Reviewerly/exHarmony huggingface dataset:
Power Data Integrity Verification Model Based on Blockchain Technology

The increasing amount of power data has a negative impact on the current power data integrity verification. Because the tag verification technology used in the current data model is relatively backward, the response time of model verification is long. Aiming at the problem of too much time cost of the model, a power data integrity verification model based on blockchain technology is constructed. ECC algorithm is used to encrypt the power data key. With the help of RSA accumulator, the power data authentication dictionary is constructed. In the network layer of the blockchain structure, virtual machine is added. Combined with the power data integrity verification algorithm and data authentication dictionary, the power data integrity verification process is completed. The experimental results show that the time cost of using the proposed model to verify the integrity of power data is short, and the verification effect is good.
Learning the eigenstructure of quantum dynamics using classical shadows

Learning dynamics from repeated observation of the time evolution of an open quantum system, namely, the problem of quantum process tomography is an important task. This task is difficult in general, but, with some additional constraints could be tractable. This motivates us to look at the problem of Lindblad operator discovery from observations. We point out that for moderate size Hilbert spaces, low Kraus rank of the channel, and short time steps, the eigenvalues of the Choi matrix corresponding to the channel have a special structure. We use the least-square method for the estimation of a channel where, for fixed inputs, we estimate the outputs by classical shadows. The resultant noisy estimate of the channel can then be denoised by diagonalizing the nominal Choi matrix, truncating some eigenvalues, and altering it to a genuine Choi matrix. This processed Choi matrix is then compared to the original one. We see that as the number of samples increases, our reconstruction becomes more accurate. We also use tools from random matrix theory to understand the effect of estimation noise in the eigenspectrum of the estimated Choi matrix.
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where

While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines.
ANDROID-BASED DISTRICT E-GOVERNMENT APPLICATION MODEL UI/UX PROTOTYPE USING DESIGN THINKING METHOD

The positive impact of rapid technological developments is widely felt in various aspects of human life. One example is improving the quality of public services at sub-district offices through the use of appropriate technology. E-Government is a new development in an effort to improve information and communication technology-based public services. This aims to make public services more transparent, accountable, effective and efficient. The E-Government application in this sub-district office requires a UI/UX design prototype model to help ease users in accessing the application. This study aims to design a UI/UX design prototype model in the sub-district office using design thinking. Researchers will analyze the problems faced by sub-districts in providing e-government services to the community, and develop a prototyping model of e-government applications that can provide a solution to the problem. The design thinking method will be used in the development of the prototype model. The Design Thinking method is an innovation-based software product design method that is based on finding solutions to solve certain problems. After successfully finding and understanding the problems found. The results of this report will later display a prototype in the form of an e-government website design that has been made. The Design Thinking method is an innovation-based software product design method that is based on finding solutions to solve certain problems. After successfully finding and understanding the problems found. The results of this report will later display a prototype in the form of an e-government website design that has been made. The Design Thinking method is an innovation-based software product design method that is based on finding solutions to solve certain problems. After successfully finding and understanding the problems found. The results of this report will later display a prototype in the form of an e-government website design that has been made.
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Tracking using bio-inspired event cameras has drawn more and more attention in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The first category needs more cost for inference and the second one may be easily influenced by noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically, a teacher Transformer-based multi-modal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then, we design a new hierarchical knowledge distillation strategy which includes pairwise similarity, feature representation, and response maps-based knowledge distillation to guide the learning of the student Transformer network. Moreover, since existing event-based tracking datasets are all low-resolution ($346 \times 260$), we propose the first large-scale high-resolution ($1280 \times 720$) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pongs, etc. Extensive experiments on both low-resolution (FE240hz, VisEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method. The dataset, evaluation toolkit, and source code are available on \url{https://github.com/Event-AHU/EventVOT_Benchmark}
Fractal analysis to determine JRC on sandstones and its correlation to SRF, Ende-Lianunu Regency, East Nusa Tenggara Province, Indonesia

The study area is located in Ende-Lianunu Regency, East Nusa Tenggara Province, Indonesia, with mountainous and hilly morphology, and is part of the Kiro Formation which is composed of tuffaceous sandstones. Discontinuities in the sandstone are in the form of bedding planes and joints, which affect the mechanical properties of the rock mass, reduce its strength, and affect slope stability. The discontinuity condition that affects mechanical behavior is surface roughness. This research aims to define the fractal dimension value of surface roughness using a box-counting method, joint roughness coefficient (JRC), and simulate the strength reduction factor (SRF) value on slopes that have a certain JRC. The fractal dimension of fine-grained samples = 1.0010 and coarse-grained = 1.0056. The average JRC value is 6.25 (Range 4-6). Simulation at JRC 0-20, gives different SRF values. On slopes with JRC = 0, the critical SRF = 1.35. If JRC = 20, the critical SRF = 1.47. It can be inferred, that the fractal dimension of the roughness of the sliding plane correlates with JRC and SRF. As the fractal dimension value increases, so do the JRC and SRF values, resulting in more stable slope conditions.
A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability.
XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%.
Examining the Values Reflected by Children during AI Problem Formulation

