Reviewerly/exHarmony huggingface dataset:
A Pilot Program Utilising a “Chatbot “ to Support Patients in the First 30 Days Following an Admission to Hospital With Acute Decompensated Heart Failure

Heart failure (HF) admissions are associated with mortality and are the key driver of HF-related costs. Patients typically deteriorate 2 weeks prior to readmission. The majority have no healthcare contact during this period. Patients and carers rarely recognise deterioration or potential triggers for deterioration; this, coupled with declining access to timely primary/specialist care, is a recipe for disaster. Could a “chatbot” intervention improve outcomes? This study partnered with Novartis and Flipside (USA) to custom build a post-discharge HF chatbot. The chatbot utilised the psychological “nudge” strategy to encourage patients to perform three self-checks daily: weight, swelling, breathing, and report changes compared to discharge. Symptom reporting had three options: better, worse, and same. The algorithm automatically responded to a significant weight gain or symptom change, alerting the patient and recommending that they phone the HF nurse. Medication and appointment compliance was supported. This study reports on the first seven enrolled patients: age 64±8 years, HF preserved ejection fraction (3), and HF reduced ejection fraction (HFrEF) (4). All patients with HFrEF received the four pillars of guideline-directed medical therapy at discharge. Polypharmacy was high, mean number of medications at discharge was 13±6.4. There was one readmission in the first 30 days (atrial flutter). These pilot data suggest that a chatbot may be a viable and low-cost tool for some patients with HF, helping them with self-care, compliance, and recognition of deterioration in the early post-discharge phase.
Interaction-Driven Design: A Case Study of Interactive Lighting

Due to the rapid development of screen-based interaction, the word “inter-action” is interpreted narrowly as a screen or touching interaction. In this paper, we present six interactive lighting design cases adopting the “Interaction-driven design”, which is initiated and driven by interaction behaviours rather than the user’s needs or technology. Each design case consists of design background, interactive process, and prototyping. Based on the exploitation of the six interactive lighting design cases, we aim at expanding the interaction methods and boundaries, increasing the bonding between users and products, and creating a brand-new user experience. In the discussion section, we reflect on design considerations of interaction-driven design, including conducting lighting design aiming at creating a new type of user behaviour, the input behaviour, as the core of the interaction, creating peak experience, and reflections on the relationship between interaction behaviour and product form, and the evaluation criteria for the interaction-driven pattern.
Comprehensive Research Report Management Application Study In UMk Manufacturing Batik Blimbing Malang

This home business or what is often called a home industry is a company that is included in a manufacturing company because where plain cloth will be changed and designed into various batik cloths, not only that, Batik Blimbing Malang also produces various accessories such as bangkiak, batik bracelets, cloth brooches and many more. Batik Blimbing Malang was founded in 2011 by Mrs. Wiwik Winarsih who is located at Jl. Candi Jago no. 6 Blimbing, Malang. This Batik Blimbing business was started by Mrs. Dra Wiwik Winarsih in 2009 and inaugurated in 2011. As well as various problems experienced by Batik Blimbing MSMEs, namely not knowing whether to rely on manual bookkeeping of product sales or by using detailed application bookkeeping, because it is private. Then during the production process of written and stamped batik cloth, it is also very influential with uncertain weather at times like this in the Malang City area where it rains every day. There is also a lack of labor in MSME Batik Blimbing Malang. Continuous improvement in all aspects of the business by business owners and workers is certainly a good synergy for Batik Blimbing to become feasible and attract the attention and popularity of the surrounding community.
Rancang Bangun Aplikasi E-Commerce Berbasis Android Pada Toko Suryamart Menggunakan Framework Flutter

Suryamart is one of the business entities of the Directorate of Business Development and Investment (DPBI) UMSIDA engaged in retail. Suryamart not only serves the needs of its internal buyers - employees and lecturers, but also the needs of the general public. In carrying out its business, the author found that many general people still feel unfamiliar with Suryamart stores. On the other hand, it was found that there were problems related to the obstruction of the ordering process because internal shoppers were too busy to come to the store to shop in person, so they were less interested in shopping. This research was conducted with the aim to help admins manage online orders, make it easier for shoppers to shop, and increase Suryamart's interaction with the wider general public. The author uses waterfall software development method to minimize errors and speed up the development process because it follows a sequential process starting from analysis, design, implementation, testing, and maintenance. The result of this study is an e-commerce system consisting of applications for users and admins. Based on the results of black box testing, it can be concluded that the functional features of the application can run well.
Pengaruh Model Pembelajaran ARIAS (Assurace, Relevance, Interest, Assessment, Satisfaction) Terhadap Kemampuan Menulis Teks Ulasan Cerpen Siswa Kelas XI SMK Negeri 1 Lubuk Pakam Tahun Ajar 2022/2023

