Reviewerly/exHarmony huggingface dataset:
DOSA: Organic Compilation for Neural Network Inference on Distributed FPGAs

The computational requirements of artificial intelligence workloads are growing exponentially. In addition, more and more compute is moved towards the edge due to latency or localization constraints. At the same time, Dennard scaling has ended and Moore’s law is winding down. These trends created an opportunity for specialized accelerators including field-programmable gate arrays (FPGAs), but the poor support and usability of today’s tools prevents FPGAs from being deployed at scale for deep neural network (DNN) inference applications. In this work, we propose an organic compiler — DOSA — that drastically lowers the barrier for deploying FPGAs. DOSA builds on the operation set architecture concept and integrates the DNN accelerator components generated by existing DNN-to-FPGA frameworks to produce an overall efficient solution. DOSA starts from DNNs represented in the community standard ONNX and automatically implements model- and data-parallelism, based on the performance targets and resource footprints provided by the user. Deploying a DNN using DOSA on 9 FPGAs exhibits a speedup of up to 52 times compared to a CPU and 18 times compared to a GPU.
Electronic Information Resources of Libraries of Higher Education Branch Institutions in Ukraine During the Quarantine Restrictions and Martial Law

The aim of the article is to determine the leader among the libraries of higher education branch institutions in Ukraine in the provision of electronic information resources.&#x0D; Research methods. The study used the following methods: synthesis, complexity, comparative analysis, comprehensiveness of knowledge, information diagnostics, generalization, abstraction, systematization, as well as statistical, survey-analytical and benchmarking methods; systemic, functional, social and communication, critical and constructive approaches.&#x0D; Scientific novelty consists in determining the set of electronic information resources provided by libraries during quarantine restrictions and martial law, which allows to establish a leader library among book collections of higher education branch institutions in Ukraine, the experience of which is necessary to analyze and spread to other book collections.&#x0D; Main conclusions. Websites of libraries in higher education branch institutions of Ukraine were studied. A comparative analysis of electronic information resources of libraries during quarantine restrictions and martial law was carried out. The research made it possible to determine the leader among the libraries of higher education branch institutions in Ukraine in providing users with information resources during quarantine and martial law. It turned out to be the library of Kyiv University named after Borys Grinchenko. The library provides its users with a number of remote electronic information resources: an electronic catalog, an institutional repository, audio books, virtual exhibitions, a new accession in the form of presentations, full-text collections of rare publications and B. Grinchenko’s works, a test access to scientometric platforms and databases: Scopus, Web of Science, EBSCOhost, Bentham Science, ScienceDirect, a corporate electronic library Alpina Digital. It is appropriate for the leading library to share its experience with other book collections.
Sistem Informasi Pemasaran Produk Pada Toko Online Agsel

The marketing information system at the existing Agsel Online Store still uses a manual system (dor to bang, wa, brochures and facebook), so the product marketing process takes a lot of time, does not update, is not efficient and spends a lot of money to print banners and brochures resulting in high income. accepted by the minimal agsel shop. For this reason, Agsel online shop wants to use web-based marketing to increase revenue, save time because the web can be accessed at any time, is accurate and more efficient. The purpose of this research is to analyze and design a Product Marketing Information System at Agsel Online Stores. The method used by the author is the prototype design method according to the title that the author takes, namely the analysis and design of a marketing information system at the Agsel online store. It is hoped that with this web-based marketing and sales, it can increase revenue at the Agsel store with more and more Agsel store products being known more widely by the entire community. It is hoped that further researchers at the Agsel store can create a website application that can combine product sales predictions with the Weka application and real web creation, not just design.
Analyzing Adaptive and Non-Adaptive Online Learners on Imbalanced Evolving Streams