Understanding how children design and what they value in AI interfaces that allow them to explicitly train their models such as teachable machines, could help increase such activities' impact and guide the design of future technologies. In a co-design session using a modified storyboard, a team of 5 children (aged 7-13 years) and adult co-designers, engaged in AI problem formulation activities where they imagine their own teachable machines. Our findings, leveraging an established psychological value framework (the Rokeach Value Survey), illuminate how children conceptualize and embed their values in AI systems that they themselves devise to support their everyday activities. Specifically, we find that children's proposed ideas require advanced system intelligence, e.g. emotion detection and understanding the social relationships of a user. The underlying models could be trained under multiple modalities and any errors would be fixed by adding more data or by anticipating negative examples. Children's ideas showed they cared about family and expected machines to understand their social context before making decisions.
Enhancing Document-Level Relation Extraction with Relation-Specific Entity Representation and Evidence Sentence Augmentation

Document-level relation extraction (DocRE) is an important task in natural language processing, with applications in knowledge graph construction, question answering, and biomedical text analysis. However, existing approaches to DocRE have limitations in predicting relations between entities using fixed entity representations, which can lead to inaccurate results. In this paper, we propose a novel DocRE model that addresses these limitations by using a relation-specific entity representation method and evidence sentence augmentation. Our model uses evidence sentence augmentation to identify top-k evidence sentences for each relation and a relation-specific entity representation method that aggregates the importance of entity mentions using an attention mechanism. These two components work together to capture the context of each entity mention in relation to the specific relation being predicted and select evidence sentences that support accurate relation identification. Finally, we re-predicts entity relations based on the evidence sentences, called relationship reordering module. This module re-predicts entity relationships based on the predicted set of evidence sentences to form k sets of relationship predictions, and then averages these k+1 sets of results to obtain the final relationship predictions. Experimental results on the DocRED dataset demonstrate that our proposed model achieves an F1 score of 62.84% and an lgn F1 score of 60.79%, outperforming state-of-the-art methods.
Specializing Small Language Models Towards Complex Style Transfer via Latent Attribute Pre-Training

In this work, we introduce the concept of complex text style transfer tasks, and constructed complex text datasets based on two widely applicable scenarios. Our dataset is the first large-scale data set of its kind, with 700 rephrased sentences and 1,000 sentences from the game Genshin Impact. While large language models (LLM) have shown promise in complex text style transfer, they have drawbacks such as data privacy concerns, network instability, and high deployment costs. To address these issues, we explore the effectiveness of small models (less than T5-3B) with implicit style pre-training through contrastive learning. We also propose a method for automated evaluation of text generation quality based on alignment with human evaluations using ChatGPT. Finally, we compare our approach with existing methods and show that our model achieves state-of-art performances of few-shot text style transfer models.
Recommender System Based on Matrix Factorization on Twitter Using Random Forest (Case Study: Movies on Netflix)

In this day and age, there is a lot of entertainment that can be done, one of which is watching movies using the Netflix platform. When you want to watch, sometimes users can be confused about which movies to watch according to their tastes and interests, which requires a solution, namely by using a recommendation system. The recommendation system is a system that emerged as a solution to provide information by learning data from users with previously stored data items. One of the recommendation system techniques is Collaborative Filtering. By using Collaborative Filtering, this study will focus on using Matrix Factorization-based because it is considered more efficient and allows the incorporation of additional information in the data. This study will use the Random Forest algorithm to improve the results of good predictions. In this study, a recommendation system based on Matrix Factorization on Twitter will be made using Random Forest in a case study of films on Netflix. The experimental results have shown that the use of the system gets a Mean Absolute Error (MAE) value of 0.7641 to 0.8496 and a Root mean squared error (RMSE) of 1.0359 to 1.1935.
Enhancing Sharpness-Aware Optimization Through Variance Suppression

