Reviewerly/exHarmony huggingface dataset:
The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal

Consider deploying a team of robots in order to visit sites in a risky environment (i.e., where a robot might be lost during a traversal), subject to team-based operational constraints such as limits on team composition, traffic throughputs, and launch constraints. We formalize this problem using a graph to represent the environment, enforcing probabilistic survival constraints for each robot, and using a matroid (which generalizes linear independence to sets) to capture the team-based operational constraints. The resulting “Matroid Team Surviving Orienteers” (MTSO) problem has broad applications for robotics such as informative path planning, resource delivery, and search and rescue. We demonstrate that the objective for the MTSO problem has submodular structure, which leads us to develop two polynomial time algorithms which are guaranteed to find a solution with value within a constant factor of the optimum. The second of our algorithms is an extension of the accelerated continuous greedy algorithm, and can be applied to much broader classes of constraints while maintaining bounds on suboptimality. In addition to in-depth analysis, we demonstrate the efficiency of our approaches by applying them to a scenario where a team of robots must gather information while avoiding dangers in the Coral Triangle and characterize scaling and parameter selection using a synthetic dataset.
DUEL: Depth visUal Ego-motion Learning for autonomous robot obstacle avoidance

Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL .
A method for classifying commodity information with the fusion of attribute features

In this paper, we propose a deep learning-based method for classifying commodity information to improve the problem that the model needs to understand the semantics of commodity information sufficiently, which leads to a reduced classification effect when the commodity descriptions are similar and complex. The research context is set to describe commodity information containing precious metals in the wording. The method enhances the commodity classification effect by extracting attribute entities in the commodity information, including identification attributes, commodity descriptions, and commodity specifications, and using a self-attention mechanism to fuse the attribute entity features with the overall semantic features to highlight the category features in the commodity information. The experiments show that the method improves the F1 value and accuracy rate by 2.28% and 1.81%, respectively, compared with typical classification methods, proving that fusing the attribute features of commodities can improve the semantic understanding of the model for different kinds of commodities under similar descriptions, which has some practical application value for dealing with complex semantic problems in commodity information.
Cryptocurrency Portfolio Management Based on Usage Characteristics Criteria Applying R-Vine Copula

This study proposes cryptocurrency portfolio management with asset selection based on usage criteria, as the direction of price variations between usage categories varies according to conventional financial concepts. The study used data from seven major types of cryptocurrencies: the storage of value, Smart contracts, Decentralized finance, Value transfer, Oracle, and Stable coins and the Meme type. The data is collected from a daily time series for the period from 20 November 2019 to 21 March 2022. The study used the Vine Copula method to analyze dependence before estimating the optimal portfolio with the least variance based on Markowitz’s theory. These results imply that the least variance portfolio should incorporate ETH, BUSD, and BTC. It further suggests that the portfolio include some DiFi. In addition, Doge coin (Meme type) is the root node of all cryptocurrency assets. When Doge coin volatility is high, investors should be cautious.
An Improved Bi-RRT* Trajectory Planning Algorithm for UAVs

In order to enable the UAV(Unmanned Aerial Vehicle) to plan an economical, feasible, and precise obstacle avoidance trajectory for the current environment during the cruise, a bidirectional expansion approach is adopted to increase iteration speed on the basis of the RRT* algorithm. At the same time, flight dynamics models are considered in the planning process, and a trajectory optimization algorithm is designed to optimized and smoothed the rough trajectory obtained initially. Finally, simulation was conducted to compare the new algorithm with traditional RRT* algorithm, and the results showed that the new algorithm has significant advantages in trajectory quality and stability. At the same time, it was verified that the new algorithm can meet the needs of online planning.
Association Rules Algorithm Based on Dual Support and Compression Matrix

Aiming at the characteristics of "more frequent and more ordinary" of traditional Chinese medicine materials, this paper proposes an association rule algorithm based on double support degree and compression matrix to improve the execution efficiency of drug search. Specifically, by introducing the principle of compression matrix, adding arrays to improve the matrix storage mode, and compressing the matrix according to the correlation theorem of the association rule, so as to reduce the number of scans of the matrix and the number of candidate sets generated. By setting the maximum and minimum support threshold, the generation of infrequent itemsets and overfrequent itemsets is limited to ensure the validity of the rules, thereby improving the practicability of the obtained rules. Experiments show that the proposed algorithm has obvious advantages in the data set with high support degree and high number of transactions.
Computational Complexity Reduction Techniques for Deep Neural Networks: A Survey

Deep-learning (DL) has been extensively applied in multiple fields such as medicine, space, agriculture and industry. It has proven to be able to successfully extract unique and discriminative features of targets from structured or unstructured data and provide useful and precise predictions. However, the computational complexity of DL remains a burden for realizing such approaches on battery-powered resource-constrained mobile, wearable, and other Internet-of-Things (IoT) edge devices. In this paper, we present a survey of the significant approaches for reducing the complexity of DL models including pruning, quantization, knowledge distillation, multiplication reduction, resource efficient architectures, low rank approximation and network architecture search. Further, we discuss the limitations of these approaches and demonstrate potential solutions.
Ranking of Countermeasures Against Cyberattacks in Cyberphysical Systems of Critical Infrastructure Facilities

