Reviewerly/exHarmony huggingface dataset:
A novel cell contour-based instance segmentation model and its applications in HER2 breast cancer discrimination

Breast cancer is a vital disease that seriously threatens women's health. Fluorescence In Situ Hybridization (FISH) is an effective method to detect breast cancer and can assist in utilizing effective targeted therapies. However, manual and computer vision-based methods have shortcomings in accuracy and real-time performance in post-processing screening samples for breast cancer detection by FISH. We obtain highly accurate cell contour polygons using our original method, called Bio-Snake. Specifically, we first apply B-channel Topology Analysis on breast cancer FISH samples based on boundary tracking algorithm in order to automatically determine the initial cell contour. Afterwards, we apply Adaptive LSTM Alignment method on cell contour optimization in order to more accurately utilize the local contours information. The Weighted Loss is used to train the cell contour segmentation model. Finally, we propose HER2 & Chromosome 17 copy number cell attribution model to assist in sample statistics. Our experimental results demonstrate that our proposed Bio-Snake achieves 51.4 average precision (AP) and 34.0 Mask-AP on the standard benchmarks with an inference speed of 11.1 FPS, which is 34% higher and 14% faster than the current state-of-the-art method.
YOLF-ShipPnet: Improved RetinaNet with Pyramid Vision Transformer

Abstract In the field of ship detection, the intricate nature of ship images arises from a multitude of factors, including variations in ship orientation, color contrasts, and diverse shapes. These factors collectively contribute to the challenge of achieving high detection precision. Thus, it is necessary to investigate the application of advanced networks for ship image detection. In this paper, we have put forward an improved network called YOLF-ShipPnet, which utilizes a popular pyramid vision transformer with increased depth as the backbone for the RetinaNet network. To increase the model’s generalization ability, You Only Look Once eXtreme’s (YOLOX’s) hue, saturation, and value (HSV) random augmentation technique is employed to simulate light and color effects on ship images during the construction of the network. Ablation experiments were conducted on the model with two popular datasets: High-Resolution Ship Collections 2016 (HRSC2016) and SAR Ship Detection Dataset (SSDD). The YOLF-ShipPnet network has been verified to improve detection precision and generalization ability in ship detection by $$5.22\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5.22</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$5.46\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5.46</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , respectively, compared to RetinaNet baseline, exhibiting strong robustness and high effectiveness. The proposed network is applicable to the field of fine-grained ship detection and achieves an accuracy improvement of $$10.03\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10.03</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> compared to the baseline network.
Design and Validity of a Smart Healthcare and Control System for Electric Bikes

This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output, training time). The developed system aims to monitor the health state of the rider, analyze data in real-time, and provide electric assistance, thus diminishing muscular exertion. Furthermore, such a system can recover the same physiological data used in medical centers and program it into the e-bike to track the patient's health. System validation is conducted by replicating a standard medical protocol used in physiotherapy centers and hospitals, typically conducted in indoor conditions. However, the presented work differentiates itself by implementing this protocol in outdoor environments, which is impossible with the equipment used in medical centers. The experimental results show that the developed electronic prototypes and the algorithm effectively monitored the subject's physiological condition. Moreover, when necessary, the system can change the training load and help the subject remain in their prescribed cardiac zone. This system allows whoever needs to follow a rehabilitation program to do so not only in their physician's office, but whenever they want, including while commuting.
Spectral clustering with robust self-learning constraints

Spectral clustering is a leading unsupervised classification algorithm widely used to capture complex clusters in unlabeled data. Additional prior information can further enhance the quality of spectral clustering results to satisfy users' expectations. However, it is challenging for users to find the prior information under unsupervised scenes. To get rid of the deficiency, we propose a spectral clustering model with robust self-learning constraints. In this model, we first extend the optimization problem of spectral clustering by seeing label constraints as variables to learn the constraints and the clustering result simultaneously. Furthermore, we add a robust term to the proposed model so that we can learn multiple groups of label constraints to guide the clustering process and find a robust self-constrained spectral clustering result. The robust term can reduce the impact of uncertainty in the quality of a single set of label constraints on the performance of the proposed model. An iterative strategy with update formulas for variables is proposed to solve the self-constrained spectral clustering problem. We provide the theoretical analysis to explain the importance of the learned constraints in spectral clustering. Furthermore, we analyze the convergence of our optimization scheme. Finally, we have done many experiments on benchmark data sets to illustrate the effectiveness of the proposed algorithm.
CoADS: Cross attention based dual-space graph network for survival prediction of lung cancer using whole slide images

