Reviewerly/exHarmony huggingface dataset:
Pengembangan Sistem Monitoring Tingkat Salinitas Air Pada Tambak Rumput Laut Berbasis Website

The level of salinity in water quality is one of the determining factors in the success of seaweed aquaculture production. The purpose of this study was to develop a website-based monitoring system for the level of water salinity in seaweed ponds. Monitoring system development is carried out by designing and developing an integrated website. Measurement of water salinity in ponds uses a sensor with a microcontroller connected via the internet network so that monitoring can be carried out without the need to go to the pond location. This type of research is development (Research and Development) which refers to the waterfall model (Planning, Analysis, Design, Implementation, Maintenance). The results of the research show that website development as a monitoring system has been successfully developed. There are four pages that can be displayed on the website for monitoring seaweed pond water salinity, namely the login page, homepage, viewing data, and monitoring water salinity. This research is expected to provide an overview in monitoring water quality in increasing the productivity of seaweed cultivation.
MODEL FOR THE DEVELOPMENT OF THE METHODOLOGICAL PREPARATION OF COMPUTER SCIENCE TEACHERS BASED ON THE INTEGRATION OF PEDAGOGICAL AND INFORMATION TECHNOLOGIES

In this article, a complex integrative process that includes the methods of problem analysis and planning, evaluation of the solution to the problem, and the development of methodological training of future teachers based on the integration of pedagogical and information technologies creation of the model and training of a professional and pedagogically prepared competent informatics teacher on the basis of integrative knowledge and its existing conditions were researched.
Delta and “Boom”: Novels of Mario Vargas Llosa through the Prism of Stylometry

The paper deals with the poetics of the novels of the Peruvian writer Mario Vargas Llosa and the evolution of his style. To solve research problems, it is used the Delta method which is the most authoritative and reliable stylometric tool that allows you to compare texts based on the distribution of most frequent words. The work poses three questions: 1) Do Vargas Llosa's early total novels The Time of the Hero, The Green House, and Conversation in the Cathedral differ at the level of style from the later novels, forming a separate group, as the literary hypothesis claims? Answer: the hypothesis is confirmed; 2) Is it possible to divide the fifteen texts of the second period into smaller groups based on stylistic similarities? Answer: novels of the second period are divided into three large groups. Moreover, an interesting correlation is revealed: the novels grouped into separate groups have similarities not only at the level of style, but also at the level of themes, composition and narrative features; 3) Whether the “boom” is a stylistically isolated period in the poetics of its key authors? Answer: the novels written by Latin American authors during the boom period (1960–1970s) are not stylistically isolated: the authorial signal is stronger, and the case of Vargas Llosa with a clear differentiation of groups of novels is unique.
An Efficient, Cost-Effective Methodology for Expedited Marketing Copy Review Leveraging Advancements in NLP

The rapidly advancing online marketplace necessitates adaptation from sellers and consumers alike. Oftentimes in established markets, sellers without grand funding or an extremely experienced marketing team will struggle despite offering products of equal or greater value than established competitors. This research paper presents a complete methodology, harnessing data mining, AI, and statistical techniques, for inexperienced sellers to augment their marketing copy and produce high-level, competitive marketing content. With the aid of existing materials and research, this paper’s proposed model can offer a balancing factor between new and established sellers in the constantly evolving online market.
QKD Key Provisioning with Multi-level Pool Slicing for End-to-End Security Services in Optical Networks

Quantum key distribution (QKD) provides future-proofed security based on the quantum physics. The end-to-end security services in the fields such as finance, government, and military are mainly carried by optical networks. Limited by technical and economical obstacles of QKD deployment, partially-QKD-deployed optical networks (pQKD-ONs) are much more practical. Key pooling is a technique to decouple keys from the underlying infrastructure for timely key provisioning. However, traditional QKD key provisioning (QKPR) methods are specific to QKD-deployed nodes. In this paper, we propose the pQKD-ON architecture and design the multi-level key pool slicing scheme for key management in pQKD-ONs. Then, we propose a general auxiliary-graph-based QKPR algorithm for key supply in pQKD-ONs. Based on the algorithm, we design four dynamic QKPR policies by adjusting edge weights of the auxiliary graphs including Quantum-Key-Pool-First (QKP-First), Key-Pool-Slice-First (KPS-First), Key-Availability-First (KA-First), and Quantum-Key-Pool-All (QKP-All) policies. We evaluate the performances of the QKPR policies in terms of key consumption, security level, and QKPR latency under two network topologies. Simulation results show that the KA-First policy can achieve 9.9% lower key consumption ratio than the KPS-First policy; the KPS-First policy sacrifices the security level but can achieve lower QKPR latency than the QKP-First and QKP-All policies.
IoT Technology Recognition Using Deep Clustering

