Reviewerly/exHarmony huggingface dataset:
Violence Detection for Smart Cities using Computer Vision

There is a need for developing deep learning solutions to analyze videos to identify any violence being present. This paper proposes a method for the detection of the presence of violent activities in videos using Deep Neural Networks. Recently there has been a rapid development happening in the field of Deep Neural networks, but the number of solutions that have been developed for violence detection is very few. The proposed solution will play a major role in transforming the way law enforcement works and support the government’s initiative to make cities smarter. The model is built using CNN for video frame feature extraction and LSTM to capture localized features present in the video frames. The LSTM extracts the localized features using the spatiotemporal relationship between the video frames. The local motion present in the video is analyzed. This work focuses on accuracy and fast response time. The performance was evaluated on the hockey fight dataset to detect violent activities.
Applications of Artificial Intelligence in Robotics

Artificial Intelligence is a theory of the cognitive perspective in the province with robotics to human communication with the perception of action. The ability to develop computer systems would require human intelligence to perform tasks [1]. Artificial Intelligence plays a prominent role in robotics in providing effective analytical business solutions like human behavior in the real world. The common root of artificial intelligence and robotics has a scientific interaction that transforms technological improvement in robotics application and utilization and has a potential for future robotics in various applications and AI technologies. The study of the creation of intelligent robots in Artificial Intelligence is an entity for different objectives and applications. It is known to many people that artificial intelligence is a subset of robotics. Robots have human-like behavior by which they can perform tasks like a human if enabled with Artificial Intelligence.
Democratising AI via Purpose Limitation for Models

This paper proposes the concept of purpose limitation for models as an approach to democratising AI via effective regulation. We aim to define the purposes of machine learning models built for predictive analytics and generative AI in democratic processes. Unregulated (secondary) use of specific models creates immense individual and societal risks, including discrimination against individuals or groups, infringement of fundamental rights, or distortion of democracy through misinformation. We argue that possession of trained models, which in many cases consist of anonymous data (even if the training data contains personal data), is at the core of an increasing asymmetry of informational power between data companies and society. Combining ethical and legal aspects in our interdisciplinary approach, we identify the trained model as the object of regulatory intervention instead of the training data. This altered focus adds to existing data protection laws and the proposed Artificial Intelligence Act, which are inefficient in preventing the misuse of trained models due to their focus on the procedural aspects of personal data or training data. By enabling the concept of risk prevention law and the principle of proportionality, we argue that the potential use of trained models in ways that are damaging to society by powerful actors warrants preventive regulatory interventions. Thus, we seek to balance the asymmetry of power by enabling democratic control of where and how predictive and generative AI capabilities may be used by identifying beneficial purposes.
Adaptive multi-object tracking based on sensors fusion with confidence updating

Multi-object tracking (MOT) systems typically rely on object detection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to unstable tracking. Regarding this problem, we proposed an adaptive MOT method based on detection confidence. At first, we use a simple data fusion method to combine the detection results of LiDAR and camera to reduce the large number of false detections. And then we used a factor based on confidence to adjust the estimating covariance matrix and measurement covariance matrix adaptively. The algorithm can judge which is more reliable between prediction and detection, and choose which is more important in the update step. Meanwhile, we set a factor based on confidence to control the search range in the data association module. Our method reduces the impact of detector error while ensuring accuracy and speed, and improves the robustness of the MOT algorithm. Through experiments conducted on the KITTI multi-object tracking dataset, our method has demonstrated significant advantages over state-of-the-art (SOTA) methods in terms of both accuracy and processing speed. The results of MOTA for 90.02% and FPS for 262.
‘Forms of Conditionality’: Response to ‘Truth-Maker Semantics for Some Substructural Logics’ by Ondrej Majer, Vít Punčochář and Igor Sedlár

In the light of the contribution of Majer, Punčochář and Sedlár, I develop a path-theoretic semantics for the conditional and briefly consider some of its applications.
Fine’s Semantics for Relevance Logic and Its Relevance

The challenge of giving a semantics for relevance logic in terms of worlds or situations intrigued several logicians. As a solution, Fine gave a two-sorted semantics. We overview the semantics as well as some further work of Fine in the area of relevance logic. Then we show that beyond supplying technical results such as soundness, completeness and the finite model property (fmp) for many logics, the operational–relational semantics provides footing for an informal interpretation and it naturally leads to an interpretation in terms of relevance.
‘Selective Programming’: Response to ‘From Felicitous Models to An﻿swer Set Programming’ by V. Lifschitz

