Reviewerly/exHarmony huggingface dataset:
Smart Health Consulting Online System Using Blockchain

Abstract: The current healthcare industry col-lects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hid-den information for effective decision making. BlockChain has been a current trend for at-taining diagnostic results because it keeps the data secure and immutable. BlockChain further extend the support for the process of extracting hidden information from massive dataset, cate-gorizing valid and unique patterns in data. By means of this fruitful insights Blockchain is used in healthcare sector for better decision making. Researchers all over the world are working in either multi agents or in ontologies for developing system in health care domain with help of blockchain. It might have happened so many times that you or someone need doctor help but they are not available due to some reasons. The work proposed here suggests a health management system which supports end user to effectively use online consultation and uses blockchain for storing the patients insights and doctors information. Through this intelligent healthcare system the patient gets guidence from the doctor related their health issues.
Construction of Derivation Trees of Plus Weighted Context Free Grammars

Abstract: The core of this paper is to construct Derivation Trees of Plus Weighted Context Free Grammars and the bonding between Plus weighted Context Free grammar and Plus weighted Context Free Dendrosystem is established.
Efficacy of the elementary, middle, and high school students’ persuasive speech: Evidence from South Korea

&lt;span lang="EN-US"&gt;This paper aims to analyze the efficacy of elementary, middle, and high school students with respect to using persuasive speech and establish a direction for the development of relevant education. It examines the case of South Korea, where the government has presented the importance of persuasive speech education and implemented considerable measures since 2015. It utilizes the survey results on the perception around persuasive speech, based on the achievement standard set within the Korean language curriculum for elementary, middle, and high school students. The research offers several recommendations. First, it is necessary to specify educational content so that it applies differentially, according to the conditions of students. Second, educational content that addresses students’ attitudes, self-awareness, and participation in community issues must be reinforced. Third, educational content related to persuasion preparation, content organization methods, strategies, and meta-cognitive aspects must be presented sequentially in terms of beginning, development, and application, as suited to each level of education. Fourth, it is necessary to review the teaching-learning methods to increase the effectiveness of persuasive speech education. Fifth, it is necessary to especially consider the disadvantages of middle school students, who have huge gaps in efficacy with respect to their knowledge of persuasive speech.&lt;/span&gt;
Bioaccumulation of heavy metals using aquatic plants in wastewater

Heavy metals are very harmful to the environment if present in excessive concentrations and will have an effect on humans if they interact directly. Knowing the findings of heavy metal bioaccumulation in aquatic plants when reducing pollutants in liquid waste is the main goal of this study. Various types of data were developed and analyzed to answer the specific goals set through the EBSCO, ProQuest, and Science Direct Search Engine, by looking at several parameters. Bioaccumulation data for aquatic plants in liquid waste were obtained from five articles that met the criteria. The findings showed that there are six types of plants that can be used as heavy metal bioaccumulators in wastewater, including Limnobium laevigatum, Salvinia auriculata, Lemna minor, Colocasia esculenta, T. latifolia, and T. palustris. Data was also obtained that most research was carried out using liquid waste originating from within the city with a research time ranging from 1-45 days. The heavy metals that were most commonly accumulated in these plants were Zn, Pb, Cr, Ni, Cu, Fe, Mn, and Cd. It can be concluded that bioaccumulation using aquatic plants is very reliable for various types of heavy metals that can be removed.
Android-based augmented reality in science learning for junior high schools: Preliminary study

&lt;span lang="EN-US"&gt;Technology in education encourages more effective learning facilitation processes. The lack of variation and innovation in the use of technology used in the form of learning media as a means of supporting learning activities raises many new problems for educational institutions. This research aims to identify the need to create innovations in the implementation of science learning, whose research focus is to analyze the needs for developing smartphone-based innovative augmented reality (AR) learning media. This research is descriptive qualitative. Data collection techniques are observation, interviews, and questionnaires. Teachers and students of junior high schools in Surakarta became the subject of this research. The results showed that students were less focused and unmotivated when participating in the learning process. In addition, the use of learning media is still conventional and not interactive, even though currently all students have smartphones. Based on field facts, the media used by teachers still tends to be conventional, and the majority of students have technological devices such as smartphones, there is great potential for developing innovative technology-based learning media. Smartphone ownership and the lack of interactive learning media are important foundations in innovating the development of smartphone-based augmented reality.&lt;/span&gt;
The Degree of Application the Principles of Knowledge Economy at Taibah University from the Perspective of Faculty Members

