Reviewerly/exHarmony huggingface dataset:
Portable Device for Monitoring the Health of Pregnant Women

The paper discusses the development of a portable device for monitoring the condition of a pregnant woman, the relevance of the topic, selection of the main indicators on which the patient's health status is assessed. Also, methods for registration of these parameters are described. As a result of the work performed, a device for monitoring the condition of a pregnant woman was proposed, its performance was evaluated and advantages are highlighted in comparison with the existing analogues, and possible ways to improve the accuracy of removal and evaluation of indicators are presented.
Clustering Kabupaten/Kota di Jawa Tengah Tahun 2022 berdasarkan Jumlah Kasus Kemunculan Penyakit dengan Algoritma K-Means

This research aims to conduct clustering or grouping of Regencies/Cities in Central Java Province based on the number of occurrences of specific diseases in 2022 using the K-Means algorithm. The research results obtained 3 clusters, namely high, medium, and low for 29 Regencies and 6 Cities. The percentage for cluster 1 is 34.29%, consisting of 10 regencies and 2 cities, cluster 2 is 40.00%, consisting of 11 regencies and 3 cities, and cluster 3 is 25.71%, consisting of 8 regencies and 1 city. These clustering results can be used as a basis for making effective strategic decisions in the development of prevention and control efforts for diseases in each region.
Klasifikasi Besar Potensi Kemunculan Batu Ginjal Menggunakan Fuzzy Inference System (FIS) Metode Mamdani

The phenomenon of modernization that arises in the lives of society has an impact on unhealthy lifestyles. This has led to an increased prevalence of kidney stone disease in recent decades. In this study, a fuzzy inference system will be developed using the Mamdani method for the classification of the large potential occurrence of kidney stones based on urine conditions. The input variables used are urine pH, urea content in urine, and calcium content in urine, while the output variable used is the magnitude of the potential occurrence of kidney stones. The variables included in the fuzzy set are formed by combinations of each variable with its respective linguistic terms. These fuzzy sets are then processed with the help of Matlab software to produce a classification result indicating small or large potential. The classification results from the developed fuzzy inference system provide accurate classifications for users with an accuracy level of 94%. Thus, in general, this fuzzy system can be used to assist in classifying the large potential occurrence of kidney stones, thereby minimizing the number of individuals suffering from this disease as much as possible.
Testing Black Boxes: Verification and Validation of AI Models in Wireless Communication Systems

Fifth generation (5G) cellular communication system provides a flexible system architecture that enables wireless communication technology to penetrate different verticals and connect various devices. Applying machine learning to develop AI models applicable to various application scenarios becomes a promising direction for designing 5G systems. However, developing AI models for wireless communication algorithms brings new challenges to system design and verification. This paper provides an overview of studies on the verification and testing of wireless communication algorithms developed by machine learning methodology. Due to the black-box nature and scenario-specified data-based model development of the machine learning approach, standardization of testing and verification procedures becomes a new challenge when the device under test implements AI models. We explain the general issues in the context of use cases studied by 3GPP standardization group to illustrate how the application of machine learning to wireless communication systems design emerging as a unique problem from the testing and verification perspective. We then summarize the challenges to develop effective testing and verification procedures. Through explaining the issues and challenges, this paper aims to provide a framework for future studies on resolutions to enable wireless communication AI model testing and verification.
Category based Fast Retrieval for Point-of-Interest Search

In the quest of achieving a seamless e-commerce and ride-hailing experience, the significance of effective Point-of-Interest (POI) search cannot be overstated. Yet, the relentless growth in number of POIs over the years, has given a rise to increased latency concerns within modern POI search engines. This, in turn, has resulted in an unsatisfactory search journey for users on the receiving end. In response to this challenge, we introduce a novel approach in the form of a category-based POI retrieval pipeline by mapping POI and query in the same space, effectively improving retrieval efficiency. Through several online A/B experiments, we showcase that our proposed pipeline outperforms by concurrently elevating NDGC@k and recall@k metrics while reducing the latency from 4.1--14.33% that affect POI searches.
News Informing on Open Science as a Strategic Direction for the Development of a Scientific Library

