Reviewerly/exHarmony huggingface dataset:
Evaluation of Bunga Bali Florist Website Usability Using The Website Usability Evaluation (Webuse) Method

Bunga Bali Florist is a MSME in Bali to need improving its digital marketing strategy by creating stalls through a media of a website. Website with a high level of usability will often be visited by users. Based on the usability aspect, the Bunga Bali Florist website still found several problems in the interface in terms of design, navigation and effectiveness. Then based on interviews with the owner of Bunga Bali Florist, it was found that the website had never been evaluated about usability factors. Therefore, it was necessary to conduct research related to the usability of the website using the Webuse procedure with the aim of analyzing the aspects that affect the usability of the Bunga Bali Florist site which refers to Webuse Theory. This research technique was carried out by distributing online questionnaires to 120 respondents. The results of the analysis of 120 respondent data obtained that the total value of the web usability on the Bunga Bali Florist website was 0.80 which was included in the “Good” usability level, which means the Bunga Bali Florist website was good and did not require recommendations for improvement.
Multi Access Edge Computing Task Unloading Algorithm Based on Genetic Ant Colony Algorithm Fusion

Aiming at the problem that the computing resources of network terminals are insufficient and the task unloading time is too long, a multi access edge computing task unloading algorithm based on genetic ant colony algorithm is proposed. In the scenario of multi-user and multi MEC server, a edge computing task unloading model is established, and a multi access edge computing model is constructed. A computing task can be processed by the user equipment itself or transmitted to the MEC server for execution. In order to improve the quality of user experience, the task offloading problem is formulated to minimize the average delay of user tasks under the constraints of network and resource conditions, and the objective function and constraints are designed according to this. The task offloading algorithm is designed based on the fusion of genetic-ant colony algorithm, and the paths deviating from the optimal point are eliminated by calculating the fitness, which reduces the calculation amount of the algorithm and speeds up the algorithm iteration. The test results show that the performance of task offloading algorithm based on genetic-ant colony fusion is better than ant colony and particle swarm algorithm in terms of task offloading energy consumption and task average delay.
Scalability in Blockchain - Hyperledger Fabric and Hierarchical Model

The growth of blockchain technology has been quite fast because of its decentralized nature to keep data in the most immutable, confidential and secured form. But with more participants in the chain, the technology is suffering from issues like scalability. Our work is a novel study to understand issues related to scalability. We propose hierarchical model with hyper- ledger fabric implementation as a solution to scalability in blockchain keeping healthcare sector as a use case.
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources

Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Commonly, a parameter server (PS) is deployed for aggregating model parameters contributed by different clients. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for vehicular networks as vehicles communicate with each other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of vehicle routes and communication distances, it is hard for individual vehicles to sufficiently exchange models with others. Data sources contributing to models on individual vehicles may not diversified enough resulting in poor model accuracy. To address this problem, we propose the DFL-DDS (DFL with diversified Data Sources) algorithm to diversify data sources in DFL. Specifically, each vehicle maintains a state vector to record the contribution weight of each data source to its model. The Kullback–Leibler (KL) divergence is adopted to measure the diversity of a state vector. To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved. Finally, the superiority of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL and improve the model accuracy significantly compared with state-of-the-art baselines.
MR-MEET: Mixed Reality Collaborative Interface for HMD and Handheld Users

Mixed Reality (MR) collaborative interface allows remote collaboration between an HMD (Head Mounted Display) user in Virtual Reality (VR) and a non-HMD user in Augmented Reality (AR). Both users to have a meeting with each other despite the distance between one another. It enables users to interact in a virtual environment that can replace the traditional way of physical meeting by using appropriate collaborative tools. However, the problem arises when the user is far apart from each other and needs an effective way or tools to collaborate. This paper discusses about a collaborative tool and proposed a prototype called MR-Meet as a collaborative MR interface. MR-Meet has been designed with a tool that can aid both users from different settings to work together. It provides the designing stage to actualize MR collaborative interface with two basic tools such as sticky notes and a whiteboard to help distant people between HMD and handheld users from different locations can be available in one shared environment. To study the user's behavior and experience when they perform collaborative tasks using the two basic collaborative tools. This paper aims to invoke the collaborative tools in MR-Meet meeting space. It describes the users from two different settings, VR user wears HMD and AR user wears handheld device. The methodology describes the interaction of HMD by using a ray pointer while Non-HMD was using a handheld device to utilize its touch-based interaction.
Data analytics system for product management in retail business

