Reviewerly/exHarmony huggingface dataset:
UGV-UAV Cooperative 3D Multi-Object Tracking Based on Multi-Source Data Fusion

Unmanned aerial vehicles (UAVs) and Unmanned ground vehicles (UGV s) have complementary sensing characteristics, and their cooperative sensing can effectively solve the problems of target loss due to view occlusion or distance in multi-target tracking tasks. In this paper, we propose a track by detection framework based on a multi-source data fusion approach. In the detection phase, a modified ImvoxelN et network with attention mechanism is proposed to fuse the multi-view image data of unmanned vehicles and UAVs to estimate the position and size of the target. In the tracking stage, the method involves correlation processing of both 2D and 3D detection data. It utilizes an interactive multi-mode unscented Kalman filter (IMM-UKF) algorithm that incorporates the Minimum-update Successive Shortest Path (muSSP) method to enhance the accuracy and real-time performance of dynamic target tracking. The experimental results demonstrate the effectiveness of the proposed approach in significantly improving the performance of multi-target tracking tasks.
Design and Validations of Stage Lighting Rail-Hanging Robots

A rail-hanging robot is developed to address the characteristics and limitations of traditional stage lighting booms. This robot, suspended with moving lights, is designed to enhance motion stability and deployment performance. The motor connects to the guide rail to achieve robot movement. The conductive rail achieves power supply and communication functions to reduce external lines. Communication among the robot, lighting console, and motors is achieved through the DMX512 and RS485 protocols. The main control MCU(Microcontroller Unit) decodes and processes DMX512 signals from the lighting console, enabling synchronized control of lights and the robot. MCU collects collision and laser signals during movement to ensure safety and stability. Experimental results show that the rail-hanging robot achieves smooth motion, enhances stage lighting effects, and effectively meets operational requirements.
Path Planning of Mobile Robot Based on Improved A-Star Bidirectional Search Algorithm

A path planning algorithm based on improved A-star bidirectional search algorithm is proposed to ensure that the robot can work quickly and safely in a complex environment with known obstacles on the road. The heuristic function of A-star algorithm is improved by introducing dynamic weights. In addition, the corner optimization of the algorithm is carried out in the case of the least turn at the same path cost. The dynamic bidirectional search is introduced to search the path from the beginning and end of the path at the same time. Then smoothing at corners in the selected path is more efficient than right-angle steering. Finally, the simulation results show that the improved algorithm has better effect than the traditional A-star algorithm in terms of time consuming and path optimization.
Multi-Agent Cooperative Search in Multi-Object Uncertain Environment

Multiagent multi-object search (MAMOS) in uncertain rescue environment has always been considered a challenge, which can be modeled as an Object Oriented-Multiagent Partially Observable Markov Decision Process (OO-MPOMDPs). This paper proposes a Guidance and Belief Synchronization-Object Oriented-Multi-agent Partially Observable Upper Confidence Bound Apply to Tree (GBS-OO-MPOUCT) algorithm which contains two parts. One is belief decomposition, update and synchronization to achieve environment representation, update and multi-agent internal communication, another is deadlock recognition and resolution when robots are getting trapped due to insufficient depth of Monte Carlo tree. We construct many examples to test the performance of the algorithm. The results show that our algorithm can find all objects in fewer steps and observation noise has little impact on algorithm performance.
PotScript: a visual grammar for sculpting with functions

We present a playful 3D modeling tool that integrates a visual grammar with shaping curves to model forms as parametric functions which can be created and manipulated by novice users. We created a web-based1, open-source2, end-to-end design environment for 3D printable forms which were fabricated on clay 3D printers. Users can manipulate forms by directly editing curves which drive transformations to the form’s profile as it grows in the z-direction. These transformations can be stacked and composed in a visual postfix language which allows for the creation of parametric designs driven by directly manipulable curves. We pilot tested our program with a small group of ceramicists, artists, and digital fabrication practitioners, which suggested applications in education and art making. PotScript demonstrates an approach to integrating direct manipulation into parametric design which allowed users to create complex forms in small sets of user actions. We aim to make modeling complex 3D forms as approachable as stacking a handful of blocks.
Demonstrating SquiggleDraw - On Generating Art and Documentation

SquiggleDraw is a generative art project that explores documentation techniques for digital fabrication workflows. Participants are invited to make a scribble drawing based on the scribbles’ qualities: line length, turn radius, point compression; then, they watch as a 2D pen plotter recreate their scribble. The generative art is deterministic – the same parameters yield the same drawing – and the parameters to recreate their scribble are printed on a thermal receipt printer. The choice to document user parameters on a receipt paper is intentional; the physical receipt can be kept alongside the fabricated object to recall settings and track outcomes when testing different parameters.
Adjectival Retention: Persuasive Texts Students Universitas Negeri Makassar