News is the most accessible, timely and popular information in the world, it affects many spheres of social life and reflects their current state. News in the field of science is no exception. People were interested in news long before the widespread use of information technology, and some current forms of information and library services were introduced in the 20th century. Defining and strategizing the direction of news information is a significant part of information support for researchers. Scientific libraries, as important social institutions specializing in providing access to information, can contribute to the search for and provision of this kind of information. The basic principles of building a news strategy in modern libraries are the relevance of the news topic to the library’s vision and its relevance to the target audience, taking into account the use of modern information technologies. In a scientific library, open science can be used as such a topic due to its demand among researchers and specialists. The purpose of this article is to develop a set of information products designed to achieve information support of scientific library users (by the example of open science). A study of 100 websites of foreign university libraries included in the Top 100 Leiden ranking of universities’ openness in 2023 was carried out to identify the practices of news informing users about open science. Five best examples of foreign experience were identified. An important part of the information strategy — the technology of selecting channels for receiving information — was developed. On its basis, a set of information products was prepared and published on the website of the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences “Library for Open Science”: news feed, quarterly digest “Open Science News” and issues of “Live News of Open Science” in video material.
Library in the Space of Books and Events of the 36th Moscow International Book Fair

From August 30 to September 3, 2023, the 36th Moscow International Book Fair was held at the Expo Centre Central Exhibition Complex on Krasnopresnenskaya Embankment. The Forum brought together participants from various regions of Russia, as well as representatives of 21 countries, including seven countries of the Commonwealth of Independent States, Germany, China and Iran, and was attended by more than 34 thousand people. The VKontakte social network has become a partner of online broadcasts and the official MIBF network. More than 300 events were held at seven venues. Publishers from Moscow, St. Petersburg and 26 other Russian regions presented their books. The article presents an annual review of the activities of the MIBF: The National competition “Book of the Year — 2023”, the competition of the Russian Library Association “The Best professional Book of the Year”, the 3rd Readers’ Assembly of the Commonwealth, the industry conference “Book Market of Russia — 2023”, the Moscow International Children’s Book Fair. In the event context of the most relevant events for the library industry, libraries’ understanding of their role in the socio-cultural life of society is revealed: achievements and initiatives aimed at preserving heritage, promoting books and reading are noted, new projects, including book publishing, are presented, the results of the participation of the Russian State Library and the publishing house “Pashkov Dom” in the MIBF-2023 are summed up.
An Unrolled and Pipelined Architecture for SHA2 Family of Hash Functions on FPGA

Cryptographic hash function is an important part of information security due to the need of integrity verification. Message compression is the most time-consuming process in the SHA2 family of hash functions. Reducing the processing time is important in high-speed applications, and the Field Programmable Gate Array (FPGA) provides a platform that can be used for this purpose. In this paper, a high-speed message compression module is proposed using pipelining and loop unrolling design methods on FPGA. A procedure to generate a pipelined design with two unrolling levels is shown, and the resultant architecture is presented as the proposed design. As a result, the block cycle of the SHA512, consisting of 80 steps, is completed in 42 steps. Performance analysis results show that the proposed unrolled and pipelined architecture for SHA512 significantly improves throughput compared to the studies in the literature.
A Blockchain Based Decentralized Identity, Access Management, and Trust Evaluation Framework for IoT

The number of connected Internet of Things (IoT) devices has been rising rapidly in recent years due to the establishment of novel and more capable communication protocols. It is estimated that the adoption of 5G and beyond communication technologies will result in the number of globally connected IoT devices surpassing 30 billion by the end of 2025. This proliferation, coupled with the fact that most of these devices are being deployed in intricate and interconnected networks, amplifies the challenges associated with designing an identity and access management (IAM) system that is self-executing, globally unique, and interoperable between different devices, networks, and systems. Blockchain technology, characterized by its decentralization, immutability, and cryptographic capabilities, emerges as a promising solution to address these challenges. In this context, we propose a blockchain-based decentralized identity (DID) and trust evaluation framework for IoT devices. This framework enables globally connected devices to authenticate each other and authorize interactions in a decentralized manner. Additionally, it implements a reputation-based trust evaluation mechanism by calculating trust scores from interactions within the blockchain network.
Adaptation of n-out-of-n secret sharing scheme into IoT network

