Reviewerly/exHarmony huggingface dataset:
Age-Invariant Face Recognition Based on Self-Supervised Learning

Face recognition across aging has grown into an extremely prominent and challenging job in the field of facial recognition in recent times. Many researchers have made improvements to this field, but there is still a massive gap to fill in. This study explores the effectiveness of Self-Supervised Learning (SSL), specifically the Bootstrap Your Own Latent (BYOL) technique, to improve age-invariant facial recognition models. The experimental results demonstrate that this method greatly enhances the model’s performance, achieving accuracy gains from up to more than 5%, even on challenging datasets such as FGNET. These findings highlight the potential of SSL methods such as BYOL in advancing the field of age-invariant facial recognition.
DarkMDE: Excavating Synthetic Images for Nighttime Depth Estimation Using Cross-Domain Supervision

Self-supervised monocular depth estimation (MDE) has recently gained significant attention, demonstrating remarkable results, particularly in daytime scenarios. However, MDE in nighttime images remains challenging due to the sensitivity of photometric loss to noise and undiffused light illumination. In this paper, we propose a simple but highly effective representation using a novel Generative Adversarial Network (GAN). Specifically, we utilize the GAN to generate synthetic nighttime images from daytime images for network inference of depth. Additionally, we leverage daytime images to compute photometric error, benefiting from the reliable performance of monocular depth estimation in the day domain. We further introduce a pseudo-depth constraint derived from the day image, ensuring depth consistency between paired daytime and nighttime images. Extensive experiments conducted on the Oxford RobotCar datasets validate the effectiveness of our approach, yielding competitive results compared to state-of-the-art methods.
Self-Supervised Visual Representation Learning via Residual Momentum

Self-supervised learning (SSL) has emerged as a promising approach for learning representations from unlabeled data. Momentum-based contrastive frameworks such as MoCo-v3 have shown remarkable success among the many SSL methods proposed in recent years. However, a significant gap in encoder representation exists between the online encoder (student) and the momentum encoder (teacher) in these frameworks, limiting the performance on downstream tasks. We identify this gap as a bottleneck often overlooked in existing frameworks and propose “residual momentum” that explicitly reduces the gap during training to encourage the student to learn representations closer to the teacher’s. We also reveal that a similar technique, knowledge distillation (KD), to reduce the distribution gap with cross-entropy-based loss in supervised learning is useless in the SSL context and demonstrate that the intra-representation gap measured by cosine similarity is crucial for EMA-based SSLs. Extensive experiments on different benchmark datasets and architectures demonstrate the superiority of our method compared to state-of-the-art contrastive learning baselines. Specifically, our method outperforms MoCo-v3 0.7% top-1 in ImageNet, 2.82% on CIFAR-100, 1.8% AP, and 3.0% AP75 on VOC detection pre-trained on the COCO dataset; it also improves DenseCL with 0.5% AP (800ep) and 0.6% AP75 (1600ep). Our work highlights the importance of reducing the teacher-student intra-gap in momentum-based contrastive learning frameworks and provides a practical solution for improving the quality of learned representations.
<i>k</i>-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases

Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min_sup</i> ) and maximum periodicity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max_per</i> ) constraints. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Min_sup</i> controls the least number of transactions in which a pattern must appear in a database. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Max_per</i> controls the maximum time interval within which a pattern must reappear in the database. The popular adoption of this task has been hindered by an open problem, which involves setting appropriate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min_sup</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max_per</i> values for any given database. This paper addresses this open problem by proposing a solution to discover top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> periodic-frequent patterns in a temporal database. Top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> periodic-frequent patterns represent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> periodic-frequent patterns with the lowest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">periodicity</i> value in a database. An efficient depth-first search algorithm, Top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> Periodic-Frequent Pattern Miner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -PFPMiner), which takes only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> threshold as an input, was presented to find all desired patterns in a database. Experimental results on synthetic and real-world databases demonstrate that our algorithm is memory and runtime-efficient and also highly scalable.
Learning Visual Representation Clusters for Cross-View Geo-Location