I make use of the truthmaker framework in providing a selective semantics for programs with disjunction and compare it to the minmalist semantics.
‘Reflections on Filtration’: A Response to ‘Modern Faces of Filtration’ by Johan van Benthem and Nick Bezhanishvili

I consider some different forms of filtration and raise some questions concerning the scope of the method of filtrations in propositional modal logic.
Permissive Updates

David Lewis asked in “A problem about permission” about the effects on context, specifically on the “sphere of permissibility,” of allowing behavior that had previously been forbidden. The framework of truthmaker semantics sheds useful light on this problem. Update procedures are definable in the truthmaker framework that capture more than Lewis was able to just with worlds. Connections are drawn with epistemic modals, belief revision and the semantics of exceptives. We consider how a truthmaker account of permissive update might be integrated into a larger semantic/pragmatic account of deontic language. Bridge principles are articulated associating types of deontic act with distinct update rules. Distinct forms of permission trigger distinct procedures because they aim at spheres more or less strongly verifying $$\Diamond \varphi $$ , the claim that $$\varphi $$ is permissible
Preserving Indonesian Culture in the Digital Age: Implementing Augmented Reality to Address Cultural Appropriation Issue

Abstract—Cultural appropriation is a complex issue that can lead to the loss of a nation's cultural identity. With the widespread adoption of mobile phones in Indonesia, there is an opportunity to use technology, specifically augmented reality (AR). To address this issue, our user-friendly AR app allows users to interact with Indonesian culture in a virtual environment. Through AR technology, users can engage with Indonesian culture in an immersive and fun way. Our target market is the younger generation, and our app provides a user-friendly interface that allows users to scan AR markers to access different cultural settings. Users can choose from two cultural regions, Bali and Papua. Once a region is selected, a scene showcases the desired region's iconic places and objects accompanied by a traditional folk song. In developing the AR app, we used the agile software development method for approximately two months with four developers. At the end of the development phase, we conduct four testing aspects of the application—namely, angle testing, functionally testing, usability testing, and hardware testing. The testing revealed that the application could perform well under various testing conditions. Through our AR solution, we aim to raise awareness of the importance of Indonesian culture and provide an educational experience that can inspire users to take ownership of their cultural heritage.
Flood-Prone Susceptibility Analysis In Garut Using Fuzzy Inference System Mamdani Method

Floods frequently occur in The District of Garut, representing a natural disaster. Currently, the National Disaster Management Agency (BNPB) is actively engaged in monitoring the landscape conditions of the Watershed River (DAS) to mitigate the impact of floods. To better understand and map this specific natural disaster, a program has been developed for in-depth analysis. This program has successfully assessed the susceptibility level to floods and categorized various areas in Garut as safe, prone, or flood-prone. To elaborate further, this study utilizes the Fuzzy Inference System Mamdani as a calculation process. Several factors influence this calculation process, including rainfall intensity, watershed area, land slope, altitude, population density, and risk level. Overall, the research findings indicate a 92.30% level of accuracy compared to the flood-prone catalog released by the national government, demonstrating its applicability in real-world scenarios.
Sentiment Analysis E-commerce Review

Reviews give customers the opportunity to tell the rest of the world about how they love or hate a product. While there is a limit to the number of words, there is no limit to the words that reviewers can use to express their anger, frustration, excitement or joy in buying the product. Online reviews have become such a standard part of the buying process for many people these days that every online retailer needs to think about optimizing them in their business strategy. The sheer number of reviews requires a special method or technique that can automatically categorize reviews, whether they are positive or negative. This research tries to classify e-commerce reviews with Naïve Bayes Classifier (NBC). The results of this research show that the accuracy with NBC is 72%, Recall 72% and Precision 78%.
Empowering Military in Tactical and Warfare Area with Virtual Reality Technology: A Systematic Literature Review