Objectives: The study aimed to identify the degree of application of the principles of Knowledge Economy at Taibah University from the perspective of the faculty members.&#x0D; Methods: To achieve this, the researcher used the descriptive survey approach. The study sample consisted of (122) faculty members from Taibah University in the College of Education and the College of Science. The researchers prepared a questionnaire to measure the principles of Knowledge Economy at Taibah University in the following domains: research, innovation, ICT implementing and governance. Validity and reliability of the study tool was verified.&#x0D; Results: The study showed that the degree of applying the principles of Knowledge Economy medium. Employing ICT was large, followed by governance, and the principle of research and innovation with a medium degree. The results revealed statistically significant differences in the degree of application of Knowledge Economy Principles due to the faculty variable in all fields in favor of education faculties, and there were statistically significant differences in the degree of application of Knowledge Economy Principles due to the academic rank variable. And there were differences in the ICT employment, in favor of the rank of associate professor. However, the results showed that there were no statistically significant differences in the degree of application of knowledge economy principles in general, due to the experience variable.&#x0D; Conclusion: The need for a comprehensive revision for applying the Principles of Knowledge Economy in the University to identify the factors that lowered these results.
Improving Logical Structure Analysis of Visually Structured Documents with Textual Features

This paper introduces a new model to improve the quality of logical structure analysis of visually structured documents.To do that, we extend the model of Koreeda and Manning [1].In order to enhance textual features, we define a new feature that uses the font size of texts as an indicator.As our observation, the font size is an important indicator that can be used to represent the structure of a document.The new font size feature is combined with visual, textual, and semantic features for training an analyzer.Experimental results on four legal datasets show that the new font size feature contributes to the model and helps to improve the F-scores.The ablation study also shows the contribution of each feature in our model.
Automated Code Generation from Use cases and the Domain Model

In this paper, we propose a method to automatically generate source code files from a use case model and a domain class diagram named USLSCG (Use case Specification Language (USL) based Code Generation).In our method, a use case scenario is precisely specified by a USL model.The USL model and the domain class diagram then are used as inputs to generate source code files automatically.These source code files include classes following three-layer applications and a SQL script file to create a database and store procedures.
Directional wavelet packets originating from polynomial splines

The paper presents a versatile library of quasi-analytic complex-valued wavelet packets (WPs) which originate from polynomial splines of arbitrary orders. The real parts of the quasi-analytic WPs are the regular spline-based orthonormal WPs designed in [1]. The imaginary parts are the so-called complementary orthonormal WPs that are derived from the Hilbert transforms of the regular WPs and, unlike the symmetric regular WPs, are antisymmetric. Tensor products of 1D quasi-analytic WPs provide a diversity of 2D WPs oriented in multiple directions. For example, a set of the fourth-level WPs comprises 62 different directions. The properties of the presented WPs are refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies. Directional WPs have a strong potential to be used in various image processing applications such as restoration of degraded images and extraction of characteristic features from the images.
Emotion analysis method based on emotion intensity fusion and BiGRU

In Chinese sentiment analysis, sentiment words are just a drop in the ocean compared with the whole corpus. In order to solve the problem of insufficient emotion lexicon and prior knowledge, proposes a method to predict the emotion intensity of target words based on neural network model (Neural Network Emebdding Score, NNES). By training a small number of labeled samples, using clustering algorithm to find the seed words, calculate the similarity between the target words and the seed words, and using it as the input of neural network to predict the emotional intensity of the unlabeled words. Compared with the traditional machine learning regression models, it has smaller mean square error. Meanwhile, a BiGRU model based on attention mechanism and convolution is proposed by integrating the predicted emotion intensity with word vector (Neural Network Emebdding Score with CNN and Attention-BiGRU, NNESC-Att-BiGRU). To compare several popular models on product and hotel review data sets, and the proposed model has better classification effect on Chinese sentiment classification task.
Human fall detection based on multi-algorithm fusion

