Reviewerly/exHarmony huggingface dataset:
An Energy-Efficient Visual Object Tracking Processor Exploiting Domain-Specific Features

Nowadays, visual object tracking (VOT) has become a key technology in many AI applications such as intelligent surveillance and mobile robots. In the past, general AI accelerators have been developed and used for accelerating VOT. However, as the domain-specific knowledge is not well utilized, the energy efficiency of general AI accelerators is limited when accelerating VOT. To address this issue, in this work, an energy-efficient VOT processor is proposed by exploiting diverse domain-specific features of VOT processing. Several techniques have been proposed to improve the energy efficiency and processing speed, including an efficient sparse computing architecture with channel-wise data and weight compression, a Siamese-core processing technique and a bounding box (bbox) early-drop technique. Implemented and fabricated with a 55nm CMOS technology, the proposed VOT processor achieves high energy efficiency (15.08 TOPS/W), high frame rate (69.8 fps) with high accuracy, outperforming several state-of-the-art designs.
Differential Privacy-Preserving of Multi-Party Collaboration Under Federated Learning in Data Center Networks

The unit of federated learning and differential privacy protection technology can ensure that the published aggregation has sufficient anonymity. While realizing privacy protection, users' data can be fully utilized. However, attackers may still steal user information by eavesdropping on the shared model of federated learning participants. In this paper, we mainly study the multi-party collaborative privacy protection mechanism based on federated learning. Firstly, we design a stochastic forest algorithm satisfying differential privacy in a distributed computing framework. The algorithm first segments the nodes in the weak classifier into the best segmentation points, and then perturbs the leaf nodes through the Laplace mechanism to protect the classification results. Secondly, we present a differential privacy-preserving mechanism based on XGBoost, aiming at the privacy leakage problem that may occur during the construction of weak classifiers. Finally, we propose a multi-party collaborative construction scheme based on federated learning to meet differential privacy. At last, we empirically confirm that our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL. Moreover, numerous experimental results indicate that the proposed framework can ensure data security and also have high accuracy.
A Smart Public Service Optimization Model Using Linear Programming to Reduce the Number of Deaths

In everyday life, it is possible for people to need public services. What is meant by public services in this research are health, security, protection, and assistance services that threaten the safety of people's lives. Public services are needed by the community in every normal or emergency incident that must receive life-saving services as soon as possible, and every service that will be provided is not necessarily the same for all communities. This is because the incident, rescue needs, and location of the victim may vary, such as the occurrence of health problems, traffic accidents, crime incidents, and fires. This research uses the concept of “Smart Health and Safe City” which aims to optimize the smart public service model using linear programming to reduce the number of deaths by utilizing hospital agencies' limited resources to provide treatment as soon as possible, police agencies, fire agencies and Basarnas agencies, where when going to carry out public services, medical (m), police (p), fire brigade (f), and Basarnas (b) depart from the hospital location (ih), police location (ip), fire extinguisher location (if), and Basarnas location (ib) to location of victim (j) to provide public services in order for the modeling's output to address the issue of delivering intelligent services as early as possible which can reduce the number of deaths, minimize costs incurred, minimize service time and maximize resource utilization. possible, as well as obtaining the maximum value of the model's objective function. The simulation results, the maximum value of the objective function was obtained at 164,00000 at the 20th iteration step and produced a model that minimized the arrival time at the location of the service recipient.
Blockchain-Based Distributed Hybrid Cloud Identity Management for Securing IoT Devices in the Cloud

The growing usage of Internet of Things (IoT) devices has led to a need for secure and decentralized identity management systems. Scalability and security are two major problems with traditional centralized identity management systems. In this paper, we propose a distributed hybrid cloud identity management system based on the blockchain for protecting IoT devices in the cloud. To achieve scalability, reliability, and performance, our system makes use of the advantages of distributed architecture and blends the characteristics of public and private cloud infrastructures. Blockchain technology is used for generating an auditable and tamper-proof record of identity management transactions. Our technology offers a decentralized and reliable identity management system, ensuring the privacy and security of IoT devices. Our proposed system provides a promising solution for managing the identities of IoT devices in a secure and decentralized manner, thus mitigating the security risks associated with centralized identity.
A Risk evaluation and control method of highway based on digital twin