This paper proposes a fully unsupervised technology recognition method using deep clustering for identifying wireless technologies to learn from raw data without requiring manual label annotations. Identifying and recognizing wireless technologies is important to realize effective spectrum management. Recent scientific publications utilized supervised deep learning with great success to train AI models to recognize different wireless technologies based on labeled RF signal datasets. However, assigning labels to wireless technology signal datasets for supervised deep learning is time consuming and may not always be practical. To remedy this issue, the proposed method combines an autoencoder and clustering layers, extended with a Gaussian Mixture Model, to learn low-dimensional salient features from raw IQ data and cluster them into distinct wireless technologies. The optimal input dimensions and performance of the method in different signal-to-noise ratio conditions are analyzed for a set of several different low-power wide area network (LPWAN) technologies: Sigfox, LoRa, IEEE 802.11ah, and IEEE 802.15.4g. Extensive simulations show that the proposed method for wireless technology recognition exhibits a recognition accuracy exceeding 90%, surpassing the performance of the state-of-the-art deep clustering algorithms by 4 to 5% in terms of accuracy.
Detecting Anomalous Robot Motion in Collaborative Robotic Manufacturing Systems

Anomalous robot motions caused by cyber attacks and inherent defects can lead to task failures as well as harmful accidents in collaborative human-robot workplaces. External Internet-of-Things (IoT) sensors are essential for reliably monitoring robot tool paths and detecting deviations as early as possible, especially in anomalous situations where the robot system and its internal sensors may not function as expected. However, affordable external IoT sensors may suffer from poor-quality measurements, necessitating data-driven algorithmic enhancements. In addition to external sensors providing independent monitoring, to effectively detect trajectory deviations, we need anomaly detection methods that can capture complex dynamic patterns in the nonlinear trajectory time-series data. In this work, we propose a framework to accurately estimate robot trajectories during normal robot operations as well as to detect various types of anomalous trajectory changes using an external camera. The proposed framework efficiently incorporates marker-based pose estimation, Long Short-Term Memory (LSTM), and residual control charts. The framework was evaluated in a shared human-robot assembly task. The results show that we can accurately estimate robot trajectories by enhancing camera-based measurements. Moreover, it effectively detects anomalous trajectory changes in their early stages. The motion deviation upon detection assists in determining a safe working distance. The framework is also generalizable to previously unseen trajectory deviations and allows the use of a variety of IoT sensors.
KianNet: A violence detection model using an attention-based CNN-LSTM structure

Violent behaviour is always an important issue that threatens any society. Therefore, many organizations have used surveillance cameras to monitor such events to preserve public safety and mitigate potential harm. It is difficult for human operators to monitor the copious camera feed manually, however, automated systems are employed to enhance the accuracy of violence detection and reduce errors. In this paper, we propose a novel model named KianNet that effectively detects violent incidents inside recorded events by combining ResNet50 and ConvLSTM architectures with a multi-head self-attention layer. The utilization of ResNet50 enables robust feature extraction, while ConvLSTM makes it easier to take advantage of the temporal dependencies in the video sequences. Furthermore, the multi-head self-attention layer enhances the model’s ability to focus on relevant spatiotemporal regions and their discriminatory capacity. Empirical investigations on large datasets UCF-Crime and RWF confirm that the proposed model outperforms its competitors.
Towards Equitable Peering: A Proposal for a Fair Peering Fee Between ISPs and Content Providers

Disagreements over peering fees have risen to the level of potential government regulation. ISPs assert that content providers should pay them based on the volume of downstream traffic. Transit providers and content providers assert that consumers have already paid ISPs to transmit the content they request and that peering agreements should be settlement-free. Our goal is to determine the fair payment between an ISP and an interconnecting network. We consider fair cost sharing between two Tier-1 ISPs, and derive the peering fee that equalizes their net backbone transportation costs. We then consider fair cost sharing between an ISP and a transit provider. We derive the peering fee that equalizes their net backbone transportation costs, and illustrate how it depends on the traffic ratio and the amount of localization of that content. Finally, we consider the fair peering fee between an ISP and a content provider. We derive the peering fee that results in the same net cost to the ISP, and illustrate how the peering fee depends on the number of interconnection points and the amount of localization of that content. We dispense with the ISP argument that it should be paid regardless of the amount of localization of content.
Concepções de acadêmicos de enfermagem sobre o processo saúde-doença mental

