Reviewerly/exHarmony huggingface dataset:
PERANCANGAN SISTEM DIGITAL SIGNAGE SEBAGAI PAPAN INFORMASI TERKAIT PROTOKOL KESEHATAN DAN DATA UP TO DATE COVID-19 DI MASJID AL-IRSYAD JAKARTA

During the Covid-19 pandemic, places of worship have the potential to become clusters for spreading the virus. It is necessary to have a digital information board related to health protocols and up to date data on Covid-19 installed in places of worship so that people can always be alert and obedient to implementing health protocols. Therefore, in this research, a digital signage has been designed using a raspberry pi microcontroller and a pisignage platform. This digital signage server uses web hosting from Pisignage to manage connected clients, make changes to the content displayed and can be accessed using smartphones and laptops. The digital signage client uses a raspberry pi connected to an LED TV to display the information sent by the server. Based on the results of the design, the display of information on each client is as planned. All information on the spread of covid-19 and image and video content can be seen and displayed on the digital signage client up to date. Based on the test results, this system has very good performance when using LAN and WLAN transmission media according to the TIPHON standard with an index value of 4. Based on the results of QoS analysis according to ITU-T G.1010 the comparison between image content and video content using wi-fi networks is obtained The conclusion is that the Qos parameter value of image content is smaller than video content due to different data sizes and bandwidths.
Special issue on Reasoning under Partial Knowledge

This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability and comparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures.
USING VIRTUAL WORLDS TO DEVELOP THINKING IN PRESCHOOL CHILDREN

During the first seven years of his life, the child receives a huge amount of knowledge about the world around him. The development of the child's thinking during this period and the results of his knowledge of the world around him affect the further educational activities of the preschooler. Thanks to the development of thinking, a child is able to imagine an image of an object and its properties in the absence of the object itself, based only on previously received information. In a child in the early and preschool years of life, game is the main activity for forming a personality. Didactic games facilitate the process of all-round development of children. Various tasks solved in didactic games are presented for children in an interesting and attractive form of activity. In a computer game, using simulation, it becomes possible to demonstrate objects and phenomena that are inaccessible to a child in the real world. The use of didactic computer games not only increases the preschooler's motivation to learn new material, but also contributes to the development of thinking. An educational computer game acts as a mentor for a child. However, the lack of communication with a living, and not virtual, mentor causes a negative effect both on the development of the child's speech and the formation of his communicative abilities. Therefore, a computer game cannot be perceived as a complete alternative to the classical means of teaching and educating preschoolers.
Personal Viewing History Collection Method for Video Streaming Services in User-Centric Model

The personal viewing history of broadcast and internet content can be used to improve the quality of online services for the user; consequently, enhancing a person's experience in their daily life. This study proposes a collection method of the personal viewing history of online video streaming services in a user-centric model, where the user owns and controls their data. By obtaining event data from a video element of HTML5 in a video streaming player on a web browser, users can store their viewing history separately from the service provider's system. Because this method employs standardized web technologies, it can be applied to multiple viewing devices and player implementations. Additionally, a prototype of a video streaming player extension is developed. Implementation of the proposed method and verification tests are conducted on the prototype. The results obtained suggest that the proposed method is applicable to existing HTML5 compliant web browsers.
What Is a Problematic Ethical Situation?

In Chapter 1, we briefly consider the question, What is a problematic ethical situation? In that chapter, we give attention to the relationship of diverse expectations in reflective inquiry, including seeking to resolve or solve ethical problems. In a related vignette, we note that Irene Sebastian, a third-grade Academy teacher, had been reported for screaming at her students. From that chapter, a person could say that an ethical situation involves making a practical decision about what should or should not be done (LW 7, 9), e.g., What needs to be investigated to clarify what Irene had done, and how should Maria and Academy colleagues respond to the situation? For Dewey, a problematic situation involves these kinds of ought questions and more, including complexities that may not be readily apparent. While tackling this subject, he notes how his views differ on occasion from others’ conclusions. For example, in Ethics, one of his interests is to explain that moral situations entail doubts and perplexities brought about when “different desires promise opposed goods” (164).
A Machine Learning Method Based on 3D Local Surface Representation for Recognizing the Inscriptions on Ancient Stele

