Reviewerly/exHarmony huggingface dataset:
Model of best practice representation for any knowledge area by using pre-conceptual schemas

Graphical representation of best practices allows for explicitly exposing a set of activities agreed by experts in an area of knowledge and leading to successful results. Some authors use graphical representations of best practices for synthesizing knowledge. Although, some drawbacks impede extraction and interpretation of best practice information. Some of such drawbacks are improperly usage of representation symbols, incomplete information presented by authors, non-standard representation, absence of essential elements of best practices, non-homogeneous level of specificity, and imprecise denomination of practice. Consequently, in this paper we propose a model of best practice representation by using pre-conceptual schemas. Our model can be applied to any knowledge area with a generic structure and it is appropriate for human and computational comprehension. Our model is intended to allow for completely representing knowledge related to best practices, creating, selecting, updating, extending, and combining best practices. We apply our best practice model to a set of best practices in orthopedist teams against COVID-19 in order to validate it.
Methods for Prediction Optimization of the Constrained State-Preserved Extreme Learning Machine

Finding the maximum testing accuracy in Machine Learning has been the goal since its conception. From this goal, neural networks have been the primary source of continual improvements in prediction performance. Traditionally, backpropagation has been the primary way of training neural networks and the Levenberg-Marquardt (LM) backpropagation has become the fastest method. Recently, the Extreme Learning Machine was introduced which randomizes weights and biases of hidden layers and uses the Moore-Penrose generalized inverse of a matrix to calculate the output weights and biases, providing competitive results at significantly faster training times. In this study, we continue our work on the Constrained State-Preserved Extreme Learning Machine (CSPELM) with a Forest optimization (CSPELMF) and ε constraint Rangefinder (CSPELMR). Furthermore, we provide hyper-parameter settings for the CSPELM to optimize accuracy over training time. Our results show that our methods outperformed the LM backpropagation in a majority of the 13 tested datasets and that the CSPELMF and CSPELMR matched or outperformed the CSPELM in all classification datasets.
Improving Patient Cohort Identification using Neural Word Embedding with Structured Analysis

Patient cohort is similar symptoms of group of patients over a time period. It is important to correctly identify patient cohort for observational study or interventional study. To identify patient cohort, we can easily retrieve information from large structured and unstructured data tables, but this information may not fulfill our interest. We need to obtain knowledge from the unstructured medical data for further detailed inspection. In this work, we have improved structured extraction by presenting context on a health record dataset for identifying cohort of diabetic patients. Results shows that traditional structured query-based data extraction methods accurately identified 97.14% positive patients to our question of interest where adding natural language processing supported technique have retrieved 98.37% precisely.
The Development of Engine Management System’s Learning Media Video in Lightweight Vehicle Engineering Expertise Nasional Berbah Vocational High School

Abstract This research aimed at (1) developing the engine management system’s learning media video, (2) knowing the feasibility of developing the engine management system’s learning media video with assessment from media and material, also (3) knowing the responses of the lightweight vehicle engineering students of National Berbah Vocational High School toward the engine management system’s learning media video. The research of the engine management system’s learning media video is a research and development (R&amp;D) study that employed 4D development model (Define, Design, Develop and Disseminate). Data collection technique carried out front analyses and questionnaires then analysis of feasibility is carried out by describing it into five categories such as the Likert scale. The results of this development study are the engine management system’s learning media video contained the three materials which were characteristics, functions, and the working of sensors, actuators, and electronic control unit, the media feasibility of this learning media is determined by assessment of material expert (4.3), media feasibility based on media expert (4.1), then student responses as the user get a grade (3.5), so the engine management system’s learning media video was feasible to be used in the learning process.
Website application for locating and reserving house and boardroom for rent based on selected location or region

Abstract The establishment of house and boardroom for rent in Jakarta is needed for students who enroll tertiary institution, and worker who came from remote area. The use of information systems in house and room for rent business is needed for the development of marketing and sales. This study aimed at evaluating the effect of website application for locating and reserving house and room for rent. Data was collected by using questionnaire survey on Universitas Tarumanagara students chosen through random sampling, and primary data by photograph of the property.
The Intelligent Preparation of Scene Matching Guidance Reference Map Based on Prior Knowledge

