Reviewerly/exHarmony huggingface dataset:
A Track Filtering Algorithm Based on α − β Filtering

Track filtering is to smooth the current state of the target according to the existing valid observations and predict the next state of the target. Whether the selection of the track filtering algorithm is reasonable has an important influence on whether the correct track can be obtained. In this paper, the main method of α − β filtering gain calculation under multi-target tracking is given. MATLAB simulation is designed for radar detection scenarios such as tracking low and slow small UAVs. Data processing and analysis of simulation results prove that α − β filtering effectively reduces measurement error and improves detection accuracy. Finally, the simulation results are verified by comparing the measured radar data before and after adding the radar track filtering.
Investigating the Malay Traditional Jewellery: A Complementary Element in Malay Women's Costumes in Malaysia

This paper explores the significance of traditional Jewellery as a complementary element in Malay women's costumes in Malaysia. This study examines the factors associated with selecting Jewellery to complement Malay women's costumes. The approach of this study is qualitative, applying Malay aesthetic principles to understand how to design Jewellery in Malay culture. The study finds that traditional Jewellery serves multiple functions, including enhancing the wearer's beauty, expressing social status, and embodying cultural identity. The authors argue that the continued use of traditional Jewellery in Malay women's costumes reflects the enduring importance of cultural heritage and the value placed on maintaining traditional practices in contemporary Malaysian society. The authors found that traditional Jewellery serves multiple functions, including enhancing the wearer's beauty, expressing social status, and embodying cultural identity. The continued use of traditional Jewellery in Malay women's costumes reflects the enduring importance of cultural heritage and the value placed on maintaining traditional practices in contemporary Malaysian society. In conclusion, this study sheds light on the significance of traditional Jewellery in Malay women's costumes in Malaysia. It has provided a comprehensive analysis of the factors influencing the selection of Jewellery to complement Malay women's costumes. The study provides insights into contemporary Malaysia's complex interplay between tradition and modernity.
Perancangan Aplikasi Ukumulasi Tentatif Pimpinan PROKOPIM Bebasis Web

At the prokopim office of the Regional Secretariat of Central Lombok Regency, incoming letters are still stored in files that are still manual in nature, so that it is possible for these letters to be scattered and when looking for the desired letter you have to search for it one by one, and it will take a long time in the search process. The methodology used in making this system is a data collection method in the form of observation, interviews and literature study, while the analytical method used is PIECES analysis, for the design method using the System Development Life Cycle (SDLC). Based on the results of the research that the mail archiving application can accommodate the storage process becomes more effective, so that the search for incoming and outgoing letters is faster, the system can back up data, so that it can minimize damage or loss of data making incoming and outgoing mail reports as needed, so Facilitate reporting to superiors.
Uzaklık Metriklerinin Performansı Üzerine Ampirik Bir Çalışma

Metrics are used to measure the distance, similarity, or dissimilarity between two points in a metric space. Metric learning algorithms perform the finding task of data points that are closest or furthest to a query point in m-dimensional metric space. Some metrics take into account the assumption that the whole dimensions are of equal importance, and vice versa. However, this assumption does not incorporate a number of real-world problems that classification algorithms tackle. In this research, the existing information gain, the information gain ratio, and some well-known conventional metrics have been compared by each other. The 1-Nearest Neighbor algorithm taking these metrics as its meta-parameter has been applied to forty-nine benchmark datasets. Only the accuracy rate criterion has been employed in order to quantify the performance of the metrics. The experimental results show that each metric is successful on datasets corresponding to its own domain. In other words, each metric is favorable on datasets overlapping its own assumption. In addition, there also exists incompleteness in classification tasks for metrics just like there is for learning algorithms.
A Classifier Model to Detect Phishing Emails Using Ensemble Technique

Phishing attacks usually take advantage of weaknesses in the way users behave. An attacker sends an email to the recipient that mimics a genuine email with phishing links. When the recipient clicks on the embedded links, the attacker can harvest critical information like credit card numbers, usernames or passwords as a result of entering the compromised account. Online surveys have put phishing attacks as the leading attack for web content, mostly targeting financial institutions. According to a survey conducted by Ponemon Institute LLC 2017, the loss due to phishing attacks is about $1.5 billion annually. This is a global threat to information security, and it’s on the rise due to IoT (Internet of Things) and thus requires a better phishing detection mechanism to mitigate these losses and reputation injury. This research paper explores and reports the use of multiple machine learning models by using an algorithm called Random Forest and using more phishing email features to improve the accuracy of phishing detection and prevention. This project will explore the existing phishing methods, investigate the effect of combining two machine learning algorithms to detect and prevent phishing attacks, design and develop a supervised classifier to detect and prevent phishing emails and test the model with existing data. A dataset consisting of benign and phishing emails will be used to conduct supervised learning by the model. Expected accuracy is 99.9%, with a rate of less than 0.1% for False Negatives (FN) and False Positives (FP).
Home Automation System with IoT Stack and ChatGPT for People with Reduced Mobility