It is challenging to extract reliefs from ancient steles due to their rough surfaces, which contain relief-like noise such as dents and scratches. In this paper, we propose a method to segment relief region from 3D scanned ancient stele by exploiting local surface characteristics. For each surface point, four points that are apart from the reference point along the direction of the principal curvatures of the point are identified. The spin images of the reference point and the four relative points are concatenated to provide additional local surface information of the reference point. A random forest model is trained with the local surface features and, thereafter, used to classify 3D surface point as relief or non-relief. To effectively distinguish relief from the degraded surface region containing relief-like noise, the model is trained using three-class labels consisting of relief, background, and degraded surface region. The initial three-class result obtained from the model is refined using the k-nearest neighbors algorithm, and, finally, the degraded region is re-labeled to background region. Experimental results show that the proposed method performed better than the state-of-the-art, SVM-based method with a margin of 0.68%, 3.53%, 2.25%, and 2.36%, in accuracy, precision, F1 score, and SIRI, respectively. When compared with the height- and curvature-based methods, the proposed method outperforms these existing methods with accuracy, precision, F1 score, and SIRI gains of over 4%, 20%, 11%, and 12%, respectively.
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities

An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous class of safety risks are learned shortcuts, i.e. spurious correlations a network exploits for its decisions that have no semantic connection to the actual task. Networks relying on such shortcuts bear the risk of not generalizing well to unseen inputs. Explainability methods help to uncover such network vulnerabilities. However, many of these techniques are not directly applicable if access to the network is constrained, in so-called black-box setups. These setups are prevalent when using third-party ML components. To address this constraint, we present an approach to detect learned shortcuts using an interpretable-by-design network as a proxy to the black-box model of interest. Leveraging the proxy’s guarantees on introspection we automatically extract candidates for learned shortcuts. Their transferability to the black box is validated in a systematic fashion. Concretely, as proxy model we choose a BagNet, which bases its decisions purely on local image patches. We demonstrate on the autonomous driving dataset A2D2 that extracted patch shortcuts significantly influence the black box model. By efficiently identifying such patch-based vulnerabilities, we contribute to safer ML models.
An Ensemble Classification Algorithm for Imbalanced Data Streams with Unlabeled Data

In practical applications, many data in imbalanced data streams are unlabeled. For this reason, this paper presents an ensemble classification algorithm for imbalanced data streams with unlabeled data . The algorithm uses the K-means clustering algorithm to label unlabeled instances, uses the re-sampling technique of dividing negative instances to establish some balanced data subsets, uses the Hoeffding Bounds inequality to detect concept drifts, and uses the ensemble technique to build an ensemble classifier. Experimental results show that the algorithm has good classification accuracy and adaptability, and it is an effective classification algorithm.
Blockchain-Based Smart Inventory

Over the past industrial Automation have created paradigm changes in the manufacturing industry worldwide. Besides, a similar shift in expectations and radical change in our approach to manufacturing and development could be accomplished by the blockchain integration. They are bearing in mind the 4.0 industrial revolution needed for further enhancement of the manufacturing inventory. The tools of industry 4.0 are likely to shift blockchain-based. The aim of this study is to analysis and understanding of the blockchain model and integration in inventory management. Also, this research constructs a conceptual framework for inventory management. Finally, initiative and introduce an integrated model for blockchain-based inventory management.
Research on the Application of Factor Analysis Model

Abstract This article uses a factor analysis model to conduct an empirical analysis using data analysis. The data comes from a questionnaire survey. The subjects of the survey are college students in Guangxi, China. The research topic is the dual “3+1” training model for compound talents. Based on the SPSS analysis that KMO=0.812 and spherical Bartlett=0.000, the contribution rates of the extracted 8 common factor variances are: H =13.487%, H 2 =8.535%, H 3 =8.303%, H 4 =6.152%, H s =5.814%, H 6 =5.622%, H 7 =4.925%, H 8 =4.870%, the above data results indicate: government support and social support, school publicity, government and university implementation efforts, student satisfaction with the training model, students’ willingness for future development, etc. The Factor Analysis Model provides reference and enriches the theoretical basis.
Ways of modernization of informational training for university students

