Reviewerly/exHarmony huggingface dataset:
Displaying Latent Classes in Figures: Consideration of Practices

While latent class analysis (LCA) has gained popularity in social sciences, including psychology, the visualization of latent classes has been the subject of limited discussions.This article reviews the elements of LCA figures, covering issues such as graph type, axis labels, and subgroup naming.Bar charts and line graphs have been identified as two major visualization approaches in LCA studies.It is concluded that LCA figures serve as an important visual vehicle to display subgroup characteristics.However, the elements of LCA figures need careful consideration as they could furnish the text with additional information.A checklist is summarized for LCA figure preparation.
FINITE AXIOMATIZABILITY OF TRANSITIVE MODAL LOGICS OF FINITE DEPTH AND WIDTH WITH RESPECT TO PROPER-SUCCESSOR-EQUIVALENCE

Abstract This paper proves the finite axiomatizability of transitive modal logics of finite depth and finite width w.r.t. proper-successor-equivalence. The frame condition of the latter requires, in a rooted transitive frame, a finite upper bound of cardinality for antichains of points with different sets of proper successors. The result generalizes Rybakov’s result of the finite axiomatizability of extensions of $\mathbf {S4}$ of finite depth and finite width.
Interactive Chatbot for Space Science Using Augmented Reality—An Educational Resource

Traditional method of learning by reading books is becoming a thing of the past. Everything is becoming increasingly digitized and is being driven by technology innovations. Bringing new learning formats based on technology, in the classrooms can lead to increased student engagement which in turn will make the knowledge and skills stay longer. Thus, adapting technological solutions to education is very important. Augmented Reality (AR) is an enhanced version of the real world which is achieved using digital visual elements, audio and other sensory stimuli. This is a growing technology. Since it provides many powerful visualizations, AR can be used as a powerful learning tool. In this paper, we are proposing to use Augmented Reality technology to develop a book and an AR application for space science that will help students easily acquire, process and remember the information. Space science is one of the most exciting and rapidly evolving branches of science. It encompasses all of the scientific disciplines that involve space exploration and study natural phenomena and physical bodies occurring in outer space. Learning space science is very important because it has led to development of various technologies such as GPS, weather prediction, solar cells. Students are fascinated to understand these. So, in the work proposed we are developing a whole book from scratch on basics of space science, which is our physical data (Image Targets). We are developing an AR application which will detect the Image Targets and layer virtual content (AR Visualizations) over them. These visualizations will help to understand the physical data better thus providing a fun way of learning. To make the learning interactive, a chatbot will be added.
Image Encryption Based on Watermarking and Chaotic Masks Using SVD

In an era of large information and systems administration, it is an important to create a safe and hearty computerized watermarking plan with high computational productivity to secure copyrights of advanced works. Nonetheless, a large portion of the current strategies centre on strength and implanting limits, dismissing security or requiring huge computational assets in the encryption cycle. It’s very crucial for any user to protect not only the data, but also images, audio and video files. Protection of the data is equally important to protect all the other files. Even though, there are numerous encryption and decryption algorithms, attacking the cryptosystem are rapidly increasing in the same frequency. This paper proposes a cryptosystem for images which also includes computerized picture watermarking model dependent on scrambling calculation, bit plane slicing and compression. In the place of random phase masks in double random phase masks (DRPE), chaotic masks have been generated. DRPE in chaotic phase masks embedded with watermarking and SVD could be a great support for image encryption and decryption systems. It has been proposed in fractional Fourier transforms. It has been performed to guarantee the safety of the top-secret data at the establishment of enormous installing limit, great strength and high computational efficiency. For encryption and decryption of images, cryptosystem has been proposed which uses the watermarking and singular value decomposition. Many analyses have been conducted to show the robustness of the system.
Proposing the Use of Hazard Analysis for Machine Learning Data Sets

There is no debating the importance of data for artificial intelligence. The behavior of data-driven machine learning models is determined by the data set, or as the old adage states: “garbage in, garbage out (GIGO).” While the machine learning community is still debating which techniques are necessary and sufficient to assess the adequacy of data sets, they agree some techniques are necessary. In general, most of the techniques being considered focus on evaluating the volumes of attributes. Those attributes are evaluated with respect to anticipated counts of attributes without considering the safety concerns associated with those attributes. This paper explores those techniques to identify instances of too little data and incorrect attributes. Those techniques are important; however, for safety critical applications, the assurance analyst also needs to understand the safety impact of not having specific attributes present in the machine learning data sets. To provide that information, this paper proposes a new technique the authors call data hazard analysis. The data hazard analysis provides an approach to qualitatively analyze the training data set to reduce the risk associated with the GIGO.
Currency Identifier for Visually Impaired People