Cross-view geo-location is a crucial research field that determines the geographic location from images taken from different viewpoints. It is often studied as a retrieval task, where the query images are with unknown locations, and the database includes images with geo-tags from a different platform. Learning image representations by neural networks is an important step, and one typical training method is using a classification loss, where cross-view images of the same locations are considered the same category. However, existing methods only focus on pushing the representation distances of different categories while ignoring the intra-category representation distances of samples from different platforms. Considering that controlling the intra-category distance can help to guide the model to extract compact category-sharing representations from cross-view images, we propose a categorized cluster loss to learn separate and compact representation clusters. Categorized cluster loss can supervise the network to learn invariant information from samples of different platforms by constraining both the inter-category and intra-category feature distances. Meanwhile, we design a category-view-stratified sampling strategy, which samples balanced inputs in terms of both category and view in each batch during the learning process. We implemented our approach with a lightweight OSNet-based network and achieved higher accuracy with fewer parameters on a typical and challenging cross-view geo-location dataset than most state-of-the-art (SOTA) methods.
CSS-EM: A Comprehensive, Secured and Sharable Education Management System for Schools

Scores, honors, and certificates are crucial for displaying pupils’ abilities in school. Obtaining authentic records is difficult, yet falsifying such data is easy. Scorechain, a blockchain-based academic data management system, addresses this issue. Our system manages scores, awards, examinations, classes, and certifications, as well as a secure multi-role hierarchy, to improve Scorechain’s security and reliability. Scorechain also lets parents, recruiters, and colleges communicate information. Our blockchain system was built using Substrate and Rust. Our implementation, testing, and analysis show Scorechain’s efficiency.
Evolving Into a Transformer: From a Training-Free Retrieval-Based Method for Anomaly Obstacle Segmentation

As a key problem of auto-vehicle applications, the goal of Anomaly Obstacle Segmentation (AOS) is to detect some strange and unexpected obstacles (possibly are unseen previously) on the drivable area, thereby equipping the semantic perceptual model to be tolerant of unknown things. Due to its practicality, recently AOS is drawing attentions and a long line of works are proposed to tackle the obstacles with almost infinite diversity. However, these methods usually focus less on the priors of driving scenarios and involve image re-generation or the retraining of perceptual model, which lead to large computational quantity or the degradation of perceptual performance. In this paper, we propose to pay more attention to the characteristics of driving scenarios, lowering the difficulty of this tricky task. A training-free retrieval based method is thereby proposed to distinguish road obstacles from the surrounding road texture by computing the cosine similarity based on their appearance features, and significantly outperforms methods of the same category by around 20 percentage points. Besides, we find that there is a deep relation between our method and self-attention mechanism, and as a result a novel Transformer evolves from our retrieval based method, further boosting the performance.
Hashing for Geo-Localization

In this paper, we undertake the task of fast geo-localization of a query ground image by using geo-tagged aerial images. To this end, we propose a hashing strategy that fast searches the database of geo-tagged aerial images for the ground image’s matches, whose geo-tags are exploited to estimate the ground geographic location. Specifically, we commence by converting the aerial images into ground-view aerial images that have the common angle of view (i.e., horizontal view) with the ground image. We then develop a feature extraction model and a hash encoder for generating hash codes for the images. Based on these models, the ground image and the geo-tagged aerial images are transformed to hash codes that comprehensively reflect their visual content similarity. Fast searching the geo-tagged aerial image database for the ground image’s matches is conducted subject to small Hamming distance between the hash codes. We extract a geographical cluster from the matched aerial images subject to their geo-tags. In this way, the geographic location of the ground image is efficiently retrieved according to the geographical cluster. Experiments on two datasets validate the efficiency and effectiveness of our proposed framework. We have released our implementation code at https://github.com/taoyiminR/Hashing_for_geo-localization for public evaluation.
ZUni: The Application of Blockchain Technology in Validating and Securing Educational Credentials

The rapid development of blockchain technology in IoT applications, including finance, government, and education, is attributed to its decentralized, secure, and transparent features. Within the IoT context, blockchain technology plays a crucial role in enhancing the management and verification of educational credentials. We propose the ZUni system as a potential solution that applies blockchain technology to transform how verifiers authenticate students’ credentials and validate their education, ensuring integrity and data security. We utilize IPFS to store credential details, Zero-Knowledge Proofs (ZKP) and Decentralized Identifiers (DID) on the Ethereum Blockchain to establish a clear and immutable record of the certification process, enabling confident verification of credential authenticity. This article aims to contribute to the growing understanding of the IoT-based application of blockchain technology in the educational system. It addresses prevailing issues associated with centralized systems used to manage student certification data in educational institutions, such as data security deficiencies and the potential for human error. ZUni resolves these challenges by establishing a decentralized system through blockchain technology, securely issuing, storing, and verifying academic credentials.
Bayesian Approach for Static Object Detection and Localization in Unmanned Ground Vehicles