The online class imbalance and concept drift (OCI-CD) has recently received much interest.The impact of this combined problem on state-of-the-art of online adaptive and non-adaptive learners has received little attention.This study investigates the effect of parameters such as current imbalance ratio, stream length, drift type, drift levels, and imbalance state (static or dynamic) on adaptive and non-adaptive online learners.The experimental results show that each parameter considered for the study has a significant impact on learner performance: (a) minority class performance decreases as the degree of imbalance increases, (b) non-adaptive learners are much susceptible to class imbalance, concept drift, and the combined problem of both drifts than adaptive learners, (c) adaptive learners are only susceptible to class imbalance drifts, and (d) the impact of the dynamic degree of imbalance is more on learner than static (e) the adaptive large scale support vector machine yields stable performance to all the parameters considered for the study.Based on these findings, directions for developing new approaches are also presented.Povzetek: Analizirane so razne metode strojnega učenja glede na parametre učenja, recimo spreminjanje neuravnoteženja razredov. MotivationWang et al. [3] recently conducted a systematic analysis to determine the impact of CI on three different types of drifts (i.e., p(y), p(y/x), and p(x/y)) while ignoring their counterparts.The effect of different levels of imbalance (static and dynamic) and coupled dynamic OCI-CD drift on online learner's performance has yet to be empirically investigated.They only gave advice based on observations of cases with a high level of imbalance (i.e., 1:9). ContributionsTo fill in the gaps mentioned above, the impact of the training stream's characteristics, including the degree of imbalance, length at the time t, drift types (CI, CD, and OCI-CD), and the state of imbalance (static and dynamic) on state-of-the art adaptive and non-adaptive learners used for minority class prediction, is explored.This study explores various static and dynamic imbalanced streams with gradual and abrupt drift levels.
THE EFFECTIVENESS OF ECONOMIC TEXTBOOK BASED ON 5.0 USING WORDPRESS FOR FIRST GRADE OF SMA 1 KOTA SALAK DHARMASRAYA

This research is motivated by the needs of students in developing online-based textbooks with more interesting learning resources.The purpose of this research is to develop a wordpress website-based textbook as an online learning resource that can be used in the learning process.then another goal is how student learning outcomes using online-based learning resources.This type of research is the development of wordpress-based textbooks using the ADDIE model.Where there are five stages, namely the stages of analysis, design, development, implementation and evaluation.Validation questionnaires, practicality questionnaires, effectiveness questionnaires and student learning outcomes assessments were used to assess product feasibility.For the validation questionnaire, 6 other people were used, consisting of 2 economics teachers, material experts, 2 economics lecturers as media experts, and 2 language lecturers as linguists.Meanwhile, the effectiveness assessment carried out an assessment of the activeness of class XI III students spread over 3 locales.And for learning outcomes, an assessment of the test questions given to students is carried out.Based on the results of the analysis of the effectiveness assessment of Economics Subject learning tools using the Wordpress website media at SMA Negeri 1 Koto Salak which was assessed by 30 students, the most students answered very effectively (90%) and as many as (10%) students stated effective.This shows that students understand well and easily learn economics textbooks using Wordpress website media.
Securing Message Steganography by using Modified LSB Method

Message steganography is one of the popular methods used to protect secret message. In this research paper a modified LSB method of message steganography will be introduced. The introduced method will use a complicated private key to protect the message from being hacked, the key will provide an excellent key space capable to resist hacking attacks, it will contain 6 values with a double data type. The private key will be used to divide the covering image into segments and divide the secret message into partitions; each message partition will be hidden in one image segment. The hiding and extracting processes will be implemented in a patch way, by inserting the binary version of the message partition in burst way and extracting the binary version of the message partition also in burst way. The proposed method will keep the quality of the stego image as in LSB method. The efficiency of the proposed method will be closed to the efficiency of LSB method. The proposed method will be implemented and tested using various images and various covering images, the obtained results will be analyzed to prove the advantages provided by the proposed method.
On Guiding Search in HTN Temporal Planning with non Temporal Heuristics

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context. In response to these inconveniences, we propose in this paper a new general POCL (Partial Order Causal Link) approach to represent and solve a temporal HTN problem by using existing heuristics developed to solve non temporal problems. We show experimentally that this approach is performant and can outperform the existing ones.
GloptiNets: Scalable Non-Convex Optimization with Certificates

We present a novel approach to non-convex optimization with certificates, which handles smooth functions on the hypercube or on the torus. Unlike traditional methods that rely on algebraic properties, our algorithm exploits the regularity of the target function intrinsic in the decay of its Fourier spectrum. By defining a tractable family of models, we allow at the same time to obtain precise certificates and to leverage the advanced and powerful computational techniques developed to optimize neural networks. In this way the scalability of our approach is naturally enhanced by parallel computing with GPUs. Our approach, when applied to the case of polynomials of moderate dimensions but with thousands of coefficients, outperforms the state-of-the-art optimization methods with certificates, as the ones based on Lasserre's hierarchy, addressing problems intractable for the competitors.
Cervical Intraepithelial Neoplasia Grading from Prepared Digital Histology Images