With the aim of enhancing the level of safety management during the operation phase of highways, the application of digital twin technology platform is employed. Following the principles of intelligent perception of information, risk assessment, and intelligent decision-making, a set of intelligent risk assessment and control methods for highway traffic is established based on the integration of static and dynamic traffic infrastructure information. Firstly, IoT (Internet of Things) technology is utilized to achieve intelligent perception of dynamic information such as traffic and environment based on the foundation of static data. Multiple-source fusion cloud databases are built in the digital twin system. Secondly, a road segment risk identification using KNN-SVR (K-Nearest Neighbor Support Vector Regression) neural network is proposed. Additionally, a vehicle risk coupling model is introduced to determine the form of traffic risk. Lastly, a rule-based reasoning (RBR) and case-based reasoning (CBR) technology are employed to establish decision support libraries for system and case decisions, aiming to provide specialized support for control decisions. The research outcomes can offer safety improvement decisions and suggestions for transportation safety management departments.
DAPSPNet: Deep Aggregation Pyramid Strip Pooling Network for Real-time and Accurate Segmentation

This paper introduces an efficient Convolutional Neural Networks (CNN) architecture named DAPSPNet for Real-time semantic segmentation. We propose a novel dual-resolution network, DAPSPNet, and augment it with strip pooling in the multi-scale feature extraction module to extract strip-shaped features more effectively. The convolution kernels have lengths of 5, 9, and 17, with a width of 1. We chose strip pooling as the supplement for two reasons. First, strip pooling is a lightweight technique that reduces the number of parameters and computations involved in pooling operations. Second, there are also some strip-shaped features in the contextual information, which are in line with the needs of real-time semantic segmentation of road scenes. Extensive experimental evaluations on the Cityscapes dataset demonstrate the competitive performance of DAPSPNet compared to several state-of-the-art methods in most scenarios.
Secure Mobile Facial Recognition Attendance System with Edge Computing: A Comparative Study of Face Recognition Models for Android Application Integration

Attendance systems are designed to monitor and record the presence of individuals within an organization, such as a company or educational institution. These systems are usually in the form of biometric scanners or RFID cards. This research focuses on the development of a secure mobile based facial recognition attendance system by employing an edge computing approach. More specifically, the purpose of the research is to compare different facial recognition models and their suitability in an android application without sending confidential data such as images of the user's faces into a separate server. The final model chosen will be taking into account the model size, F1 score, and inference time. This model will be used to correctly identify a user in the android application strictly for attendance purposes. This research method involves comparing the performance of FaceNet, VGGFace, and MobileFaceNet for facial recognition. This research will also focus on designing and implementing a backend database system in order to facilitate the mobile based attendance application. The results of this study shows that the MobileFaceNet model was the best choice for this application as it is significantly smaller in size compared to the other two models while still having high F1 score. The model will then be integrated to the android application and the required classes and functions to facilitate this will be developed in Kotlin.
IT Infrastructure Dashboard Monitoring Application Development Using Grafana And Promotheus, a Case Study at Astra Polytechnic School

IT infrastructure play an important role in this digitally transformed era in relation to support business day to day operation. Businesses were forced to adapt information technology to get efficiency in all aspects. By having an optimal IT infrastructure business may maintain its system stability company-wide. Handling infrastructure malfunction takes time without a real-time monitoring tool, since all maintenance action taken based on users complaints. This study tries to develop a solution of how to achieve infrastructure monitoring within the business, from network peripherals, servers and inhouse application system. Method used in this research is iterative development by using Prometheus used to collect data for certain metrics while Grafana used as real time visualization of those data gathered. By having all vital information on a single dashboard related to IT infrastructure, business may react and resolve issues that will cause service interruption if not handled properly. The result of this study is a web based centralized dashboard for monitoring all infrastructure related that is accessible from within the school computer network.
Using Deep Learning and Word Embedding to Detect Clickbait in Indonesian Headline News

