Reviewerly/exHarmony huggingface dataset:
Effectiveness of Smart Two-Dimensional Barcodes in Designing Cardiopulmonary Resuscitation Posters

Introduction: Today, the use of new technologies has expanded in various fields. One of these cases is the use of smartphones and barcode reader software and two-dimensional barcodes. In this study, we tried to evaluate the influence of barcode utilization in the training of residents. Methods: This was a descriptive cross-sectional study conducted in the emergency department of Ghaem Hospital, in which the effect of using educational barcodes on 30 medical residents of the emergency, internal medicine, and neurology was investigated. A questionnaire recording the performance of residents in medical emergency procedures was used. Also, demographic variables of residents were recorded. Data analysis was performed using descriptive and inferential statistics in SPSS software. Results: A total of 30 residents completed the study questionnaire before installing the poster, and 29 of them completed the post-test questionnaire. Of these, 10 (33.3%) were emergency medicine residents, 13 (43.3%) Internal medicine and 7 subjects (23.3%) were neurology residents. All of them (100%) found the use of QR codes practical and easy. Analysis of data related to pre-test and post-test scores of residents shows that their average scores in the pre-test, 7 ±2.39, and their average scores in the test post 10.86 ±2.431, which indicates a significant increase after using the QR code poster (P-value = 0.000) Conclusion: The use of QR codes has been practical, easy, and user-friendly. Also, comparing the average scores of participants before and after the installation of these codes, shows that it has had a significant effect on training residents.
Automatic Speech Recognition for Portuguese with Small Data Set

Voice recognition has become more and more popular in various systems and applications. To further promote Macau tourism worldwide, a mobile Macau tourism APP is being developing that supports voice control to facilitate Portuguese users. Consequently, this paper is about the research and implementation of an Automatic Speech Recognition (ASR) engine for Portuguese language. In this research, three well-known open-source ASR platforms were evaluated and compared. The complete ASR development procedure using Kaldi platform is discussed. Due to the limitation of collected voice data, a novel few-shot learning and transfer learning is implemented in this project. The final model achieved a stable 95.25% accuracy which is good enough for production use. The novel technics implemented in this research can be used for ASR trainings with limited training data and can be extended to a wide range of applications in the future.
The Rise and the Demise of the Iowa Caucus

The origins of even familiar objects are frequently mired in mystery. The violin is one such example. Nevertheless, we have recently learned much more about the evolution of the violin over time – specifically, its overall shape and the length of its twin sound-holes. The research yielding this increased understanding focused on the makers of the violin, its so-called luthiers, thus putting behavior prominently into the spotlight. Each of the two studies under consideration proposed provocative parallels between structural changes in the violin and the Law of Natural Selection. But, the more appropriate analysis rests on the Law of Effect shaping the behavior of the luthiers. What is revealed is, first, that increasing the length of the sound-holes amplified the acoustic power of the violin, thus leading luthiers to lengthen the sound-holes; and, second, that the aesthetic tastes of their customers led luthiers to modify the overall shapes of their violins.
On the Present Situation and Future of Digital Image Intelligent Processing System

Computer technology is widely used in various industries. Since the digital image processing technology is realized based on computer technology, its future development space is very large. For some users who are new to digital image processing technology, the theoretical knowledge of this technology will be more difficult to understand. For the purpose of better solving this problem, the system is based on the Windows processing system in the design period so that it can further process images and import or export them. Thus, this system can be easier to understand and operate for people.
Robust time-varying Kalman predictor for descriptor system with uncertain-variance noise and packet dropout

For the linear stochastic descriptor system with packet dropout and uncertain-variances noise, the robust prediction problem is addressed. Applying the singular value decomposition (SVD) method, augmented state approach and the fictitious noise approach, the original descriptor system is transformed to new standard system only with uncertain-variance fictitious noises. Based on Kalman filtering and minimax robust estimation principle, the robust time-varying Kalman predictor is presented by replacing the unrealization conservative measurement by the actual measurement in the conservative Kalman predictor. Further, the robustness is proved by the Lyapunov equation approach, i.e., the actual estimation error variance are guaranteed to have minimal upper bounds for all admissible uncertainties. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.
Methods for Aggregating Crowdsourced Ontology-based Item Annotations