This paper aims to develop a data analytics system for product management in retail businesses based on the Winters' Method and tests the milk sales forecasting model with machine learning using linear regression, random forest, and eXtreme Gradient Boosting (XGBoost). Using Minitab, apply machine learning test data to separate milk statistical analysis with five forecasting methods. Actual sales figures were compared to the difference using the results of the machine learning model and statistical analysis. The results of machine learning show that random forests have more minor deviations than a statistical analysis performed with Minitab that showed Winters' Method (Additive Method) with UHT of less than 7.9 percent. Pasteurized was less than 9.54 percent. Soymilk was less than 12.57 percent. Fermented milk was less than 10.47 percent. The result is a desirable user satisfaction rating of 4.47 out of 5.
CNN Acceleration Through Flexible Distribution of Computations Between a Hardwired Processor and an FPGA

Accelerating CNN computations on IoT devices is highly needed due to the constrained resources associated with such devices and the demanding applications they might need to run. This paper proposes a flexible model for CNN acceleration by distributing the computations between a hardwired processor and an FPGA communicating through a bus. Three different computation distribution scenarios are suggested. Four combinations for each scenario are implemented and evaluated to demonstrate the flexibility of the proposed approach. While all twelve combinations are shown to provide some acceleration ranging between 3.26 and 19.73 times compared to the hardwired processor, they do have different FPGA resource requirements. Flexibility is introduced by allowing the system designer to pick the most suitable combination based on acceleration and resource constraints.
DGS: Communication-Efficient Graph Sampling for Distributed GNN Training

Distributed GNN training tends to generate huge volumes of communication. To reduce communication cost, the state-of-the-art sampling-based techniques sample and retrieve only a subset of the nodes. However, our analysis shows that current sampling algorithms are still inefficient in network communication for distributed GNN training, which is mainly because of three problems: first, they overlook the locality of the sampled neighbor nodes in the cluster; second, they sample data only at the coarse-grained graph node level; and third, some mechanisms they adopted fall short in distributed scenarios. This paper presents a graph sampling framework (DGS) for distributed GNN training, which effectively reduces network communication cost while preserving the final GNN model accuracy. To achieve this, DGS samples neighborhood information based on the locality of the neighbor nodes in the cluster, and samples data at the levels of not only graph nodes but also node features based on explanation. Specifically, DGS constructs an explanation graph which preserves the relationship between the local graph and remote nodes, and leverages the recently-proposed model explanation technique to design an online explanation scheme that interprets the importance of nodes and features. Evaluation results show that DGS achieves up to 1.25× throughput speedup over the state-of-the-art FastGCN and reduces the communication cost by up to 28.3%, while preserving the final model accuracy almost the same as that of full-batch training.
Photonic-aware Neural Networks for Packet Classification in Beyond 5G Networks

The benefits introduced by novel network technologies such as 5G and beyond, including low latency and support for billions of devices, have the potential to transform the lives of people. However, the features promised by these new technologies have also attracted malicious actors, with various motivations for attacking the network infrastructure, from cybercrime-based frauds to political goals. Thus, to enable the full potential of the emerging network technologies, it is necessary to take into accounts these attacks and develop tailored countermeasures. One future direction in mitigating the risks of potential attacks is the automatic classification of malicious packets, with the possibility to drop them if classified in the attack category. Hence, in this context, we propose a solution based on Neural Networks (NNs) to automatically classify packets into two classes, i.e., benign and attack, directly in the Radio Access Network (RAN), specifically inspecting packets when they are relayed at the next generation eNB (gNB)-Central Unit (CU) level. Since NNs can be computationally intensive algorithms, potentially increasing the latency of the network, we decide to leverage Photonic-Aware Neural Network (PANN), photonic accelerators able to perform NN computations in the analog optical domain and with time-of-flight latency. We devised two different PANN architectures, considering different photonic constraints. The classification performance of the two architectures has been assessed on the CICIDS-2017 dataset and compared with electronic counterparts. Results proved that the F1-score loss due to underlying hardware constraints is negligible, paving the way for PANN applications in next generation networks.
Blockchain Application in Education Data Security Storage Verification System