The rapid advancement of virtual reality technology offers numerous real-world benefits across various sectors worldwide, including the military. As early adopters of this technology, the military has found multiple medical and practical applications. However, the research contributions of virtual reality technology, particularly in practical military applications, remain limited. This is primarily due to limited data availability and a lack of interest from researchers and military institutions. To investigate this issue, the authors are conducting a systematic literature study focused on practical military virtual reality, specifically tactical and combat operations. Following the PRISMA framework and employing searching and inclusion-exclusion techniques, the authors identified twenty-five relevant research outputs that extensively discuss these topics, both qualitatively and quantitatively. Notably, fifteen of these studies focus on combat and weapon training simulators, and interestingly, researchers are beginning to integrate other technologies such as robotics, big data, haptics, body area networks, and signal processing. The majority of the quantitative research conducted in this area has demonstrated success, with eight out of ten papers showing positive outcomes. To improve research quality, the authors propose two main solutions: implementing combat and weapon training within the military system and encouraging innovative and in-depth research in this domain. In conclusion, the analyzed virtual reality products can serve as a platform for military forces to support personnel in enhancing and maintaining their personal development and skills.
Mobile Based Brand and Car Type Detection Using Convolutional Neural Network

Although the parking lot is equipped with CCTV surveillance, car theft incidents still occur occasionally. These thieves exploit a loophole in the system that prevents the identification of the car's brand and model upon entering and exiting the parking lot using the same ticket. Therefore, the aim of this research is to develop a system capable of detecting the brand and model of cars. The study utilizes convolutional neural network technology and specifically focuses on the EfficientNet, MobileNet, and MobileNetV2 architectures due to their low resource requirements. Training images for the study are sourced from an online marketplace for used cars, with each of the 20 car brand and model variations consisting of 100 photographs. creates a model after being processed. For the prototype, the approach is put into practice through a mobile application. This study compares EfficientNet, MobileNetV1, and MobileNetV2 as the end result. EfficientNet scored 74%, MobileNetV1 74%, and MobileNetV2 66% on the test of accuracy. The accuracy increases to 80% after the EfficientNet has been tuned.
Signature Authentication Model using Adaptive Moment Estimation Optimization in Multilayer Backpropagated Artificial Neural Networks

A signature serves as a unique symbol of consent used to authenticate and formally bind written materials. Due to its importance, a thorough verification process is required to establish the authenticity of a signature. In this research, we examined a dataset consisting of 2000 signatures, encompassing both genuine and custom-made signatures. By extracting their distinct digital characteristics, we conducted an analysis and developed an optimized model to assist in the manual and labor-intensive verification procedure. The outcome is a verification process that accurately determines the authenticity of a signature, differentiating between genuine and forged signatures. Our proposed method is based on nine essential features, including ratio, x-axis centroid, y-axis centroid, eccentricity, solidity, x-axis skewness, y-axis skewness, and x-axis kurtosis. These features are incorporated into the Multilayer Backpropagated Artificial Neural Networks (BP-ANN) methodology, resulting in an efficient verification system.
Feasible Technology for Augmented Reality in Fashion Retail by Implementing a Virtual Fitting Room

Augmented Reality has become a bridge from the virtual to the real world. This technology has been an implementable choice for fashion companies to provide more information and customization for their user's shopping experience. From the COVID-19 pandemic alongside technological globalization, fashion industries have to find a new way for customers to purchase products. All things considered, AR helped fashion industries keep customers attracted and satisfied. This paper is conducted in a Systematic Literature Review manner to review and compare the AR technologies used by fashion industries. Furthermore, the result would be a proposition of which Augmented Reality technology or platform option is feasible and implementable for the fashion industry. After comparing the technologies used in past papers and their technical and economic feasibility, we propose a 3-term plan. The short-term plan with zero cost and mobile platform-focused application, the medium-term plan with an upgraded version of the garment object from 2D images to 3D objects, and the long-term plan changing the platform used from mobile to a new device. We also conclude that artificial intelligence can also support and improve the augmented reality experience for customers in the fashion retail industry by implementing an AI-powered virtual assistant.
Development of Augmented Reality Application in Physics through Newton's Laws and Object Interaction