Visually impaired people are struggling to identify the denominations of the currency. The transactions that are done are basically out of trust but then the current scenario survey says that many are getting cheated because of the cruel people in this society. Visually impaired people are confident enough to rely on the existing system. The braille system is not flooded everywhere, even though people teach braille many who lacks education or cannot afford it cannot learn it. Nowadays, everything has become automated with the implementation of IoT and machine learning so new systems for visually impaired people are integrated with mobile phones so in order to overcome the existing crisis we bring the hardware prototype method The datasets of various currencies are taken and trained to the model; if the shown currency matches with the currency in the database, then the denomination is recognized, and information is passed to the user.
Research-Practice Partnership in Tanudan, Kalinga, Philippines

Knowledge management (KM) is the documentation/storage, dissemination/sharing, and development/scaling up of knowledge. This is essential for Indigenous knowledge (IK), for which Indigenous knowledge management (IKM) is proposed in line with the International Decade of Indigenous Languages (IDIL) 2022-2032. This chapter reckons an initiative originally called knowledge management for Kto12 (KM4K12). It primarily sought to assist Department of Education (DepEd) teachers and senior high school (SHS) students during the K-12 transition period (2016-2021). Incidentally, it engaged Indigenous peoples (IPs) in Tanudan, Kalinga, Philippines. More specifically, it (1) presents a timeline of relevant activities (2015-2022). Further, it (2) lists paper presentations and publications of the authors as partners in the name of Research-Practice Partnership (RPP). These texts constitute RPP, which is a sustainable education practice, particularly the research function/mandate of education agencies. Lastly, it (3) notes instances that can make or break RPP.
Optimizing Data Distribution to Balance Scalability-Redundancy Trade-off for Collaborative Blockchain Storage

Blockchain protocol ensures security and reliability through immutability and replication of a constantly growing database in a trustless environment. Storage burden of individual nodes keeps increasing with the growing size of data. Collaborative storage schemes for blockchain have shown potential as a way forward to solving the storage challenge. However, most of the existing techniques tend to indiscriminately sacrifice redundancy, which is a basic determinant of blockchain's security and decentralization. Level of redundancy of a collaborative storage system depends on optimization of data distribution. In this paper, we outline a mechanism for the evaluation of a data distribution scheme in terms of its effectiveness for redundancy. We also present a scalable collaborative storage model with optimized data distribution for a balanced trade-off between scalability and redundancy. The proposed model assigns data portions proportional to the storage capacities of nodes. Experiment results show that the storage capacity of the proposed model linearly scales with the number of nodes maintaining an optimum level of redundancy.
Review on the Research Status of Intelligent Level Classification

The intelligent level grading standard plays a basic, supporting and leading role in the development of artificial intelligence. Based on the development status of artificial intelligence, this paper combs and summarizes the classification standards and evaluation methods of intelligent level. Firstly, the basic concept of artificial intelligence, the classification of intelligent level and the significance of classification are discussed. Secondly, taking different application fields of artificial intelligence as the research object, this paper focuses on the classification of intelligent level, typical characteristics and evaluation methods in the fields of unmanned system, network, industrial automation instruments and intelligent products. Finally, the development prospect of intelligent level classification and evaluation is given, which provides a new research direction for intelligent level classification and evaluation.
Path Planning of Autonomous Driving Based on Quadratic Optimization

In order to deal with the scenario that require high flexibility in obstacle avoidance, such as urban roads, this paper proposes an autonomous driving path planning algorithm based on quadratic programming (QP). The algorithm proposes an obstacle avoidance cost based on Frenet frame, which can not only satisfy the characteristics of the positive definite quadratic form of the cost function, but also add the obstacle avoidance cost as a soft constraint, and then adapts an iterative solution strategy. The candidate paths are generated by solving the QP problem, the algorithm will output the optimal path, which satisfy the collision detection. The simulation test shows that the algorithm can deal with nudge, lane change and complex obstacle avoidance scenarios.
An Energy Efficient Artificial Intelligence Based Innovation Detection for Complex Data Communication Model