The times have changed education to be more modern with the existence of technology. The rapid development of influential technology in data storage (verification) is developed through Blockchain technology (ledger) which consists of a Blockchain containing a ledger and three elements, namely the hash, the hash of the previous block, and data. Blockchain is a new technology in storing valid data. This study aims to minimize errors and falsification of data in the field of education that has been verified inaccurate data storage. This study uses a workflow analysis method for valid data storage verification systems. The novelty of this research is that Blockchain offers additional security of central data storage as a cryptographic network regulator to provide verification and authentication that occurs in data exchange in the network. It can be concluded that online verification of data storage has become a more modern and secure container.
MIMIC: SmartNIC-aided Flow Backpressure for CPU Overloading Protection in Multi-Tenant Clouds

In multi-tenant clouds, off-the-shelf x86 boxes are widely deployed as middleboxes. With the rapid growth of cloud traffic and the migration to NFV deployment in recent years, CPU overloading at middleboxes becomes more of an issue. From our data centers, we observed that the CPU overloading was caused by heavy hitters. To address this issue, we propose MIMIC, a cloud-scale flow backpressure system, implemented onto our existing SmartNIC with FPGA acceleration. MIMIC rate-limits the selected heavy hitters through a new per-flow backpressure protocol and a new heavy-hitter detection system, to protect the other tenants. The detection system is based on hierarchical memory design, leveraging on-chip SRAM and off-chip DRAM, which can handle highly concurrent cloud traffic without the losses of flow information. We extend the design by adding a pre-filtering procedure for rapid detection. To avoid CPU being flooded by FPGA through frequent heavy-hitter reporting, due to their performance disparity, the CPU queries the FPGA on demand. The backpressure protocol is non-invasive to protect tenant privacy and allows controllable rate-limiting through the novel use of ECN and meter tables. The SmartNIC acts as a man in the middle to facilitate heavy-hitter detection and per-flow backpressuring. In a production setting, we observe that MIMIC can react quickly and bring down CPU load to the normal level within 10ms without packet losses.
Performance Impacts of JavaScript-Based Encryption of HTML5 Web Storage for Enhanced Privacy

The HTML5 Web Storage API provides the ability for web applications to store data on client machines. This storage is commonly used for caching, local state tracking, and offline support that allows web applications to work when the web server cannot be contacted. The HTML5 Web Storage is becoming increasingly popular with the majority of new web applications using at least some features provided by this standard. Unfortunately, the local storage provided by HTML5 Web Storage is not entirely secure and does not sufficiently ensure the confidentiality of the user’s data. Encrypting data prior to storage is a common approach to protecting local user data. However, as browser-based applications become more complex and demanding the impact of data encryption may adversely impact application performance. Furthermore, the average web developer is generally not proficient in cryptographic best practices in web applications. First, we provide a simple design approach for encryption of local storage that supports offline web applications. Second, we analyze the impact of various symmetric encryption algorithms and implementations on the performance of the HTML Web Storage API. We show that there are several viable options that will increase the confidentiality and privacy of user data within local storage without imposing significant performance penalties.
Work-in-Progress: High-Performance Systolic Hardware Accelerator for RBLWE-based Post-Quantum Cryptography

Ring-Binary-Learning-with-Errors (RBLWE)-based post-quantum cryptography (PQC) is a promising scheme suitable for lightweight applications. This paper presents an efficient hardware systolic accelerator for RBLWE-based PQC, targeting high-performance applications. We have briefly given the algorithmic background for the proposed design. Then, we have transferred the proposed algorithmic operation into a new systolic accelerator. Lastly, field-programmable gate array (FPGA) implementation results have confirmed the efficiency of the proposed accelerator.
Prediction of Solar Photovoltaic Energy Output Based on Thin-Film Technology Utilizing Various Machine Learning Techniques

