Reviewerly/exHarmony huggingface dataset:
Voter Authentication Persistent In Mobile Voting

Indonesia's general election system requires voters to go directly to a polling station to vote. Traditional elections have several weaknesses, such as relatively high costs, various frauds at polling stations, lack of voter enthusiasm, etc. Electronic voting/e-voting may be a solution to overcome existing weaknesses, but the electronic voting system may still have problems such as system security. Persistent mobile voting is a method to develop a general election electronic voting system on the Android platform to facilitate the election process, as voters can vote anywhere without going to a polling station. In continuous mobile voting, voters confirm that they are entitled to vote by checking the registration family and personal code are in the system, if they are recognized as valid, continue with the registration process to obtain a username and password. In addition to using usernames and passwords, this application also implements a fingerprint system and security algorithms to hide the identity of voters. In mobile voting, permanent authentication of the voter replaces the role of the recorder as the rightful owner of the vote, uses verification of the family number and ID card, as well as the scanning of the voter's fingerprints, and ensures verification of the right of the voter. to vote election results of voters. Permanent voter per-authentication, namely layered verification, is proposed where fingerprint identification is matched with a voter ID generator to achieve a layered authentication process that does not rely solely on fingerprints. It has been proven that fingerprints are not only detected on the device, but also verified by the voter ID generator. The authentication process becomes more secure.
Blockchain Based Traceability in Computer Peripherals in Universities Scenarios

Tracing and tracking of the peripheral devices help in terms of manufacturing and providing services to the users. However, most of the current systems which are suggested by Food and Drug Administration (FDA) are loT and Hardware-Assisted Tracing data which are being stored as required. The time and effort on duplicating record keeping and third-party validations are wasting of time and effort towards verification process. Record-keeping systems can be attacked by fraud and cyber-attacks whereas Blockchain is ideal for delivering that information securely and in a distributed fashion. This research has proposed a Blockchain based traceability of the Computer Peripherals in Universities scenarios. It is well-known that the Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. Smart contract is a good feature for maintaining this kind of peripheral data. In addition to providing quality information, the proposed traceability system enables transparency, safety, and security. We have done security analysis also.
A Quasi-Newton Matrix Factorization-Based Model for Recommendation

Solving large-scale non-convex optimization problems is the fundamental challenge in the development of matrix factorization (MF)-based recommender systems. Unfortunately, employing conventional first-order optimization approaches proves to be an arduous endeavor since their curves are very complex. The exploration of second-order optimization methods holds great promise. They are more powerful because they consider the curvature of the optimization problem, which is captured by the second-order derivatives of the objective function. However, a significant obstacle arises when directly applying Hessian-based approaches: their computational demands are often prohibitively high. Therefore, the authors propose AdaGO, a novel quasi-Newton method-based optimizer to meet the specific requirements of large-scale non-convex optimization problems. AdaGO can strike a balance between computational efficiency and optimization performance. In the comparative studies with state-of-the-art MF-based models, AdaGO demonstrates its superiority by achieving higher prediction accuracy.
RDF(S) Store in Object-Relational Databases

The Resource Description Framework (RDF) and RDF Schema (RDFS) recommended by World Wide Web Consortium (W3C) provide a flexible model for semantically representing data on the web. With the widespread acceptance of RDF(S) (RDF and RDFS for short), a large number of RDF(S) is available. Databases play an important role in managing RDF(S). However, there are few studies on using object-relational databases to store RDF(S). In this paper, the authors propose the formal definitions of RDF(S) model and object-relational databases model. Then they introduce the approach for storing RDF(S) in object-relational databases based on the formal definitions. They implement a prototype system to demonstrate the feasibility of the approach and test the performance and semantic retention ability of this prototype system with the benchmark dataset.
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models