The Artificial Intelligence (AI) based innovation detection model for complex data communication model is a revolutionary approach to identifying and exploiting opportunities for innovation in data communication systems. This model is based on the principle of AI-assisted data mining, which allows for the automated detection of patterns and correlations in data sets. By applying AI-based algorithms, this model can identify patterns or relationships between elements of large data sets which may not be obvious to human analysts. The AI-based innovation detection model for complex data communication model is designed to help organizations identify areas in which they can capitalize on opportunities for innovation. This model can identify complex patterns or relationships between data-driven variables such as customer behavior, product performance, and market trends. By leveraging the data-driven insights, organizations can create new products or services that are tailored to their customer’s needs and desires. The AI-based innovation detection model for complex data communication model can be used to identify opportunities for innovation in a variety of industries. For example, the model can be used to identify potential opportunities for new product development in the healthcare sector. By analyzing patient records, AI-based algorithms can uncover patterns or correlations between different types of patient data that may not be obvious to human analysts. Additionally, this model can also be used in the retail sector to identify potential opportunities for developing new products or services that are more tailored to customer needs and desires.
Multi-Sensor Adaptive Birth for Labeled RFS Filters using Bistatic Range-Only Measurements

Recently, a Monte Carlo importance sampling-based approach has been established to achieve scalable, multi-sensor, measurement adaptive track initialization for labeled random finite set filters. However, previously suggested proposal distributions require every sensor's measurement function to have a differentiable inverse in the observable dimensions of the target's state space. This assumption is valid for measurement modalities such as position or angle-range sensors, but not for many common non-invertible measurement modalities such as bistatic range-only, angle-only, and range-only sensors. This paper provides an alternative proposal distribution for Monte Carlo importance sampling-based, multi-sensor, measurement adaptive track initiation that is not restricted to invertible measurement functions. The solution for a bistatic range-only measurement function is provided, and simulation results are shown to verify the efficacy of the solution.
Analysis of The Various Techniques Used for Breast Segmentation from Mammograms

Studies show that the cancer that causes the breast is the most frequent type of cancer found among women. X-ray imaging, called mammography, is an imaging technique that is commonly used to detect and classify breast abnormalities. However, accurate segmentation of breast tissues and abnormalities in the mammogram is a challenge, and consequently, many techniques have been employed over the years to extract these tissues and abnormalities and classify breasts based on their vulnerability to breast cancer. In this paper, we present different approaches used for breast segmentation from mammograms. Various methods ranging from modern deep learning-based techniques like UNet, and Atlas-based techniques are reviewed, and the classical techniques such as active contour, global threshold, machine learning based methods, etc. The results of these techniques are compared in order to provide an insight into the challenges of breast tissue classification and the future challenges are highlighted.
Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm

Lung cancer is a curable disease if diagnosed early, and early treatment of lung cancer reduces mortality. Early detection of the disease depends on imaging techniques that can be used to detect the disease easily and accurately. An image of the cancer in the affected area is taken from the patient and analyzed using image processing technology. This new Unsupervised Diffusion Classification Algorithm (UDCA) is used to enable the system to easily detect victim crimes. It uses an adaptive median filter algorithm to improve image quality of cancerous areas. Preprocess and segment the image using a supervised image edge detection algorithm. Finally, we use feature extraction to examine the cancer mean, RMS, and regions affected by system performance. UDCA classification method and three main database levels or images can be classified using the proposed method to detect lung cancer enhanced and achieve accurate value in lung cancer diagnosis. Results are obtained through the most standard in-hospital real-time analysis. Hence, this new technique for rapid detection of lung cancer by filter separation method.
Detecting Repetitive Human Actions by Neural Networks Trained on Composite Data Only

We propose a neural network-based method for detecting the temporal locations of human repetitive actions in time-series data obtained from a body-worn accelerometer. Because labeling the temporal locations of human repetitive actions in the accelerometer data is costly, resulting in difficulties in preparing training data of the neural network, we generate composite labeled training data using existing accelerometer datasets. The proposed neural network uses the U-Net architecture to acquire a feature map capturing action-independent features of repetitions from the input data and processes the feature map in its 1D Fully Convolution Network, which outputs a binary time-series indicating the temporal locations of repetitions. Our experiments demonstrated that our proposed network is able to detect repetitions with high accuracy even though it is trained only on composite data.
MSQA: An Unsupervised Domain-Adapted Question Answering Method Based on Multiple Source Domains

