Reviewerly/exHarmony huggingface dataset:
Obstacle-aware Simultaneous Task and Energy Planning with Ordering Constraints

To improve the practical performance of task planning for unmanned ground vehicles, geographical features, limited onboard energy, and ordering constraints are urgent to be considered. This paper studies the simultaneous task and energy planning (STEP) problem considering obstacles and ordering constraints. The STEP problem is a sequential decision-making problem using the Markov decision process. A new multi-head self-attention-based deep reinforcement learning (DRL) method is proposed to solve this problem. A distance estimator calculates the distance between any two task points considered obstacles. A relational network pairs ordering constraints with feature vectors of each task to reason their relationships. The simulations compare solutions of our method, a recurrent neural network (RNN) based DRL, and the CPLEX method. Results demonstrate that our approach can obtain approximated performance compared with RNN-based DRL but in a shorter training time.
On Improving Malaria Parasite Detection from Microscopic Images: A Comparative Analytics of Hybrid Deep Learning Models

Malaria, a life-threatening mosquito-borne disease, contributes to a significantly high number of fatalities in tropical/sub-tropical regions due to inadequate detection technology, lack of laboratory experience, and other barriers. From the design perspective of a general-purpose point-of-care solution for detecting malaria along with other tropical diseases, malaria parasite detection from blood work needs to integrate accurate and fast detection capabilities. In this vein, in this paper, we develop three hybrid data-driven models in this paper that combine a convolutional neural network (CNN) with long short-term memory (LSTM), bi-directional LSTM (BiLSTM), and gated recurrent unit (GRU), respectively. CNN is employed in all three proposed models to extract the relevant features that are passed to two cascaded layers of Recurrent Neural Networks (RNNs) in each model that acts as a classifier. Based on the experiments conducted with a public dataset, we demonstrate that our designed CNN-GRU-GRU hybrid model outperformed the other models in terms of accuracy (96.01%), less type-I error rate (1.81%), and type-II error rate (2.18%). On the other hand, the CNN-LSTM-LSTM model was attributed to a low computing (training) time of just 4 minutes and 46 seconds. Our findings clearly elucidate the potential of combining classifiers in biomedical analytics research and pave the way for portable point-of-care devices with reasonable accuracy and fast computation times, enabling them to be used for collaborative learning for large-scale, real-time disease modeling.
Personalized Matching System of Learning Resources Based on Multi-Dimensional User Portrait Using Hybrid Recommendation Algorithm Combining Artificial Intelligence

The key to the realization of user personalized learning is the precise placement of learning resources. In this paper, artificial intelligence recommendation algorithm is integrated to establish a personalized recommendation system for learning resources. This paper firstly classifies the interest tags of users in the recommendation system by combining the multi-label classification algorithm of user portrait technology. Then from the personalized learning data acquisition, personalized learning label system construction, learner portrait modeling and other aspects of in-depth mining user personalized learning era of precise service demand. An accurate personalized learning path planning system based on learner portrait is designed. On this basis, the relationship model between teaching resources and learner portrait is established by using adaptive algorithm. The experimental results show that the model can recommend the courses that similar learners have learned according to the portraits of learners and provide personalized course recommendation services.
Towards a High-Performance Object Detector: Insights from Drone Detection Using ViT and CNN-based Deep Learning Models

Accurate drone detection is strongly desired in drone collision avoidance, drone defense and autonomous Unmanned Aerial Vehicle (UAV) self-landing. With the recent emergence of the Vision Transformer (ViT), this critical task is reassessed in this paper using a UAV dataset composed of 1359 drone photos. We construct various CNN and ViT-based models, demonstrating that for single-drone detection, a basic ViT can achieve performance 4.6 times more robust than our best CNN-based transfer learning models. By implementing the state-of-the-art You Only Look Once (YOLO v7, 200 epochs) and the experimental ViT-based You Only Look At One Sequence (YOLOS, 20 epochs) in multi-drone detection, we attain impressive 98% and 96% mAP values, respectively. We find that ViT outperforms CNN at the same epoch, but also requires more training data, computational power, and sophisticated, performance-oriented designs to fully surpass the capabilities of cutting-edge CNN detectors. We summarize the distinct characteristics of ViT and CNN models to aid future researchers in developing more efficient deep learning models.
Research on Small Target Detection in Optical Remote Sensing Based on YOLOv7

