with images of students in class. For face recognition and detection, the Haar- Cascade classifier and the local binary pattern histogram method are utilized, respectively. Faces are perceived and distinguished in the homeroom's live-transferred video. Attendance records will be mailed to the relevant lecturers at the conclusion of the session.
Credit Card Fraud Detection Using Machine Learning Techniques

Credit card fraud poses a persistent challenge in today's e-commerce market, with a significant surge in fraudulent activities observed over recent years. This surge has resulted in financial losses for numerous organizations, companies, and government agencies. As digital payments continue to gain popularity, it is expected that the problem of credit card fraud will only intensify in the future. To address this issue, this study concentrates on the early identification of fraudulent online credit card transactions, utilizing advanced machine learning techniques. However, detecting credit card fraud proves to be a complex undertaking due to the highly imbalanced nature of the dataset. In other words, the frequency of genuine cases far exceeds that of fraudulent cases. To tackle this problem, predictive models such as logistic regression, random forest, k-nearest neighbors, and XGBoost, in conjunction with various resampling techniques, have been employed on the imbalanced dataset to ascertain whether a transaction is fraudulent or genuine. The experimental findings demonstrated that the ensemble method of max voting, combined with a hybrid resampling approach known as the synthetic minority oversampling technique (SMOTE), yielded the most favorable outcomes.
Secured Data Transaction for Agriculture Harvesting using Blockchain Technology

One of the biggest sectors in the world is agriculture, and a lot of data is produced every day in this industry. However, because there aren't adequate data management systems, this data is frequently underused, which causes inefficiencies in the sector. A safe and open data management solution for the farming sector could be constructed using block chain based to solve this problem. The built-in security features of block chain can aid in ensuring that data is gathered, stored, and shared securely, preventing hacking or illegal access. Farmers, agricultural businesses, and other stakeholders may safely share data on crop yields, weather patterns, soil conditions, and other crucial parameters thanks to block chain technology. Besides that, this data can be utilized to enhance farming methods, boost productivity, and eventually raise crop yields and profitability. Block chain technology can provide farm produce comprehensive visibility in as well as safety and accessibility, making it simple for customers to confirm the legitimacy and provenance of the goods they buy. All things considered, the application of block chain technology in the agricultural sector has the potential to transform the sector by delivering safe and transparent data management and enhancing production.
Video-Based Person Re-identification with Improved Temporal Attention and Spatial Memory

Video-based Person Re-identification (reID) aims to retrieve and query videos of people with the same identity across multiple cameras. Temporal attention mechanism is widely used in video-based person re-identification, but it cant effectively use the effective features in poor-quality frames. In this paper, we propose a video-based Person Re-identification model with improved temporal attention and spatial memory(ITASM), which mainly includes encoder, spatial memory module, attention residual module, feature fusion optimization module and weight distribution module. In the feature fusion optimization module, we extract the effective features of each frame, and fuse them with the features output by the attention residual module, so that the final temporal sequence features will contain more effective features of poor-quality frames during the weighted summation. In this way, we strengthen the effective information in poor-quality frames, and solve the above problem to a certain extent. The validity of the model proposed in this paper has been verified on MARS and iLIDS-vid datasets.
Deep-Reinforcement-Learning-Based Path Planning for Industrial Robots Using Distance Sensors as Observation

Traditionally, collision-free path planning for industrial robots is realized by sampling-based algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap), etc. Sampling-based algorithms require long computation times, especially in complex environments. Furthermore, the environment in which they are employed needs to be known beforehand. When utilizing these approaches in new environments, a tedious engineering effort in setting hyperparameters needs to be conducted, which is time- and cost-intensive. On the other hand, DRL (Deep Reinforcement Learning) has shown remarkable results in dealing with complex environments, generalizing new problem instances, and solving motion planning problems efficiently. On that account, this paper proposes a Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We propose an easily reproducible method to train an agent in randomized scenarios achieving generalization for unknown environments. We evaluated our model against state-of-the-art sampling- and DRL-based planners in several experiments containing static and dynamic obstacles. Results show the adaptability of our agent in new environments and the superiority in terms of path length and execution time compared to conventional methods. Our code is available on GitHub [1].
MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders

Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder models have overcome the GAN limitations, leading to high reconstruction fidelity. This theoretically makes them a perfect candidate to perform representation-level face morphing. This work investigates using diffusion autoencoders to create face morphing attacks by comparing them to a wide range of image-level and representation-level morphs. Our vulnerability analyses on four state-of-the-art face recognition models have shown that such models are highly vulnerable to the created attacks, the MorDIFF, especially when compared to existing representation-level morphs. Detailed detectability analyses are also performed on the MorDIFF, showing that they are as challenging to detect as other morphing attacks created on the image- or representation-level. Data and morphing script are made public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .