Reviewerly/exHarmony huggingface dataset:
Product-Centered Language Style in Chinese Women Online Reviews

The relationship of language style and online review has drawn increasing academic attention recently as it can provide customers with a guide to make the purchase. Extant research attaches importance to the language style that is presented in the use of function words, instead of product-related content words. This study aimed to examine the language style generated by customers' comments relating to the product based on content words, that is, product-centered language style (PCLS). We built a corpus of Chinese women clothes online reviews to explore the general picture and distinct features of PCLS and the distinct feature of PCLS. A content-word-centered Linguistic Inquiry and Word Count (LIWC) in terms of product performance is established. PCLS is calculated based on the language style matching (LSM) algorithm. Our results show that the PCLS in women clothes online review is featured by diverse and polarized language styles among three groups of women clothes buyers, and the prioritized arrangement of words of importance contributes to the PCLS. The findings benefit the women clothes industry in which it can help companies quickly find the distinctive and the transition of PCLS and offer an approach for companies to indiscriminately look into the significance of the product category from the linguistic perspective, which can help with product sale strategy and product design.
Preservice teacher cluster memberships in an edtech course: A study of their TPACK development

Researchers have been examining preservice teachers’ development of TPACK in edtech courses for years. However, most empirical studies used small samples to look at preservice teachers’ TPACK development in the edtech courses. With a small dataset, it is hard to know whether there consistently exist knowledge gaps in preservice teachers’ prior knowledge and TPACK development in the edtech course. Studies with large samples are needed because they provide a large amount of empirical data that can be used as strong evidence for justifying whether the edtech course has any impact on preservice teachers’ TPACK development across all demographic and prior knowledge groups. Thus, the purpose of this study was to examine eighteen cohorts of preservice teachers’ pre-TPACK scores and their cluster assignment, as well as their post-TPACK scores in a required edtech course. A two-step cluster analysis was used based on pre-TPACK scores and demographics to identify groups of preservice teachers that share common characteristics in their pre-TPACK scores. A two-cluster model was a good fit (cluster 1 - lower pre-TPACK scores and cluster 2 - higher pre-TPACK scores). Independent-sample t-tests were run to test whether there were differences in the post-TPACK scores. Findings revealed that cluster 2 preservice teachers had higher post-TPACK scores. These results might help teacher educators make data-driven decisions to design targeted instructions for diverse preservice teacher groups. Details on potential actions for designing the edtech course and practices for developing TPACK were discussed.
Improving Information Extraction on Business Documents with Specific Pre-training Tasks

Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Extraction in business documents. However, most pre-training tasks proposed in the literature for business documents are too generic and not sufficient to learn more complex structures. In this paper, we use LayoutLM, a language model pre-trained on a collection of business documents, and introduce two new pre-training tasks that further improve its capacity to extract relevant information. The first is aimed at better understanding the complex layout of documents, and the second focuses on numeric values and their order of magnitude. These tasks force the model to learn better-contextualized representations of the scanned documents. We further introduce a new post-processing algorithm to decode BIESO tags in Information Extraction that performs better with complex entities. Our method significantly improves extraction performance on both public (from 93.88 to 95.50 F1 score) and private (from 84.35 to 84.84 F1 score) datasets composed of expense receipts, invoices, and purchase orders.
Blockchain Based Framework for Secure Data Sharing of Medicine Supply Chain in Health Care System

The potential for blockchain technology in the healthcare sector is summarize in this study. It covers a wide range of technology themes, including storing medical information in blockchains, patient personal data ownership, and patient outreach via mobile apps. Blockchain was created to safeguard digital money transactions, but it has since gained popularity in a variety of other industries, including tourism, real estate, voting, the stock market, and supply chain management. In the healthcare industry, blockchain technology is rapidly gaining traction. Threats to integrity as well as threats to threats to threats to threats to threats to threats to Data management and medicine traceability are two of the most well-known blockchain uses in healthcare. We've explored the problems with standard data processing and drug tracing approaches in this paper.
Action Learning Strategy to Enhance Students Speaking Skill: A Classroom Action Research

The aim of this study was improve the students’ English speaking skill through action learning strategy at 9 of SMP Islam Assya’roniyyah. The method used Classroom Action Research (CAR) that developed by Kemmis and Mc Taggart (2013). It consists of four steps namely, planning, acting, observing, and reflecting. Improvement the problem in this research was brought by the series of cycle. The procedures of research were performs by administrating two cycles. Each cycle contain four steps which are planning, action, observation, and reflection. The collecting data used test, observation, and interview. Analyzing data used the mean each post-test in cycle. The result showed that the students’ speaking skill improved. It can be noticed that there was an improvement in every cycle from the students’ behavior and achievement. Action learning strategies facilitate the students to speak in way, they reduce students’ anxiety so they can speak freely without being afraid to make a mistake, they increase the students’ motivation in learning speaking, and the most important the students do not think that English is a difficult subject.&#x0D; Keywords: Action Learning Strategy, English Speaking Skill, Classroom Action Research
Penerapan Metode Dempster Shafer Pada Sistem Pakar Diagnosa Penyakit Usus Halus