Aiming at the difficulty of target detection in optical remote sensing images due to excessive small targets, dense distribution and environmental factors, YOLOv7 target detection algorithm is used to identify small targets in optical remote sensing. Based on the YOLOv1~6 series algorithms, YOLOv7 algorithm introduces efficient layer aggregation network E-ELAN, cascade-based model scaling, module-level reparameterization, training with auxiliary header and positive and negative sample allocation strategy to enhance the performance of the model and improve the inference speed. Because the optical remote sensing images often have the problems of single type and inaccurate labeling, the DOTA dataset is selected as the target detection dataset. However, the pixel of DOTA data set is too high, resulting in too much training calculation. In this paper, the DOTA data set is divided. After 150 iterations, the YOLOv7 algorithm achieved an average accuracy of 77%. In order to prove the effectiveness of the proposed algorithm, a comparison experiment with the YOLOv5 series algorithm is carried out. The results show that the YOLOv7 algorithm has an average accuracy 1.3% higher than the superior YOLOv5x algorithm, and has a faster detection speed. Therefore, YOLOv7 algorithm can show better performance and stronger robustness in solving the problem of small target detection in optical remote sensing.
A Corpus-Based Study of the Dependency Distance Differences in English Academic Writing

Dependency distance has increasingly become a key measure of interest in cross-linguistic corpus studies from multiple perspectives. Based on a syntactically annotated corpus of 400 PhD dissertation abstracts written by native English (L1) and English as a foreign language (L2) academic writers, the current study investigated the mean dependency distance (MDD) variation across language backgrounds and disciplines, which is followed by a grammatical description based on fine-grained indices related to particular syntactic structures. The findings include: (1) L2 academic writers produce an averagely longer MDD than L1 academic writers because of their heavy use of prepositional phrases; (2) The MDD of the linguistics abstracts is significantly longer than that of the physics &amp; chemistry abstracts because of the relatively higher syntactic complexity of the language of linguistics. The findings suggest that MDD can effectively differentiate academic texts with different language backgrounds and disciplines, that both L1 and L2 academic writers write under the constraint of dependency distance minimization, and that L2 PhD dissertation writers have achieved native-like writing proficiency in extending nominal structures.
Comparative analysis of Vid2Vid and Fast Vid2Vid Models for Video-to-Video Synthesis on Cityscapes Dataset

Video generation refers to the process of creating videos using computational methods with the help of deep learning algorithms. With the advancement of deep learning techniques and the availability of large datasets, video generation has become more sophisticated and has the potential to revolutionize various industries, including entertainment, advertising, and education. This comparative review of video-to-video synthesis models is done to understand the working of both the Vid2Vid and Fast Vid2Vid model. In this comparative review, Vid2Vid delivers 3.44 FID and 0.92 Human Preference Score. In contrast, Fast Vid2Vid, with a two-stage training process, fine-tunes the Vid2Vid model on segmented images and gains 3.10 FID and 0.97 Human Preference Score. Fast Vid2Vid generates high-quality videos at a faster speed than Vid2Vid, making it more suitable for various applications like video editing, video creation, virtual try-on and so on.
NS4: a Novel Security approach for extracted video keyframes using Secret Sharing Scheme

Due to the vast employment and high impacts of videos in the multimedia era, video security has become a key research area in multimedia security. Hence, it is necessary to prevent sensitive data from being misappropriated, used, or destroyed in diverse fields like the military, finance, and education. Secure communication is not easy as various mediums produce much multimedia (video) data. Users must spend a lot of time and effort scanning an enormous amount of video information in search of essential information. Therefore, a secure keyframes-based generic video summarization (VS) model is proposed to generate a secure video summary. First, secret keyframes are extracted from the video through the proposed Saliency-Based Video Summarization (SBVS) technique. Second, secret image sharing is provided to the secret keyframes by the proposed EBSS (Encoding Based Secret Sharing) scheme, which uses encoding and XOR concepts for keyframes security. The proposed model outperforms generated summary and security compared to the existing keyframe-based VS and Secret Sharing Scheme (SSS) techniques.
Abnormal Behavior Prediction in Elderly Persons Using Deep Learning