The relevance of cybersecurity to critical infrastructure, such as nuclear facilities, cannot be overstated. A distinctive feature of cybersecurity at nuclear facilities is the severe consequences of its violation, and the consequences of cyber-attacks can have catastrophic consequences and even reach national proportions. A study of the field of cybersecurity of nuclear power facilities has shown that the problem of ensuring the security of such facilities has not been fully solved. To solve this problem, we have developed models and algorithms for creating attack vectors for cyber-physical systems of critical infrastructure facilities. The models and algorithms were developed considering the specifics of a particular object and statistical data, distinguishing them from previously proposed vectors of attacks on cyber-physical systems. Generated vectors allow one to analyze possible security impacts and rank countermeasures to these vectors. The countermeasure ranking method uses lists of attack vectors and open vulnerability databases, which makes it possible to rank countermeasures according to various criteria. Such approaches have not previously been used in the specifics of the objects under study.
Convolutional Neural Networks for Automated Retrieval of Fuel Price Data from Captured Images at Gas Stations

Fuel price fluctuations, including both increases and decreases, are a common occurrence when it comes to driving a vehicle. The Waze navigation application provides a platform for users to input and share fuel prices through crowd-sourced data manually. In a recent research study, an automated approach was developed to supply accurate fuel price information. This approach involved training a Single Shot MultiBox Detector (SSD) to recognize fuel-related signs, and Google’s Cloud Vision API was employed for Optical Character Recognition (OCR). These technologies collectively offered an efficient means for users to contribute to and access crowd-sourced fuel price details. A web application was created to extract fuel prices from these signs, achieving a notable Mean Average Precision (mAP) score of 66.6% through the trained SSD model. Through user evaluations, the research demonstrated a mean average satisfaction score of 4.6, indicating alignment with ISO 25010 and 25012 standards encompassing functional suitability, performance efficiency, and accuracy. The research study effectively designed a web-based solution that proficiently extracts fuel prices from images of fuel station price boards using a combination of object detection and OCR.
Online Sexism Detection and Classification by Injecting User Gender Information

This paper investigates the potential effects that user gender information has on online sexism detection, in terms of both binary and multi class detection. Social media has recently developed into a center for sexist posts that target especially women. Since most sexist comments are made especially against people of a particular gender, whether the gender information of the users could be effective or not for sexism detection, is still an important question. Here we try to address this issue using Natural Language Processing (NLP) and machine learning models. Experiments showed that combining user gender information with textual features improved classification performance both in terms of binary classification and multi class classification. The effectiveness of the proposed strategy is demonstrated by the experimental results reported in this research.
An new image histogram regularization based on probabilistic transformation

Usually, when contour enhancement is performed on an image, it is necessary to emphasize the image contour by widening the gray level gap or highlighting sensitive gray levels. The common method is to use the histogram regularization method, but this method will face the problem that the transformed histogram cannot obtain a high overlap with the target histogram, which leads to the insufficient effect of contour enhancement. In this paper, we propose a forced probabilistic histogram transformation method, which uses probabilistic transformation to achieve a forced mapping of the same gray level to different gray levels of the target histogram, effectively mapping the gray levels into the restricted interval while obtaining a higher degree of overlap. The effectiveness of the forced probability histogram regularization algorithm be derived from the experiments of actual contour enhancement and the comparison of SML (Single Mapping Law) histogram regularization method in terms of overlap and time complication .
Full Cycle Data Protection Scheme of Power Parameters in Smart Grid Based on Lattice Encryption

Power parameter data protection is an important guarantee for the smooth operation of smart grid. In order to realize the full life cycle data protection of power parameters, based on lattice cipher and DALCM model, this paper proposes a full life cycle data protection scheme for power parameters generated by smart terminals in power grids. The scheme in electricity parameter data phase, the electricity parameter for data encryption, the encrypted cryptograph no longer decrypt data in data processing stage, ciphertext survival to data disposal, scheme proposed can provide data security support for the operation of the smart grid, from the perspective of data security to ensure the smooth operation of the smart grid.
An Active Learning Model Production Method for Electric Power Inspection Image Multi-target Detection

With the development of intelligent electric power inspection technology, electric power companies use UAVs, cameras, robots and other equipment for power transmission line inspections, and take a large number of inspection images. In recent years, the image multi-target detection model has been maturely applied in the intelligent inspection of electric power. The current image intelligent detection model mainly relies on a large number of image sample labels. Power companies have a small number of labeled image samples, and a large number of unlabeled. But manual data annotation is very expensive, and cost lots of time and money. Moreover, there will be certain errors in manual annotation, so it is necessary to repeat the annotation and verification to reduce the error. But this will also increase the cost. This article proposes an active learning model production method for electric power inspection image multi-target detection. We select samples for annotation according to some strategies, and select labeled samples for verification. This method can reduce the cost of sample annotation, and improve the model. productivity. Compared with the traditional random labeling strategy, our method can achieve the same accuracy with only 20% of the annotation cost. With 50% of the annotation cost, the mAP index will increase by 12%.
Research on Calligraphy Style Recognition Based on Adaptive Image Enhancement and Improved DenseNet Algorithm

