Reviewerly/exHarmony huggingface dataset:
Upcycling Fabric Waste for Home Decoration by Implementing Islamic Art Approach

Humanity's activities cause the destruction that occurs in the world as a caliphate, as stated in Surah Ar-Rum (30), verse 41 of the Quran. One of the products that might harm the ecosystem is waste. Various countries, including the Indonesian Government, have competed with numerous initiatives and policies to save environmental sustainability. The author of this study provided training on how to conserve the environment by processing trash, particularly fabric waste. Bina Nusantara University's community development initiative invites numerous assisted MSMEs to participate in a waste management training session. Participants are instructed on how to transform fabric waste into useful interior products. The techniques conducted start from joining fabric waste with patchwork, quilting, and applique connection techniques. From the connection results, the product's value can be increased again by adding a digital screen printing technique. Islamic Art is one of the many motifs that can be used. Implementing Islamic geometric patterns and floral, vegetal, and calligraphy designs on interior accessories are some examples of Islamic art motifs. Apart from being able to decorate the interior of the home, particularly by giving it an Islamic feel, the objective of creating this product is to conserve the environment through upcycling technology.
Efficient Meta-Tuning for Content-Aware Neural Video Delivery

Recently, Deep Neural Networks (DNNs) are utilized to reduce the bandwidth and improve the quality of Internet video delivery. Existing methods train corresponding content-aware super-resolution (SR) model for each video chunk on the server, and stream low-resolution (LR) video chunks along with SR models to the client. Although they achieve promising results, the huge computational cost of network training limits their practical applications. In this paper, we present a method named Efficient Meta-Tuning (EMT) to reduce the computational cost. Instead of training from scratch, EMT adapts a meta-learned model to the first chunk of the input video. As for the following chunks, it fine-tunes the partial parameters selected by gradient masking of previous adapted model. In order to achieve further speedup for EMT, we propose a novel sampling strategy to extract the most challenging patches from video frames. The proposed strategy is highly efficient and brings negligible additional cost. Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications. We conduct extensive experiments based on various efficient SR architectures, including ESPCN, SRCNN, FSRCNN and EDSR-1, demonstrating the generalization ability of our work. The code is released at https://github.com/Neural-video-delivery/EMT-Pytorch-ECCV2022 .
Updatable NIZKs from Non-Interactive Zaps

In ASIACRYPT 2016, Bellare, Fuchsbauer, and Scafuro studied the security of NIZK arguments under subverted Structured Reference String (SRS) and presented some positive and negative results. In their best positive result, they showed that by defining an SRS as a tuple of knowledge assumption in bilinear groups (e.g. $$g^a, g^b, g^{ab}$$ ), and then using a Non-Interactive (NI) zap to prove that either there is a witness for the statement $$\textsf{x}$$ or one knows the trapdoor of SRS (e.g. a or b), one can build NIZK arguments that can achieve soundness and subversion zero-knowledge (zero-knowledge without trusting a third party; Sub-ZK). In this paper, we expand their idea and use NI zaps (of knowledge) to build NIZK arguments (of knowledge) with updatable, universal, and succinct SRS. To this end, we first show that their proposed sound and Sub-ZK NIZK argument can also achieve updatable soundness, which is a more desired notion than the plain soundness. Updatable soundness allows the verifier to update the SRS one time and bypass the need for a trusted third party. Then, we show that using a similar OR language, given a NI zap (of knowledge) and a key-updatable signature scheme, one can build NIZK arguments that can achieve Sub-ZK and updatable simulation soundness (resp. updatable simulation extractability). The proposed constructions are the first NIZK arguments that have updatable and succinct SRS, and do not require a random oracle. Our instantiations show that in the resulting NIZK arguments the computational cost for the parties to verify/update the SRS is negligible, namely, a few exponentiations and pairing checks. The run times of the prover and verifier, as well as the size of the proof, are asymptotically the same as those of the underlying NI zap.
A Comparison of Text Classification Methods: Towards Fake News Detection for Indonesian Websites

Fake news reports false or distorted information that aims to mislead us and undoubtedly has a negative impact on society. For example, medical research in Taiwan shows that fake news about the COVID-19 vaccine reduces the number of doses absorbed by the public significantly as those exposed to fake news become hesitant and even anti-vaccine. Therefore, early automatic detection of fake news on the internet is crucial. In detecting fake news, machine learning algorithms, especially text classification algorithms, are used as a solution for this problem. Searching for the best method with high accuracy needs to be done continuously. This paper compares four main algorithms, namely Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Logistic Regression (LR), and Naïve Bayes. The experiment was carried out using 200 Indonesian news datasets, consisting of 100 fake news and 100 real news. The performance matrix of each algorithm was evaluated with accuracy, recall, precision, and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score as the harmonic mean. The results showed that Logistic Regression was able to separate fake news and real news with the highest F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -scorc reaching 90.9%. This paper also proposes a framework of detecting fake news that can be implemented on public websites.
Smart Waste Management Systems by Using Automated Machine Learning Techniques