In the dynamic field of smart home automation, the attainment of seamless integration among numerous devices and the utilization of artificial intelligence are of great importance. This research introduces an innovative methodology combining commercial and prototype-based sensors into an open-source, cost-effective framework. By utilizing an open-source IoT Stack, we can achieve a level of interoperability that surpasses traditional limitations imposed by device limits. Our approach integrates the OpenAI API, which complements the static decision-making processes with intelligent and contextually aware decision-making. Our system prioritizes the implementation of advanced artificial intelligence-driven answers and lays the foundation for future initiatives focused on improving the user experience, particularly for individuals with reduced mobility.
Conscious perception of environmental threats: the role of environmental psychology in the formation of environmental consciousness

The article examines the role of environmental psychology in the formation of the environmental consciousness and conscious perception of environmental threats by humans. It examines how psychological factors influence people's perception and understanding of environmental issues, including barriers that prevent effective interaction with the environmental information. This article will help reveal the importance of environmental psychology in the formation of environmental consciousness and understanding of environmental threats. It will contribute to the understanding of how psychological factors affect the perception and interaction with the environmental information, and will provide practical recommendations for increasing environmental consciousness in the society. The purpose of the article is to emphasize the importance of targeted scientific and practical efforts in using the knowledge of environmental psychology to improve the conscious perception of environmental threats and stimulate sustainable environmental behavior. The scientific novelty of the article consists in the combination of a theoretical overview of the main concepts of environmental psychology with an experimental research to study the level of conscious perception of environmental threats among respondents. In addition, the article focuses on understanding the interconnection between the concepts of environmental psychology and their role in the formation of environmental consciousness. The obtained results of the study reveal the level of understanding of the respondents of the basic concepts of environmental psychology, which can be of practical importance for the development of programs and interventions aimed at increasing the level of environmental consciousness and changing behavior. Such a comprehensive approach to the study and understanding of environmental psychology contributes to the expansion of knowledge in the field of environmental psychology and the identification of ways to improve human interaction with the environment. The article examines the role of environmental psychology in the formation of environmental consciousness and conscious perception of environmental threats. Using the testing method, the level of understanding of the main concepts of environmental psychology among the participants was determined. The results showed a low level of understanding of some concepts, which indicates the need for increased consciousness and education about environmental issues. This article contributes to the field of environmental psychology by combining a theoretical overview with an experimental study that is important for the further development of programs and interventions aimed at increasing the level of conscious perception of environmental threats and the formation of environmental consciousness.
Networked Architectures for Localization-Based Multi-User Augmented Reality

Multi-user augmented reality (AR) seeks to enhance the user experience in shared activities, whether collaboration for training, entertainment, or safety applications such as autonomous driving and driver assistance. AR involves a number of tasks that require both sensing and complex computations. A key task is localizing an AR device in the real world, in order to render the virtual holograms at the correct locations on the display. With cloud computing moving closer to the edge, a number of architectural options are available to aid in these complex computations, ranging from performing all of the computation at the end device to moving it all to the cloud, each with a different level of dependency on the communication link. In this work, we outline several architectural alternatives for localization in multi-user AR and their applicability to a number of important usage scenarios. The considered usage scenarios (entertainment, autonomous vehicles, and medicine) reflect a range of latency and spatial accuracy requirements. Within this context, we discuss our recent work on an edge-cloud-centric solution – SLAM-Share – and its applicability to various use cases. In addition, we evaluate its resilience to variations in communication delay. Overall, this article seeks to provide an overview of network architectures for localization-based AR to inform the design of future AR systems.
IEEE International Mediterranean Conference on Communications and Networking (IEEE MEDITCOM 2023)