This paper introduces a statistical and probabilistic framework in approaching for an automatic search and estimation of static object’s location in an unmanned ground vehicle setting. It relies on the probabilistic nature of the Bayesian approach in statistical inference and decision-making. In Bayesian statistic, probabilities are assigned to model parameters, hypotheses, and predictions, making them inherently probabilistic. In the context of machine learning, the Bayesian approach plays a crucial role in addressing various challenges, particularly in the realm of uncertainty estimation, parameter optimization, and decision-making. Bayesian method permits a more comprehensive exploration of the parameter space. In this paper, the proposed approach utilized a lidar sensor and a pan/tilt camera as the primary hardware components. The lidar sensor, with its wide angle of view and high 2D accuracy, first detected the object in the 2D plane. Subsequently, the camera captured and analyzed object features to confirm its presence before guiding the robot to the precise position and orientation. The Recursive Bayesian Estimation (RBE) algorithm was leveraged to continuously track and determine the target’s location, ensuring accurate and reliable updates throughout the process. A confidence level was assigned to each detection technique, and the lidar sensor enables continuous detection by incrementally increasing the probability of detection in a generalized manner. The approach was evaluated through the detection of a static object with specific features, and the results were demonstrated using a multi-space Bayesian approach and the lidar detection method.
Building an AI-Powered IoT App for Fall Detection Using Yolov8 Approach

Falls are a serious health concern, particularly among older people and the disabled, because they contribute to increased mortality, hospitalization, and loss of independence. This is especially true for the elderly. For the purpose of solving this issue, we have developed a brand new artificial intelligence system for fall detection. To reliably detect falls, our system employs the powerful and state-of-the-art YOLOv8 algorithm, as well as advanced software development techniques. The YOLOv8 algorithm, which serves as the foundation for our deep learning model, which helps us be able to detect if an individual person captured by surveillance cameras is falling in acceptable counting time at the staging level. In our opinion, this model successfully differentiates falls from other movements and activities, exhibiting excellent accuracy and a low false positive rate across a wide range of settings and levels of illumination. Our fall detection method has the potential to dramatically improve the safety and well-being of vulnerable groups by giving immediate aid and support in the event that a fall occurs.
A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation

Detecting prohibited items via X-ray screening at airports and sensitive venues is essential for preventing smuggling and breaches of security. The difficulty in prohibited items inspection lies in accurately detecting prohibited items in complex X-ray images and limited access to X-ray images containing prohibited items. Few-shot detection aims at learning with limited examples and assigning a category label to each object. However, most few-shot learning methods do not focus on the edge information of the occluded object in X-ray images, which is crucial for the model to detect prohibited items in the X-ray images. In this paper, we presents a method (RVViT) for few-shot prohibited items detection tasks which fully acknowledges the significance of X-ray penetrability and increases the stability of few-shot learning model. Specifically, a Transformer encoder is firstly adopted for generating high-level semantic features that contain global information. At the same time, an edge detection module is devised for enhancing the edge information of prohibited items. Moreover, to further improve the stability of the few-shot learning model and ensure prototype consistency between the support and query samples, a reverse validation strategy is proposed to assist training. Extensive experiments demonstrate our method outperforms state-of-the-art approaches in terms of detection with a small number of samples.
A High-Speed Barret-Based Modular Multiplication with Bit-Correction for the CRYSTAL-KYBER Cryptosystem

The CRYSTAL-Kyber algorithm is the only Public-key Encryption algorithm selected by the National Institute of Standards and Technology (NIST) in the third round of the Post-Quantum Cryptography (PQC) Standardization Process. Hardware architectures for the CRYSTAL-Kyber cryptosystem must be designed to hasten the execution of the operands of cryptographic algorithms, particularly high-order polynomial multiplication. All operands are reduced modularly to produce finite groups, rings, or fields. This paper utilizes an optimized variant of the Barret reduction algorithm, combined with a bit-correction module, to calculate the precision of the estimated quotient q̂. As a result, only one conditional subtraction is required without DSPs. The proposed structure is simulated and synthesized using Xilinx Vivado 2021.2 suite software and implemented on the Artix7 FPGA board, demonstrating that this design has the highest frequency achieving 239 MHz with an area of 75 LUTs, 50 FFs, and 28 Slices, respectively. The hardware efficiency metric is slightly higher than the K-RED design and significantly improved compared to other methods, at least $$1.53\times $$ , $$2.67\times $$ , and $$2.74\times $$ , respectively. This design is suitable for both NTT and Point-Wise-Multiplication (PWM) modes.
A High-Performance Pipelined FPGA-SoC Implementation of SHA3-512 for Single and Multiple Message Blocks

