Reviewerly/exHarmony huggingface dataset:
Evaluasi Kapabilitas Sistem Informasi Pasien ICU dan HCU Menggunakan COBIT 5 dengan Domain BAI

The hospital's Intensive Care Unit (ICU) and High Care Unit (HCU) systems are very important systems and are always used, so it is necessary to know the extent of their performance. Thus, the study aims to provide reference for xyz hospital regarding the condition (performance) of the current ICU and HCU patient systems. COBIT 5 is an information system capability evaluation framework with the build, acquire and implement (BAI) domain. Domain BAI focuses on developing, acquiring, and implementing information systems, applications, and services. It covers the entire IT project life cycle, starting with the planning and requirements gathering phases and continuing through the design, development, testing, and deployment phases. This study also uses qualitative approach with literature studies and quantitative approach for data analysis to measure capability level. Research respondents were heads and staff from the IT, ICU, and HCU divisions. The results of this study describe the level of capability achieved by hospitals in the BAI Domain, with an average score of 3.30 at the established level. This level illustrates that the application of information technology can achieve process results that are in accordance with the wishes of raft xyz management. This stage also illustrates that the xyz hospital has an information technology process that has been standardized thoroughly to achieve all processes. The results of this evaluation can also be guideline for the xyz raft house to make improvements and improve the performance of the ICU and HCU patient systems in meeting the needs of intensively treated patients.
Analisis Usability pada Human Capital Management System PT. XYZ menggunakan WEBUSE Model

Human Capital Management System (HCMS) has become a necessity for organizations to perform administrative personnel services. PT XYZ started implementing HCMS in 2019 as an online self-service for personnel administration, which was previously done manually. During this transition phase, not all employees understand how to use the HCMS system due to its confusing interface. As a result, many employees still prefer assistance from the HR/IT team to make changes to their data rather than doing it themselves. This research was conducted to analyze the usability of the HCMS system using the WEBUSE method. There are four categories used: Content, Organization, and Readability (COR); Navigation and Links (NAL); User Interface Design (UID); and Performance and Effectiveness (PAE). Each category serves as a basis for measuring usability. Data was collected through questionnaires and interviews with respondents to obtain values and levels of WEBUSE usability based on user satisfaction aspects In the case study of this research, it was obtained from the results of the questionnaire distributed as many as 13 respondents stated that in general, the usability of the HCMS system was at the Good level. This research provides recommendations for improving the self-service HCMS based on the needs or experiences of employees using the system. Also contributes to the scientific study of UI/UX and provides an ideal overview practitioner in developing an HCMS system.
Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron

Corn is one of the substitute staple foods in Indonesia after rice. Maize crops grown in Indonesia often experience considerable losses due to maize plant diseases. Generally, plant diseases are initially caused by morphological changes in the leaves. Accurate detection and classification of diseases that appear on the leaves will prevent the widespread spread of the disease. This study will compare classification algorithms, namely Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron to find the best algorithm in the classification of leaf disease in corn plants, namely, cercospora leaf spot gray, common rust, and northern leaf blight using the VGG-16 deep learning model used as image feature extraction. The results showed that the Multilayer Perceptron algorithm produced the best values with accuracy, precision, and recall of 97.4% each.
Optimasi Algoritma Support Vector Machine Berbasis PSO Dan Seleksi Fitur Information Gain Pada Analisis Sentimen

Sentiment analysis is a method for processing consumer reviews. This study examines the application of the Support Vector Machine (SVM) algorithm based on PSO and Information Gain as feature selection to filter attributes as a form of optimization. Algorithm implementation in sentiment analysis is carried out by applying a test scenario to measure the level of accuracy of the several parameters used. Selection of the Information Gain feature using the top-k parameter yields an accuracy value of 85.3%. Algortima optimization applying information gain feature selection on the PSO-based SVM resulted in an optimal accuracy rate of 86.81%. The resulting increase in accuracy is 18.84% compared to the application of classic SVM without PSO-based information gain feature selection. Applying information gain feature selection on the PSO-based SVM algorithm can increase the accuracy value in the online sentiment review analysis.
‘Insight unlocked’: Applying a collective intelligence approach to engage employers in informing local skills improvement planning