Accurate overall survival (OS) prediction for lung cancer patients is of great significance, which can help classify patients into different risk groups to benefit from personalized treatment. Histopathology slides are considered the gold standard for cancer diagnosis and prognosis, and many algorithms have been proposed to predict the OS risk. Most methods rely on selecting key patches or morphological phenotypes from whole slide images (WSIs). However, OS prediction using the existing methods exhibits limited accuracy and remains challenging.In this paper, we propose a novel cross-attention-based dual-space graph convolutional neural network model (CoADS). To facilitate the improvement of survival prediction, we fully take into account the heterogeneity of tumor sections from different perspectives. CoADS utilizes the information from both physical and latent spaces. With the guidance of cross-attention, both the spatial proximity in physical space and the feature similarity in latent space between different patches from WSIs are integrated effectively.We evaluated our approach on two large lung cancer datasets of 1044 patients. The extensive experimental results demonstrated that the proposed model outperforms state-of-the-art methods with the highest concordance index.The qualitative and quantitative results show that the proposed method is more powerful for identifying the pathology features associated with prognosis. Furthermore, the proposed framework can be extended to other pathological images for predicting OS or other prognosis indicators, and thus delivering individualized treatment.
A Decidable Fragment of First Order Modal Logic: Two Variable Term Modal Logic

First order modal logic (𝖥𝖮𝖬𝖫) is built by extending First Order Logic (𝖥𝖮) with modal operators. A typical formula is of the form \(\forall x \exists y \Box P(x,y)\) . Not only is 𝖥𝖮𝖬𝖫 undecidable, even simple fragments like that of restriction to unary predicate symbols, guarded fragment and two variable fragment, which are all decidable for 𝖥𝖮 become undecidable for 𝖥𝖮𝖬𝖫. In this paper we study Term Modal logic (𝖳𝖬𝖫) which allows modal operators to be indexed by terms. A typical formula is of the form \(\forall x \exists y~\Box _x P(x,y)\) . There is a close correspondence between 𝖳𝖬𝖫 and 𝖥𝖮𝖬𝖫 and we explore this relationship in detail in the paper. In contrast to 𝖥𝖮𝖬𝖫, we show that the two variable fragment (without constants, equality) of 𝖳𝖬𝖫 is decidable. Further, we prove that adding a single constant makes the two variable fragment of 𝖳𝖬𝖫 undecidable. On the other hand, when equality is added to the logic, it loses the finite model property.
Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is an active area of research that has the potential to improve the efficiency and reliability of wireless communication systems significantly. AMC is the approach used in a communication system to detect the type of modulation format at the receiver end. This paper proposes a voting-based deep convolutional neural network (VB-DCNN) for classifying M-QAM and M-PSK signals. M-QAM and M-PSK signal waveforms are generated and passed through the fading channel in the presence of additive white Gaussian noise (AWGN). The VB-DCNN extracts features from the input signal through convolutional layers, and classification is performed on these features. Multiple network instances are trained on different subsets of training data in the VB-DCNN. A network instance predicts the input signal during testing. Based on the votes, the final prediction is made. Different simulation experiments are carried out to analyze the performance of the trained network, and the DCNN is designed with the Deep Neural Network Toolbox in MATLAB. The generated frames are divided into training, validation, and test datasets. Lastly, the classification accuracy of the trained network is determined using test frames. The proposed model’s accuracy is near to 100% at lower SNRs. The simulation results show the superiority of the proposed VB-DCNN compared to existing state-of-the-art techniques.
Higher Grassmann codes II