Crowdsourcing plays an important role in modern IT landscape, enabling the use of human information processing abilities to solve problems that are still hard for machines. One of the specific (and most demanded) applications of crowdsourcing is collecting item annotations, i.e., describing the contents of complex items with a help of labels (tags). Input received from crowdsourcing participants is typically unreliable, therefore, to increase the quality of annotations, each item is typically processed by several participants and the obtained annotations have to be aggregated. The paper considers a special case of annotating, where a set of possible labels, as well as the set of relationships between the labeled items and the labels are defined by an OWL 2 ontology (OWL QL). Such semantic item annotations turn out to be very useful in organizing large collections of items and enabling semantic search in them. In order to increase annotations quality, the paper proposes two aggregation methods - Onto Voting and OntoSB, differing in that the first one is agnostic with respect to participants' reliability and the second one accounts for variations in reliability. Simulation experiments with ontology-based annotations of varying quality show that the proposed aggregation methods increase the quality of collected ontology-based item annotations.
An Improved Median Filter Method Based on Multi-directional Template Combined with Image Feature

Median filtering algorithms have been widely used in image preprocessing. According to the multi-directional characteristic of images, we propose a two-stage improved median filtering method based on multi-directional templates. Firstly, image pixels are divided into suspicious noise pixels and signal pixels based on the adaptive median filter algorithm with detail protection characteristic. Secondly, thirteen directional templates are proposed according to the directional characteristics of the images. By comparing the standard deviations of pixel values in different templates, the suspicious noise pixels are filtered with median value in the template with lowest deviation. The experimental results show that denoising performance of the proposed method is better than other commonly used algorithms.
The influence of land use change on flood events in Tanah Periuk Village, Grogot District, Paser Regency, East Kalimantan

Humans exploit natural resources in many ways excessively to meet their needs, which can lead to the imbalance of nature. As a consequence, nature will form a new balance, which, in fact, will be detrimental to humans themselves. Due to the inevitable massive exploitation, environmental degradation and the number of disaster events significantly increase. This research specifically focuses on flood events caused by environmental degradation. Flood events are not simply triggered by extreme rainfall but also land conversion, watersheds damage (DAS), development of floodplains, environmental damage, and other factors. For this purpose, a case study has conducted in Tanah Periuk Village, Grogot District, Paser Regency, East Kalimantan. This village is one of the villages that has experienced flooding several times. According to the BPBD Tanah Grogot’s data in 2020, the floods occurred at Tanah Periuk Village resulted in as many as 646 families, and about 3000 people fled to safer places since the water stagnated to a height of 50-150 cm and caused crops failure. This study aims to determine land-use changes from 2011-2020 associated with flood events in the Paser Regency and its surroundings. An approach of Geographical Information System (GIS) was applied by comparing changes in land use each year, rainfall and, flood events. Based on this preliminary study, it demonstrated that the GIS approach provided reliable results which, later could be useful for local governments in planning and preparing proper land use regulation and policy as part of the efforts of disaster risk reduction.
Building our worlds: co-developing future scenarios as a methodology for fashion researchers and designer-innovators

In the context of climate and social injustice at global and local scales; commitment to change is being translated into resource efficiency and problem-minimising activities.These actions are not enough to make a required radical shift from the current trajectory of fashion industry-related impacts.This paper presents interim insights from collaborative research, undertaken by academic researchers with designer-innovators, in designing context, over designing in the current context of fashion.Thus product, services and business models are part of transformation design that extends the value of fashion's social, economic, environmental, and cultural dimensions.Designing is an iterative, dynamic process, responding and shaping context, based on key concerns and areas of interest and opportunity.The research describes a scaffolding of context through a scenarios methodology, in which participants are co-contributors, practicing in flux, designing products, services, systems, and contexts in which we might thrive together.
Improving Particle Swarm Optimization with Self-adaptive Parameters, Rotational Invariance, and Diversity Control

Particle Swarm Optimization (PSO) algorithms are swarm intelligence methods that are effective in solving optimization problems. However, current techniques have some drawbacks: the particles of some PSO implementations are sensible to their input hyper-parameters, lack direction diversity in their movement, have rotational variance, and might prematurely converge due to rapid swarm diversity loss. This article addresses these issues by introducing Rotationally Invariant Attractive and Repulsive eXpanded PSO (RI-AR-XPSO) and Rotationally Invariant Semi-Autonomous eXpanded PSO (RI-SAXPSO) as improvements of Rotationally Invariant Semi-Autonomous PSO (RI-SAPSO) and eXpanded PSO (XPSO). Their swarm behavior was evaluated with classic functions in the literature and their accuracy was tested with the Congress on Evolutionary Computation (CEC) 2017 optimization problems, in whose results a statistical significance test was applied. The results obtained attest that strategies such as diversity control, automatic hyper-parameter adjustment, directional diversity, and rotational invariance improve performance without accuracy loss when adequately implemented.
Cloud Disruption Impacts Business IT Role Requirements