This study examined the impact of the ARIAS learning model (Assurance, Relevance, Interest, Assessment, Satisfaction) on the writing ability of short story reviews among class XI students at SMK Negeri 1 Lubuk Pakam. Using Cluster Random Sampling, 72 students were selected for the control and experimental classes. The experimental method with a Posttest-Only Control Design was employed, and a posttest served as the data collection instrument. The results showed that the experimental class (mean = 85.94, SD = 5.89) outperformed the control class (mean = 57.33, SD = 8.38). Preceding hypothesis testing, normality and homogeneity tests confirmed the suitability of the data. The t-test indicated a significant difference (t = 16.702, df = 70, p &lt; 0.05), rejecting the null hypothesis and accepting the alternative hypothesis. Consequently, the ARIAS learning model positively influenced the writing proficiency of class XI students at SMK Negeri 1 Lubuk Pakam.
A study on the prediction of user participation and game outcome of Wordle guessing game based on Arima time series and BERT neural network model

Wordle is a popular daily puzzle game in which players try to guess a five-letter word six times or less. This report develops predictive models for the distribution of reported outcomes and the number of reported outcomes reported. The data were first preprocessed, and then an ARIMA(1,1) prediction model was developed by ADF testing and plotting ACF and PACF plots, and calculated that there would be 7,260 reported outcomes on March 1, 2023. In addition, two attributes were extracted, namely the number of repeated letters and the information entropy. Through Pearson correlation analysis, the report investigates the relationship between the attributes and the variance of the scores. It can be seen that entropy is significantly negatively correlated with variance and the number of repeated letters is significantly positively correlated. The report uses the One-hot method to encode words, applies the BERT neural network architecture, and uses the SGD algorithm for optimization to predict the distribution of the reported results. For the word EERIE, the calculated percentages were 1.16%, 5.75%, 20.68%, 32.60%, 24.72%, 12.39%, and 2.70%. The calculated MSE errors indicate the high confidence level of the model. The study provides insight into user behavior and improves game design.
Liner Regression to Predict World Cup

The 2022 World Cup competition has come to an end. The winning team is not the most expensive team. In order to study the relationship between team strength and average worth, this paper uses the linear fitting method to analyze the two. Team strength: We can derive a relatively fixed strength based on the team's ranking in the world. This fitting has achieved certain results. There is a certain linear relationship between the two. The predictions speak to a team's long-term strength. To understand the relationship between the two, we need to collect data from Kaggle to come up with a more accurate ranking. The author used some preliminary data to analyze the strength of each team. Then match their ratings to their worth. Preliminary linear fitting is carried out first. With such a linear model, we can get the approximate level of a team's worth and their winning percentage after many games.
Hardware Architectures of FPGA-based Accelerators for Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been widely used in Artificial Intelligence applications due to their unsupervised feature, which automatically identifies relevant features without human intervention. Outperforming power-hungry GPUs and inflexible ASICs in lightweight CNNs, FPGAs serve as a promising platform on balancing peak performance, energy efficiency and flexibility. In the last decade, several frameworks have been proposed to optimize the global performance of CNN on hardware platforms. This paper presents a survey on hardware architectures generated by various software frameworks designed for mapping CNN on FPGAs. Classic architectural cases of the streaming architecture and the single computation engine from traditional CNN-specific processors to end-to-end mapping using High Level Synthesis (HLS) tools which emerged in recent years are carefully analyzed in a sequential order. Moreover, adaptability of existing frameworks to upcoming challenges and future directions of FPGA-based CNN accelerators are identified, providing an in-depth evaluation on the topic of hardware architectures of FPGA-based CNN accelerators.
Research on the Queuing Theory based on M/M/1 Queuing Model

A collection of mathematical theories and models make up queueing theory. The theory, which is derived from several models, makes an effort to recreate numerous typical queue waiting models. The purpose of this study is to do research on the M/M/1 queuing model-based queueing theory. After a brief introduction to the queuing theory, the independent indexes and the dependent variables will be shown. This paper contains some fundamental queuing theory formulas, some of which are specifically derived for the M/M/1 queuing model. This study then presents a simulation of the M/M/1 queuing model. Since some results are attained, this paper revealed some probability distributions and distribution functions of several indexes. The data characteristics displayed by these figures are very consistent with the daily life situation, which also proved the rationality and M/M/1 queuing model's applicability. By investigating this queuing theory, people shall be able to understand, analyze, and predict the phenomenon of waiting in a line.
K-anonymous Mathematical Model Based on Greedy Algorithm