The article substantiates the need for informatization of education in general, and higher professional education in particular, requiring the modernization of forms, methods and content of information training of students in the light of the implementation of the program “Digital Economy of the Russian Federation”. The article presents the ways of improving the informational training of university students. The research methods are as follows: analysis of literature, documents, and electronic resources; observation, questioning of participants in the educational process, methods of mathematical statistics. The analysis shows that the project-based method of training, monitoring and control of the content of electronic resources, the integrated use of distant learning systems, online software for collaborative work, the implementation of artificial intelligence technology in education will allow to form the media literacy of future specialists that meets the modern requirements of the economy and society. The study was conducted on the basis of S.A. Yesenin Ryazan State University in various areas and profiles of education. One of the types of project activities based on modern digital technologies is a hackathon. The goal of such a project is to develop and create a media product in a given professional field. This can be a website, an application, or an interactive software. A team of students is working on a socially significant task. Preparing students for the project-based work by using modern digital technologies has shown its effectiveness for being included in the “Startup as a Degree” Program. The final qualification work prepared in this format may represent a project that is in demand in the field of professional activity. The experimental work of the study was carried out by dividing students into a control group and an experimental group. The range, difference, and significance of changes in the level of informational training in the control and experimental groups were evaluated by the Fischer – Yates criterion. The analysis of individual and collective projects developed by the students, the qualitative analysis of the students’ answers from the both groups to the questions of questionnaires and tests showed that the students of the experimental group revealed a higher level of readiness for professional activity, self-study, self-education, which indicates the effectiveness of the proposed approach.
Dual bidirectional mutual distillation based on dense connections

Abstract In the field of deep neural network, model compression has been widely used. As a model compression method, knowledge distillation has been studied more and more widely. Most of the existing knowledge distillation methods need a lot of data to train the teacher model in advance, and the interaction between their teacher model and student model is often weak. The existing methods such as DML are constrained by loss to realize soft weight sharing, and there is little work to discuss hard weight sharing. Therefore, a new deep interactive online distillation method is proposed in this paper. In this method, the teacher network generates the auxiliary student network and establishes the connection between the two, and the student network generates the auxiliary teacher network and establishes the connection between the two. Then the two auxiliary networks are combined and trained from scratch. Finally, the performance of the model is improved by using back gradient propagation. The effectiveness of this method is verified by experimental tests on common image classification tasks.
Supervised Classification of Diseases Based on an Improved Associative Algorithm

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles.
Information Technology Augmentative and Alternative Communication Using Smart Mobile Devices

The article describes the information technology of alternative communication implemented by non-contact text entry using a limited number of simple dynamic gestures. Non-contact text entry technologies and motion tracking devices are analysed. A model of the human hand is proposed, which provides information on the position of the hand at each moment in time. Parameters sufficient for recognizing static and dynamic gestures are identified. The process of calculating the features of the various components of the movement that occur when showing dynamic hand gestures is considered. Common methods for selecting letters with non-contact text entry are analysed. To implement the user interaction interface, it is proposed to use a radial virtual keyboard with keys containing alphabetical letters grouped. A functional model and a model of human-computer interaction of non-contact text entry have been developed. It enabled to develop an easy-to-use software system for alternative communication, which is implemented by non-contact text entry using hand gestures. The developed software system provides a communication mechanism for people with disabilities.
An Approach for Buyer Name Normalization in Pharmacy Sales Data