Cloud computing (Cloud) introduces new information technology (IT) optimization challenges for business management. The traditional supply of IT services is disrupted by more diverse and cost-efficient options where all IT services can be rented or leased on-demand to suit a business requirement. The implication is for a forecasted discontinuation of many IT workplace roles and the formulation of others to fit the new requirements. This exploratory research shows that the IT service supply chain has been disrupted and that Cloud is a driver for business structural change. We asked: What are the impacts of cloud computing on business information system (BIS) IT services roles? A targeted sample of industry Cloud professionals were consulted, and the data thematically analyzed. The data analysis showed seven general business impact categories and six classes for BIS IT role impacts. These findings contribute understanding of uncertainty around the new business IT role requirements.
Learning to Combine Answer Boundary Detection and Answer Re‐ranking for Phrase‐Indexed Question Answering

Phrase-indexed question answering (PIQA) seeks to improve the inference speed of question answering (QA) models by enforcing complete independence of the document encoder from the question encoder, and it shows that the constrained model can achieve significant efficiency at the cost of its accuracy. In this paper, we aim to build a model under the PIQA constraint while reducing its accuracy gap with the unconstrained QA models. We propose a novel framework—AnsDR, which consists of an answer boundary detector (AnsD) and an answer candidate ranker (AnsR). More specifically, AnsD is a QA model under the PIQA architecture and it is designed to identify the rough answer boundaries; and AnsR is a lightweight ranking model to finely re-rank the potential candidates without losing the efficiency. We perform the extensive experiments on public datasets. The experimental results show that the proposed method achieves the state of the art on the PIQA task.
Impact of various IBM Quantum architectures with different properties on Grover's algorithm

Quantum computing and quantum algorithms have been said to be able to provide significant speed-up over classical computation with classical algorithms by exploiting the nature of quantum mechanical systems. In this work, we will study the qubit architecture of quantum systems available at IBM Quantum (IBM-Q) such as the properties of the qubit system, the qubit layout and connectivity as well as the transpilation process of circuits and its effects to the circuits. We investigate the correspondence between limited and sparse connectivity and connected physical qubits to Grover's algorithm implementation of 2 and 3 qubits. We will also test Grover's algorithm on the different connectivity available on four IBM-Q systems for 2, 3, 4 and 5 qubits using optimization level of 0, 1, 2 and 3 to compare the circuit depth of the transpiled circuits as well as their execution times. This work will provide guidance for researchers on the important factors to consider before implementing a quantum algorithm on a real quantum system.
An Application of Control- Tutored Reinforcement Learning to the Herding Problem

We exploit a control-tutored reinforcement learning (CTRL) algorithm to solve the herding problem. We illustrate the benefits of our approach and its effectiveness by using the problem of controlling one or more agents to herd and contain within a goal region a set of target free-roving agents in the plane.
TimeElide: Visual Analysis of Non-Contiguous Time Series Slices

We introduce the design and implementation of TimeElide, a visual analysis tool for the novel data abstraction of non-contiguous time series slices, namely time intervals that contain a sequence of time-value pairs but are not adjacent to each other. This abstraction is relevant for analysis tasks where time periods of interest are known in advance or inferred from the data, rather than discovered through open-ended visual exploration. We present a visual encoding design space as an underpinning of TimeElide, and the new sparkbox technique for visualizing fine and coarse grained temporal structures within one view. Datasets from different domains and with varying characteristics guided the development and their analysis provides preliminary evidence of TimeElide’s utility. We provide open-source code and demo at https://github.com/UBC-InfoVis/time-elide and supplemental materials at https://osf.io/yqvmf/.
Text Aspect-level Sentiment Analysis based on Multi- task Joint Learning