In response to the problems of complex implementation, low accuracy, poor applicability and high latency of some contemporary human fall detection algorithms that do not achieve real-time results, this paper proposes a fall detection based on Tiny-YOLO target detection algorithm, Kalman target trajectory tracking, Alphapose human pose recognition and spatio-temporal convolutional network, a combination of multiple algorithms. It solves the problem that human target features are difficult to extract and subsequent tracking is lost. And the training speed is accelerated to achieve fast detection of human falls through video and improve the accuracy. The algorithm is tested on the publicly available datasets UR Fall Detection Dataset and Lei2 fall Detection Dataset, and the experimental results prove that the algorithm in this paper has high detection accuracy and effectively reduces the false detection rate.
Learning anisotropy and asymmetry geometric features for medical image segmentation

Finding contours of interest from medical images is an important task in the field of medical image analysis. The current deep learning-based image segmentation approaches have obtained promising results. However, most of these models do not take into account the anisotropy and asymmetric features which play an important role in describing the target contours. In order to address this issue, we propose new loss-function applied to the deep learning model with dense distance regression, which can benefit the edge-based features, thus able to improve the stability of the segmentation procedure and to reduce the probability of outliers in the segmentation results. The introduced loss function is embedded into the deep learning model, which can perform an end-to-end image segmentation procedure for medical images. Ablation experiments were done with other loss functions and three datasets were used to verify whether this loss function is effective. SOTA results were obtained for the proposed loss function in this paper compared to the recently designed method for reducing the boundary error.
Can Incremental Learning help with KG Completion?

Knowledge Graphs (KGs) are a type of knowledge representation that gained a lot of attention due to their ability to store information in a structured format. This structure representation makes KGs naturally suited for search engines and NLP tasks like question-answering (QA) and task-oriented systems; however, KGs are hard to construct. While QA datasets are more available and easier to construct, they lack structural representation. This availability of QA datasets made them a rich resource for machine learning models, but these models benefit from the implicit structure in such datasets. We propose a framework to make this structure more pronounced and extract KG from QA datasets in an end-to-end manner, allowing the system to learn new knowledge in incremental learning with a human-in-the-loop (HITL) when needed. We test our framework using the SQuAD dataset and our incremental learning approach with two datasets, YAGO3-10 and FB15K237, both of which show promising results.
An Intelligent and Data-Based Skate Analyzer to Assist in Analyzing Movements of Skate on Ice

Ever since the start of Figure Skating, there has been an emphasis on skating technique, especially in the step sequences of a skater’s choreography [1]. But Figure Skaters often are not able to detect the motion, edge, or placement of their blade on the ice without watching themselves skate. The solution to this problem would be to have a skate analyzer. A skate analyzer would record the movements of a skate on ice and one would be able to playback the recorded data and view their skate motion precisely [2]. Three main components that my project links together are the QTPY-ESP 32 microcontroller, the sensor that combines the accelerometer, gyroscope, and magnetometer, and the SD card reader. The QTPY-ESP32 is a microcontroller that acts as a main computer controlling the whole board. The QTPY is then connected to a sensor board through an I2c protocol. Then, through an SPI protocol, the QTPY is connected to an SD card reader. After the skater is finished recording, they can insert the SD card in a computer, upload the data into the app, and play it back. There is also a slider on the top of the screen that the skater can slide back and forth to view the skate at specific times in the file. This would be a great technology to use for skaters as they can playback their movements on ice and improve their technique [3].
Towards Scalable Anomaly Detection for Embedded Devices through Synthetic EM Fingerprinting