This research contributes to language development, especially adjectival retention as a very basic resilience in the text. Research on adjectives has been conducted by (Kennedy, 2013), (Schiff et al., 2014), (Sharma et al., 2015), (Sato et al., 2016), (Cutillas &amp; Tolchinsky, 2017) Compared to the previous studies, however, the present study focuses on adjectival retention. In addition, the research findings also provide an understanding of the use of adjectives with the meaningfulness contained in the sentence. This research is a qualitative one, using a content analysis method. The data consist of persuasive essays written by 83 students of Indonesian Language and Literature Education Program in the Faculty of Languages and Literature at State University of Makassar, submitted during March to September 2019 period. While the research data were in the form of adjectives in the essays submitted, the research instrument was the researchers themselves. Data collection was done by giving assignments to the students to write persuasive essays. Data analysis techniques were identifying, reducing, presenting the data, verifying, and drawing conclusions about adjectival retention. The findings of the study indicate the following cases of adjectival retention: (a) weakening of positive adjectival retention; (b) intensive retention occurring not only with the precursor which precedes it, but also with the constituent following it; (c) elative retention appearing only after the use of verbs in the sentence; (d) excessive adjectival retention having a formation that occurs due to a conjunction stating the cause; (e) retention of argumentative adjectives placing emphasis on the part of the core sentence; (f) retention of attenuate adjectives reinforcing one another and not weakening the other parts. Overall, some 42% of the essays contain adjectival retention and 58% of them were without adjectival retention.
I “…I Felt More Challenged to Keep Practicing”: An Exploration of the Students’ Voice towards Elsa Speak for Supplementing Students’ Speaking Willingness

This study aims at discovering out the students’ perceptions about using Elsa Speak in learning English. Sources of data for this study were 15 junior high school students. In collecting data, the researcher spread the questionnaires by using Google Forms format and the researcher used an interview to support the questionnaire result towards the students’ voice in utilizing Elsa Speak. Statistical analysis was performed using SPSS software. The results show that students have a fairly positive perception towards the use of Elsa Speak in learning English. It indicates that students like the use of Elsa Speak in learning process, they can read the words, phrases, and situational sentence patterns themselves and then be assessed and corrected by ELSA's artificial intelligence, they can find out how much they can pronounce sentences according to native standards. Students also get many advantages with Elsa Speak.
Instantaneous Velocity Estimation of Multiple Targets using Coordinated OFDM RADAR Nodes

Joint communication and sensing receives a lot of attention in the research community. Sensing the environment with communication networks can save a lot of resources. The sensing capability of OFDM radar has been investigated by many research groups and shows great potential. Although tracking the trajectory of the target is important, the instantaneous velocity of the target can be beneficial for many applications. If the instantaneous velocity of the target is known, the direction of movement can be more accurately predicted to avoid potential hazards. Therefore, in this paper, multiple OFDM radar nodes are used to jointly estimate the instantaneous velocity of multiple targets. A Gaussian mixed probability hypothesis density filter is employed to estimate the number and state of targets from observations with many false estimates. Subsequently, the instantaneous velocity estimation algorithm based on the least squares method efficiently estimates multiple targets’ instantaneous velocity vectors with an error rate as low as about 11.48 %. There are many potential applications, such as robotic arms in factories or self-driving cars.
Integrating Communication and Collaboration of 21st Century Learning Skills in Narrative Writing by Using an Online Learning Platform via Mobile Device among Upper Secondary Students in Penang, Malaysia

This research aimed to find out the effectiveness of integrating the main skills of 21st century learning model which are communication and collaboration for developing the main ideas, and organising the ideas into a few paragraphs to write a narrative essay using an online learning platform via mobile device. Henceforth, a narrative writing module was predominantly created to carry out this study which is known as 4C’s to WRITE module. This research used a case study which incorporated a mixed-method research design. The sample of this study consisted of the Form 4 students in a national secondary school in Butterworth, Penang. Ihe study involved 30 students as a sample of this study. In the process of data collection, the researcher used pre-test, post-test, participant observation, and document analysis. This study found that there was an improvement in students’ narrative writing skills from the post-test. The outcomes from participant observation and document analysis by using a few common themes revealed that students were able to progress in identifying the main ideas and organising the ideas into a few paragraphs. Therefore, it could be concluded that using communication and collaboration skills via technologies significantly affect students’ narrative writing skills.
When MetaVerse Meets Computing Power Networking: An Energy-Efficient Framework For Service Placement