This investigating demonstrates how a Smart Waste Manag ement system may use automated machine learning to handle a global question. This investigation focuses on detecting i.e., binary classification which is the removal of a recycling container using detector accumulation. A wide variety of data driven soluti ons for dealing with the difficulty are examined in a realistic context where most natural events are not actual emptying. Among the tactics investigated are the existing manually generated framework and its alteration as well as typical machine learning algorithms. Machine learning improves the assortment quality and reminiscence of the existing enchiridion constructed model from 86.8% and 47.9% to 99.1% and 98.2%, respectively, when utilizing the advanced performing resolution. This method utilizes a Ran dom Forest classifier to categorize a set of attributes based on the quantity of filling at assorted period of time intervals. Ultimately, when similitude to the present enchiridion constructed baseline framework the foremost performing solution amend the quality of prognostication for recycling container evacuation period of time.
Resource-Efficient FPGA Implementation of Advanced Encryption Standard

Advanced encryption standard (AES) is today’s most widely used symmetric-key block cipher. This paper proposes an AES intellectual property (IP) design with high utilization efficiency of field-programmable gate array (FPGA). The round-based AES design is used to optimize the utilization of design resources. We analyzed and optimized MixColumns and SubBytes. In addition, we explored the tradeoff relationship between resource utilization and clock cycles in terms of the datapath variation of the round module. Based on the analysis, we propose an area-optimized AES implementation considering the characteristics of FPGA. The implementation results show up to 36% better look-up table (LUT) utilization efficiency than the Xilinx AES IP, and up to 17.9 times better than the existing AES implementation results.
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algorithms are important in multiple domains; hence evaluation needs to reflect the broad applications. We propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms. We comprehensively analyze the impacts of freezing layers, different architectures, and different pre-training datasets on FSOD performance. Our empirical results show several key factors that have not been explored in previous works: 1) contrary to previous belief, on a multi-domain benchmark, fine-tuning (FT) is a strong baseline for FSOD, performing on par or better than the state-of-the-art (SOTA) algorithms; 2) utilizing FT as the baseline allows us to explore multiple architectures, and we found them to have a significant impact on down-stream few-shot tasks, even with similar pre-training performances; 3) by decoupling pre-training and few-shot learning, MoFSOD allows us to explore the impact of different pre-training datasets, and the right choice can boost the performance of the down-stream tasks significantly. Based on these findings, we list possible avenues of investigation for improving FSOD performance and propose two simple modifications to existing algorithms that lead to SOTA performance on the MoFSOD benchmark. The code is available here .
360° Virtual Reality Video Tours Generation Model for Hostelry and Tourism based on the Analysis of User Profiles and Case-Based Reasoning

This paper proposes an adaptive software architecture focused on hotel marketing based on immersive virtual reality (VRI) with 360°videos, which includes a component based on Case-Based Reasoning (CBR) to provide experiences that correspond to the analysis of user profiles.For the validation of the system, considering that the use of VR can trigger experiences in several dimensions, affective, attitudinal and behavioral responses, as well as the cognitive load were evaluated using visualizations of 2D photographs contained in hotel websites , which were compared with 360°videos in a VRI environment.To test the hypotheses, a quasi-experimental study was conducted with an independent sample group, in which subjects were randomly assigned to the two types of visualizations.The contribution of the article lies in the incorporation of marketing concepts and approaches in VRI experiences with 360°videos through virtual objects that are used by the software architecture, as well as in the proposed validation of the effectiveness of the proposal.
Immersive View and Interface Design for Teleoperated Aerial Manipulation

The recent momentum in aerial manipulation has led to an interest in developing virtual reality interfaces for aerial physical interaction tasks with simple, intuitive, and reliable control and perception. However, this requires the use of expensive subsystems and there is still a research gap between interface design, user evaluations and the effect on aerial manipulation tasks. Here, we present a methodology for low-cost available drone systems with a Unity-based interface for immersive FPV teleoperation. We applied our approach in a flight track where a cluttered environment is used to simulate a demanding aerial manipulation task inspired by forestry drones and canopy sampling. Through objective measures of teleoperation performance and subjective questionnaires, we found that operators performed worse using the FPV interface and had higher perceived levels of cognitive load when compared to traditional interface design. Additional analysis of physiological measures highlighted that objective stress levels and cognitive load were also influenced by task duration and perceived performance, providing an insight into what interfaces could target to support teleoperator requirements during aerial manipulation tasks.
Label-Guided Auxiliary Training Improves 3D Object Detector

Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently. In this paper, we propose a Label-Guided auxiliary training method for 3D object detection (LG3D), which serves as an auxiliary network to enhance the feature learning of existing 3D object detectors. Specifically, we propose two novel modules: a Label-Annotation-Inducer that maps annotations and point clouds in bounding boxes to task-specific representations and a Label-Knowledge-Mapper that assists the original features to obtain detection-critical representations. The proposed auxiliary network is discarded in inference and thus has no extra computational cost at test time. We conduct extensive experiments on both indoor and outdoor datasets to verify the effectiveness of our approach. For example, our proposed LG3D improves VoteNet by 2.5% and 3.1% mAP on the SUN RGB-D and ScanNetV2 datasets, respectively. The code is available at https://github.com/FabienCode/LG3D .
BIBLIOMETRIC ANALYSIS OF RESEARCH TREND ON AGILE IT GOVERNANCE

This paper was prepared to analyze the scientific trends of research on Agile IT Governance through bibliometric studies in the Scopus and Google Scholar databases. From the search results using Publish or Perish in 2016 to 2021, samples were obtained consisting of 132 documents in the Scopus database and 962 documents in the Google Scholar database. The results of querying five years of data showed that scientific publications on Agile IT Governance had increased significantly since 2016. Based on the Scopus database, researchers from Germany contributed the most documents on Agile IT Governance. Still, the number of citations was dominated by the UK for research on this topic. Then, the visualization of research trends on Agile IT Governance also resulted in four clusters of co-occurrence from Scopus and Google Scholar, which lead into some topic areas mentioned in the result; 1) Agile IT Governance management in the world, 2) Agile IT Governance in relating to digital innovation and digital transformation, 3) Agile IT Governance in implementing technological and environmental innovations, and 4) Agile IT Governance in relating to its effect and benefit to society. The findings in this study will undoubtedly help related researchers recognize trends in Agile IT Governance research globally and recommend directions for further research in the future.&#x0D; &#x0D; &#x0D; Keywords: Agile, IT Governance, Bibliometric Study, Research Trend, VOSviewer
What’s Inside a Cluster of Software User Feedback: A Study of Characterisation Methods

Feedback from software users is vital for engineering better software requirements. One tool for extracting requirements from online user feedback is clustering, where the most mentioned topics are found by grouping similar feedback together. For these topics to be understood, clusters have been summarized in previous work using characterizing phrases or sentences. This work evaluates which method of characterization (unigrams, bigrams, trigrams, or sentences) is most effective for understanding the semantic meaning of a whole cluster using feedback from multiple feedback sources. We evaluate multiple characterization methods to determine the ability of each method to create distinct, descriptive characterizations. We further evaluate the amount of requirements relevant characterizations created by each characterization method. We find that unigrams, bigrams, trigrams, and full sentences all perform similarly in distinguishing clusters from each other. However, we find that fewer and more expressive characterizations, such as full sentences, contain more requirements relevant information from a feedback cluster compared to more numerous but less expressive unigrams, meaning a sentence will better summarize the important requirement relevant information from a cluster. Our findings inform the future development of user feedback clustering tools, with different cluster characterization methods being quantitatively measured for the first time.
GradXcepUNet: Explainable AI Based Medical Image Segmentation

Medical images segmentation is an important research area. Physicians and radiologists can diagnose diseases in their patients by observing the visual features using various imaging methods like CT, MRI, X-ray, and Ultrasound. AI-based medical image segmentation models can help radiologists and experts analyze various ailments. However, the prediction results are only trustworthy when the results can be interpreted by a doctor. Our work utilizes “Explainable AI(XAI)." We propose GradXcepUNet, an XAI-based medical image segmentation model, that couples the segmentation power of U-Net and explainability features of the Xception classification network by Grad-CAM. The Grad-CAM trained images highlight the critical regions for the Xception classification network. Then, as the guidance, the visualized results for critical regions are combined with an existing segmentation model (U-Net) to produce the final segmentation results. With the assistance of XAI analysis and visualization, our GradXcepUNet outperforms the original U-Net and many state-of-the-art methods. The evaluation results show that we can reach a Dice coefficient of 97.73% and an Intersection over Union (IoU) score of 78.86% on the 3D-IRCADb-01 database.
BMKS: A Blockchain Based Multi-Keyword Search Scheme for Medical Data Sharing