The paper proposes an integrated approach to the automated diagnosis of cervical intraepithelial neoplasia (CIN) in epithelial patches extracted from digital histology images. Experiments were conducted to determine the most suitable deep learning model for the dataset and fuse patch predictions to decide the final CIN grade of the histology samples. Seven candidate CNN architectures were assessed in this study. Three fusion methods were applied to the best CNN classifier. The model ensemble, combined CNN classifier and highest performing fusion method achieved an accuracy of 94.57%. This result shows significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images. It is hoped that this work will contribute towards further research to automate diagnosis of CIN from digital histopathology images.
An Ontology Design Pattern for Modeling Experimental Paradigms

We present an ontology design pattern for modeling scientific experiments and examinations conducted in a clinical research study. Integrating heterogeneous data into a common ontological model is a challenge, redoubled if we want them to be explored later. In order to facilitate the development of dedicated ontological modules, this design pattern relies on invariants, is centered on the event of the experiment, and keeps the link to the original data.
An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection

This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.
SNA: The Optimal Nodes to Raise Nurses’ Infosec Awareness

Social Network Analysis (SNA) can promote Infosec awareness. A sample of 164 nurses selected the most trusted actors to get Infosec updates. UCINET 6 and NetDraw were used for mapping and PSPP 1.6.2 was used for data analysis. Nurses tend to trust managers, colleagues and IT professionals for retrieving Infosec updates.
Leveraging Multi-Word Concepts to Predict Acute Kidney Injury in Intensive Care

Acute kidney injury (AKI) is an abrupt decrease in kidney function widespread in intensive care. Many AKI prediction models have been proposed, but only few exploit clinical notes and medical terminologies. Previously, we developed and internally validated a model to predict AKI using clinical notes enriched with single-word concepts from medical knowledge graphs. However, an analysis of the impact of using multi-word concepts is lacking. In this study, we compare the use of only the clinical notes as input to prediction to the use of clinical notes retrofitted with both single-word and multi-word concepts. Our results show that 1) retrofitting single-word concepts improved word representations and improved the performance of the prediction model; 2) retrofitting multi-word concepts further improves both results, albeit slightly. Although the improvement with multi-word concepts was small, due to the small number of multi-word concepts that could be annotated, multi-word concepts have proven to be beneficial.
Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results

The comprehensive epidemiology and global disease burdens reported recently suggest that chronic lymphocytic leukemia (CLL) constitutes 25-30% of leukemias thus being the most common leukemia subtype. However, there is an insufficient presence of artificial intelligence (AI)-based techniques for CLL diagnosis. The novelty of this study is in the investigation of data-driven techniques to leverage the intricate CLL-related immune dysfunctions reflected in routine complete blood count (CBC) alone. We used statistical inferences, four feature selection methods, and multistage hyperparameter tuning to build robust classifiers. With respective accuracies of 97.05%, 97.63%, and 98.62% for Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), and XGboost (XGb)-based models, CBC-driven AI methods promise timely medical care and improved patient outcome with lesser resource usage and related cost.
Experience from the Development of HL7 FHIR IG for Gatekeeper Project

The GATEKEEPER (GK) Project was financed by the European Commission to develop a platform and marketplace to share and match ideas, technologies, user needs and processes to ensure a healthier independent life for the aging population connecting all the actors involved in the care circle. In this paper, the GK platform architecture is presented focusing on the role of HL7 FHIR to provide a shared logical data model to be explored in heterogeneous daily living environments. GK pilots are used to illustrate the impact of the approach, benefit value, and scalability, suggesting ways to further accelerate progress.
User Experiences on the Implementation of Exoskeletons in Care Work

This article describes the results of a field test of an exoskeleton in care work. Qualitative data about the implementation and use of exoskeletons, with the help of interviews and user diaries, were collected both from nurses who used the exoskeleton and managers at different levels of the care organization. On the basis of these data, there are relatively few obstacles in and many opportunities for the implementation of exoskeletons in care work, taken that the introduction, orientation and continuous support for technology use are given enough weight.
Microbiological and Virological Knowledge-Based Alert Service