In order to solve the problems that ancient calligraphy fonts are missing due to the influence of historical factors, there are many kinds of fonts, but the amount of data is small, and the deep learning technology is not used to realize better recognition, an adaptive image enhancement and DenseNet calligraphy font style recognition algorithm is proposed, and better font recognition effect is achieved through optimization model. Through image processing and other technologies, more than forty thousand pictures of six types of calligraphy were collected, each type of calligraphy font contains at least three thousand common words, and the current popular neural network models: AlexNet, GoogleNet, SimpleNet, ResNet, DenseNet models were compared by training self-built data set. Finally, DenseNet network was selected for optimization, and Lion optimizer was used to optimize the network model. The training accuracy reached 97.24%, and the training effect was better than the popular neural network.
Attribute-based Certificateless Aggregation Signcryption Scheme

The advanced measurement system is a complete network that measures, counts, stores, analyzes and uses the user's power information. In the system, smart meters need to send the user's power data to the control center in real time, and the data security and transmission efficiency are extremely high during the transmission process. Therefore, in order to ensure the safe and fast transmission of data, this paper studies the protection methods of user identity and electricity data in the system: aiming at the huge workload problem caused by the correctness of the ciphertext in turn before the control center designs, an attribute-based certificateless aggregation sign-off scheme is proposed, and the security verification in the data transmission process is passed,and the amount of calculation in the transmission process is reduced and the transmission efficiency is improved.
Quantifying Object-Oriented System Complexity: Introducing a Powerful Measurement Tool

A lot of metrics and tools have been devised to measure the complexity of software systems. Through measurement, software professionals can have a better understanding and control of a software’s complexity. In this paper, we reviewed complexity metrics and tools for object-oriented systems. The existing tools measure either implemented systems which may be source code or compile binaries, or class diagrams. The tools that measure implemented systems already offer a wide variety of features to satisfy different needs. However, the tools that measure class diagrams are not only poor in amount, but all take in class diagram XMI files, with none of them taking in PlantUML class diagram source code. PlantUML is a popular text-based UML diagramming tool that enables users to define UML diagrams with a domain specific language then renders the diagram for them. Hence, we developed a web application that measures class diagram complexity via PlantUML class diagram source code. It measures five class metrics and seven system metrics visualized as tables and bar charts, with the results exportable to PDF and CSV. Our tool’s measurements were also evaluated to be correct when it is within our scope of supported syntax.
Implicit Modeling Network of Human Critical Points based on Attention Mechanism

Human pose estimation tasks require the use of visual cues and anatomical relationships between joints to locate key points, but convolutional neural network-based approaches have difficulty focusing on remote contextual cues and modeling the dependencies between distant joints. In this paper, we propose an implicit modeling method based on an attention mechanism to implicitly model the constraint relationships between key points by computing feature correlations between joints through multi-stage iterations. To address the problem that the network may weaken the invisible key points during training, a focal loss function is used to make the network more focused on complex key points. The experimental results show that the implicit modeling network can improve the performance of the human posture estimation network, and the algorithm with the HRNet network as the backbone network can improve the performance of the human posture estimation network in the MPII dataset, the benchmark dataset for human posture estimation, compared with the original network. The accuracy of the algorithm with HRNet network as the backbone was improved by 1.7% compared to the original network.
Augmented Reality and Machine Learning in Health: A Systematic Review

Augmented Reality (AR) is a useful technology for providing an information-rich reality by superimposing digital objects and giving a virtual interpretation of the physical environment. AR has played a key role in reducing cognitive load and the applications of AR have been useful in various fields ranging from manufacturing, advertisement, education, military, and health. AR has also been deployed on various platforms like mobile, computer screens, and head-mounted displays (HMD). In this paper, we systematically reviewed research papers that have applied AR systems with machine learning (ML) in various health-related domains within the past 12 years (2010–2021). We present a review of the state-of-the-art AR implementation and research in the area of health by (1) identifying various AR approaches, (2) uncovering various areas of health where AR have been applied, (3) determining the current trend, gaps, and areas for future work, (4) highlighting the artificial intelligence (AI) and machine learning (ML) algorithms used in the AR systems and how they are used, and (5) comparing the different visualization modalities (web, mobile, and HMD). This review adds to the existing literature by shedding light on the common tools, successful approaches used in implementing previous AR projects, and evaluation methods. We uncover how AI and object tracking was implemented in AR for health. Finally, we identify gaps and offer recommendations for advancing research in this area.
An Interactive Mobile Augmented Reality for Obstetrics Learning

The learning process for medical students in obstetrics, specifically in Malaysia's universities are divided into two parts, the first is theoretical learning where students will have to follow lectures as usual, and the latter is clinical training where students will undergo training with real patients in hospital.
Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Based on WHO’s data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient’s diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection.