Reviewerly/exHarmony huggingface dataset:
Grey Wolf Optimizer Based Deep Learning for Pancreatic Nodule Detection

At an early point, the diagnosis of pancreatic cancer is mediocre, since the radiologist is skill deficient.Serious threats have been posed due to the above reasons, hence became mandatory for the need of skilled technicians.However, it also became a time-consuming process.Hence the need for automated diagnosis became mandatory.In order to identify the tumor accurately, this research proposes a novel Convolution Neural Network (CNN) based superior image classification technique.The proposed deep learning classification strategy has a precision of 97.7%, allowing for more effective usage of the automatically executed feature extraction technique to diagnose cancer cells.Comparative analysis with CNN-Grey Wolf Optimization (GWO) is carried based on varied testing and training outcomes.The suggested study is carried out at a rate of 90%-10%, 80%-20%, and 70%-30%, indicating the robustness of the proposed research work.Outcomes show that the suggested method is effective.GWO-CNN is reliable and accurate relative to other detection methods available in the literatures.
Calibration of a Catadioptric System and 3D Reconstruction Based on Surface Structured Light

In response to the problem of the small field of vision in 3D reconstruction, a 3D reconstruction system based on a catadioptric camera and projector was built by introducing a traditional camera to calibrate the catadioptric camera and projector system. Firstly, the intrinsic parameters of the camera and the traditional camera are calibrated separately. Then, the calibration of the projection system is accomplished by the traditional camera. Secondly, the coordinate system is introduced to calculate, respectively, the position of the catadioptric camera and projector in the coordinate system, and the position relationship between the coordinate systems of the catadioptric camera and the projector is obtained. Finally, the projector is used to project the structured light fringe to realize the reconstruction using a catadioptric camera. The experimental results show that the reconstruction error is 0.75 mm and the relative error is 0.0068 for a target of about 1 m. The calibration method and reconstruction method proposed in this paper can guarantee the ideal geometric reconstruction accuracy.
An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

In order to solve the problem of the low action recognition accuracy of passengers' unsafe behaviors caused by redundant joints, this study proposes an efficient recognition method based on a Kinect sensor. The method uses the pelvis as the starting point of the vector and high-frequency bone joints as the end point to construct the recognition feature vector. The joint angle difference between actions is obtained by using the cosine law, and the initial test result is converted into action similarity combined with the DTW similarity algorithm. Considering the combination of 3 angle features and 4 joint feature selection methods, 12 combined recognition models are formed. A comparative experiment was carried out to identify five types of unsafe behaviors of metro passengers-punch, one-armed call for help, arms for help, trip forward and trip backwards. The results show that the overall selection of joints has a poor similarity effect and cannot achieve the purpose of recognition. The overall recognition model effect of the local "pelvis divergence method" is higher than that of the local "adjacent joint method", and the local "pelvis divergence method" has the highest recognition result of the maximum angle difference model, and the recognition results of the five unsafe behaviors are 86.9%, 89.2%, 85.5%, 86.7%, and 88.3%, respectively, and the recognition accuracy of this method is 95.7%, indicating the feasibility of the model. The recognition results are more concentrated and more stable, which significantly improves the recognition rate of metro passengers' unsafe behavior.
Computational Optimization of Image-Based Reinforcement Learning for Robotics

The robotics field has been deeply influenced by the advent of deep learning. In recent years, this trend has been characterized by the adoption of large, pretrained models for robotic use cases, which are not compatible with the computational hardware available in robotic systems. Moreover, such large, computationally intensive models impede the low-latency execution which is required for many closed-loop control systems. In this work, we propose different strategies for improving the computational efficiency of the deep-learning models adopted in reinforcement-learning (RL) scenarios. As a use-case project, we consider an image-based RL method on the synergy between push-and-grasp actions. As a first optimization step, we reduce the model architecture in complexity, by decreasing the number of layers and by altering the architecture structure. Second, we consider downscaling the input resolution to reduce the computational load. Finally, we perform weight quantization, where we compare post-training quantization and quantized-aware training. We benchmark the improvements introduced in each optimization by running a standard testing routine. We show that the optimization strategies introduced can improve the computational efficiency by around 300 times, while also slightly improving the functional performance of the system. In addition, we demonstrate closed-loop control behaviour on a real-world robot, while processing everything on a Jetson Xavier NX edge device.
An Ontology-Based Question Answering System for University Admissions Advising