The Third IEEE International Mediterranean Conference on Communications and Networking (IEEE MEDITCOM 2023) took place in September 2023 in Dubrovnik, Croatia. This article explains the main highlights of the four-day Technical Program of the conference that took place on the shore of the Mediter-ranean, addressing many of the outstanding challenges that exist in the areas of communications and networking.
Human Operator Gaze Movement Characteristics Analysis for Fatigue Detection

The paper investigates relationships between gaze movement characteristics and human fatigue. Gaze movement characteristics are calculated from such basic concepts of gaze as fixation and saccade. Characteristics can also be divided by the types of physical events on which they are based. It is possible to distinguish such characteristics as speed, time, quantity, size, percentage, frequency, and ratio characteristics. To search for correlations between gaze movement characteristics and fatigue, a dataset of gaze movements and the results of the VAS-F fatigue questionnaire were analyzed in 10 subjects, The dataset consists of operator parameters such as gaze movements, scene camera image, and gaze direction. To detect the level of fatigue, the participant completed the VAS-F questionnaire. Each record from the dataset corresponds to a questionnaire result. A total of 60 characteristics of eye movements and the corresponding VAS-F test values were analyzed and the correlation between them was calculated. The characteristics of eye movements were then sorted in descending order of the obtained correlation values. For further analysis, the first 20 characteristics with the highest correlation were selected from each participant. A search was then made for characteristics that were found in 60% or more of the participants among the first 20 characteristics. As a result, 9 characteristics of eye movements were found that correlated with VAS-F test scores for each participant. The significant correlation was shown by speed characteristics, size characteristics, time characteristics, frequency characteristics, ratio characteristics, and proportion characteristics.
Data Driven Track Before Detect Using Artificial Neural Networks

We present two neural network solutions for data driven track before detect applications. The detected tracks may be used to estimate good initial states for traditional trackers such as Kalman filters. We evaluate the method on different scenarios with multiple targets, non-linear trajectories, and different signal to noise ratio (SNR) values. Depending on scenario, the presented method achieves 99% detection probability on Swerling 3 and 4 targets at 5 - 13 dB SNR, with 0.04 - 0.001 false tracks per frame. The presented method is compared to a theoretically optimal detector.
Error Function Analysis and Simulation of Radar Range Discriminator for the RGPO/I Range Deception

The range deception against the range tracker is used in electronic warfare (EW). The radar range discriminator (RRD) error originates while tracking a target with a radar. RRD error is minimized while tracking the target. Hence, the range deception requires the presence of RRD error. Unidirectional range deception (URD) that increases or decreases the distance between radar and target can be used to introduce RRD errors. EW technique can be defeated by the electronic counter-counter measurements (ECCM) capabilities of a radar. A recent study proposed bi-directional range deception (BRD) with different velocities in opposite directions to overcome ECCM. However, the limited simulation was performed without the radar automatic gain control (AGC) in the low-jamming-to-signal ratio. This study analyzed the RRD error function due to the radar AGC by the BRD with acceleration and velocity based on the jamming-to-signal (JSR). The result showed that the values of the error function of RRD differed depending on overlap, separation, and gate departure within the early-late gate. Simulation and analysis results agreed at JSR=20dB.
ComESN: Compressive Echo State Network for Multi-task Time-series Prediction

Time-series prediction has been widely studied for single-variable applications, but shows poor performance for multi-variable applications due to the complex correlation. Multivariable prediction is often transferred into many single-variable tasks, and results into high computational cost. The prediction performance decreases exponentially as the dimensions increase. The compression of multiple prediction tasks is necessary to provide a trade-off between computational cost and prediction accuracy. For this purpose, compressive echo state neural network (ComESN) is proposed, which consists of a compressive module and a multi-task echo state network (MT-ESN). In this study, the locally linear embedding algorithm is adopted as compression method to reduce the number of prediction tasks for improving the computational efficiency. A novel MT-ESN is developed to further improve the performance of ESN for multi-task prediction. Due to highly efficient compression and unveiling correlation of multiple tasks, ComESN could cut down the computational cost while providing a promising prediction accuracy. Experiments on three real-world datasets illustrate that the proposed ComESN could compete with state-of-the-art methods with regard to computational cost and accuracy.
Comparative Study on User Preference of Food Delivery Mobile Application in Malaysia