Text sentiment analysis is an important research topic in the field of natural language processing. Compared with coarse-grained text sentiment analysis with a single sentiment tendency based on sentences or documents, fine-grained aspect- level sentiment analysis is more suitable for practical application scenarios and becomes more difficult at the same time. In this paper, we propose a multi-task-based joint learning model for aspect-level sentiment analysis of text, using the BERT_CBiGRU composite network with stronger text semantic representation as the main learning task for aspect-level sentiment analysis, and designing an auxiliary learning task for aspect target identification. For the sample imbalance problem, we introduce the focal loss function Focal Loss. Final experiments show that our model has a certain improvement compared with previous models.
Research on Reform of "Electrical Engineering and Electronics" Based on CDIO Education Model

research-article Research on Reform of "Electrical Engineering and Electronics" Based on CDIO Education Model Share on Authors: Miao Zhang Army Military Transportation University, China Army Military Transportation University, ChinaView Profile , Liyun Zhang Army Military Transportation University, China Army Military Transportation University, ChinaView Profile , Deshan Wang Army Military Transportation University, China Army Military Transportation University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 178–182https://doi.org/10.1145/3482632.3482669Online:22 November 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Biometrically Trusted Personal Health Status for Pandemic Management

Undoubtedly COVID-19 is one of the most disruptive pandemics in recent history, adversely affecting individuals, societies, and economies through severe limitations imposed on activities involving gatherings and travel. Among many tools envisioned in battling such pandemics are immunity passports allowing vaccinated or immune individuals to bypass the restrictive measures. However, this proposal also raises many security, privacy, and ethical issues. Due to the sensitive medical records, companies and recreation centres do not have direct access to the COIVD vaccine database; on the other hand, the vaccine results are easily forgeable. One solution is public access to the vaccination database, which is not acceptable for privacy reasons. Our solution securely combines the vaccination results with the user’s biometric authentication result to generate a binary result, such that 0 means this person either not authenticated or not vaccinated, and 1 means this person is vaccinated and authenticated so he/she may use an intended service. Our scheme is based on Secure MultiParty Computations (MPC) and preserves both the privacy of the biometric query and the database. At the same time, it also prevents the inquiring service provider from learning about the vaccination result.
Advanced Watermarking o f Digital Images Showing Robust, Semi Fragile a nd Fragile Behaviour

Digital Image Watermarking plays an important role when it comes to maintaining digital color picture authentication information. The proposed paper consists mainly of a digital watermarking scheme consisting of discrete wavelet transformation and involving the generation of pn sequence number to embed the watermark and also to extract the watermark from the host image. The technique suggested includes both embedding the watermark and removing it from the host file. Both the method of embedding and extraction consists of generating the pn sequence number values using the random numbers. The technique for all three of the RGB signal sources is included. The watermark symbol is located independently within the RGB image's red, green, and blue channels. The suggested technique further reveals the improved mode of digital watermarking of images through fragile watermarking of images and semi-fragile digital watermarking of images.
FOCT: Fast Overlapping Clustering for Textual Data

Text clustering is used to extract specific information from textual data and even categorizes text based on topic and sentiment. Due to inherent overlapping in textual documents, overlapping clustering algorithms have become a suitable approach for text analysing. However, state-of-the-art algorithms are not fast enough to analyse a large volume of textual data within tolerable time limits. In this research, we propose our text clustering algorithm, FOCT, which is a fast overlapping extension of SOM, one of the best algorithms for clustering textual data. We apply some heuristics to extract special characteristics presented in textual data and establish a very fast overlapping clustering algorithm. We use fast methods to represent the vectors of documents, compute the similarity of documents and neurons and update the weights of neurons. In our algorithm, each document can belong to one or more neurons and this is in line with what many documents have in their essence. We analyse the efficiency of the proposed algorithm over k-means, OKM, SOM and OSOM clustering approaches and experimentally demonstrate that it runs 12 to 690 times faster, and the overlap size of FOCT clusters is closer to the overlap size of the original data. The quality of clusters is also measured by four different internal and external evaluation criteria where FOCT clusters represent up to 64% better quality.
Methods to Identify Topic Sentences in Paragraphs in TESOL

In current Chinese TOESOL education, many university students put more efforts on words recitation, but taking little attention of the logical structuring of a text. Therefore, the necessity of topic sentence is of great importance. As it portrays the subject and focus of paragraphs. Problems puzzle many Chinese English teachers are that: first, what is a topic sentence? How to identify the topic sentence? Can a topic sentence be positioned in the end of a paragraph? If not, what is it? Qualitative analysis tells us that first, a topic sentence can be in the initial and medial position of a paragraph; second, the last sentence of a paragraph often acts the role of concluding sentence; third, the last sentence in the introduction usually is thesis statement of the text; last, the topic sentence doesn't appear at the end of the paragraph.