Clickbait is a term used to describe provocative news article headlines designed to entice readers. Clickbait titles or headlines are frequently used to increase the number of clicks on articles or news stories so that the author or platform owner can profit. This will help potential readers avoid being provoked by a headline that contains clickbait. This study aims to detect clickbait in Indonesian article headlines by considering the effect of using Word2Vec and FastText as word embedding and analyzing the results of hyperparameter tuning performed on the model. The Bidirectional Long Short-Term Memory model will be applied to categorize news headlines in Indonesian article. The variation of the BiLSTM unit hyperparameter, dropout rate, learning rate, batch size, and epoch variation on the classification of clickbait in Indonesian news headlines will also be investigated in this study. The optimal hyperparameter value can be used in developing studies on models and future research. The best evaluation results are obtained when the model uses Word2Vec as word embedding with an accuracy of 0,892. For precision and recall obtained are 0,870 and 0,828.
A Programmable Hybrid Simulation Environment for Coordination of Autonomous Vehicles

The objective of this paper is to investigate a programmable hybrid simulation environment (based on PX4 and ROS2, two commonly-used packages for autonomous vehicle simulation) that can enable efficient coordination of multiple autonomous vehicles or systems. This work also aims to demonstrate the potential benefits and significance of effective coordination and communication among these autonomous vehicles. In the paper, a typical mission context featuring two autonomous vehicles, a drone and a rover, interacting with each other, is considered and used to showcase the possibility of autonomous operation and the performances of their behavior and coordination. To achieve effective and flexible communication among the ROS2 (Robotic Operating System 2) and PX4 platforms, new messaging topics and computing nodes that allow bi-directional communication between two vehicles were developed. The paper will detail two scenarios where the drone and rover interact actively in the PX4 and ROS2 platforms, respectively. In either platform, the drone and rover can publish and subscribe to the topic to facilitate effective communication. The presented work is useful in various fields, such as aerospace systems, transportation, logistics, agriculture, and search and rescue operations, where efficient coordination of autonomous vehicles could significantly enhance the efficiency, safety, and performance.
A Patient Specific Algorithm for Plasmodium Malaria Detection on Cell Images

Computer-aided detection of plasmodium malaria on cell images from digital microscopy provides an invaluable second opinion to medical experts. Traditionally, well-established deep learning algorithms are widely used to detect plasmodium, but these techniques have not been deployed due to the uninterpretable nature of the decisions made by the network. To address this, we present an ensemble method that fuses the predictions based on the class activation mapping (CAM) results from multiple networks via a novel selector network. We study the performance on a publicly available dataset of cell images by using three convolutional neural network architectures, Xception, ResNet50, and InceptionV3,to produce CAM results for the selector network and performance comparison. Our proposed approach helps enhance doctors' trust and outperforms the traditional methods by 1%.
Computer Vision-assisted Evaluation Method for Solar Photovoltaic Installation on Building Rooftops

The use of solar energy is becoming prominent as a cleaner solution to address huge energy demands due to urbanization. One method to collect the sun's energy in an urban setup is through the installation of solar photovoltaic (PV) modules on building rooftops. However, designing an optimal PV system in an urban setting necessitates an accurate potential assessment of both existing and potential installation areas. Conventional methods used to gather data in assessing the potential of solar PV installations are tedious and expensive. Hence, this study proposes a design framework to address these gaps. A self-integrated unmanned aerial vehicle (UAV) is used to automate data acquisition. Next, the collected images are processed to create a high-quality orthophoto using the structure-from-motion (SfM) technique. Then, a web-based design software tool is utilized to generate a preliminary design model. In addition, analysis based on the hierarchical approach assessment criterion (geographical, physical, and technical potential) from the generated model and simulations of different case studies is also carried out. It is discovered that the existing solar PV system within the prospect site has covered almost 50% of their energy consumption and can still be potentially improved through maximizing current locations or extending to other available rooftops. It is found that the key elements, such as the UAV, its flight plan, the generated orthophoto, and the preliminary solar PV design, are crucial for the methodology. In conclusion, this study allows for developing an advanced alternative to conventional assessment methods, addressing their drawbacks.
Comparing Deep Learning Performance for Aircraft Detection in Satellite Imagery

The recent public availability of satellite imagery has pushed forward the field of deep learning allowing the application of novel object detection methods. However, object detection in satellite imagery, such as aircraft detection, presents significant challenges including large image variability due to orientation, scale, and shape. This work makes a summarized comparison of known object detection algorithms including You-Only-Look-Once (YOLO) and RetinaNet-ResNet variants performing aircraft detection on the publicly available RarePlanes dataset. RarePlanes was assessed to be a highly effective training dataset with similar bounding box size distributions across classes. Results indicate that YOLO variants generally provided better performance than RetinaNet-ResNet variants in identical testing scenarios. YOLO variants were especially effective when using high numbers of classes and with classes containing large numbers of annotations.
Modified Agile User Experience for Developing Student Medical Report