In recent years, electronic medical records(EMRs) sharing has played a vital role in formulating optimized treatment plans, providing data sets for researchers, and accelerating the development of biomedical science. However, This unprecedented era of technological confluence poses significant data security and privacy challenges. To solve these problems, we propose a blockchain-based multi-keyword search scheme for medical data sharing called BMKS, focusing on ensuring medical data confidentiality and realizing secure data retrieval. In BMKS, we introduce a two-level search scheme to achieve efficient and verifiable keyword searches. The introduction of the improved Bloom filter significantly improves query efficiency. In addition, we take advantage of blockchain technology to realize ciphertext search and pre-decryption, which reduces user's decryption overhead. Moreover, blockchain's transparency and tamper-preventing characteristics help record the access control process in a traceable and auditable way. The performance evaluation and security analysis show that BMKS is comprehensively safe, efficient, and practical.
The Essential Factors of Web 3.0 Affecting 7 Layers of Decentralized Web in Business or Industry

The new web 3.0 or Web3 is a distributed web technology mainly operated by decentralized blockchain and Artificial Intelligence. The Web 3.0 technologies bring the changes in industry 4.0 especially the business sector. The contribution of this paper to discuss the new web 3.0 (not semantic web) and to explore the essential factors of the new Web 3.0 technologies in business or industry based on 7 layers of decentralized web. The Layers have users, interface, application, execution, settlement, data, and social as main components. The concept 7 layers of decentralized web was introduced by Polynya. This research was carried out using SLR (Systematic Literature Review) methodology to identify certain factors by analyzing high quality papers in the Scopus database. We found 21 essential factors that are Distributed, Real-time, Community, Culture, Productivity, Efficiency, Decentralized, Trust, Security, Performance, Reliability, Scalability, Transparency, Authenticity, Cost Effective, Communication, Telecommunication, Social Network, Use Case, and Business Simulation. We also present opportunities and challenges of the 21 factors in business and Industry.
Secure Decentralized Online Gaming with Lending Functionalities

We present a decentralized online gaming platform implemented as a Decentralized Application (DApp) on the Ethereum blockchain. The gaming platform enables secure gaming, where the account balances and the stakes of the players are secured by a smart contract. Moreover, the fair enforcement of the game rules and the deposit of the winnings of the players and the gaming platform into their accounts are guaranteed by the smart contract. The gaming platform proposes lending functionalities that allow players to securely borrow tokens from the gaming platform in order to participate in the games.
Trading Positional Complexity vs Deepness in Coordinate Networks

It is well noted that coordinate-based MLPs benefit—in terms of preserving high-frequency information—through the encoding of coordinate positions as an array of Fourier features. Hitherto, the rationale for the effectiveness of these positional encodings has been mainly studied through a Fourier lens. In this paper, we strive to broaden this understanding by showing that alternative non-Fourier embedding functions can indeed be used for positional encoding. Moreover, we show that their performance is entirely determined by a trade-off between the stable rank of the embedded matrix and the distance preservation between embedded coordinates. We further establish that the now ubiquitous Fourier feature mapping of position is a special case that fulfills these conditions. Consequently, we present a more general theory to analyze positional encoding in terms of shifted basis functions. In addition, we argue that employing a more complex positional encoding—that scales exponentially with the number of modes—requires only a linear (rather than deep) coordinate function to achieve comparable performance. Counter-intuitively, we demonstrate that trading positional embedding complexity for network deepness is orders of magnitude faster than current state-of-the-art; despite the additional embedding complexity. To this end, we develop the necessary theoretical formulae and empirically verify that our theoretical claims hold in practice. Project page at https://osiriszjq.github.io/complex_encoding .
Credit Card Fraud Detection Based on Random Forest Model

This paper uses a classifier named random forest to detect credit card fraud.Credit card fraud is one of the main issues in the economic industry.To construct a credit card fraud detection system, a certain amount of samples is required.In this paper, a dataset containing 284,807 credit card transactions is used.This dataset has gone through the PCA transformation and includes 492 frauds out of 284,807 transactions.Based on the huge amount of data and imbalanced samples, this paper compresses the dataset and uses the synthetic minority over-sampling technique (SMOTE) to address the problem of imbalanced samples.Also, in this paper, we use random forest as a classification model while constructing the fraud detection system/method.