The extensive applications of deep neural network (DNN) and its increasingly complicated architecture and supply chain make the risk of backdoor attacks more realistic than ever. In such an attack, the adversary either poisons the training data of a DNN model or manipulates its training process to stealthily inject a covert backdoor task, alongside the primary task, so as to strategically misclassify inputs carrying a trigger. Defending against such an attack, particularly removing the backdoor effect from an infected model, is known to be hard. For this purpose, prior research either requires a recovered trigger, which is hard to come by, or attempts to fine-tune a model on its primary task, which becomes less effective when the clean data is scarce. In this paper, we present a simple yet surprisingly effective technique to induce "selective amnesia" on a backdoored model. Our approach, called SEAM, has been inspired by the problem of catastrophic forgetting (CF), a long standing issue in continual learning. Our idea is to retrain a given DNN model on randomly labeled clean data, to induce a CF on the model, leading to a sudden forget on both primary and backdoor tasks; then we recover the primary task by retraining the randomized model on correctly labeled clean data. We analyzed SEAM by modeling the unlearning process as continual learning and further approximating a DNN using Neural Tangent Kernel for measuring CF. Our analysis shows that our random-labeling approach actually maximizes the CF on an unknown backdoor in the absence of triggered inputs, and also preserves some feature extraction in the network to enable a fast revival of the primary task. We further evaluated SEAM on both image processing and Natural Language Processing tasks, under both data contamination and training manipulation attacks, over thousands of models either trained on popular image datasets or provided by the TrojAI competition. Our experiments show that SEAM vastly outperforms the state-of-the-art unlearning techniques, achieving a high Fidelity (measuring the gap between the accuracy of the primary task and that of the backdoor) efficiently (e.g., about 30 times faster than training a model from scratch on the MNIST dataset), with only a small amount of clean data (e.g., with a size of just 0.1% of training data for TrojAI models).
Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning

In the realm of solar photovoltaic system image segmentation, existing deep learning networks focus almost exclusively on single image sources both in terms of sensors used and image resolution. This often prevents the wide deployment of such networks. Our research introduces a novel approach to train a network on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m. Using extensive hyperparameter tuning, we first determined the best possible parameter combinations for the network based on the DeepLabV3 ResNet101 architecture. We then trained a model using the wide range of different image sources. The final network offers several advantages. It outperforms networks trained with single image sources in multiple test applications as measured by the F1-Score (95.27%) and IoU (91.04%). The network is also able to work with a variety of target imagery due to the fact that a diverse range of image data was used to train it. The model is made freely available for further applications.
E-Authentication System using QR Code and OTP

This paper proposes an authentication system that combines One-Time Password (OTP) and Quick Response (QR) code technologies to enhance security and user experience. The system generates an OTP and a unique QR code for each authentication attempt, which can be scanned using a mobile device to complete the authentication process. The QR code contains encrypted information about the user's identity and the OTP, which is verified by the server. The proposed system provides a secure, convenient, and efficient method for user authentication, which is crucial in today's digital world. An e-authentication system that uses OTP and QR code technology is a secure and efficient method for authenticating users in online transactions. This system combines the benefits of OTP and QR code technology to provide a two-factor authentication mechanism that is convenient for users and effective in preventing unauthorized access. This system aims to address the vulnerabilities of traditional username and password authentication by providing an additional layer of security through two-factor authentication. the system aims to prevent unauthorized access to online services and transactions. The system aims to provide a userfriendly and convenient authentication method that can be easily integrated into existing online platforms. It protect sensitive information and ensure that only authorized users can access online services and transactions
Robust meta gradient learning for high-dimensional data with noisy-label ignorance

Large datasets with noisy labels and high dimensions have become increasingly prevalent in industry. These datasets often contain errors or inconsistencies in the assigned labels and introduce a vast number of predictive variables. Such issues frequently arise in real-world scenarios due to uncertainties or human errors during data collection and annotation processes. The presence of noisy labels and high dimensions can significantly impair the generalization ability and accuracy of trained models. To address the above issues, we introduce a simple-structured penalized γ -divergence model and a novel meta-gradient correction algorithm and establish the foundations of these two modules based on rigorous theoretical proofs. Finally, comprehensive experiments are conducted to validate their effectiveness in detecting noisy labels and mitigating the curse of dimensionality and suggest that our proposed model and algorithm can achieve promising outcomes. Moreover, we open-source our codes and distinctive datasets on GitHub (refer to https://github.com/DebtVC2022/Robust_Learning_with_MGC ).
Ensemble Text Summarization Model for COVID-19-Associated Datasets

The work of text summarization in question-and-answer systems has gained tremendous popularity recently and has influenced numerous real-world applications for efficient decision-making processes. In this regard, the exponential growth of COVID-19-related healthcare records has necessitated the extraction of fine-grained results to forecast or estimate the potential course of the disease. Machine learning and deep learning models are frequently used to extract relevant insights from textual data sources. However, in order to summarize the textual information relevant to coronavirus, we have concentrated on a number of natural language processing (NLP) models in this research, including Bidirectional Encoder Representations of Transformers (BERT), Sequence-to-Sequence, and Attention models. This ensemble model is built on the previously mentioned models, which primarily concentrate on the segmented context terms included in the textual input. Most crucially, this research has concentrated on two key variations: grouping-related sentences using hierarchical clustering approaches and the distributional semantics of the terms found in the COVID-19 dataset. The gist evaluation (ROUGE) score result shows a significant and respectable accuracy of 0.40 average recalls.
User Fee Programs: Design Choices and Processes