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields.However, these systems depend on learning methods and training data.Therefore, it is necessary to prepare such a good dataset, but it is not an easy job.An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efficient responses automatically.A case study of admissions advising at the International University-VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generating answers to users' questions.Two experiments are carried out to test how the system reacts to different questions.The first experiment examines questions made from some templates, and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.
Proposed Privacy Preservation Technique for Color Medical Images

Nowadays, the security of images or information is very important.This paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image security.First, the secret medical image is encrypted using Advanced Encryption Standard (AES) algorithm.Then, the secret report of the patient is embedded into the encrypted secret medical image with the Least Significant Bit (LSB) watermarking algorithm.After that, the encrypted secret medical image with the secret report is concealed in a cover medical image, using Kekre's Median Codebook Generation (KMCG) algorithm.Afterwards, the stegoimage obtained is split into 16 parts.Finally, it is sent to the receiver.We adopt this strategy to send the secret medical image and report over a network securely.The proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (C r ), Feature Similarity Index Metric (FSIM), and Structural Similarity Index Metric (SSIM).Histogram estimation is used to confirm the matching between the secret medical image before and after transmission.Simulation results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.
Person-Dependent Handwriting Verification for Special Education Using Deep Learning

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However, in crowded classrooms, it is difficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However, the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development, characteristics, and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition (HWR) more difficult.To overcome this problem, we propose a new personalized handwriting verification system that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network (CNN) created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwritten figures of the students are collected under the supervision of special education teachers.The system is designed as a person-dependent system as every student has their writing style.Overall, the system achieves promising results, reaching a recognition accuracy of about 94%.Overall, the system can verify special education students' handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.
Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Every day, websites and personal archives create more and more photos.The size of these archives is immeasurable.The comfort of use of these huge digital image gatherings donates to their admiration.However, not all of these folders deliver relevant indexing information.From the outcomes, it is difficult to discover data that the user can be absorbed in.Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner.Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.Hence, in this paper, Adaptive Convolutional Deep Learning Model (ACDLM) is developed for automatic image annotation.Initially, the databases are collected from the open-source system which consists of some labelled images (for training phase) and some unlabeled images {Corel 5 K, MSRC v2}.After that, the images are sent to the pre-processing step such as colour space quantization and texture color class map.The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation (JSEG).The final step is an automatic annotation using ACDLM which is a combination of Convolutional Neural Network (CNN) and Honey Badger Algorithm (HBA).Based on the proposed classifier, the unlabeled images are labelled.The proposed methodology is implemented in MATLAB and performance is evaluated by performance metrics such as accuracy, precision, recall and F1_Measure.With the assistance of the proposed methodology, the unlabeled images are labelled.
Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Due to the development of technology in medicine, millions of healthrelated data such as scanning the images are generated.It is a great challenge to store the data and handle a massive volume of data.Healthcare data is stored in the cloud-fog storage environments.This cloud-Fog based health model allows the users to get health-related data from different sources, and duplicated information is also available in the background.Therefore, it requires an additional storage area, increase in data acquisition time, and insecure data replication in the environment.This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloom filter and provide the health data security using the Advanced Signature-Based Encryption (ASE) algorithm in the Fog-Cloud Environment (WCA-BF + ASE).This WCA-BF + ASE eliminates the duplicate copy of the data and minimizes its storage space and maintenance cost.The data is also stored in an efficient and in a highly secured manner.The security level in the cloud storage environment Windows Chunking Algorithm (WSCA) has got 86.5%, two thresholds two divisors (TTTD) 80%, Ordinal in Python (ORD) 84.4%, Boom Filter (BF) 82%, and the proposed work has got better security storage of 97%.And also, after applying the de-duplication process, the proposed method WCA-BF + ASE has required only less storage space for various file sizes of 10 KB for 200, 400 MB has taken only 22 KB, and 600 MB has required 35 KB, 800 MB has consumed only 38 KB, 1000 MB has taken 40 KB of storage spaces.
Genetically Trained Fuzzy Cognitive Maps for Effects Based Operations

Effects Based Operations (EBO) is an umbrella term for operational environments in which an action or series of actions cascade through chain-effects to produce notable changes in some key end-chain effects. Important examples are wartime theaters, city planning, politics, and advertising. The key component of EBO analysis is determining the best set of actions for maximization of a desired effect or minimization of an undesired effect. EBOs are analyzed using knowledge graphs. Fuzzy Cognitive Maps (FCM) are one method of analytically representing and solving knowledge graphs. Integrating a fuzzy inference system on each node of a knowledge graph allows for the traditional relational weight matrix to be replaced with fuzzy rule sets allowing for more uncertainty in relational effect weights. This reflects real world uncertainty in wartime, political, or other planning environments. Using a genetic algorithm also decreases the vast search space of possible knowledge graph solutions. This allows for a robust analysis of an FCM-EBO system with a high level of explainability retained in the system.
Adaptable Security Maturity Assessment and Standardization for Digital SMEs

Small and Medium-sized Enterprises (SMEs) constitute a very large part of every country's economy and play an essential role in economic growth and social development. SMEs are frequent targets of cyberattacks. Unlike large enterprises, SMEs generally have limited capabilities regarding cybersecurity practices. Assessment and improvement of cybersecurity capabilities are crucial for SMEs to survive and sustain their operations. Despite the availability of maturity assessment models and standards to assess and improve cybersecurity capabilities, SMEs' specific requirements and roles in the digital ecosystem are often neglected. This paper presents high-level SME requirements regarding cybersecurity maturity assessment and standardization and translates them into an Adaptable Security Maturity Assessment and Standardization (ASMAS) framework to address this gap. The framework is demonstrated by a web-based software prototype. In the evaluation study conducted with SMEs, we obtained positive results for perceived usefulness, perceived ease of use of the framework, and intention to use it.
Algorithmic Application of Evidence Theory in Recommender Systems

With the development of wireless network and various communication technologies, the information on the Internet is expanding rapidly. The development of wireless network and various communication technologies has promoted the development of e-commerce, and people can understand a large amount of commodity information without leaving home. However, due to the complex information on the network, users need to pass a lot of screening to obtain the information they want, and a large amount of irrelevant information will cause users to consume a large amount of irrelevant information. To solve these problems, the personalized recommendation system is created, but the recommendation system is recommended according to the characteristics of users’ interest and shopping behavior. However, users’ interests will change, so they need to use other technologies to screen for relevant commodity information. Evidence theory has a strong ability to distinguish between true and false information and to deal with uncertain information. To this end, this article studies the application of the evidence theory in the recommendation system and finds that the evidence theory algorithm can infer the information needed by users based on the uncertain information. Moreover, the experiment in this article proves that the algorithm application of the evidence theory in the recommendation system can well grasp the interests of users and recommend the information needed by users. This improves the efficiency of users to obtain the required information and achieves 80 points for content recommendations.
Aesthetic and Implication Analysis of the Traditional Poetic Environment Based on Natural Language Emotion Analysis

The uniqueness of aesthetic implication in Zhou Dynasty poetics lies in that it is the basic forming stage of the concept of formal beauty of the whole Chinese nation, and the aesthetic implication of the Zhou Dynasty poetics art has fundamental significance for the whole ancient Chinese aesthetic implication theory. In the discipline of natural language processing, text emotion analysis is a crucial topic. Artificial neural network research is where the idea of "deep learning" (DL) first emerged. In view of the problems that semantic information is easy to be lost and emotional information may be ignored in the traditional Chinese short text emotion analysis model, this paper introduces the AM (attention mechanism) and proposes a CNN-LSTM (convolutional neural network-long short-term memory) poetic aesthetic implication analysis method based on self-attention. For the IL (input layer), word vectors trained by Word2Vec are used and then input into the CNN-LSTM joint model. Then, the output of the joint model is weighted and summed by self-attention and finally input into the Softmax classifier, so as to realize the emotion classification of the text. By creating and putting into practise pertinent comparative experiments, the usefulness of the proposed model is confirmed. The outcomes demonstrate that this model outperforms the other three comparison models for the quantification of evaluation indices in terms of overall performance. The accuracy and F1 of this paper are 93.362% and 90.886%, respectively, which are higher than other models.
A high-accuracy calibration method for fusion systems of millimeter-wave radar and camera

Abstract Multi-sensor information fusion is widely used in the field of unmanned aerial vehicles obstacle avoidance flight, particularly in millimeter-wave (MMW) radar and camera fusion systems. Calibration accuracy plays a crucial role in fusion systems. The low-angle measurement accuracy of the MMW radar usually causes large calibration errors. To reduce calibration errors, a high-accuracy calibration method based on a region of interest (ROI) and an artificial potential field was proposed in this paper. The ROI was selected based on the initial calibration information and the MMW radar’s angle measurement error range from the image. An artificial potential field was established using the pixels of the ROI. Two moving points were set at the left and right ends of the ROI initially. The potential forces of the two moving points are different because the pixels of the obstacle and the background are different in the image. The two moving points were iteratively moved towards each other according to the force until their distance was less than the iteration step. The new calibration point is located in the middle of the final position of the two moving points. In contrast to the existing calibration methods, the proposed method avoids the limitations of low angle measurement accuracy by using image pixels. The experimental results show that the calibration errors decrease by 83.95% and 75.79%, which is significantly improved compared to the traditional methods and indicates the efficiency of the proposed method.
FAIR data and metadata: GNSS precise positioning user perspective

ABSTRACT The FAIR principles of Wilkinson et al. [1] are finding their way from research into application domains, one of which is the precise positioning with global satellite navigation systems (GNSS). Current GNSS users demand that data and services are findable online, accessible via open protocols (by both, machines and humans), interoperable with their legacy systems and reusable in various settings. Comprehensive metadata are essential in seamless communication between GNSS data and service providers and their users, and, for decades, geodetic and geospatial standards are efficiently implemented to support this. However, GNSS user community is transforming from precise positioning by highly specialised use by geodetic professionals to every-day precise positioning by autonomous vehicles or wellness obsessed citizens. Moreover, rapid technological developments allow alternative ways of offering data and services to their users. These transforming circumstances warrant a review whether metadata defined in generic geospatial and geodetic standards in use still support FAIR use of modern GNSS data and services across its novel user spectrum. This paper reports the results of current GNSS users’ requirements in various application sectors on the way data, metadata and services are provided. We engaged with GNSS stakeholders to validate our findings and to gain understanding on their perception of the FAIR principles. Our results confirm that offering FAIR GNSS data and services is fundamental, but for a confident use of these, there is a need to review the way metadata are offered to the community. Defining standard compliant GNSS community metadata profile and providing relevant metadata with data on-demand, the approach outlined in this paper, is a way to manage current GNSS users’ expectations and the way to improve FAIR GNSS data and service delivery for both humans and the machines.
Emergency Decision-making Method of Unconventional Emergencies in Higher Education Based on Intensive Learning

In order to improve the emergency decision-making and management ability of unconventional emergencies in Colleges and universities, an emergency decision-making method for unconventional emergencies in Colleges and Universities based on reinforcement learning is proposed. The function of the emergency decision-making model of unconventional emergencies in Colleges and universities is constructed, and the optimal control of the emergency decision-making process of unconventional emergencies in Colleges and universities is realized by using the queuing theory model. Based on the sub-sequence decomposition method, the reinforcement learning function is optimized. Combined with big data scheduling and the empirical mode decomposition method, the effective probability density function of emergency decision-making for unconventional emergencies in higher education is calculated, and the optimal solution vector analysis of emergency management and scheduling decision-making for unconventional emergencies in Colleges and universities is realized according to parameter estimation and quantitative optimization results. The test results show that this method has a good ability to optimize and schedule the emergency decision-making of unconventional emergencies in higher education, and has strong convergence. This method improves the emergency response-ability, and the time cost is short.