Aiming at the problem that the automatic processing level of reference map preparation in image matching guidance is not high enough, our work proposes a novel reference map preparation based on the prior knowledge such as infrared imaging conditions and trajectory information. In the image preprocessing stage, the deep convolution network is used to extract the regions of interest automatically and meanwhile, combined with the analysis of connected region, the ROI extraction results can be optimized; Then, the natural scene is classified by texture of material, and the periodic temperature change of the feature is automatically mapped to the gray level, which conforms to the actual imaging characteristics and reduces the artificial gray level error. Next, combined with trajectory information and perspective transformation, multi-scale and multi-view scene images can be automatically obtained, which can avoid the situation that the manually selected target or landmark is not obvious or does not exist in the actual imaging. With comprehensive analysis from three aspects of reliability, stability and uniqueness, the best matching area is automatically determined, which improves the accuracy of matching algorithm and the time performance of reference map preparation system.
Statistically Secure and Massively Scalable Fingerprinting Scheme for Mobile Game Broadcasting Content

Digital fingerprinting is a technique to prevent customers from redistributing multimedia contents illegally. Main attack for fingerprinting is the collusion attack, where multiple users collude by creating an average or median of their individual fingerprinted copies, and escape identification. Previous research such as ACC (anti-collusion code) cannot support large number of users, and also vulnerable to LCCA (linear combination collusion attack). We present a practical SACC (scalable ACC) scheme to generate codebooks for supporting large number of users; and angular decoding scheme to be robust on LCCA. We implemented the SACC codebook using a Gaussian distributed random variable for various attack robustness, and the fingerprint embedding using human visual system based watermarking scheme. We experimented with commercial mobile game video sequences for collusion detection performance, and it shows good collusion detection performance over average, median attacks. For LCCA collusion attack on SACC, our angular decoding scheme identifies the correct colluder set under various WNR (watermark to noise ratio).
Nothing Standard About It: An Analysis of Minimum Security Standards in Organizations

Written security policies are an important part of the complex set of measures to protect organizations from adverse events. However, research detailing these policies and their effectiveness is comparatively sparse. We tackle this research gap by conducting an analysis of a specific user-oriented sub-component of a full information security policy, the Minimum Security Standard. Specifically, we conduct an analysis of 29 publicly accessible minimum security standard documents from U.S. academic institutions. We study the prevalence of an extensive set of user-oriented provisions across these statements such as who is being addressed, whether the standard is considered binding and how it is being enforced, and which specific procedures and practices for users are introduced. We demonstrate significant diversity in focus, style and comprehensiveness in this sample of minimum security standards and discuss their significance within the overall security landscape of organizations.
The Future of Robots in a Super-Aged Society

A Super-ageing population and challengesIt is estimated that the world population aged 65+ will triple from 6.9% to 20% by 2050 [1].Across the world, different countries are at various stages in this super-ageing journey.Japan is the most rapidly ageing society with 28.2% now in this age group; in contrast, emerging economies such as India have some of the world's youngest populations (6.1% aged 65+) [2].The creation of super-aged societies will be without parallel in human history.While people may be living longer, it is also important to consider their Quality of Life (QoL), whether these additional years are 'healthy life years ' [3].Looking into the future, China is the country faced with the largest challenge in the next 30 years with a growth in older citizens to 26% of the population (366 million by 2050) [4].An additional challenge associated with super-ageing is the group of increasingly frail older population (aged 85+) which is currently the fastest growing population world-wide; in the UK this is predicted to double in size by 2041 and treble by 2066 [5].Policy makers consider that the challenges of a superageing population can partly be addressed through a policy of empowering senior citizens to continue living independently through use of assistive technology or 'ageing in place ' [6].But once frail older people need 24/7 care, they need to move into some form of Long-Term Care (LTC) facility.These facilities need to be staffed, and a super-ageing society is inevitably faced with the additional challenge of fewer young people to enter this workforce.World-wide, the ratio of people aged 20-64 years to those aged 65+ is expected to halve by 2050 [1].
A Comparative analysis of appearance based subspace methods for facial expression recognition

Facial expression recognition is like a pattern recognition problem, which is divided into two parts dimensionality reduction and classification. It is very much challenging to find out an efficient combination of dimensionality reduction technique and classifier. Many appearance based dimensionality reduction techniques have been proposed in recent years. This research focuses on the comparative analysis of appearance based subspace methods (PCA, KPCA, PCA+MI, KPCA+MI) for facial expression recognition in completely equal working conditions. PCA is a vastly used feature extraction technique which is linear and unsupervised. A kernelized version of PCA is called KPCA increases classification accuracy by handling non-linear data which is an unsupervised feature extraction method. KPCA shows the highest accuracy among these two methods. Combining a feature selection method MI with KPCA which can highly increase the recognition rate. The accuracy of KPCA+MI was above 95.24% when the moderate number of training features were used to train the SVM with RBF kernel.
Multi-aircraft Collaborative Route Planning Scheme Based on Coevolution