This report, commissioned by ACUS in the spring of 2023, examines the design and implementation of user fee programs in the executive branch. The underlying project had two goals. The first goal was to describe user fee design and implementation across a range of federal agencies —identifying design decisions that must be made and the choices available; identifying practical and legal constraints on the choices available; exploring the relationship between these choices and core values of administrative law, as applicable; and explaining how program designers might make these choices, both as a procedural matter and as a substantive matter. The second goal was, if appropriate in light of the research findings, to offer recommendations for agencies, and potentially Congress, relating to user fee design and implementation. This final report to ACUS therefore (1) provides a history of user fee programs and various issues resolved through litigation especially in the 1970s and an explanation of the relationship between legislative appropriations and user fee collections, (2) discusses threshold questions for decision makers, specifically, whether to impose a user fee at all and whether to use the existing mid-century authorizing statute or seek separate authorizing legislation, (3) works through design and implementation choices, grouped around four topics -- setting user fees, collecting user fees, using collected fees, and reviewing user fees and user fee programs, and (4) offers four broad recommendations to decision makers at both the legislative and administrative levels. A lengthy appendix lists many user fee programs in place today.
Urban Seismic Networks: A Worldwide Review

Seismic networks in urban areas today represent key infrastructure to better address the tasks of earthquake preparation and mitigation in the pre-event phase, and are an important knowledge tool supporting disaster risk management during seismic crises and post-disaster recovery. In the last fifteen years, a decrease in instrumentation costs and the development of new low-cost devices have enhanced the deployment of several monitoring and experimental networks worldwide. This paper conducts a review of scientific work that refer to the deployment of Urban Seismic Networks (USN) in order to define the current state of the art. We collected a list of more than one hundred USNs worldwide that were operative within the period from 1994–2023. For each USN, we report the locations and objectives along with information about the timing, coverage, geometry, and technical characteristics (sensors and transmission). By reviewing all these aspects, this paper offers important insights to provide guidelines for new implementations, bearing in mind that the interest in monitoring urban areas is expected to continue to increase in the near future driven by population growth in urbanized areas.
Dual‐branch feature extraction network combined with <scp>Transformer</scp> and <scp>CNN</scp> for polyp segmentation

Abstract To overcome the difficulty of accurate polyp segmentation, a novel encoder–decoder network DFETC‐Net is proposed, in which two encoders based on Swin Transformer and CNN are utilized to extract the global and local features respectively. Further, a new self‐attention and convolution feature fusion module is designed to fuse the two branch features to enhance the feature representative capability and alleviate the influence of the semantic gap. In the bottleneck, a new multi‐feature pyramid pooling module fuses all deep features from two branches to obtain multi‐scale information and promote segmentation accuracy. The coordinate attention is used in the skip connections between two shallow CNN blocks and corresponding decoder blocks to pay more attention to doubtful and complicated regions. Extensive experiments demonstrate the proposed network outperforms several state‐of‐the‐art methods in terms of both qualitative effects and quantitative measurements. All codes are available on https://github.com/LYJieH/DFETC-NET .
Stackelberg Security Game for Optimizing Cybersecurity Decisions in Cloud Computing

As it is difficult to cover all cybersecurity threats, an optimal defense strategy is one of the focal issues in cloud computing due to its dynamic abstraction and scalability. On this basis, Stackelberg security games (SSG) have received significant attention for their better deployment of limited security. To deal with uncertainty and incomplete information, we introduce a modified quantal response (Mod-QR) approach that incorporates bounded rationality and preference into the decision-making process. Formally, this can be done by using the quantal response equilibrium (QRE) framework to find a trade-off between the effectiveness and operating costs of cloud computing. In this case, the most effective countermeasures to defend the cloud can be viewed as a mixed strategy in which all the actions of the defender are played with a nonzero probability. This framework has been evaluated using an experimental study on MATLAB optimization toolbox to understand the behavioral aspects of cybersecurity actors and then to proactively protect cloud computing.
Applications, Approaches, and Ethics of the Extended Reality in Urban Design and Planning