This paper demonstrates how the innovative application of a Collective Intelligence approach enhanced Local Skills Improvement Planning information for employers, education and skills training organisations and regional economic policy organisations. This took place within a Knowledge Transfer Partnership between a Chamber of Commerce and a University. This aimed to develop and deploy regional business intelligence for enhanced policy and decision-making in enterprise and economic development. The project converged knowledge from several research centres including economics, entrepreneurship and innovation, data science, and Artificial Intelligence. The paper presents a project case study which provides two contributions to applied knowledge. Firstly, it demonstrates how a Collective Intelligence (CI) approach can be applied to achieve rapid results in resolving the real-world problem of local skills information availability. Useful real-time data was gathered from employers in three sectors on skills requirements, supply and training. This was analysed using Artificial Intelligence tools, then shared publicly via an automated Internet portal, providing a scalable model for wider use. Secondly, it explores and evaluates how the knowledge exchange (KE) process can function effectively and quickly in applying CI-based innovation in practical ways which create new value, within a Knowledge Transfer Partnership between a University and Chamber of Commerce.environment.
GEOSPATIAL ESTIMATION OF ROOFTOP SOLAR PHOTOVOLTAIC POTENTIAL IN CALABAR MUNICIPAL, CROSS RIVER STATE, NIGERIA

The availability of significant solar resource and the continuous hike in economic activities as well as an increased population in Calabar Municipal holds a substantial solar photovoltaic (PV) potential of rooftops in the city. This is as a result of the consequent increase in urban households. This Research work is aimed at estimating solar PV potential of rooftops in the study area using geospatial techniques. It adopted Geospatial techniques’ approach in identifying rooftops available for PV deployment in Calabar Municipal. High resolution satellite imagery gotten from google Earth, Digital surface model (DSM)/ Digital elevation model (DEM) and ArcGIS 10.8 were employed in this study. In other to estimate the PV potential of rooftops, criteria such as Solar Radiation/ Irradiance, Total Roof Area (Building footprints), Slope (rooftop inclination angle) and Aspect (Buildings’ orientation) were analyzed. The results gotten from these analyses were analyzed further and rooftops that fall below the acceptable range of the aforementioned criteria were excluded from the analysis. Results showed that the total roof area in Calabar Municipal was estimated as 5,965,916.44 m2 (5.97 km2) while the available roof area suitable for PV installations was evaluated as 5,964,966.42 m2 (5.96 km2). The findings of this research indicates that Calabar Municipal has good solar radiation as well as enough suitable rooftop area hence a very good place for solar PV deployments. Also, this study is accurate enough to be used as a guide in decision making on power (electricity) generation in Calabar Municipal of Cross River State.
A Privacy-Preserving and Edge-Collaborating Architecture for Personalized Mobility

Driven by technologies and demands, the modern transportation system has developed from intelligent transportation systems (ITS) to autonomous transportation systems (ATS) to resolve intertwined demands and supplies with few human interventions. In ATS, personal mobility service (PMS) is the service that can sense real-time traffic conditions comprehensively, learn travelers’ preferences accurately, recommend multimodal travel options appropriately, and provide service responses timely to elevate the level of personalization and intelligence in smart mobility services. Since current PMS widely employs centralized approaches (CPMS) to process massive sensitive data from individuals and support diverse edge devices, resulting in high pressure in privacy protection and performance balancing, this paper presents a federated PMS (FPMS) and its design architecture in logical and physical views by adopting federated learning to provide multimodal, dynamic, and personalized travel options with system-saving safety and efficiency guaranteed. Moreover, through an extensive evaluation, the performances of CPMS and FPMS are compared to reveal the merits of FPMS in reducing costs and latency.
ZGridBC: Zero-Knowledge Proof Based Scalable and Privacy-Enhanced Blockchain Platform for Electricity Tracking

The total number of solar power-producing facilities whose Feed-in Tariff (FIT) Program-based ten-year contracts will expire by 2023 is expected to reach approximately 1.65 million in Japan. If the facilities that produce or consume renewable energy would increase to reach a large number, e.g., two million, blockchain would not be capable of processing all the transactions. In this work, we propose a blockchain-based electricity-tracking platform for renewable energy, called ‘ZGridBC,’ which consists of mutually cooperative two novel decentralized schemes to solve scalability, storage cost, and privacy issues at the same time. One is the electricity production resource management, which is an efficient data management scheme that manages electricity production resources (EPRs) on the blockchain by using UTXO tokens extended to two-dimension (period and electricity amount) to prevent double-spending. The other is the electricity-tracking proof, which is a massive data aggregation scheme that significantly reduces the amount of data managed on the blockchain by using zero-knowledge proof (ZKP). Thereafter, we illustrate the architecture of ZGridBC, consider its scalability, security, and privacy, and illustrate the implementation of ZGridBC. Finally, we evaluate the scalability of ZGridBC, which handles two million electricity facilities with far less cost per environmental value compared with the price of the environmental value proposed by METI (=0.3 yen/kWh).
Single Image Dehazing Based on Sky Area Segmentation and Image Fusion