This research paper presents a comparative study on user preferences for food delivery mobile applications in Malaysia. The study collected data from 116 respondents through an online survey distributed via social media platforms such as Facebook, Instagram, and WhatsApp. The findings provide valuable insights into the factors influencing users' adoption and usage of popular food delivery apps such as Food Panda, Grab Food, MYOTO, McDelivery, and Lalamove. The results show that user interface design is a significant factor in determining user satisfaction with food delivery apps. Additionally, perceived usefulness and ease of use are also important factors that influence users' adoption and usage of these applications. The study highlights the strengths and weaknesses of each application based on user feedback and provides recommendations for developers to improve their UI/UX design. Overall, this study provides valuable insights into user preferences for food delivery mobile applications in Malaysia that can assist developers in creating applications that better align with users' needs and preferences. Therefore, by improving the UI/UX design of these applications, developers can enhance overall user satisfaction and increase adoption rates.
Enhancing Continual Deep Open-Set Recognition with Perceptive Unknown Feature Search

Open-Set Recognition (OSR) and Continual Learning (CL) have recently gained attention from researchers aiming to advance sustainable Artificial Intelligence (AI). However, there has been limited research focused on the performance of rejecting unknown classes in continual OSR models. This paper provides empirical evidence that continual OSR models tend to experience a decline in their ability to reject unknown classes over successive task periods. To address this issue, we propose an initial solution of using two memory replays: one for samples of known features and another for pseudo-features associated with unknown classes. For generating features of these unknown classes, we propose a method called Perceptive Unknown Feature Search (PUFS) that involves locating features based on the positions of existing prototypes and then feeding them into an inverse network to obtain backbone features. To enhance the model, we modify the loss function by incorporating contrastive learning for unknown features. This improved model, named as Class-Incremental Quad-Channel Contrastive Prototype Networks (CI-QCCPN), outperforms its predecessor QCCPN as well as softmax-based classifiers with memory replay and achieves the highest average AUROC scores across various tasks and datasets. Our source code is available on https://github.com/gusti-alfarisy/ContinualDeepOSR.
Performance Analysis of Forensic Programs on SQLite Corpus

In this study, the objective was to meticulously assess the efficacy of multiple forensic programs when applied to a diverse corpus of SQLite databases. The study's author meticulously curated databases of varying sizes and intricate structures, striving to create a representative sample. The forensic programs were rigorously tested across different versions of SQLite, revealing nuanced performance nuances. It is crucial to acknowledge that the programs' efficiency is inherently intertwined with the unique attributes of the analyzed database and the specific version of SQLite in use. This study not only delves into the intricate details of these performance variations but also offers invaluable insights. By shedding light on the dynamic interplay between forensic tools and SQLite databases, this research equips professionals with essential knowledge, enabling them to make informed decisions when selecting tools for forensic analyses.
The Adoption of the “Buruan Sae” Agricultural Extension Application in Bandung City Using the UTAUT

The “Buruan Sae” program in Bandung is an integrated urban farming initiative by the Department of Food and Agriculture. This research investigates the factors influencing the adoption of the agricultural extension application “Buruan Sae” in Bandung City, utilizing the UTAUT model. The study collected data from 14 agricultural extension agents working under the Department of Food Security and Agriculture. The analysis identified trust and performance expectancy as the key determinants of adoption, complemented by attitude and dependence. The study investigated the impact of different factors on users' intention to use the “Buruan Sae” app. The study investigated the impact of UTAUT variables on Behavioral Intention towards the “Buruan Sae” app. Four out of five variables (Effort Expectancy, Social Influence, Facilitating Conditions, and Perceived Technology) positively influenced Behavioral Intention, while Performance Expectancy had a weaker impact. The Performance Expectancy's regression coefficient was 0.410 with a significance level of 0.000 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$( &lt; 0.05)$</tex> , leading to the acceptance of Hypothesis 1. The same holds true for Effort Expectancy and Social Influence with t-values of 6.013 and 3.668, respectively, both significant at sig <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 0.05$</tex> , confirming Hypotheses 2 and 3. Facilitating Conditions' influence on Perceived Technology was established with a t-value of 5.425 and sig <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt; 0.05$</tex> , accepting Hypothesis 4. The F-test further supported the impact of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions on Perceived Technology, as Hypothesis 5 was accepted due to a sig of 0.002 and a calculated F-value of 10.296 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$( &gt; 3.59)$</tex> . The study concludes that due to limitations in the data, further research is recommended for a more thorough exploration of how “Buruan Sae” app is used by extension workers in the Food and Agriculture Resilience Agency. This could involve methods like direct observations, interviews, or case studies to gain insights into farmers' use of the app, along with the associated challenges and benefits.