This research propose a user-centered combinatorial data anonymization method. whereas a data matrix is said to be k-anonymous if each row occurs at least k times. Therefore, the authors propose PATTERN-GUIDED k-ANONYMITY, an improved k-anonymization problem. It allows users to designate the combinations in which suppressions may occur, building on prior work and addressing relevant shortcomings. Users of anonymous data can indicate that the aspects of the data are valued differently. The so-called K-anonymity is usually realized by Generalization and Suppression techniques. Generalization refers to Generalization and abstraction of data so that specific values cannot be distinguished, for example, the age data group can be generalized into an age group.
Multi-scale, multi-dimensional binocular endoscopic image depth estimation network

During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) simulation model for generating image datasets and acquiring depth information in real-time. Here, we proposed an end-to-end multi-scale supervisory depth estimation network (MMDENet) model for the depth estimation of pairs of binocular images. The proposed MMDENet highlights a multi-scale feature extraction module incorporating contextual information to enhance the correspondence precision of poorly exposed regions. A multi-dimensional information-guidance refinement module is also proposed to refine the initial coarse disparity map. Statistical experimentation demonstrated a 3.14% reduction in endpoint error compared to state-of-the-art methods. With a processing time of approximately 30fps, satisfying the requirements of real-time operation applications. In order to validate the performance of the trained MMDENet in actual endoscopic images, we conduct both qualitative and quantitative analysis with 93.38% high precision, which holds great promise for applications in surgical navigation.
Progress in Object Detection: An In-Depth Analysis of Methods and Use Cases

Object detection, a fundamental task in computer vision, involves identifying and localizing objects within images or videos. This paper provides a comprehensive review of traditional and deep learning-based object detection techniques and their applications, challenges, and future directions. We first discuss traditional object detection methods, which rely on handcrafted features and classical machine learning algorithms. We then explore the advancements brought by deep learning, including convolutional neural networks (CNNs) and transformer-based architectures, which have significantly improved the accuracy and efficiency of object detection tasks. A thorough comparison and evaluation of different object detection techniques are presented, considering performance metrics, speed, and robustness to object size, orientation, and occlusion variations. We also examine the diverse applications of object detection across various domains, such as robotics, autonomous vehicles, surveillance, medical imaging, and augmented reality. We outline open challenges and future research directions, emphasizing the need to combine object detection with other tasks, develop few-shot and zero-shot learning approaches, and address issues related to fairness, accountability, and transparency. This paper aims to comprehensively review the most prominent object detection techniques, their evolution, and their applications in diverse domains. We discussed traditional methods and recent deep learning-based approaches, emphasizing their strengths and limitations.
STUDY ON CAPTURE EFFICIENCY FOR RESIDENTIAL ELECTRIFIED KITCHENS (PART 1) : CAPTURE EFFICIENCY OF RANGE HOODS BY NUMERICAL FLUID MODEL CONSIDERING BOILING WATER

In this paper, capture efficiency of range hoods is measured for boiling water and non-boiling salad oil at 100℃ . The influence of difference in thermal updraft over a pot with and without boiling at the same temperature on capture efficiency is discussed. Analytical conditions for CFD using RANS to consider effect of boiling water on thermal updraft are studied.
Artificial Neural Network Models for Image Recognition

Image recognition is an important application of artificial intelligence. Due to the continuous development of chips and algorithms, this field has made great progress in the past decade. At the same time, AI based image recognition has been widely used in the emerging fields like robotics, autonomous vehicles, and surveillance cameras, and their demand for AI has promoted the development of image recognition technology. Resent research has found that the convolutional neural network model is particularly effective for image classification and detection and smaller convolution kernels with deeper network structures are conducive to improving the accuracy. However, problems such as overfitting and activation function gradient descent need to be solved during the operation process. The latest convolutional neural network model ResNet applies residual units to reduce the redundant calculations and improve the efficiency of the model. In general, different variants of convolutional neural network structures have different effects on image recognition, but regional convolutional neural network structures are preferred in engineering applications for its balance between processing speed and recognition accuracy.
User Interest Based Contextual Conversational Recommender System

The Conversational recommender system is for item recommendation during human-machine dialogue, where items can be movies, music, and goods. Scenarios can be intelligent customer service and shopping assistants. The system's goal is to generate high-quality recommendations in short-round interactions. There are two main problems with the system. Firstly, recommending through dialogues is a hard problem as less contextual information is available, resulting in fewer signals for recommendation and less precision for user interest modeling. Secondly, the recommender system considers the absence of interaction between a user and an item as a negative sample. However, this approach can introduce bias in the objectives because non-interaction could potentially be a positive sample. To tackle the previously mentioned concerns, this paper suggests a new model. This model adds an extra knowledge graph to enrich contextual information. Then, this model introduces contrastive learning to separate the distribution between positive and negative samples and improve learning efficiency. This paper further tests the model on the ReDial dataset, and experiments have shown that the method is effective and performs better than previous work.
Spatially heterogeneous learning by a deep student machine