A "smart home" is one in which conveniences like remote monitoring and helpful services are installed to help their residents maintain their sense of autonomy and privacy. When helping an elderly person, it's important to be able to distinguish between the person's typical and atypical actions. Abnormal behaviours noticed while performing ADLs are an excellent predictor of the presence of health and behavioural issues requiring care and support. In this paper, we offer a method for automatically detecting aberrant behaviours in the elderly using long short-term memory recurrent neural networks (LSTM). Our technique can be used to simulate the time-dependent information contained in longitudinal data sets. The purpose of this research is to assess how well LSTM can detect and foresee inappropriate behaviour in the elderly within connected homes. We conducted extensive experiments on a dataset to show that the suggested strategy is effective in predicting abnormal behaviours with a high degree of precision. We also showed that our method is far better than the current gold standard.
Anomaly Detection: A Machine Learning and Deep Learning Perspective

Anomaly detection involves finding data points that don’t follow the usual trends followed by the rest of the dataset. There exist several computationally efficient anomaly detection methods in data science. This paper explores and compares various machine learning and deep learning techniques. Anomaly detection can be a tricky task due to a multitude of reasons including highly imbalanced datasets, low amounts of data on actual frauds, unpredictable nature of the problem. The transaction in question does not necessarily need to be an outlier, which makes the problem contextual as well. Having performed fraud detection on credit card fraud and healthcare provider fraud datasets, we have been able to draw certain inferences as to which algorithms work best under which scenarios. Random Forest Classifier, XGBoost Classifier, Isolation Forests, Artificial Neural Networks, and Autoencoders are few of the prominent algorithms tested. After testing the algorithms, the results show that some algorithms work better with imbalanced datasets, while others can handle noise very well. We have also tried using certain dimensionality reduction methods in order to remove noise and improve efficiency, and certain sampling methods to handle class imbalances and then compared their results on these datasets.
Self-Governing Assessment Network (SGAN) Based Super-Resolution for CT chest images

Chest cancer is a common and potentially life-threatening disease that affects humanity worldwide. Early detection of chest cancer is critical for successful treatment and improved survival rates. Medical technology advancements in recent years have resulted in the development of various methods for detecting chest cancer. These methods have significantly improved the accuracy and reliability of chest cancer detection. However, the challenge remains to identify chest cancer at an early stage when it is most treatable. Therefore, ongoing research is focused on developing new and innovative approaches to enhance the resolution of CT chest images for early detection of chest cancer. In this paper the Self- Governing Assessment Network (SGAN) model is proposed to produce super resolution images from the low-resolution CT chest images. The proposed SGAN model is tested on 900 chest CT images dataset for super resolution factors 2, 4 and 6 respectively. The proposed model outperformed the existing models with PSNR values 35.82, 36.732, 37.196 and SSIM values 0.914, 0.9421, 0.9653 for super resolution factors 2,4 and 6 respectively.
Performance Metrics Assessment in Sentimental Analysis over Machine Learning Approaches

This article compares four different classification algorithms based on their performance in determining the sentiment based on the subjective context of the IMDB Movie Review Data. The classification algorithms used are logistic regression, decision tree, support vector machine (SVM), and XGB. Text data pre-processing including the determination of the weight of each word has been done using the Term Frequency Inverse Document Frequency (TFIDF) Vectorizer. Sentiment analysis may also know to be opinion mining, which is the procedure for determining whether the text reveals positive, negative, or neutral sentiment. Through this analysis, business managers can acquire a deep perception of customer opinions about their products. Consumer feedback can influence how well a product performs and the decision to track it can be the difference between a successful product launch and a missed opportunity. By highlighting possibilities to reimagine the user experience, it can also guide marketing and product development. Few metrics measures have been applied to test the efficacy of considered four classifiers and experimented outcome is presented in terms of superior accuracy.
Impact and Performance Analysis of Various Activation Functions for Classification Problems

The human brain serves as the inspiration for artificial neural networks in deep learning, which have a similar structure and operation as the brain’s network of neurons. These networks have layers of cells that are connected and process information in a similar way to real neurons. An activation functions plays a important role in these deep learning based neural network architectures. This paper examines the impacts and performance of various activation functions: step function, sigmoid, Tanh, soft-sign, soft-max, Relu and its variants. These activation functions are tested on four publicly available biomedical data sets. This paper studies the impacts of these activation functions and finds the best activation function for binary and multi-class classification problems in deep artificial neural networks. The results show that sigmoid has good performance for binary classification and soft-max has good performance for multi-class classification tasks. However, building a successful neural network depends on choice of parameter setting but, in this study, all other parameters are kept unchanged.
Multi-Body Collaborative Scheduling Strategy Based on Bessel Curve and Grey Wolf Algorithm