The secure hash algorithm 3 (SHA-3) is an important technique for ensuring data authentication, integrity, and confidentiality. Improving the round function to enhance speed and resource efficiency has been a primary concern in most studies. However, processing long messages can consume a significant amount of time when retrieving data from external memory. Specifically, after completing one block of the message, the processor, such as the Central Processing Unit (CPU), is required to prepare the input for handling the next block. In this research, we present a high-performance and flexible hardware architecture for SHA3-512, specifically designed for applications with short and long messages without software intervention. We contribute two techniques. Firstly, we introduce an architecture designed to handle multiple messages, each with either a single block or multiple blocks. Secondly, we utilize a sequential processing technique for padding, catering to both short and long messages. Additionally, we implement the pipeline technique for the round function. The proposed SHA3-512 architecture is synthesized on Cyclone 5CSXFC6D6F31C6, achieving an impressive throughput of 12.05 Gbps at a clock frequency of 125.57 MHz, with each hash computation taking six clock cycles.
Unsupervised Tensor Based Feature Extraction From Multivariate Time Series

Clustering and outlier detection for multivariate time series are essential tasks in data mining fields and many researchers have developed a lot of algorithms. However, these tasks are still challenging because both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-wise</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">variable-wise</i> associations should be taken into account to appropriately treat multivariate time series. We propose a tensor based feature extraction method, called UFEKT, which focuses on subsequences to account for the time-wise association and constructs a feature vector for each subsequence by applying tensor decomposition to account for the variable-wise association. This method is simple and can be used as an effective preprocessing for clustering and outlier detection algorithms. We empirically show that UFEKT leads to superior performance on various popularly used clustering algorithms such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means and DBSCAN and outlier detection algorithm such as the κ-nearest neighbor and LOF.
Design of a Secure Firmware Over-The-Air for Internet of Things Systems

This paper presents the development of the Firmware Over-The-Air (FOTA) remote firmware update method for IoT-embedded system devices. The proposed method provides a secure environment for updating IoT system memory over the Internet. The proposed system employs the ED25519 digital signature creation algorithm, which incorporates the SHA3 hash function, in addition to guaranteeing the security of the FOTA process and the stability of the operating firmware versions. Large files are hashed using the Tree Hash algorithm, and data is encrypted using the ChaCha20 algorithm. In addition to modifying memory for applications, this paper provides a method for modifying the memory space containing the bootloader to satisfy the second-party boot requirements using the RoT (Root of Trust) program. The system is prototyped and evaluated on microcontrollers.
Intermed: An Efficient Interoperable Blockchain Protocol for Electronic Health Record Transferring

This paper introduces Intermed, an interoperable blockchain solution tailored specifically for the healthcare sector. Blockchain technology, which gained popularity through the introduction of Bitcoin in 2008, has expanded its scope beyond cryptocurrencies and found applications in various sectors, including healthcare. The COVID-19 pandemic has highlighted the limitations of traditional healthcare systems in securely managing large volumes of sensitive information. Blockchain has emerged as a potential solution to address this challenge, resulting in the emergence of successful blockchain startups in the healthcare industry. However, the isolated nature of blockchain networks presents a barrier to integrating them with existing healthcare platforms. While several cross-chain solutions have been proposed, most are designed for public blockchains and are unsuitable for transferring sensitive data like Electronic Health Records (EHR). Moreover, existing proposals for data sharing among private blockchains, including those in healthcare, often lack sophistication and may have security vulnerabilities. Intermed facilitates seamless data exchange and collaboration among patients, providers, researchers, and insurers, ensuring secure and standardized sharing while preserving privacy and data integrity. Through case studies and implementation examples, we demonstrate the feasibility and benefits of adopting an interoperable blockchain for healthcare, contributing to advancing blockchain technology in the healthcare sector and fostering a more connected and efficient healthcare ecosystem.