It is fundamental for pharmaceutical enterprises to make statistics and analyses on the massive sales data that are submitted by distributors at different levels when making market plans and other decisions. However, buyer names in sales data may have various forms due to aliases, abbreviations, typos, and other reasons, which severely affect the subsequent mining performance. To tackle this problem, in this paper, we propose a novel approach called BuyerNorm which can identify different expression forms for a given buyer name. The proposed model takes pairs of variable length buyer names as input and combines Bidirectional Encoder Representations from Transformers (BERT) and Bi-directional Long Short-Term Memory (BiLSTM) to obtain string representations and calculate the similarity between the two names, which indicates whether they represent the same buyer. For this task, we first build a data set including more than 80,000 pairs of buyer names. Then, extensive experiments on the data set show that BuyerNorm performs better than the state-of-the-art baseline and can obtain an average AUC of 99.84%.
Development of application Information System in Rhee District

Abstract Management of patient data at the Technical Implementation Unit of the Community Health Center (UPT. Puskesmas) of Rhee Sumbawa District has not been fully computerized. The need for the development of an Information System to store medical records (history) of patient data. The research aims to develop information systems with descriptive research methods. For software development methods using Waterfall Models and built with Microsoft Visual Studio C# software and databases stored in Microsoft SQL Server software. Software testing uses Black Box Testing, which is testing software in functional terms. The results of research into digital data technology products that function to enter, change, delete data and minimize the time to find the data needed to improve the effectiveness and efficiency of data management.
SAR image segmentation based on different gradients with nonlocal mean filter

Aiming at the problem that SAR image segmentation is seriously interfered by the speckle, this paper studies the methods of SAR image segmentation based on different gradients under nonlocal mean (NLM) filter. We focus on four gradient operators, contained ratio of average(ROA), ratio of an exponentially weighted averages (ROEWA), Unsampled Wavelet Transform (UWT) and instantaneous coefficient of variation (ICOV). Firstly, the NLM filter is used to suppress the speckle and protect the structure in the SAR image. After that, the gradient of SAR image is calculated based on the above different gradient operators. Finally, the watershed technique is used to segment the image based on the gradient of SAR image. Experimental results show that the segmentation based on ICOV and UWT obtain more accurate boundary location and fewer segmentation regions than the methods based on ROA and ROEWA.
GraphGem: Optimized Scalable System for Graph Convolutional Networks

Deep Learning (DL), especially Graph Convolutional Networks (GCNs) have revolutionized several domains and applications dealing with unstructured data with non-euclidean and graphical relationships. Constructing large-scale Deep GCNs, however, are bottlenecked by glaring systems issues due to memory blow-ups, runtime slowdowns with random access, and I/O costs. This research abstract identifies various systems and scalability issues and proposes a novel system called GraphGem to handle GCN-centric DL tasks end-to-end. GraphGem tackles the bottlenecks by elevating entire GCN workloads for convenient input declarations by the user, and is inspired by lessons from the databases and machine learning systems worlds. This abstract also highlights the bigger picture of the potential research impact alongside tacking systems constraints and what it may mean for data science and deep learning practitioners going forward.
Algorithm of Detection of Articles Left behind in Vehicles

(Purpose) In order to improve the timeliness and stability of detection of targets in a vehicle and to reduce the probability of loss of articles, (Method) the author uses PyTorch to build a full convolutional neural network model for target detection which adopts ResNet as the backbone network and FPN for extracting the feature maps of higher-order and lower-order network. (Result) After convergence of model, the comparison of the result received from validation set with the effect of target detection in original ResNet network suggests that the feature extraction capacity and stability are improved significantly with this model. (Conclusion) The improved network structure has a good application prospect in detection of targets in a vehicle.
Spatial neural network for forecasting energy consumption of Palembang area

Abstract Spatial Neural Network proposed as a new approach to determine the level of energy consumption in the future in the Palembang region, South Sumatra. Back-propagation is used to train Artificial Neural Networks. Population size, Gross Regional Domestic Product (GRDP), economic growth, Household Energy Consumption spatially per sub-district is used for this estimate. Data for 2008-2012 is used to train Spatial Neural Networks (ANN) and Data for 2013-2017 is used to validate this new approach to electricity demand prediction. The proposed Spatial ANN approach provides relatively good predictions about energy demand in acceptable errors and high accuracy for electricity demand predictions.