Since the dark channel prior (DCP)-based dehazing method is ineffective in the sky area and will cause the problem of too dark and color distortion of the image, we propose a novel dehazing method based on sky area segmentation and image fusion. We first segment the image according to the characteristics of the sky area and non-sky area of the image, then estimate the atmospheric light and transmission map according to the DCP and correct them, and then fuse the original image after the contrast adaptive histogram equalization to improve the details information of the image. Experiments illustrate that our method performs well in dehazing and can reduce image distortion.
Parallel Implementation of CNN on Multi-FPGA Cluster

We developed a PYNQ cluster that consists of economical Zynq boards, called M-KUBOS, that are interconnected through low-cost high-performance GTH serial links. For the software environment, we employed the PYNQ open-source software platform. The PYNQ cluster is anticipated to be a multi-access edge computing (MEC) server for 5G mobile networks. We implemented the ResNet-50 inference accelerator on the PYNQ cluster for image recognition of MEC applications. By estimating the execution time of each ResNet-50 layer, layers of ResNet-50 were divided into multiple boards so that the execution time of each board would be as equal as possible for efficient pipeline processing. Owing to the PYNQ cluster in which FPGAs were directly connected by high-speed serial links, stream processing without network bottlenecks and pipeline processing between boards were readily realized. The implementation on 4 boards achieved 292 GOPS performance, 75.1 FPS throughput, and 7.81 GOPS/W power efficiency. It achieved 17 times faster speed and 130 times more power efficiency compared to the implementation on the CPU, and 5.8 times more power efficiency compared to the implementation on the GPU.
A Lightweight End-to-End Speech Recognition System on Embedded Devices

In industry, automatic speech recognition has come to be a competitive feature for embedded products with poor hardware resources. In this work, we propose a tiny end-to-end speech recognition model that is lightweight and easily deployable on edge platforms. First, instead of sophisticated network structures, such as recurrent neural networks, transformers, etc., the model we propose mainly uses convolutional neural networks as its backbone. This ensures that our model is supported by most software development kits for embedded devices. Second, we adopt the basic unit of MobileNet-v3, which performs well in computer vision tasks, and integrate the features of the hidden layer at different scales, thus compressing the number of parameters of the model to less than 1 M and achieving an accuracy greater than that of some traditional models. Third, in order to further reduce the CPU computation, we directly extract acoustic representations from 1-dimensional speech waveforms and use a self-supervised learning approach to encourage the convergence of the model. Finally, to solve some problems where hardware resources are relatively weak, we use a prefix beam search decoder to dynamically extend the search path with an optimized pruning strategy and an additional initialism language model to capture the probability of between-words in advance and thus avoid premature pruning of correct words. In our experiments, according to a number of evaluation categories, our end-to-end model outperformed several tiny speech recognition models used for embedded devices in related work.
Behavior Recognition and Monitoring System for Office Workers Using Deep Learning and Two Surveillance Cameras

In many offices, starting and finishing times of workers are recorded as part of attendance control, but they do not monitor the employees' behaviors during the workday whether they are taking appropriate breaks. In this paper, we propose a behavior recognition system using the object detection algorithm YOLOv5 and the object tracking algorithm DeepSORT and two surveillance cameras. In addition, we propose a person matching method between the images from two surveillance cameras using projective transformation. We will show that the recognition works substantially well by integrating the recognition results by using two cameras.
A Proposal of Response Continuity Prediction Model for Attentive Listening Agents

For the current attentive listening agents, the main way to improve the system performance is to design the format of output utterances. However, to achieve better attentive listening performance, it is also important to predict when to start and to end attentive listening. In fact, predicting the timing of attentive listening can be also seen as predicting whether or not the speaker will continue to speak in an occupied multi-unit turn. In this paper, we propose a deep learning model to predict whether or not a speaker will continue speaking in an attentive listening dialog. We focus on the situation in which the respondent continues to respond to a question by the interlocutor or completes the response being produced. Our model has the following three features. First, the input data of our model is designed using domain knowledge about the structure of attentive listening dialog and the characteristics of the vocabulary used therein. Second, we use multimodal information such as text, acoustic features, and characteristic tokens when constructing the model. Third, considering the practicality in actual daily dialogs, we use everyday conversation data collected from a large-scale corpus for constructing the model. The experimental results show that the proposed model achieves the best prediction performance among the models we examine, providing a great potential for prediction of the timing of attentive listening for dialog agents.
A quantum resistant multi-user signcryption scheme featuring key invisibility for Internet of Things