The small intestine is a food processing factory where nutrients are digested and absorbed completely when the small intestine is in good condition. However, many of us have an unhealthy small intestine, so the small intestine does not function properly. Health problems in the small intestine can occur from different factors such as the most dominant factor is food and drink, health awareness is still low, the habit of always wanting to live a practical life and lack of knowledge of healthy living and little knowledge about the problem of early symptoms of a disease. which is the main factor in treating the disease so that it becomes severe. If the patient is late in receiving information about his illness, the patient will be treated by the doctor too late. The doctor's knowledge can be implemented in an expert system system. The expert system method used to find diagnostic confidence uses the Dempster Shafer method. With the aim of making it easier for the public to find out what small intestine they suffer from and be able to check them early. Where the method of finding diagnostic confidence uses the Dempster Shafer method where the final result of this study produces an accurate diagnosis with a sample of symptom data used by 32% with Dianoses are less likely to suffer from Small Intestine Tract Blockage yang selalu ingin menjalani hidup secara praktis, serta cara berpikir yang cenderung mengarah pada kehidupan yang tidak sehat, dan gejala awal penyakit. merupakan bagian dari faktor. sakit.
Impact of COVID-19 Pandemic News on the Cryptocurrency Market and Gold Returns: A Quantile-on-Quantile Regression Analysis

In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index associated with coronavirus outbreak and Bitcoin and gold returns. Our results reveal that the daily levels of positive and negative shocks in indices induced by pandemic news asymmetrically affect the Bearish and Bullish on Bitcoin and gold, and fear sentiment induced by coronavirus-related news plays a major role in driving the values of Bitcoin and gold more than other indices. We find that both commodities, Bitcoin and gold, can serve as a hedge against pandemic-related news. In general, the COVID-19 pandemic-related news encourages people to invest in gold and Bitcoin.
Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method

With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.
WISP, Wearable Inertial Sensor for Online Wheelchair Propulsion Detection

Manual wheelchair dance is an artistic recreational and sport activity for people with disabilities that is becoming more and more popular. It has been reported that a significant part of the dance is dedicated to propulsion. Furthermore, wheelchair dance professionals such as Gladys Foggea highlight the need for monitoring the quantity and timing of propulsions for assessment and learning. This study addresses these needs by proposing a wearable system based on inertial sensors capable of detecting and characterizing propulsion gestures. We called the system WISP. Within our initial configuration, three inertial sensors were placed on the hands and the back. Two machine learning classifiers were used for online bilateral recognition of basic propulsion gestures (forward, backward, and dance). Then, a conditional block was implemented to rebuild eight specific propulsion gestures. Online paradigm is intended for real-time assessment applications using sliding window method. Thus, we evaluate the accuracy of the classifiers in two configurations: "three-sensor" and "two-sensor". Results showed that when using "two-sensor" configuration, it was possible to recognize the propulsion gestures with an accuracy of 90.28%. Finally, the system allows to quantify the propulsions and measure their timing in a manual wheelchair dance choreography, showing its possible applications in the teaching of dance.
Stream Privacy Amplification for Quantum Cryptography

Privacy amplification is the key step to guarantee the security of quantum communication. The existing security proofs require accumulating a large number of raw key bits for privacy amplification. This is similar to block ciphers in classical cryptography that would delay the final key generation since an entire block must be accumulated before privacy amplification. Moreover, any leftover errors after information reconciliation would corrupt the entire block. By modifying the security proof based on quantum error correction, we develop a stream privacy amplification scheme, which resembles the classical stream cipher. This scheme can output the final key in a stream way, prevent error from spreading, and hence can put privacy amplification before information reconciliation. The stream scheme can also help to enhance the security of trusted-relay quantum networks. Inspired by the connection between stream ciphers and quantum error correction in our security analysis, we further develop a generic information-theoretic tool to study the security of classical encryption algorithms.
Investigation on Vibration Signal Characteristics in a Centrifugal Pump Using EMD-LS-MFDFA

Vibration signals from centrifugal pumps are nonlinear, non-smooth, and possess implied trend terms, which makes it difficult for traditional signal processing methods to accurately extract their fault characteristics and details. With a view to rectifying this, we introduced empirical mode decomposition (EMD) to extract the trend term signals. These were then refit using the least squares (LS) method. The result (EMD-LS) was then combined with multi-fractal theory to form a new signal identification method (EMD-LS-MFDFA), whose accuracy was verified with a binomial multi-fractal sequence (BMS). Then, based on the centrifugal pump test platform, the vibration signals of shell failures under different degrees of cavitation and separate states of loosened foot bolts were collected. The signals’ multi-fractal spectra parameters were analyzed using the EMD-LS-MFDFA method, from which five spectral parameters (Δα, Δf, α0, αmax, and αmin) were extracted for comparison and analysis. The results showed EMD-LS-MFDFA’s performance was closer to the BMS theoretical value than that of MFDFA, displayed high accuracy, and was fully capable of revealing the multiple fractal characteristics of the centrifugal pump fault vibration signal. Additionally, the mean values of the five types of multi-fractal spectral characteristic parameters it extracted were much greater than the normal state values. This indicates that the parameters could effectively distinguish the normal state and fault state of the centrifugal pump. Moreover, α0 and αmax had a smaller mean square than Δα, Δf and αmin, and their stability was higher. Thus, compared to the feature parameters extracted by MFDFA, our method could better realize the separation between the normal state, cavitation (whether slight, moderate, or severe), and when the anchor bolt was loose. This can be used to characterize centrifugal pump failure, quantify and characterize a pump’s different working states, and provide a meaningful reference for the diagnosis and study of pump faults.
RQCODE – Towards Object-Oriented Requirements in the Software Security Domain