The Metaverse, as the next-generation Internet, disrupts the physical world to create an immersive virtual digital realm. Its implementation relies on key enablers, including virtual reality, AI, blockchain, and 6G. However, these technologies involve computationally intensive operations with high energy consumption, posing a challenge to the sustainability of the Metaverse. A promising approach is to leverage ubiquitous and heterogeneous computing power, encompassing end-edge-cloud devices, to optimize resource allocation for computing and networking. Unfortunately, existing edge-cloud architectures fall short in meeting the diverse resource demands of Metaverse applications. In this article, we present an energy-efficient framework that delivers Metaverse services meeting quality-of-service metrics. The framework is based on computing power networking, a network-driven distributed computing environment enabling the free flow of isolated computing power. We introduce a typical Metaverse scenario involving video analytics and formulate the problem of optimal service placement, which we solve using the deep reinforcement learning algorithm, proximal policy optimization. Experimental results demonstrate the superior energy efficiency of our proposed framework. Finally, we discuss future directions for building a sustainable Metaverse, emphasizing the need for ongoing advancements in energy efficiency.
Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and A Case Study

Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain's role in sustainability, we propose a three-layers structure encapsulating four green utilities: recording and tracking, wide verification, value trading, and concept disseminating. Nori, a decentralized voluntary carbon offset project, serves as our case, illuminating these utilities. Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset. We argue that blockchain's contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.
Distributed PV Power Forecasting Considering Spatio-temporal Dependence

With the increase of installed capacity and proportion of distributed photovoltaic (PV), the influence of its random and fluctuation characteristics on the dispatching management of power grid and station cannot be ignored. However, at present, distributed PV does not require the configuration of proprietary power forecasting system, and the irradiance data that are often used to realize PV decoupling and forecasting cannot be directly obtained because the measuring devices are rarely installed in small and medium-sized PV stations. Therefore, this paper proposes a method to estimate the regional irradiance by using the actual power data of some known PV systems, and deeply mines the spatio-temporal correlation information of the irradiance/power data of the distributed PV cluster to improve the power forecasting accuracy. Simulation results show that the method is more accurate than the satellite-based method in irradiance retrieval. In the aspect of PV power forecasting, the performance of the conventional forecasting method based only on time correlation is not as good as that of the method proposed in this paper, and the distributed PV power can be tracked and predicted better after considering the spatiotemporal dependence of resources.
An Empirical Evaluation of Feature Selection Stability and Classification Accuracy

The performance of inductive learners can be negatively affected by high-dimensional datasets. To address this issue, feature selection methods are used. Selecting relevant features and reducing data dimensions is essential for having accurate machine learning models. Stability is an important criterion in feature selection. Stable feature selection algorithms maintain their feature preferences even when small variations exist in the training set. Studies have emphasized the importance of stable feature selection, particularly in cases where the number of samples is small and the dimensionality is high. In this study, we evaluated the relationship between stability measures, as well as, feature selection stability and classification accuracy, using the Pearson Product-Moment Correlation Coefficient. We conducted an extensive series of experiments using five filter and two wrapper feature selection methods, three classifiers for subset and classification performance evaluation, and eight real-world datasets taken from two different data repositories. We measured the stability of feature selection methods using a total of twelve stability metrics. Based on the results of correlation analyses, we have found that there is a lack of substantial evidence supporting a linear relationship between feature selection stability and classification accuracy. However, a strong positive correlation has been observed among several stability metrics.
Risk allocation of intellectual property pledge financing based on blockchain technology: An evolutionary strategy study

The purpose of intellectual property pledge financing is to address the funding shortage of small and medium enterprises. However, information asymmetry between financial institutions and enterprises increases credit risk and often results in government intervention as a guarantee provider, leading to an unreasonable risk allocation among stakeholders. The distribution essence of blockchain technology is suitable to enable a long-term operation mechanism. In this study, we employ an evolutionary game model between banks and guarantee agencies to investigate the effects of repayment ratio and other key parameters on the equilibrium. Model and numerical simulation results indicate that risk allocation ratios within a certain range are beneficial with the adoption of blockchain technology. Furthermore, we suggest that governments should subsidize banks' technology costs for the construction of blockchain platforms rather than directly financing their business operations.
Perancangan Aplikasi SiCitra Menggunakan Unified Modelling Language