Physics learning is often challenging for students due to its abstract and complex concepts. This research is made to overcome the problem of lack of understanding related to physics learning due to abstract concepts. This research aims to develop and evaluate an Augmented Reality (AR) application that focuses on learning Newton's Law by interacting with 3D objects like balls, cars, and rockets. AR-based technology has been recommended for use in education due to its excellent potential in helping to improve the understanding of the concept of a material to be better. Therefore, this research will develop and evaluate the use of augmented reality in physics learning related to newton's law. This paper uses the ADDIE model method as the basis for developing Augmented Reality applications by involving the stages of analysis, design, development, implementation, and evaluation. The study results show that the developed AR application can help students understand the concept of Newton's Law and increase interest in learning. Student feedback obtained through questionnaires indicated that they rated this application positively regarding ease of use, engagement, and effectiveness in supporting the learning process. The AR application developed in this study improves the quality and effectiveness of physics learning through innovative and interactive technology. Further research could focus on developing additional content and features and exploring the impact of this application on a broader group of students and in different areas of education.
Abnormal Data Classification Based on SSA-AELSTM

Maintenance of production machinery and monitoring product quality through data analysis are the key problems with smart factories. There are far more normal values in the data collected for analysis during the production process than there are aberrant values. Data preparation uses a lot of energy before data analysis in order to make use of the data. In this research, we suggest a new hybrid technique that can perform anomaly detection more efficiently by merging two techniques, effective SSA techniques for reducing time series data and LSTM Autoencoder, which demonstrated great performance in time series outlier identification.
Software Size Measurement of Smart Digital Tourism Project based on Use Case Point

In recent years, the tourism sector has evolved into a key strategic business for boosting local economic output. This industry has been dealing with the COVID-19 pandemic impact since the end of 2019. The tourism sector was, however, expected to bounce back and carry on expanding as it had in the past. For the purpose of enhancing services for stakeholders, this sector must transition from the conventional approach to digitization. Regarding the ability to include stakeholders in offering smart services, a smart digital tourism should be prepared. The creation of this clever solution should follow a quantifiable software development approach, such as measurable software design. The purpose of this study is to provide a case point method for measuring software design in relation to project budget and schedule. The study's findings showed that the size of the project would be 112.27 Use Case Points, it would take 2,133.13 hours to complete, and it would cost 28,530,613.8 Indonesian Rupiah (IDR) to develop the software. The findings will aid in the management of software development at a later stage in terms of making decisions and planning additional quantifiable projects.
Prediction of Health Insurance Claims Using Logistic Regression and XGBoost Methods

Fraud is one of the crimes in the financial industry. Health insurance claims are a kind of fraud in insurance. This research examines health insurance claims to predict fraud. The claims would be denied if there were any suspicions of fraud. Extreme Gradient Boosting (XGBoost) and Logistic Regression are the approaches employed in this research. The most recent method in machine learning, XGBoost, is an improvement over the classic statistical method, Logistic Regression, in terms of classification performance. There are nine independent variables that influence fraud in health insurance claims, according to the findings of the Logistic regression model. The comparison between Logistic regression and the XGBoost model showed that the XGBoost model is better than the Logistic model. Based on the result of accuracy, precision, and recall values on both models.
Implementation of Computer Vision of Jakarta Weather Image Categorization Using ResNet

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were.
Data-Efficient Reinforcement Learning with Data Augmented Episodic Memory

Reinforcement learning has been implemented to model a task by doing the task repeatedly to get the maximum results based on the reward and punishment policy. It has been implemented in the game and agent-based modelling. In the game, the game agent or Non-Players character can be modelled using several techniques to achieve the goal (e.g. reinforcement learning, deep neural network and Monte Carlo Tree Search). Deep neural networks and Monte Carlo Tree Search, two more sophisticated techniques in reinforcement learning algorithms, assisted the present reinforcement learning in resolving more challenging issues. However, this area has two challenges: the minimum number of data to model and generalization to different environments. Determining a minimum number of data required by the architecture to train the model is quite a cumbersome task to be applied to real- world jobs and situations since it demands substantial data to be explicitly provided and trial-and-error re-configuration. This work proposes a Data-Efficient Reinforcement Learning model by augmenting the data and implementing episodic memory. To illustrate the effectiveness of the proposed model, this research compares it to several models, such as the Deep Q-Network (DQN) with episodic memory to the same model with data augmentation and episodic memory. The model adds to the observations before being stored in the agent's memory, causing the agent to use the same logic and take the same action in comparable situations. The outcome demonstrates that the augmented model can surpass the fundamental model in speed (with an improvement of 50% quicker).