With the advent of ubiquitous computing, multiple heterogeneous devices have evolved with increased connectivity, yielding the Internet of Things (IoT). For establishing secure communication, IoT has effectively become a primary security concern. The massive connectivity of the resource-constrained devices in IoT and enormous data on the air have made information vulnerable to several types of attacks. Although several resource-intensive cryptographic algorithms have been proposed to combat this security threat, signcryption has been identified as a sound solution for such an application. In this work, we propose an isogeny-based signcryption scheme by integrating HyPKE, an isogeny-based encryption scheme with an isogeny-based signature SeaSign based on Fiat Shamir with aborts. Apart from confidentiality and authenticity requirements, our scheme also achieves ciphertext anonymity and key invisibility, unlike the existing signcryption schemes in the post-quantum world. More positively, our security model considers insider attacks, supports multi-users setting and offers non-repudiation. Our work also studies the key privacy notion of the isogeny-based encryption scheme Hashed-PKE, which can be of independent interest in designing several anonymous channels or privacy-preserving protocols. More precisely, we showed Hashed-PKE achieves indistinguishability of keys against chosen-plaintext attack. This leads to the indistinguishability of keys against chosen-ciphertext attack security of HyPKE, a hybrid encryption scheme that is obtained by applying the Fujisaki Okamoto transformation to Hashed-PKE. We have proved our result in the standard model assuming the hardness of the Commutative Supersingular Decisional Diffie–Hellman assumption.
Improving the Accuracy of Differential-Neural Distinguisher for DES, Chaskey, and PRESENT

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for SPECK32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for SPECK32/64 and SIMON32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.
Multilayered Cortical Learning Algorithm for Forecasting Time-Series Data with Probabilistically Changing Trends

In this paper, a burst-based multilayered cortical learning algorithm (BM-CLA) for forecasting trend-changing time-series data is proposed. CLA predicts time-series data while adjusting synapse relationships online. However, the forecast accuracy of the conventional CLA deteriorates with trend-changing time-series data, in which several time-series trends are switched over time. The proposed BM-CLA detects trend changes based on multilayered CLA predictors. Experimental results using multiple artificial time-series data with probabilistically changing trends showed that BM-CLA achieves results that are better or comparable to those of conventional CLAs with different specifications and the long short-term memory (LSTM), which is a neural network-based forecast algorithm.
Analysis of Industrial Sensor Data Using Statistical and Regression Methods

Today's industrial landscape is primarily driven by rapid and effective data processing and evaluation. Consequently, industries should devote considerable attention and resources towards real-time examination of the large data sets acquired, enabling timely extraction of vital information for outlier detection, fake data identification, and predictive analysis to mitigate unforeseen expenses. This rigorous process of data analysis necessitates the employment of a diverse set of algorithms that align with the specific objectives, spanning a wide spectrum of potential solutions. In this manuscript, we demonstrate how Apache Spark's unified engine can be harnessed for conducting statistical analysis of time series data, thereby expediting industrial data analysis processes. Furthermore, we examine and implement both linear and random forest regression models within the context of the demonstrated use case.
An Examination of Design Choices Intended to Improve the Auditability of a Consortium Blockchain

ABSTRACT Blockchain consortia offer firms several advantages, including the ability to maintain a shared transaction ledger that is secure, verified, and agreed-upon by key business partners. While these benefits are largely derived from blockchain’s distributed architecture, this same architecture poses challenges to auditors working to provide assurance on a technology owned, operated, and maintained across several firms’ borders. Indeed, participating in a blockchain consortium transitions a firm from relying solely on its own IT infrastructure and processes, to being vulnerable to how other firms collectively maintain the shared ledger. As blockchain consortia grow and are used to process and record material transactions, members will require assurance that other members maintain the blockchain in a well-controlled manner. Given the complexities of auditing a distributed environment, this study proposes three design choices intended to improve the auditability of consortium blockchains. Practitioners then evaluate these designs and offer additional considerations/alternative paths forward.