Embedded devices are omnipresent in modern networks, including those facilitating missioncritical applications. However, due to their constrained nature, novel mechanisms are required to provide external, and non-intrusive defenses. Among such approaches, one that has gained traction is based on analyzing the emanated electromagnetic (EM) signals. Unfortunately, one of the most neglected challenges of this approach is the manual gathering and fingerprinting of the corresponding EM signals. Indeed, even simple programs are comprised of numerous branches, making the fingerprinting stage extremely timeconsuming, and requiring the manual labor of an expert. To address this issue, we propose a framework for generating synthetic EM signals directly from machine code. These subsequent signals can be used to train an anomaly detection system. The advantage of this approach is that it completely removes the need for an elaborate and error-prone fingerprinting stage, thus, increasing the scalability of the protection mechanisms. The experimental evaluations indicate that our method provides above 90% detection accuracy against code injection attacks. Moreover, the proposed methodology inflicts only -1.3% penalty in accuracy for detecting injections of as little as four malicious instructions when compared to the same methods of training on real signals.
Fun Writer: A Context-Based Intelligent Writing Platform to Assist and Motivate Writing Activities using Artificial Intelligence and Natural Language Processing

The aim of this paper is to apply computer techniques to assist high school students' essay writing. A python program named Fun Writer is developed to assist users in paraphrasing, summarizing, and keywording. We applied it to real text sentences and paragraphs and conducted a qualitative evaluation of the approach. The results show that Fun Writer can automatically provide users with different versions of sentence paraphrases, Text or paragraph summaries, and keyword retrieval, thus being flexible to the user’s needs. The program will be a website where the user can upload his own input text and freely select to either paraphrase, summarize, or extract keywords.
marl-jax: Multi-Agent Reinforcement Leaning Framework

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax} and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: \href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}
Weakly-supervised Single-view Image Relighting

We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scenes and relighting them under the new environment lighting, which is essential for AR applications. To relight the object, we solve both inverse rendering and re-rendering. To resolve the ill-posed inverse rendering, we propose a weakly-supervised method by a low-rank constraint. To facilitate the weakly-supervised training, we contribute Relit, a large-scale (750K images) dataset of videos with aligned objects under changing illuminations. For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics. The whole pipeline is end-to-end and efficient, allowing for a mobile app implementation of AR object insertion. Extensive evaluations demonstrate that our method achieves state-of-the-art performance. Project page: https://renjiaoyi.github.io/relighting/.
Anomaly Detection under Distribution Shift

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing AD studies assume that the training and test data are drawn from the same data distribution, but the test data can have large distribution shifts arising in many real-world applications due to different natural variations such as new lighting conditions, object poses, or background appearances, rendering existing AD methods ineffective in such cases. In this paper, we consider the problem of anomaly detection under distribution shift and establish performance benchmarks on four widely-used AD and out-of-distribution (OOD) generalization datasets. We demonstrate that simple adaptation of state-of-the-art OOD generalization methods to AD settings fails to work effectively due to the lack of labeled anomaly data. We further introduce a novel robust AD approach to diverse distribution shifts by minimizing the distribution gap between in-distribution and OOD normal samples in both the training and inference stages in an unsupervised way. Our extensive empirical results on the four datasets show that our approach substantially outperforms state-of-the-art AD methods and OOD generalization methods on data with various distribution shifts, while maintaining the detection accuracy on in-distribution data. Code and data are available at https://github.com/mala-lab/ADShift.
Directional router and controllable non-reciprocity transmission based on phase and pathway coherence

A multi-channel quantum router with four nodal cavities is constructed by two coupled-resonator waveguides and four single cavities. We can achieve directional routing by adjusting the probability of photon exiting from the specified port to close to 100% based on multiple pathways between the photon from the incident port to the outgoing port in this hybrid system. Under the effect of phase difference between two classical light fields, the mutual interference between different pathways can be adjusted to destructive interference or constructive interference, which lays the foundation for the increase and decrease of the routing probability. The influence of different parameter values on single photon routing probability is also studied. By studying the analytic formula of probability amplitude, we get the physical mechanism of exiting ports being closed under certain parameter conditions and the phase relationship between the backward transmission and the original direction transmission of photons. Furthermore the non-reciprocal transmission and directional routing beyond chiral coupling can also be realized, which provides new possibilities for the study of quantum routers and new insights for the study of photon transmission characteristics.