Internet of Things (IoT) has become an established part of our daily lives by interconnecting billions of devices in diverse areas such as healthcare, smart home technologies, agriculture, etc. However, these devices are limited in memory, energy and computational capabilities. This creates a great potential for security issues to arise as being constrained prevents them from applying complex cryptographic algorithms. In this study, we propose a novel method to provide a low-cost and secure communication for constrained IoT devices. The proposed method is based on a n-out-of-n secret sharing scheme and mimicks the idea of visual cryptography in a digital set-up.Generally, when an IoT device communicates with an outer party, it establishes the communication by itself or through a mediary such as a central hub or gateway; which leads to single point of failure. Our proposed method aims for a distributed environment in which devices collaborate with each other and therefore divide the responsibility of sending a message into multiple devices, instead of just one device. Therefore, when a device plans on sending a message, all its neighbors send it on the behalf of this device.
Rural Cultural Heritage Scene Construction Based on Digital Activation Technology

China’ s rural cultural heritage has a long history and has left behind many rich resources in the process of inheritance and development. This article uses digital dynamic technologies such as computer image and video processing to construct a twin scene of rural cultural heritage, achieving multimodal, digital storage protection, and modern innovation of rural cultural connotations. On the one hand, the scene construction of rural intangible cultural heritage based on 2D animation technology realizes the modern restoration of rural intangible heritage through static painting and animation; On the other hand, the construction of rural material culture heritage scenes based on 3D animation technology realizes the digital reality application of rural cultural scenes through the method of object restoration and presentation. Finally, the commercial value and potential applications of rural cultural heritage were explored.
A resource scheduling strategy based on firefly algorithm

The Yarn scheduler is one of the core modules of Hadoop. It plays a critical role in the resource utilization efficiency and overall performance of a distributed cluster. However, with the generation of large amounts of data, the three resource schedulers it comes with can no longer well meet the needs of the industry. Therefore, this paper proposes a resource scheduling strategy based on the Firefly algorithm. Firstly, through the study of the scheduling of existing resources and cluster resource allocation mechanism, the algorithm based on the capacity scheduler is designed to achieve a more reasonable resource allocation scheme. Secondly, according to the job resource demand and the resource status of system nodes to dynamically adjust, we design the FireFlyScheduler resource scheduling strategy based on the Firefly algorithm. Finally, in order to test the effectiveness of the proposed scheduling strategy, the cultural data is tested with WordCount instances and two different types of MapReduce tasks TeraSort and TestDFSIO.Simulation results show that the performance of the proposed FireFlyScheduler scheduling strategy is better than that of other existing capacity schedulers. The above results prove the superiority of the scheduling strategy proposed in this paper, which is of great significance for improving the overall resource utilization of the system and the overall performance of the Hadoop platform.
Research on the Application of Video Affective Computing in the Field of Radio and Television

Video affective computing refers to the computer analyzes and extracts multimodal emotional features such as text, audio, and images from videos, uses pattern recognition, machine learning and other methods to perform calculation, and analyzes the emotional response and behavior of viewers after watching the video. This paper briefly expounds the key technology and architecture of video affective computing, and puts forward suggestions for the application in the field of radio and television.
Message from the General Chairs ACSOS 2023

On behalf of the ACSOS 2023 organizing committee, we would like to welcome you to the fourth edition of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems.
Implementation of the Comandos Application Accounting Information System at Bank BTPN Makassar Branch Office

This research is a type of research with case studies approach with the aim of knowing whether the application commands have been effectively applied to PT. Bank BTPN KC Makassar based on indicators of accounting information systems. Which method used in this study is a descriptive analysis. Technique data collection obtained through observation, documentation. the data that is obtained is secondary data and primary data. Based on the research results, the authors draw important conclusions, namely the application of commandos is very effectively implemented at PT. Bank BTPN KC Makassar. The Comandos app helps BTPN banks to become more efficient in managing and monitoring collateral data. This application is also well integrated with the existing system at Bank BTPN. The quality of the system produced by the Commandos application is very good and works optimally in processing customer data quickly and efficiently. The quality of the information generated by the Commandos application is also very good. Employees who use the Commandos application are very satisfied because the Commandos application is easy to understand, and especially easy to input data. After the application is updated the application performance gets better. Internal constraints use the application only on a network that is sometimes bad or slow
A New Try for Age Estimation and Gender Recognition Based on EfficientNetB4