Hospital wards need immediate information about multi-resistant pathogens and contagious viruses in their hospitalized patients. An alert service configurable with Arden-Syntax-based alert definitions passing through an ontology service to complement results from microbiology and virology with high-level terms was implemented as proof of concept. Integration into the University Hospital Vienna's IT landscape is ongoing.
Digital Tools in UMLS Metathesaurus Knowledge Processing

Clinical search engines development is actual task for medical informatics. The main issue in this area is to implement high-quality unstructured texts processing. Ontological interdisciplinary metathesaurus UMLS can be used to solve this problem. Currently, there is no unified method to relevant information aggregation from UMLS. In this research, we have presented the UMLS as graph model and performed the spot check of UMLS structure to identify basic problems. Then we created and integrated new graph metric in two created by us program modules for relevant knowledge aggregation from UMLS.
TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.
A Failure Event Virtual Learning Method that Replaces Field Experience and Its Effectiveness Measurement

Its known that 95 % of occupational accidents are caused by unsafe human behavior. Therefore, in order to prevent occupational accidents, various human resource education is implemented in each company, but due to the rut that accompanies continuation, many of them are only aimed at preventing serious accidents in steady state. Furthermore, since it takes time and manpower to conduct education, there is also a problem that these educational methods are not effectively utilized in the field. For example, in recent years, a method of supporting risk prediction using VR in high-place work has been considered, but it costs a lot of money to introduce it, and people who have little experience in the first place have the problem that it feels like a game. Merely continuing such a stereotyped education will not improve learning through experience, and it will be difficult to develop outstanding human resources with awareness. Furthermore, for those who have little experience in the field, even if they acquire knowledge through classroom lectures and group work, it is difficult to use it in the field and predict danger. Developing experienced and excellent human resources who can notice on-site is an urgent issue for the future of the company. Therefore, in this research, by investigating various conventional educational methods and sorting out their strengths and weaknesses, we will clarify the reasons why these educational methods are not being used in the field, improve the weaknesses, and improve the actual field. We propose a failure event virtual experience-based l learning method that replaces the field experience.In this study, we decided to measure the effect of the proposed learning method from the degree of awareness in the accident prevention activities carried out in Japanese companies. The reason for this is that in order to further improve the effectiveness of current safety education, it is most practical to measure awareness during activities that are actually carried out in the field, and it is not possible to measure with paper tests or questionnaires. Therefore, based on the characteristics of on-site workers, we decided to examine methods to improve their awareness and willingness to participate in safety activities. Furthermore, we decided to conduct an analysis based on an evaluation experiment to see if the subject's awareness improved by receiving the proposed educational method prior to the safety activities.
Design of Library Management System Based on MVVM Framework and ZXing Scanning Code Technology

The library is an important resource for university learning. Nowadays, the library has gradually become the place where students learn most in universities at home and abroad. Therefore, our team will design an interdisciplinary practice in the library of Huazhong University of Science and Technology. The survey found that the libraries of Huazhong University of Science and Technology and other universities achieve self-service management through self-service machines, public numbers, official websites, and other channels, but students generally think that the function is cumbersome and inconvenient to query, so we want to design a book self-service management system to bring the greatest convenience to users. This practice uses literature analysis, questionnaires, interviews, and experiments to deeply understand the pain points of library self-service machines, the design principles of relevant interfaces of libraries at home and abroad, and the needs of users to determine the information construction, interactive experience and technical needs of the system. This project is mainly an interdisciplinary practice of computer science and industrial design. In terms of technology, the system uses Android and MVVM architecture to display the front-end interface, realizes the Android network request through OkHttp and Retrofit, and uses ZXing open-source scanning technology to realize the function of borrowing and returning books on the palm; Go language and Echo Web framework is used for development, and Docker is used to deploying containers. In terms of design, the system combines ergonomics, design psychology, and so on, which not only realizes the basic functions of mobile phones, such as scanning code, borrowing, and returning books, map guide, and searching books but also adds special functions such as lost and found, recording reading time and so on, so as to enhance the user's personalized experience. After the usability test, the interviewees believe that the design can greatly improve the learning efficiency in the library.