Objective: to analyze the conceptions of the mental health process and to understand the conception of care technologies and the inclusion of those who suffer mentally, based on the narratives of students of undergraduate courses in Nursing at a Federal University of Bahia. Method: narrative investigation, with a qualitative approach, which analyzed the narratives of students of the Degree in Nursing. Results: we had 42 participants, aged between 19 and 50 years. The students' conception of the mental health-illness process was shown in different ways, such as the understanding of psychosocial interventions for the confrontation of psychic suffering, presenting themselves free of prejudices and stigmas. During the data collection, I made reports of illness in the covid-19 pandemic. The results were grouped into empirical categories: Conceptions of the health process, mental illness and quality of life, Psychosocial interventions for coping with psychological distress, Reflection on the Covid-19 Pandemic on the psychological suffering of university students, and Infrastructure/functioning university impacting health university mentality. Conclusion: This investigation suggests possible pedagogical and clinical interventions at Universities for future nursing students. It revealed that the students understand the need for their practice to be guided by the principles of the Single Health System and the anchor of nursing care.
An Improved Deep Convolutional LSTM for Human Activity Recognition Using Wearable Sensors

Behavior recognition technologies based on sensor devices show strong development potential as sensor devices are increasingly applied in people’s lives. However, some challenges in sensor-based behavior recognition, such as incomplete feature extraction, complex behavior recognition algorithms and the recognition accuracy, remain open issues. To address these issues, this paper proposes an improved deep convolutional long short-term memory (DeepConvLSTM) model that uses the squeeze and excitation (SE) module and group convolutions to perform human activity recognition (HAR). The proposed scheme uses convolutional neural network (CNN) and LSTM to simultaneously extract temporal and spatial features. The SE module is an attention mechanism that allows the network to perform feature recalibration, through which the network learns to use global information to selectively emphasize informative features while suppressing less useful features. Moreover, the model uses group convolutions to optimize the number of parameters and reduce the computational complexity. To show the benefit of the proposed model, the performance and efficiency of the improved DeepConvLSTM model and other traditional models are compared on the OPPORTUNITY dataset. In addition, this paper evaluates the impact of hyperparameters on the proposed model and designs an ablation experiment to verify the improvement of each modification. The evaluation results show that the improved DeepConvLSTM model increases the HAR accuracy up to 90.80% given that most computational resources are assigned to more informative features. Moreover, floating-point operations per second (FLOPs) are reduced by 88.93% when the number of group is set to 16 because the group convolution reduces the training parameters greatly.
GLGAN-VC: A Guided Loss-Based Generative Adversarial Network for Many-to-Many Voice Conversion

Many-to-many voice conversion (VC) is a technique aimed at mapping speech features between multiple speakers during training and transferring the vocal characteristics of one source speaker to another target speaker, all while maintaining the content of the source speech unchanged. Existing research highlights a notable gap between the original and generated speech samples in terms of naturalness within many-to-many VC. Therefore, there is substantial room for improvement in achieving more natural-sounding speech samples for both parallel and nonparallel VC scenarios. In this study, we introduce a generative adversarial network (GAN) system with a guided loss (GLGAN-VC) designed to enhance many-to-many VC by focusing on architectural improvements and the integration of alternative loss functions. Our approach includes a pair-wise downsampling and upsampling (PDU) generator network for effective speech feature mapping (FM) in multidomain VC. In addition, we incorporate an FM loss to preserve content information and a residual connection (RC)-based discriminator network to improve learning. A guided loss (GL) function is introduced to efficiently capture differences in latent feature representations between source and target speakers, and an enhanced reconstruction loss is proposed for better contextual information preservation. We evaluate our model on various datasets, including VCC 2016, VCC 2018, VCC 2020, and an emotional speech dataset (ESD). Our results, based on both subjective and objective evaluation metrics, demonstrate that our model outperforms state-of-the-art (SOTA) many-to-many GAN-based VC models in terms of speech quality and speaker similarity in the generated speech samples.
A Comparative Analysis of Feature Eliminator Methods to Improve Machine Learning Phishing Detection

This Machine-learning-based phishing detection employs statistical models and algorithms to assess and recognise phishing attacks. These algorithms can learn patterns and features that distinguish between phishing and non-phishing attacks once they are trained on vast amounts of data from both types of cases. Phishing detection systems can quickly evaluate considerable data, identify possible phishing attempts, and warn users of potential dangers. Machine-learning-based phishing detection systems have the potential to continuously improve their accuracy over time through ongoing feature refinement, iterative model evaluation, and algorithm optimization. In contrast to conventional techniques, these systems offer a more effective and efficient approach to identifying and mitigating phishing attacks. This research critically analyzes existing literature on phishing detection, aiming to identify all proposed features and determine the critical ones necessary for accurate and fast phishing attack detection. By eliminating unnecessary overhead, this research enhances our understanding of feature eliminator methods and their role in improving machine learning-based phishing detection. The findings would contribute to the development of more robust cybersecurity measures to combat phishing attacks, as well as advance the field's knowledge and application of machine learning in detecting and mitigating such threats. The study highlights the importance of feature selection and optimization in achieving accurate and efficient phishing detection, ultimately strengthening the overall security posture of organizations and individuals against phishing attacks.
Publishing and citing datasets