Although the current research is mainly focused on the single aircraft flight route planning algorithm, the multi-aircraft collaborative flight route planning should become a hot and key issue in the future. Based on CCRP algorithm, a multi-aircraft cooperative route planning scheme is proposed. The scheme consists of three algorithms: RSAVS, ECCRP and RTCCRP. Its goal is to greatly improve the survival rate and attack success rate of aircraft fleet through multi-aircraft coordinated actions. RSAVS is used to ensure the robustness of the whole route planning algorithm; ECCRP is used for static global route planning and to prepare for possible route re-planning due to unexpected threats; RTCCRP is used to ensure the real-time performance of planning. The rationality and advantages of the scheme are explained and summarized.
Visualizing the food landscape of Durham, North Carolina

Abstract In partnership with community leaders of Durham, North Carolina, the Duke World Food Policy Center is creating a Durham Food Justice Plan (DFJP) for envisioning an equitable food system. The Food Justice plan serves to incorporate Durham's local food history in terms of combating historical and present injustices in the food system. We propose creating an integrative, interactive visual for DFJP to view the food landscape of Durham, which will be utilized to study its relationships with the people living in the area where these establishments reside. We created a food landscape platform in Tableau using multisource data including Durham's food vendor data from North Carolina's Department of Commerce and census data for demographic data, neighborhood labels, and street and highways. We incorporated the Google Geocoding API and the aggregated information based on census blocks and tracts in visualization. We produced a variety of maps and graphs to highlight diverse relationships. The food landscape visualization allows us to see interactive patterns among food vendors and demographic data in Durham. We expect to utilize the developed landscape visual for insightful statistical analysis on food inequity issues.
Price look out using web browser for the objects detected in the image

Object Detection and Recognition in the field of computer vision is gaining popularity due to enormous number of applications associated with them. Number of algorithm are developed for object detection and recognition along with localization. In the current paper, a relative comparison of various object detecting algorithm in terms of speed is carried out and “You only look once (YOLO)” is selected for further studies. It is one of the prominent deep learning algorithm associated with object detection at real time. The algorithm scans the image only once to localize and detect multiple objects present in the image. Training is a memory and time intensive process, hence trained weights are directly used for computation. Based on the labels that are obtained from the detected objects their pricing information are obtained. This saves considerable amount of time when pricing information of objects are required in large numbers of multi-object images.
Adaptive enhanced affine transformation for non‐rigid registration of visible and infrared images

Non-rigid registration, performing well in all-weather and all-day/night conditions, directly determine the reliability of visible (VIS) and infrared (IR) image fusion. On account of non-planar scenes and differences between IR and VIS cameras, non-linear transformation models are more helpful to non-rigid image registration than the affine model. However, most of non-linear models usually used on non-rigid registration are constructed by control points at present. Aiming at the issue that the adaptiveness and generalization of the control-point-based models are limited, adaptive enhanced affine transformation (AEAT) is proposed for image registration, generalizing the affine model from linear to non-linear case. Firstly, Gaussian weighted shape context, measuring the structural similarity between multimodal images, is designed to extract putative matches from edge maps of IR and VIS images. Secondly, to implement global image registration, the optimal parameters of the AEAT modal are estimated from putative matches by a strategy of subsection optimization. Experiment results show that this approach is robust in different registration tasks and outperforms several competitive methods on registration precision and speed.
City Brand Image Building Strategy Based on Interactive Data Mining and Visual Saliency

City brand image building strategy based on the interactive data mining and visual saliency is discussed in this paper. In the big data environment, through the general interface and interaction design, the operation and management and also scheduling capabilities of big data can be improved. By using this feature, this paper proposes the listed novelties. (1) The GSSL method mainly relies on the Euclidean distance between point pairs to construct a graph model composed of multiple overlapping local blocks. This feature is used to estimate the distance of the visual information. (2) The simplest form of the region matching is to divide the whole image into many sub-regions, and then measure the similarity of photometric information. This has been used to construct the analytic framework. The verifications have proven the better performance.
Acoustic modeling for Thai- English code-switching speech