For the last 20 years, the number of vulnerabilities has increased near 20 times, according to NIST statistics. Vulnerabilities expose companies to risks that may seriously threaten their operations. Therefore, for a long time, it has been suggested to apply security engineering – the process of accumulating multiple techniques and practices to ensure a sufficient level of security and to prevent vulnerabilities in the early stages of software development, including establishing security requirements and proper security testing. The informal nature of security requirements makes it uneasy to maintain system security, eliminate redundancy and trace requirements down to verification artifacts such as test cases. To deal with this problem, Seamless Object-Oriented Requirements (SOORs) promote incorporating formal requirements representations and verification means together into requirements classes.This article is a position paper that discusses opportunities to implement the Requirements as Code (RQCODE) concepts, SOORs in Java, applied to the Software Security domain. We argue that this concept has an elegance and the potential to raise the attention of developers since it combines a lightweight formalization of requirements through security tests with seamless integration with off-the-shelf development environments, including modern Continuous Integration/Delivery platforms. The benefits of this approach are yet to be demonstrated in further studies in the VeriDevOps project.
Exemplar-Based Sketch Colorization with Cross-Domain Dense Semantic Correspondence

This paper aims to solve the task of coloring a sketch image given a ready-colored exemplar image. Conventional exemplar-based colorization methods tend to transfer styles from reference images to grayscale images by employing image analogy techniques or establishing semantic correspondences. However, their practical capabilities are limited when semantic correspondences are elusive. This is the case with coloring for sketches (where semantic correspondences are challenging to find) since it contains only edge information of the object and usually contains much noise. To address this, we present a framework for exemplar-based sketch colorization tasks that synthesizes colored images from sketch input and reference input in a distinct domain. Generally, we jointly proposed our domain alignment network, where the dense semantic correspondence can be established, with a simple but valuable adversarial strategy, that we term the structural and colorific conditions. Furthermore, we proposed to utilize a self-attention mechanism for style transfer from exemplar to sketch. It facilitates the establishment of dense semantic correspondence, which we term the spatially corresponding semantic transfer module. We demonstrate the effectiveness of our proposed method in several sketch-related translation tasks via quantitative and qualitative evaluation.
An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool

Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed.
Point‐convolution‐based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple‐input multiple‐output radar

Compared with traditional approaches that used vision sensors which can provide a high-resolution representation of targets, millimetre-wave radar is robust to scene lighting and weather conditions, and has more applications. Current methods of human skeletal pose estimation can reconstruct targets, but they lose the spatial information or don't take the density of point cloud into consideration. We propose a skeletal pose estimation method that combines point convolution to extract features from the point cloud. By extracting the local information and density of each point in the point cloud of the target, the spatial location and structure information of the target can be obtained, and the accuracy of the pose estimation is increased. The extraction of point cloud features is based on point-by-point convolution, that is, different weights are applied to different features of each point, which also increases the nonlinear expression ability of the model. Experiments show that the proposed approach is effective. We offer more distinct skeletal joints and a lower mean absolute error, average localisation errors of 6.1 cm in X, 3.5 cm in Y and 3.3 cm in Z, respectively.
Developing the Health Care System for Smart Drugstore Based on the IoT and the Embedded System

Recent studies have confirmed that the third cause of death is medical errors, as many patients around the world suffer from poor health care. Many medical errors are caused by taking the wrong medication, expired medication, taking damaged medication, and taking medication with foods and drinks that are incompatible with the medication. The Internet of Things (IoT) technology has been used in healthcare applications to solve healthcare problems and medical errors for the elderly and people who need special medical care. A smart drug store was established using embedded systems to control the physical factors that affect the drug and contribute to the health care system. On all smartphone operating systems and computer operating systems and supports real-time communication with all work parts based on Internet of Things technology. The efficacy of action was proven by using 12 drugs that differ from each other in properties and obtained results of enhancing dose adherence (96.45%), adherence time (96.18%), and expiration (97.81%) through temperature control and alerting all necessary information for health care. A smart pharmacy protects medication from spoilage by controlling temperature and humidity, providing needed schedules for better healthcare, sending needed alerts, sending warnings about drug expiration or drug running out, and sending notifications to three pharmacies to provide the elderly with necessary information drugs. The product can be used in various workplaces, homes, businesses, and military units. The research was practically applied and the research proved successful through the results that will be displayed.