The paper addresses issues related to the creation of data archives, their publication, placement of metadata in the Crossref bibliographic database and citation of datasets in scientific publications. Examples of popular world data archives are given and an experiment on creating and publishing datasets in KIAM is described.
Stakeholders’ coordination towards integrated functional adult education provision in Metekel Zone

The main objective of the study was to demonstrate the status of stakeholders’ coordination and identifying major hindering factors towards the provision of Integrated Functional Adult Education in Metekel zone. A mixed research method and explanatory sequential research design were employed. Simple random and purposive sampling techniques were used to select 241 respondents. Data were collected through questionnaires, interviews, and document analysis. The quantitative data were analyzed using frequency, percentage, and multiple linear regression. Whereas qualitative data were also narrated to support the analyzed data. As a result, the following major study findings were obtained. Stakeholders’ coordination towards IFAE program implementation was found very poor. Mostly the task of the IFAE program has been left to the education sectors with few participations of others. Stakeholders’ poor coordination was resulted due to the following major hindering factors. These are a) lack of integration between sector office stakeholder managers and local level workers, b) lack of stakeholders’ clarified goal being achieved through the IFAE program, and c) lack of accountability of stakeholders in the IFAE program implementation. Unless a big deal solution is given by the concerned, the situation would downgrade the quality of IFAE program provision to achieve the intended result. In the end, concerned stakeholders are advised to work with partners through recognizing and putting into account the IFAE program as the means of achieving their primary objectives and activities.
Impact of COVID-19 on Quality of Education with Visualization Techniques

The objective of this research is to examine the effects of the COVID-19 pandemic on the quality of education utilizing secondary data and visualizing the data via R. The COVID-19 pandemic had caused education to experience unprecedented disruptions, requiring a fast shift to remote learning and posing multiple challenges for students and educators everywhere. Leveraging secondary data, the study focuses on students in Delhi and the National Capital Region (NCR) of India and examines different facts of educational quality. The findings showcase that the COVID-19 pandemic had significantly affected schooling, as seen by considerable changes in online class time, self-study hours, sleep patterns, exercise routines, and gadget usage. The results highlight the urgent necessity to lessen the pandemic’s negative effects on the standard of schooling. Policymakers can use the knowledge collected from this study to develop focused efforts to improve educational quality and address issues related to distant learning.
Role of Concordance of Lexicon and Collocations in Indian Newspaper Headlines on Pulwama Crisis: A Critical Discourse Analysis

The purpose of this study is to analyse critically media headlines of leading Indian e-papers during the crisis of Pulwama phase igniting from 14th of February to 31st of March 2019. The study has included five newspapers from India. The research has examined role of concordance of lexicon and collocations for angling news headlines. The research design is qualitative in approach. For the analysis of news headlines Fairclough’s 3-D model (1992) is employed to expose ideological representation through semantic devices (concordance and collocations). Theoretical framework includes selected features of Paul Chilton’s Model of Deictic Space Theory (2004) like, Proximization, and Wieczorek’s (2013) Clusivity to analyse the text. The results give deeper understanding of indian journalistic discourse that shows Concordance of use of words like terrorist/s, attack, JeM, dangerous and phrases like punishing Pakistan, threats of surgical strike, reflects the trend to be so grave and lethal.
Monitoring Accounting Process in Organization with Blockchain Technology

Abstract: Blockchain has been well conceded for its implicit uses in the financial and banking industries. As a decentralized and distributed technology, blockchain, on the other hand, may be used as an important tool for a wide range of everyday operations. Among the multitudinous operations sectors where blockchain is anticipated to have a significant influence, finance, and banking are some of the most important. In the contemporary financial and banking systems, it produces a broad variety of options and possibilities. As a result, the purpose of our design is to use blockchain technology in present financial and banking systems and give a transparent and secure system for so. In addition, we will be outlining the hurdles and roadblocks that must be overcome in blockchain technology that can be successfully used in financial and banking institutions. In this, we will be erecting the smart contract for blockchain-predicated financial systems, which will be helpful for associations and institutions for better translucence in their association to reduce fraud