We compute the parameters of the linear codes that are associated with higher dimensional projective embeddings of Grassmannian when the degree exceeds the number of elements of the finite field. We investigate various dimension formulas for the projective Reed-Müller codes. We show that the duals of higher Grassmann codes are given by higher Grassmann codes or their simple extensions.
An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle

Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.
Stein variational gradient descent with learned direction

Recently, as a particle-based Bayesian inference method, Stein variational gradient descent (SVGD) has attracted much attention due to its strong approximation power and efficiency compared to traditional variational inference and Markov chain Monte Carlo sampling methods. However, the original SVGD method assumes the descent direction to be within an reproducing kernel Hilbert space (RKHS), which on one hand limits its expressive power, and on the other hand suffers from the curse of dimensionality. To address this issue, we propose to parameterize the descent direction using a flexible neural network. With this parameterization strategy, the descent direction can be possibly optimized in each step for updating the particles, such that the target distribution can be expected to be better approximated. Experiments on both synthetic and real datasets demonstrate the effectiveness and efficiency of the proposed method, especially in the high-dimensional situations, as compared with the original SVGD.
Verified reductions for optimization

Abstract Numerical and symbolic methods for optimization are used extensively in engineering, industry, and finance. Various methods are used to reduce problems of interest to ones that are amenable to solution by these methods. We develop a framework for designing and applying such reductions, using the Lean programming language and interactive proof assistant. Formal verification makes the process more reliable, and the availability of an interactive framework and ambient mathematical library provides a robust environment for constructing the reductions and reasoning about them.
ACoRe: Automated Goal-Conflict Resolution

Abstract System goals are the statements that, in the context of software requirements specification, capture how the software should behave. Many times, the understanding of stakeholders on what the system should do, as captured in the goals, can lead to different problems, from clearly contradicting goals, to more subtle situations in which the satisfaction of some goals inhibits the satisfaction of others. These latter issues, called goal divergences , are the subject of goal conflict analysis , which consists of identifying, assessing, and resolving divergences, as part of a more general activity known as goal refinement. While there exist techniques that, when requirements are expressed formally, can automatically identify and assess goal conflicts, there is currently no automated approach to support engineers in resolving identified divergences. In this paper, we present ACoRe , the first approach that automatically proposes potential resolutions to goal conflicts, in requirements specifications formally captured using linear-time temporal logic. ACoRe systematically explores syntactic modifications of the conflicting specifications, aiming at obtaining resolutions that disable previously identified conflicts, while preserving specification consistency. ACoRe integrates modern multi-objective search algorithms (in particular, NSGA-III, WBGA, and AMOSA) to produce resolutions that maintain coherence with the original conflicting specification, by searching for specifications that are either syntactically or semantically similar to the original specification. We assess ACoRe on 25 requirements specifications taken from the literature. We show that ACoRe can successfully produce various conflict resolutions for each of the analyzed case studies, including resolutions that resemble specification repairs manually provided as part of conflict analyses.
LTL Reactive Synthesis with a Few Hints

Abstract We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment, including hostile ones. In the variant studied here, the user provides the high level LTL specification $$\varphi $$ of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm first generalizes the user-provided examples in E using tailored extensions of automata learning algorithms, while preserving realizability of $$\varphi $$ . Second, it turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine realizing $$\varphi $$ . The examples are used to guide the synthesis procedure. We prove learnability guarantees of our algorithm and prove that our problem, while generalizing the classical LTL synthesis problem, matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize $$\varphi $$ , computed by the synthesis tool Acacia-Bonzai . We illustrate the practical interest of our approach on a set of examples.
Job satisfaction among educators: A systematic review

This systematic review of literature examined the emergent scientific literature relating to job satisfaction among educators. It is believed to provide administrators and educational leaders an overall view of educator’s job satisfaction and its related factors based on the synthesis and findings of studies across the globe for the past half decade. The findings revealed that empirical studies on job satisfaction among educators mainly focused on self-efficacy and job performance. Thus, there is a need to explore several related factors such as relationship, internal and external factors and theoretical development on job satisfaction. Therefore, it’s the researcher’s hope that future studies can explore related factors on educator’s job satisfaction thereby enriching and augmenting effectiveness and efficiency at work to better guide educational management, supervision and leadership.
Human object interaction detection based on feature optimization and key human-object enhancement