Sharpness-aware minimization (SAM) has well documented merits in enhancing generalization of deep neural networks, even without sizable data augmentation. Embracing the geometry of the loss function, where neighborhoods of 'flat minima' heighten generalization ability, SAM seeks 'flat valleys' by minimizing the maximum loss caused by an adversary perturbing parameters within the neighborhood. Although critical to account for sharpness of the loss function, such an 'over-friendly adversary' can curtail the outmost level of generalization. The novel approach of this contribution fosters stabilization of adversaries through variance suppression (VaSSO) to avoid such friendliness. VaSSO's provable stability safeguards its numerical improvement over SAM in model-agnostic tasks, including image classification and machine translation. In addition, experiments confirm that VaSSO endows SAM with robustness against high levels of label noise.
An improved video fingerprinting attack on users of the Tor network

In today’s digital age, there is less and less doubt that companies, governments, and hackers track our online patterns and collect personal data. Thus, over 2 million users daily flock to the Tor network, which secures their online identities by encrypting their traffic. Although the Tor network is often considered secure, it is vulnerable to fingerprinting attacks that threaten users’ online privacy. Using machine learning, these attacks attempt to classify which web page the victim is visiting based on the page’s unique traffic patterns. Since video streaming makes up 80% of total downstream web traffic, we aim to explore how well this content can be fingerprinted in Tor. In this paper, we develop a new video fingerprinting model. Our model is based on a random forest classifier, a supervised machine learning algorithm that assembles decision trees for various samples and classifies based on their majority vote. Our model uses 247 features from video traces to exploit the burst patterns present in video traffic that are unique to each video. Our model is able to distinguish which one of the 50 videos a user is hypothetically watching on the Tor network with 85% accuracy, which outperforms the state-of-the-art, Deep Fingerprinting model’s accuracy of 55%. This demonstrates that video fingerprinting poses a serious threat to the privacy of Tor users. Our model performs better as it is adjusted to consider the bursts that are streamed from video traffic’s DASH protocol.
A Joint Analysis of Input Resolution and Quantization Precision in Deep Learning

Deep learning models have become increasingly prevalent in various domains, necessitating their deployment on resource-constrained devices. Quantization is a promising way to reduce the model complexity in that it keeps model architecture intact and enables the model to operate on specialized hardwares(e.g., NPU, DSP). Input resolution is also essential in making a trade-off between accuracy and computation.
A Sign Language Recognition System with Pepper, Lightweight-Transformer, and LLM

This research explores using lightweight deep neural network architectures to enable the humanoid robot Pepper to understand American Sign Language (ASL) and facilitate non-verbal human-robot interaction. First, we introduce a lightweight and efficient model for ASL understanding optimized for embedded systems, ensuring rapid sign recognition while conserving computational resources. Building upon this, we employ large language models (LLMs) for intelligent robot interactions. Through intricate prompt engineering, we tailor interactions to allow the Pepper Robot to generate natural Co-Speech Gesture responses, laying the foundation for more organic and intuitive humanoid-robot dialogues. Finally, we present an integrated software pipeline, embodying advancements in a socially aware AI interaction model. Leveraging the Pepper Robot's capabilities, we demonstrate the practicality and effectiveness of our approach in real-world scenarios. The results highlight a profound potential for enhancing human-robot interaction through non-verbal interactions, bridging communication gaps, and making technology more accessible and understandable.
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation

Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feature extraction and dynamics estimation are tightly coupled when using event cameras, and no precise model is currently available for describing the complex structures generated by lines in the space-time volume of events. We solve this problem by deriving the correct non-linear parametrization of such manifolds, which we term eventails, and demonstrate its application to event-based linear motion estimation, with known rotation from an Inertial Measurement Unit. Using this parametrization, we introduce a novel minimal 5-point solver that jointly estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines. We demonstrate on both synthetic and real data that our solver generates more stable relative motion estimates than other methods while capturing more inliers than clustering based on spatio-temporal planes. In particular, our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%. The proposed eventails contribute to a better understanding of spatio-temporal event-generated geometries and we thus believe it will become a core building block of future event-based motion estimation algorithms.
Blockchain-Based Privacy-Preserving Electronics Healthcare Records in Healthcare 4.0 Using Proxy Re-Encryption

Sharing patient data across health organizations is currently not secure, and doing so without mutual trust may result in data privacy violations. Through the implementation of Industry 4.0 principles, the whole ecosystem is advancing toward Healthcare 4.0. Periodic medical data sensing, aggregation, data transfer, data sharing, and data storage were the core components of Healthcare 4.0. The blockchain provides trust in the flow of patient medical information between health institutions. In this research, a permissioned blockchain network based on the Hyperledger Fabric architecture is built among patients and medical institutions to achieve secure and trustworthy data exchange for patients. Using the Hyperledger Fabric framework, blockchain can effectively handle electronic health records by combining the customized access-control method with asymmetric cryptography to remove inconsistency in data interchange. This work provides the results with different chaincodes and assesses Fabric’s performance as the number of chaincodes and peers grow.