In recent years, with the development of knowledge bases and crowdsourcing,researchers have successfully proposed several high-quality, large-scaledatasets. The rise of pre-trained language models has extensively promoted theprogress of machine reading comprehension tasks. Even so, the need for large-scaletraining data is one of the critical challenges in this field. Existingmethods: one is to improve the transferability of downstream tasks byoptimizing and improving pre-trained language models; the other is to usemultiple existing datasets to build a more extensive dataset or generatesynthetic data. However, the effect is not ideal and lacks specific logicalgeneralization abilities. In this paper, we propose a new domain adaptationframework based on BERT, called Multi-Source Domain Adaptive QA (MSQA), whichutilizes multiple existing large datasets and uses adversarial domaintechniques to narrow the distribution of multiple sources and target domains,thereby improving performance on target domain datasets. Extensive experimentson multiple widely used English datasets demonstrate that our method exhibitssuperior adaptive performance.
Sports Video Classification and Labeling Algorithm Based on Data Mining Technology

With the explosive development of video media, sports workers have accumulated rich experience in sports competitions, training and technical analysis videos. These data contain rich sports information and personal characteristics of athletes. In these videos, how can sports workers quickly find the video materials they need? The traditional text recognition method cannot meet the demand because of its slow speed. If the conventional text recognition method is used, it is equivalent to looking for a needle in a haystack. Due to the large amount of data in sports video, the traditional text recognition method is difficult to meet people’s high demand for real-time and accuracy. Today, with the vigorous development of data mining (DM for short here) technology, using DM to classify and label a large number of sports videos can well solve the problem of low efficiency of traditional text recognition video recognition. At the same time, the trained classifier can help users find the information and action patterns of interest. Therefore, based on the support vector machine (SVM) algorithm, this paper studies the classification of moving video. Finally, take the video of ice and snow sports events in the past three years as the data sample, the experimental platform uses Windows 7 system PC, the simulation environment is Matlab 2012, and then use OpenCV (Open-Source Computer Vision Library) to process the video. At the same time, by comparing the advantages and disadvantages of different classifiers, the most suitable classifier for this study is selected. Finally, this paper applies the trained model to the analysis of the actual collected ice and snow game video. The experimental results show that the average recall and precision of the SVM algorithm are 0.9527425 and 0.945192, respectively. It can be seen that SVM algorithm can achieve the purpose of processing complexity, and a large number of sports videos have been effectively managed and classified rapidly, with high classification accuracy.
Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video

To improve the person re-identification system accuracy, an integrated approach is proposed in the formation of a training sample for convolutional neural networks, which involves the use of a new image dataset, an increase in the training examples number using existing datasets, and the use of a number of transformations to increase their diversity. The created dataset PolReID1077 contains images of people that were obtained in all seasons, which will improve the correct operation of re-identification systems when the seasons change. Another PolReID1077 advantage is the video data use obtained from external and internal surveillance in a large number of different filming locations. Therefore, the people images in the created set are characterized by the variability of the background, brightness and color characteristics. Joining the created dataset with the existing CUHK02, CUHK03, Market-1501, DukeMTMC-ReID and MSMT17 sets made it possible to obtain 109 772 images for training. An increase in the variety of generated examples is achieved by applying a cyclic shift to them, eliminating color and replacing a fragment with a reduced copy of another image. The research results on estimating the accuracy of re-identification for the ResNet-50 and DenseNet-121 convolutional neural networks during their training, using the proposed approach to form a training sample, are presented.
Real-time Multi-Camera Analytics for Traffic Information Extraction and Visualization

The density and complexity of urban environments present significant challenges for autonomous vehicles. Moreover, ensuring pedestrians' safety and protecting personal privacy are crucial considerations in these environments. Smart city intersections and AI-powered traffic management systems will be essential for addressing these challenges. Therefore, our research focuses on creating an experimental framework for the design of applications that support the secure and efficient management of traffic intersections in urban areas. We integrated two cameras (street-level and bird's eye view), both viewing an intersection, and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with a central management platform provided by Kentyou. We designed a pipeline to collect and analyze the video streams from both cameras and obtain real-time traffic/pedestrian-related information to support smart city applications. The obtained information from both cameras is merged, and the results are sent to a dedicated dashboard for real-time visualization and further assessment (e.g., accident prevention). The process does not require sending the raw videos in order to avoid violating pedestrians' privacy. In this demo, we present the designed video analytic pipelines and their integration with Kentyou central management platform.