A framework called ECA-EfficientNetB4 is proposed to solve the issue of low accuracy in age estimation and gender recognition of current facial features in this paper. First, integrates ECA attention mechanism in EfficientNetB4, which can effectively capture inter channel relationships in images while maintaining efficiency, thereby enhancing the ability of feature representation. After adding the cross entropy loss function to the network, not only does it enhance the accuracy of prediction, but also enhances the convergence speed of the model. The ECA-EfficientNetB4 model proposed in this article has an age MAE of 4.55 years on a UTKFace dataset of over 23000 images, which is 0.31 years less than VGGFace-ResNet50 and the accuracy rate of gender recognition is 93.62%. Therefore, compared with the other four excellent frameworks in the same category, this model has the lowest MAE for age and achieves first-class accuracy for gender, thus achieving excellent performance. In the future, this technology can be applied to identity verification and assisted driving judgment strategies on cars.
Cross-modality Deep Feature Matching Network for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VIReID) aims to match pedestrian images between the visible and infrared modalities. Existing methods focus on using modal transformation approaches to alleviate modality differences. However, they have not taken into account the relationship of local information across modalities. Therefore, we propose a Cross-modality Deep Feature Matching Network (CDFMN) for VIReID by adopting a feature matching method. CDFMN effectively handles both modality-specific and modality-shared information by leveraging modal invariant features. We introduce novel losses to capture modality-specific features and establish cross-modal correspondences. Additionally, we employ deep feature matching to measure the similarity between image pairs. This pioneering work applies feature matching to VIReID, demonstrating its effectiveness in addressing the challenges of cross-modal matching.
Physical Layer Security Performance Prediction Based on Deep Learning

This paper presents an innovative approach to enhance communication security by accurately predicting eavesdropper channel strength through real-time variations based on legitimate channels. Utilizing Long Short-Term Memory (LSTM) neural networks, this method trains a model to predict real-time amplitude of the channel. To address the location information of known unlicensed users within a network, an LSTM model is established by collecting channel information from licensed users in a real wireless propagation environment. This model is utilized to predict the channel strength of the unlicensed user. Experimental results demonstrate that this approach accurately predicts the strength of eavesdropping channels, thereby preventing eavesdroppers from obtaining communication activity information and enhancing communication security.
Outlier detection of vital sign trajectories from COVID-19 patients

In this work, we present a novel trajectory comparison algorithm to identify abnormal vital sign trends, with the aim of improving recognition of deteriorating health.There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems.We introduce a dynamic time warp distance-based measure to compare time series trajectories. We split each multi-variable sign time series into 180 minute, non-overlapping epochs. We then calculate the distance between all pairs of epochs. Each epoch is characterized by its mean pairwise distance (average link distance) to all other epochs, with clusters forming with nearby epochs.We demonstrate in synthetically generated data that this method can identify abnormal epochs and cluster epochs with similar trajectories. We then apply this method to a real-world data set of vital signs from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show how outlier epochs correspond well with the abnormal vital signs and identify patients who were subsequently readmitted to hospital.
An Efficient Service-based System for Hierarchical Human Activity Sensing

In this paper, we propose an end-to-end system, based on SEnsing as Service (SEAS) model, which processes continuous mobility data from multiple sensors on the client edge-device by optimizing the on-device processing pipelines. Thus, reducing the cost of data transfer and CPU usage. We also propose a classification algorithm as a part of the system to recognize Activities of Daily Living (ADL). The results indicate that our proposed system recognizes ADLs with considerable accuracy and flexibility.Clinical relevance— Measurement of Activities of Daily Living has a high correlation with independent living measures for elderly people [1] and post-event rehabilitation where an event may be heart-attack [2], stroke [3], surgical intervention [4], or trauma [5] etc.
Spatial Attention-Guided Generative Adversarial Network for Synthesizing Contrast-enhanced Computed Tomography Images

Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guided generative adversarial network (SAG-GAN), which can directly obtain corresponding CE-CT images from the patient’s NC-CT images. In the SAG-GAN, we devise a spatial attention-guided generator, which utilize a lightweight spatial attention module to highlight synthesis task-related areas in NC-CT image and neglect unrelated areas. To assess the performance of our approach, we test it on two tasks: synthesizing CE-CT images in arterial phase and portal venous phase. Both qualitative and quantitative results demonstrate that SAG-GAN is superior to existing GANs-based image synthesis methods.