Due to the effect of globalization, mixing languages between Thai and English has been commonly used in typical conversations in Thailand, even if talking with Thai natives. Consequently, Thai automatic speech recognition that is deployed in multilingual communities are able to handle Thai-English code-switching. One of the main challenges in building a system is selecting phone set for Thai-English pairs which mother tongue-like accent interferes with the English pronunciation. This paper shows evidence that an acoustic model with a Thai phoneme set improves the recognition performance for Thai-English code-mixing speech. The baseline system for comparison built with merge phoneme for Thai and English where the phones were simply combined. The experimental results shown that the word error rate of monolingual Thai phones is reduced by 4.5%.
AGAMI: Scalable Visual Analytics over Multidimensional Data Streams

As worldwide capability to collect, store, and manage information continues to grow, the resulting datasets become increasingly difficult to understand and extract insights from. Interactive data visualizations offers a promising avenue to efficiently navigate and gain insights from highly complex datasets, but the velocity of modern data streams often means that precomputed representations or summarizations of the data will quickly become obsolete. Our system, Agami, provides live-updating, interactive visualizations over streaming data. We leverage in-memory data sketches to summarize and aggregate information to be visualized, and also allow users to query future feature values by leveraging online machine learning models. Our approach facilitates low-latency, iterative exploration of data streams and can scale out incrementally to handle increasing stream velocities and query loads. We provide a thorough evaluation of our data structures and system performance using a real-world meteorological dataset.
Analyzing Continuous Integration Bad Practices in Closed-Source Projects

Continuous integration (CI) is a process widely used in projects of different types and sizes, generally aimed at improving development and also ensuring the quality of the software. However, some bad practices can mitigate or invalidate the beneficial effects of CI. In this context, we present an initial study to analyze CI bad practices in two closed-source projects. The study had as main research questions: (i) the frequency of CI bad practices in projects, (ii) the time that builds failures remain unresolved in projects, and (iii) the effects of CI bad practices on software health from the perspective of developers and project managers. For this, we carried out a mapping of the existence of bad practices through a questionnaire directed to the projects team. Also, it was realized the analysis of the pipeline history of the projects. Our findings indicate that the most common CI bad practices are related to improper management of the CI repository, and defective builds usually take a long time to fix. Besides, we found that the difficulties resulting from CI bad practices promote an increase in the development cost and reduction of software quality assurance.
Concerto: Leveraging Ensembles for Timely, Accurate Model Training Over Voluminous Datasets

As data volumes increase, there is a pressing need to make sense of the data in a timely fashion. Voluminous datasets are often multidimensional with individual data points representing a vector of features. Data scientists fit models to the data - using all features or a subset thereof - and then use these models to inform their understanding of phenomena or make predictions. The performance of these analytical models is assessed based on their accuracy and ability to generalize on unseen data. Several frameworks exist for drawing insights from voluminous datasets, but have limited scalability (which leads to prolonged training times), poor resource utilization, narrow applicability across problem domains, and insufficient support for combining diverse model fitting algorithms. In this study, we describe our methodology for scalable supervised learning over voluminous datasets. The methodology explores the effect of controlled partitioning of the feature space, as well as how analytical models can be combined to preserve accuracy. Rather than build a single, all-encompassing model, we enable practitioners to construct an ensemble of models that are trained independently in parallel over different portions of the data space. This can provide faster training times and increased prediction accuracy overall; our empirical benchmarks demonstrate the suitability of our approach using real-world data.
WITHDRAWN: Power algorithm to improve the IoT device for lightweight cryptography applications

Due to exponential growth of Internet of Things (IoT) devices, the volume of data exchanged between IoT devices has increased drastically. Most IoT systems handle sensitive and confidential information with low-resource applications. For low-resource devices conventional encryption techniques are unsuitable. Ciphers are used for encrypting information on those devices to compensate for security and power consumption requirements. The objective of this study is to explore ways of improving the effectiveness and power consumption of low-resource IoT technologies in chip design using lightweight cryptography. This research work introduces an effective power algorithm to improve the low-resource IoT device which enables critical messages to be encrypted in low-power mode. For lightweight cipher performance metrics, we have created a simple and efficient model. The experimental analysis of mathematical model for power consumption in terms of using various power parameters has been compared and validated.