This paper presents solar photovoltaic (PV) energy prediction based on thin-film technology utilizing various machine learning (ML) models. Several ML models like Support Vector Machine (SVM), Extra Tree Regression (ETR), Decision Tree Regression (DTR), K-Nearest Neighbour (kNN) and Feed-Forward Neural Network (FFNN) were utilized to evaluate each model's performance according to performance metrics. The primary input parameters such as time, solar radiation, wind speed, ambient and PV module temperatures, and the actual power generated by the thin-film PV panel based on the 2018 data set were considered for predicting solar PV output power. The ETR is proposed to predict the PV power output in this work and compared with other ML models. The results showed that ETRs outperformed the different ML algorithms, whereas DTR performed the poorest. The ETR model had the best performance, with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of 59.17 and 39.07, respectively. On the other hand, the DTR model performed poorly, with an RMSE of 81.83 and an MAE of 52.9, respectively.
Life Prediction Method Based on LDA-ELM for Mechanical Components of Door Systems

The life of door system is closely related to the capacity of rail vehicle safe operation and maintenance. Rolling pin is a built-in mechanical component of the rail vehicle door system. Its wear degree is difficult to measure so that its lifetime is hard to predict in real time. In order to predict the life of rolling pin online and provide decision support for active maintenance, this paper proposes a data-driven life prediction method based on Linear Discriminant Analysis (LDA) and Extreme Learning Machine (ELM). Firstly, features related to the wear state of the rolling pin are extracted from raw data collected from motor of the door. Then, with the LDA, the features are fused to filter out the redundant features. Finally, the ELM model for predicting the diameter of small end is built, and the life of rolling pin is calculated according to the relationship between the run times and the diameter of small end. The simulation results show that the method enables to accurately predict the life of the product, which has reliability and important engineering application value.
Study on Optimization Method of Visual Odometry Based on Feature Matching

The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided.
Super-resolution of Sentinel-2 images using Wasserstein GAN

The Sentinel-2 satellites deliver 13 band multi-spectral imagery with bands having 10 m, 20 m or 60 m spatial resolution. The low-resolution bands can be upsampled to match the high resolution bands to extract valuable information at higher spatial resolution. This paper presents a Wasserstein Generative Adversarial Network (WGAN) based approach named as DSen2-WGAN to super-resolve the low-resolution (i.e., 20 m and 60 m) bands of Sentinel-2 images to a spatial resolution of 10 m. A proposed generator is trained in an adversarial manner using the min-max game to super-resolve the low-resolution bands with the guidance of available high-resolution bands in an image. The performance evaluated using metrics such as Signal Reconstruction Error (SRE) and Root Mean Squared Error (RMSE) shows the effectiveness of the proposed approach as compared to the state-of-the-art method, DSen2 as the DSen2-WGAN reduced RMSE by 14.68% and 7%, while SRE improved by almost 4% and 1.6% for 6× and 2× super-resolution. Lastly, for further evaluation, we have used trained DSen2-WGAN model to super-resolve the bands of EuroSAT dataset, a satellite image classification dataset based on Sentinel-2 images. The per band classification accuracy of low-resolution bands shows significant improvement after super-resolution using our proposed approach.
Usability Analysis of My TelU Application Using System Usability Scale

My TelU application is a mobile application developed by Telkom University to support and improve the quality of mobile-based learning systems. This application helps students to check their schedules, grades, attendances, notifications, news, and micro social media timelines. This study was conducted to determine one aspect of user satisfactions with My TelU application using Usability Testing, specifically with System Usability Scale (SUS) and SPSS for data analysis. We ran quantitative research with primary data collection conducted by distributing questionnaires to a total of 400 Telkom University student respondents. This number of participants is sufficient based on sample calculations from the existing population. Our results showed that My TelU application received a score of 74.5 with a “Good” rating indicating that improvements can still be made. In this paper, we provided some recommendations to enhance the usability level of My TelU application.