Despite spectacular successes, deep neural networks (DNNs) with a huge number of adjustable parameters remain largely black boxes. To shed light on the hidden layers of DNNs, we study supervised learning by a DNN of width $N$ and depth $L$ consisting of $NL$ perceptrons with $c$ inputs by a statistical mechanics approach called the teacher-student setting. We consider an ensemble of student machines that exactly reproduce $M$ sets of $N$-dimensional input/output relations provided by a teacher machine. We show that the statistical mechanics problem becomes exactly solvable in a high-dimensional limit which we call a ``dense limit'': $N\ensuremath{\gg}c\ensuremath{\gg}1$ and $M\ensuremath{\gg}1$ with fixed $\ensuremath{\alpha}=M/c$ using the replica method developed by Yoshino [SciPost Phys. Core 2, 005 (2020)] In conjunction with the theoretical study, we also study the model numerically performing simple greedy Monte Carlo simulations. Simulations reveal that learning by the DNN is quite heterogeneous in the network space: configurations of the teacher and the student machines are more correlated within the layers closer to the input/output boundaries, while the central region remains much less correlated due to the overparametrization in qualitative agreement with the theoretical prediction. We evaluate the generalization error of the DNN with various depths $L$ both theoretically and numerically. Remarkably, both the theory and the simulation suggest that the generalization ability of the student machines, which are only weakly correlated with the teacher in the center, does not vanish even in the deep limit $L\ensuremath{\gg}1$, where the system becomes heavily overparametrized. We also consider the impact of the effective dimension $D(\ensuremath{\le}N)$ of data by incorporating the hidden manifold model [Goldt, M\'ezard, Krzakala, and Zdevorov\'a, Phys. Rev. X 10, 041044 (2020)] into our model. Replica theory implies that the loop corrections to the dense limit, which reflect correlations between different nodes in the network, become enhanced by either decreasing the width $N$ or decreasing the effective dimension $D$ of the data. Simulation suggests that both lead to significant improvements in generalization ability.
The Future of Graphical Symbols

Non-linguistic symbols such as emoji have been increasingly widely used of late. As a result, a popular question has been whether emoji, or something like them, could ultimately replace written language as a mode of communication. This in turn relates to a theme we introduced in Chap. 4, namely whether a system that encodes meaning directly can ever be as flexible as traditional phonologically based writing systems. A long standing dream has been the development of a perfect system of communication that can substitute for language and which, unlike writing, works by directly “encoding thought”. In this chapter we review attempts to develop a “perfect” symbolic language that would encode meaning directly. Once again, we point out that all such attempts have ultimately failed in their goals, and have never resulted in a system that had the flexibility of natural language or its graphical form, writing. We also review the social status of writing, and end with some thoughts on the future of writing and graphical symbol systems more generally.
A New Approach to Pharmaceutical Product Verification Using Barcode and QR Code

The rapid growth of pharmaceutical industry, especially after the outbreak of COVID-19 virus has become a massive threat to modern economies and public health. Medical emergency situations like pandemic will continue to have an impact on illicit trade in counterfeit and substandard medicines as there will be an exponential increase in demand for medicines, test kits, protective equipment, etc., (Interpol General Secretariat in Covid-19: the global threat of fake medicines, 2020, [1]). Currently available anti-counterfeiting solutions like holograms, color shifting inks, embedded code, images, and dyes depend mostly on customer awareness. These techniques require additional costs for awareness program and periodical change of elements to avoid forgery. This paper will solve the problems related to pharmaceutical products by introducing barcodes or QR codes to cross-verify the products. The barcode printed on the packing will be the key parameter to query the manufacturer database from the internet-facing interface and verify the product details. The basic requirements for the proposed solution to work are internet connectivity and multimedia devices with QR code scanner application.
Variable item value-based high utility itemset recommendation using statistical approach

High utility mining has become an absolute requirement for an efficient corporate management procedure. The challenge persists in identifying the top-out or bottom-out conditions in the context of the available HUM solutions, and it is critical for enterprises to manage adequate inventory to have higher yield outcomes. Taking these aspects into consideration, this paper proposed a comprehensive method named as 'variable item value-based high utility itemset recommendation (VIVHUIR)'. Unlike the contemporary models, which are focusing utility mining by constant utility factor, the proposed model is focusing on variable utility factor to perform utility mining based on profitability for an itemset. In addition, the drift (variability) in utility factor detection methodology is fundamentally based on the average true range for an itemset and the relative strength index (RSI) assessment for analysis, which is unique and novel feature of the proposal. To comprehend the elements influencing profit, the proposed four-layered filtering model depends on quantities, demand, supply, and gain/loss inventory. The experimental research of the model refers to potential solutions that are pragmatic in a real-time situation.