Aiming at the problem of unclear or missing human object interaction behavior objects in complex background, we propose a human object interaction detection algorithm based on feature optimization and key human-object enhancement. In order to solve the problem of missing human behavior objects, we propose Feature Optimized Faster Region Convolutional Neural Network (FOFR-CNN). FOFR-CNN is an object detection network optimized by multi-scale feature optimization algorithm, taking into account both image semantics and image structure. In order to reduce the interference of complex background, we propose a Key Human-Object Enhancement Network. The network uses an instance-based method to enhance the features of interactive objects. In order to enrich the interaction information, we use the graph convolutional network. Experimental results on HICO-DET, V-COCO and HOI-A datasets show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms.
Real-Time Detection of Road-Based Objects using SSD MobileNet-v2 FPNlite with a new Benchmark Dataset

This research paper presents a real-time detection of road-based objects using SSD MobileNet-v2 FPNlite. This model uses the Single Shot Detector (SSD) architecture with MobileNet-v2 as the backbone and Feature Pyramid Network lite (FPNlite) as the feature extractor. This approach combines the advantages of both SSD and MobileNet-v2 for object detection while maintaining low computational complexity. In order to evaluate the performance of the model, a new benchmark dataset is explicitly created for this study, which includes a wide range of images captured from various sources such as cameras mounted on vehicles and street-level cameras. The dataset contains a diverse set of objects and scenes, making it suitable for testing the robustness and generalization ability of the system. The results of the experiments demonstrate the effectiveness of the model. In addition, the newly developed benchmark dataset can be used as a reference for further research in the field.
Survey on Job Recommendation Systems using Machine Learning

With the development of internet technology online job hunting has been boosted as it helps to save the time and efforts, [theory providing ease of search]. It's hard for job seekers to rely solely on keyword acquisition to find a job that befit their needs. To overcome this problem, the system will be made using article-based collaborative filtering and content-based filtering job recommended algorithm. The proposed system will be notified where the information about various jobs could be scrapped from vivid websites to form a huge database comprising majority of information regarding real time job opportunities. The users' information would be fetched from the CV submitted with our data from the dataset and recommendations would be provided on the same. This would prove to be of greater help as one doesn't have to hunt across varied websites. Job recommender systems' goal is to offer suggestions based on data about the users' preferences that has been recorded. The major goal is to provide skill recommendations to users so they can learn them, discover appropriate work, and streamline the application process for both novice and seasoned job seekers. The difficulties lie in identifying the best individuals based on their skill sets.
Image Caption Generation Related to Object Detection and Colour Recognition Using Transformer-Decoder

The dependence on digital images is increasing in different fields. i.e, education, business, medicine, or defense, as they are shifting towards the online paradigm. So, there is a dire need for computers and other similar machines to interpret information related to these images and help the users understand the meaning of it. This has been achieved with the help of automatic Image captioning using different prediction models, such as machine learning and deep learning models. However, the problem with the traditional models, especially machine learning models, is that they may not generate a caption that accurately represents that Image. Although deep learning methods are better for generating captions of an image, it is still an open research area that requires a lot of work. Therefore, a model proposed in this research uses transformers with the help of attention layers to encode and decode the image token. Finally, it generates the image caption by identifying the objects along with their colours. The fliker8k and Conceptual Captions datasets are used to train this model, which contains images and captions. The fliker8k contains 8,092 images, each with five captions, and Conceptual Captions contains more than 3 million images, each with one caption. The contribution of this presented work is that it can be utilized by different companies, which require the interpretation of diverse images automatically and the naming of the images to describe some scenario or descriptions related to the images. In the future, the accuracy can be increased by increasing the number of images and captions or incorporating different deep-learning techniques.