With the rapid development of computer technology, the development of intelligent technology is getting faster and faster, and the application of intelligent applications is getting wider and wider. Intelligent trolley as one of the intelligent applications has developed rapidly and is widely used in production life. Smart trolleys and the products generated on the basis of smart trolley research have become the key equipment for automated logistics transportation. Intelligent trolley reasonable path planning can choose the best path [1]–[2], and then improve production efficiency. To pursue high efficiency in the work of smart carts, choosing the path with the shortest movement time is one of the methods to improve efficiency. In this paper, by analyzing the kinematic mechanism of the two-dimensional motion of the trolley and establishing a two-dimensional planar path planning model and a nonlinear planning model by combining the principles of multi-body collaborative scheduling [3], we study the influence of the position and attitude of the initial and termination points of the trolley and the midway path planning scheme on the formation change time, and seek the shortest time of scheduling under the premise of avoiding collision. In this paper, two main aspects are considered. First, considering the non-collision constraint, square area constraint, velocity constraint and acceleration constraint, the optimization model is established with the objective of minimizing the maximum time used by each cart to reach the target point. Second, combined with the minimum radius of curvature restriction when the intelligent cart does arc motion, the motion process of the cart is divided into arc-linear-arc three segments to establish the cart path planning model under the requirement of seeking the shortest path and the final arrival of the cart at the target azimuth. Considering the non-collision constraint, square area constraint, velocity constraint and acceleration constraint, the Bessel curve [4] is used to plan the path to avoid the obstacles in the area where collision may occur, and the optimization model is established with the goal of shortest dispatch time.
Research of professional and value orientations of youth in the conditions of Internet socialization

Relevance. We studied the professional-value orientations of young people in the conditions of Internet socialization, their place in the structure of the life values of young people. The relevance is due to the need to search for technologies for managing the process of professionally oriented training of young people with the help of the Internet. The concept of professional value orientations of young people is clarified, technologies that contribute to the Internet socialization of youth are identified, Internet technologies that contribute to the formation of professional value orientations of young people are identified. Research methods. To achieve the goals of the study, various methods were used, including the method of analyzing information sources, the method of questioning through the digital resource Google Forms. Research results. We discuss the results of a study of the professional and value orientations of young people in the context of Internet socialization with the help of professionally oriented social networks, professional communities, technologies and services for interaction on the Internet. The Internet socialization of young people using freelance technologies, online internships, online edu-cational platforms is being studied. A study is being made of the attitude of the pedagogical and business communities to the practice of university students with the help of Internet technologies. Conclusions. Based on the analysis, it was revealed that the specificity of the formation of professional and value orientations of student youth in the conditions of Internet socialization is manifested in their active position, entry into various professionally oriented communities and their adaptation, individuality and integration in this community. Socialization first manifests itself as a process of adaptation to a professionally oriented Internet community, then – a demonstration of one’s individuality in the Internet community, and finally – entry into the Internet community and influence on its life. Based on the above, a discussion was held of the results of a study of professional value orientations of young people in the context of Internet socialization in professionally oriented social networks, freelance technologies, online internships, online educational platforms, and others.
On the Use of First and Second Derivative Approximations for Biometric Online Signature Recognition

This paper investigates the impact of different approximation methods in feature extraction for pattern recognition applications, specifically focused on delta and delta-delta parameters. Using MCYT330 online signature database, our experiments show that 11-point approximation outperforms 1-point approximation, resulting in a 1.4% improvement in identification rate, 36.8% reduction in random forgeries and 2.4% reduction in skilled forgeries.
Unjustified Sample Sizes and Generalizations in Explainable Artificial Intelligence Research: Principles for More Inclusive User Studies

Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 220) published between 2012 and 2022. Most studies did not offer rationales for their sample sizes. Moreover, most papers generalized their conclusions beyond their target population, and there was no evidence that broader conclusions in quantitative studies were correlated with larger samples. These methodological problems can impede evaluations of whether XAI systems implement the explainability called for in ethical frameworks. We outline principles for more inclusive XAI user studies.