Health screening for elementary and middle school students in Indonesia is challenging for schools and Public Health Centers (PUSKESMAS). The screening process is generally manual, so it is hard to analysis based on student health in school and identify health problems. With this urgent need, we initiated a system that can manage website-based student health data with a fast development system that is easy for users to use. One method for developing systems quickly and involving users is Agile User Experience (UX). Agile UX modifications integrate UX research into every Agile system development and reduce design sprint time from the original five days to three days. This method condenses six stages of design sprint (Understand, Define, Diverge, Decide, Prototype, and Validate) and was modified into three stages (Ideate, Create, Iterate), where each stage was carried out in 1 day. The results of Minimum Viable Product (MVP) testing on basic features with real users using eye tracking analysis show no significant problems in the application created. In addition, user testing by the (Usability Metric for User Experience) UMUX-Lite questionnaire shows that user satisfaction is 74.9, which means good. This method suits small-scale developers and products that want to be launched quickly and save time.
The Influence of Accounting Information System Knowledge, User Satisfaction, and Internal Control on MySAP Implementation

This research examines the influence of accounting information system knowledge, user satisfaction, and internal control on MySAP implementation on PT PH-El. The study utilized a survey approach, employing a questionnaire as the primary tool for collecting data. The research focused on employees who are directly engaged in the utilization of accounting information systems on PT PH-El. The researchers employed purposive sampling in selecting 100 respondents for the data analysis. The findings of the research demonstrate that accounting information system knowledge and user satisfaction positively and significantly impact the implementation of MySAP. However, the study indicates that internal control does not have a significant influence on MySAP implementation. In this context, companies can consider providing training and education related to accounting information systems and enhancing user satisfaction as efforts to improve MySAP implementation.
The pedagogical advisor as a weaver of the learning design process

This chapter proposes that learning design is interdisciplinary teamwork which depends on and is influenced by two key aspects: the characteristics of university teachers, and the attributes of their universities. My argument is derived from working as a pedagogical advisor in four Colombian universities over the last 14 years, where I collaborated with university teachers designing courses aimed at undergraduate, graduate and continuing education students. &#x0D; This chapter presents a reflection on learning design in virtual, blended and face-to-face environments in Colombian higher education institutions. The first section provides background context on higher education in Colombia and briefly describes the concepts of “learning design” and “instructional design”, highlighting how each of the two approaches can guide the team responsible for learning experiences. &#x0D; The second section is about the characteristics of university teachers. Given that “university teachers” do not make up a homogeneous category (Hernandez et al., 2009), the most important thing to consider about them is what defines them individually. The first consideration is their teaching skills and training needs. The second is teachers’ beliefs about teaching and learning, and the third aspect concerns the multiple literacies demanded by incorporating ICTs into teaching. &#x0D; The third section describes the institutional contexts and attributes of the educational institution as determinants of learning design, particularly as relates to policies that emphasise educational innovation and teacher support.&#x0D; The fourth section concludes by describing how the pedagogical advisor works as a link between teacher and institution to develop an appropriate plan in a way that responds to both the needs of the teacher and the requests of the institution in a coherent way.&#x0D;
Impact of Digital Technology on Education

Conventionally education is based on sources such as schools, teachers, written script or print media. The learners enroll themselves with schools, teachers and libraries as a source of information. The era before digitalization when information was not easy to get to by the preponderance of people and even those who could access was unable to achieve current affairs and current information as compared to today’s context. The current and the more updated society want to know the news sooner as it happens, when it happens and how it happens. It would be good to say that the world has turned more informational and more knowledgeable society. Thus education is considered as the most pressing matter and the power of wisdom is becoming the most important positive feature of an association. Advancement in digital technology has straightened out many boulevards of knowledge and education. The digital technology has made information reachable or transmissible from anywhere and by or to all groups of people. Almost all over the globe education has reached and has become a fundamental part of human life. This paper describes the process of generation, formation and getting hold of knowledge and information through the technology, also describes how technology is accessible and applicable in importing knowledge. The paper establishes a link between how these technologies have been used in education and its impact in general.