Problem, research strategy, and findings Urban planners can benefit from extended reality (XR) technology to better understand data, history, design, and planning options through immersive sensory experiences. Though there are many examples of XR-assisted urban planning, there is little guidance on how to use it effectively. In this review I provide a comprehensive overview of XR in urban planning by examining recent literature. I begin by defining XR concepts and other influential technologies like metaverse, digital twins, generative artificial intelligence, and blockchain. Then, I review six key planning goals for using XR, including urban design, place marketing, planning support, education, research, and planning for metaverse platforms. In addition, practical considerations are discussed, such as public participation, ethics, and technical considerations.
Automatic Transcription of Greek Folk Dance Videos to Labanotation Based on Autoencoders

We’re creating an automatic system, based on autoencoders, to transcribe dance videos into labanotation [1], a movement notation system. Manual labanotation generation is a time-consuming process that requires specialized knowledge. Our system aims to save time and provide a valuable tool for choreographers, dancers, and anyone interested in documenting body movement. Our system analyzes RGB videos of dancers, isolates their movements, and generates labanotation as an image. The process involves extracting the 3D skeleton, segmenting movements, identifying them, and mapping them to Laban symbols. In our research, we focus on segmenting the movements of the dancer’s lower body. We calculate the angles of the legs and use them as features to train an autoencoder. This approach, inspired by [2], has not been previously explored for human movement segmentation. Human movement segmentation remains a hard problem due to the temporal complexity among the high-dimensional motion features. Our work aims to automatically generate labanotation for Greek folk dances, contributing to the preservation and transmission of dance-related Intangible Cultural Heritage (ICH). The system is integrated into the CHROMATA online platform [3], which offers AI tools for analyzing, classifying, and annotating ICH content. This integration assists designers in creating immersive experiences based on ICH.
COVID-Free Interaction with Public Displays

In response to the COVID-19 pandemic, there has been a need to reassess and adapt interaction modalities to prioritize hygiene and safety. Contactless interaction has emerged as a potentially effective approach for applications on publicly available interactive systems. This paper introduces an interactive system designed for contactless interaction with publicly accessible information displays. The system was implemented and deployed in a busy bank branch in Greece, catering to a high volume of users. Its goal is to provide a safe and hygienic way for users to engage with interactive information displays. By enabling contactless interaction, the system addresses the concerns and precautions associated with the pandemic, while maintaining a high level of accessibility and user engagement.
A Mobile Tour Guide with Localization Features and AR Support

In this paper, we present an interactive guided tour of the House-Museum of Eleftherios Venizelos located in the city of Chania, Crete, Greece. The mobile application is using a mixture of Bluetooth beacons and Augmented Reality (AR) to expand the museum experience both while visiting the museum and when planning for a museum visit. In terms of tour personalisation, several options are provided including short and long tours and audience-specific tours for younger generations.
Reviving Events, Historical Figures and Artefacts in the Context of a Physical Museum Exhibition

In this work, we present our approach towards reviving events, historical figures and artefacts in the context of a physical museum exhibition organized by the National Historical Museum in the Old Parliament House as part of the celebration of 200 years since the beginning of the Greek Revolution. In this context, several interactive systems were created to accompany the museum exhibits aiming at augmenting the museum visiting experience through an interactive dialogue with objects, places, persons and events.
THE VIRTUAL BEHAVIOR ENGINEERING MODEL: 6 BEHAVIOR INFLUENCE FRAMES FOR A REMOTE WORKFORCE

The virtual behavior engineering model builds off Gilbert’s original behavior engineering model and Chevalier’s updated model and integrates user experience qualities. While the user experience typically refers to technology, its qualities are relevant to all facets of remote work. By analyzing each of the six influence frames, we can get better insight into root causes and their relationships. This improved understanding will help us prioritize and design better solutions to close a given performance gap.
Diffusing Colors: Image Colorization with Text Guided Diffusion

The colorization of grayscale images is a complex and subjective task with significant challenges. Despite recent progress in employing large-scale datasets with deep neural networks, difficulties with controllability and visual quality persist. To tackle these issues, we present a novel image colorization framework that utilizes image diffusion techniques with granular text prompts. This integration not only produces colorization outputs that are semantically appropriate but also greatly improves the level of control users have over the colorization process. Our method provides a balance between automation and control, outperforming existing techniques in terms of visual quality and semantic coherence. We leverage a pretrained generative Diffusion Model, and show that we can finetune it for the colorization task without losing its generative power or attention to text prompts. Moreover, we present a novel CLIP-based ranking model that evaluates color vividness, enabling automatic selection of the most suitable level of vividness based on the specific scene semantics. Our approach holds potential particularly for color enhancement and historical image colorization.