Find out the live location of the family or friends that be city bus passengers is the one of city bus application users needs that hasn t been implemented yet until now, so we propose the design of software engineering that has the main feature in the form of shared live location with people that allow knowing with utilizing Unified Model Language Diagram. There are 3 (three) types of UML Diagram that have we created in this research, including Use Case Diagram, Class Diagram, and Activity Diagram. Besides that, we also produce Entity Relational Diagram and System Design to fit up the design of software engineering. Thus with modeling software before starting the development process, the system will be structured and delivered to the user as well.
Comparison of Agile Scaling Frameworks

21st century software development approaches, such as Agile has benefitted small initiatives with a single team building software in their ability to respond to change, reduce product delivery schedules, reduce product cost, increase product quality, and Increase employee morale. The industry has begun to question what the benefits and challenges could be if we use those same practices at scale for large Initiatives with multiple teams. There have been multiple studies on the benefits of Agile at Scale including quality, productivity, and shorter time to market. Over the last two decades dozens of Scaling frameworks have been created. However, given the large number of frameworks available, how do companies choose the right scaling framework? In this paper we review what the difference between the frameworks are by comparing their core principles. This paper began by analyzing results Digital.ai's 16th Annual state of Agile Survey which identifies the 10 most utilized frameworks according to their respondents. The paper presents a detailed analysis of the principles to assess the degree of differences among the frameworks and determine based on this which framework is the best for large scale organizations to choose.
Image Inpainting for Passersby Removal Based on Multi-Scale Dilated Gated Convolution with Generative Adversarial Networks

Passersby often appear in photos taken at popular scenic spots. To remove the unwanted passersby in photos and fill in the empty areas, a typical approach is manually labeling passersby and using software (Photoshop, Snapseed, Photo Retouch) for inpainting. This approach, however, takes long time and may not produce satisfactory and stable results. In this study, we propose an automatic image segmentation and inpainting algorithm for passersby removal, which greatly reduces the labeling and editing time. The passersby are identified by the Mask R-CNN image segmentation technique. The missing areas are estimated using a modified generative adversarial network (GAN) with gated convolutions and a coarse-to-fine refinement structure. A higher number of channels is used in the deep network to improve the learning of image features. Multi-scale dilated convolution is adopted to ensure the transfer of features and to relieve the discontinuity caused by the holes of traditional dilated convolution. Compared with the existing methods, the proposed method preserves the color and structural features of the missing areas more faithfully, and the objective image quality in PSNR is increased by 0.5 dB to 1.1 dB on average.
Handling Data Imbalance In Linear Modelling of Fatality Rate of Auto Collision

Learning from imbalanced data has been an ongoing hot research area. By applying techniques for handling imbalanced data, machine learning or statistical models can significantly improve their prediction performance and mitigate bias, leading to more reliable and unbiased results. Data used to predict the fatality rate of car accidents is derived from various sources, including information at the person, vehicle, and collision levels. These data are typically imbalanced, and studying this type of data is highly desirable in improving road safety. Also, predicting a fatal event is crucial for better management and allocation of limited health resources. This study explores the impact of imbalanced data handling techniques on linear statistical models.The study illustrates the significant specificity improvement when imbalanced data is appropriately managed. The findings of this study provide valuable guidelines for health resource management, illuminating the influence of data imbalance on prediction accuracy and offering insights to improve the performance of predicting auto collision fatalities.
An In-Depth Analysis of Revenge Porn and Blackmailing on Philippine Social Media and its Effects on the People Affected

Different cybercrimes such as Blackmailing, and Revenge Porn are increasing every year in the Philippines. With the current state of Cybersecurity in the Philippines, it is complicated to solve all crimes and track down the suspects. This review paper aims to analyze Revenge Porn and Blackmailing on Philippine social media and know its effects on the people affected by it. Specifically, this paper aims to determine the current state of Cybersecurity, identify the processes and intentions of the perpetrators, analyze the effects on the people affected, and propose solutions that may alleviate the occurrence of these cybercrimes to the user and country. This study's significant findings are the country's cybersecurity network and personnel cannot cater to and protect the number of Filipino social media users and are not fully capable of functioning to its highest potential, which leads to the rising number of cybercrimes. Moreover, the three main motives of suspects why they conduct these crimes: Money, Fulfillment of Sexual Desires and Fantasies, and Revenge due to hatred and other negative emotions. Doing these crimes usually requires the feeling of trust between the victim and the suspect before eventually breaking it. Finally, victims often tend to develop mental illnesses such as PTSD, Depression, Severe Anxiety, and Suicidal Thoughts.