Reviewerly/exHarmony huggingface dataset:
Research on improved algorithm for helmet detection based on YOLOv5

The continuous development of smart industrial parks has imposed increasingly stringent requirements on safety helmet detection in environments such as factories, construction sites, rail transit, and fire protection. Current models often suffer from issues like false alarms or missed detections, especially when dealing with small and densely packed targets. This study aims to enhance the YOLOv5 target detection method to provide real-time alerts for individuals not wearing safety helmets in complex scenarios. Our approach involves incorporating the ECA channel attention mechanism into the YOLOv5 backbone network, allowing for efficient feature extraction while reducing computational load. We adopt a weighted bi-directional feature pyramid network structure (BiFPN) to facilitate effective feature fusion and cross-scale information transmission. Additionally, the introduction of a decoupling head in YOLOv5 improves detection performance and convergence rate. The experimental results demonstrate a substantial improvement in the YOLOv5 model's performance. The enhanced YOLOv5 model achieved an average accuracy of 95.9% on a custom-made helmet dataset, a 3.0 percentage point increase compared to the original YOLOv5 model. This study holds significant implications for enhancing the accuracy and robustness of helmet-wearing detection in various settings.
Aerial Visible-to-Infrared Image Translation: Dataset, Evaluation, and Baseline

Aerial visible-to-infrared image translation aims to transfer aerial visible images to their corresponding infrared images, which can effectively generate the infrared images of specific targets. Although some image-to-image translation algorithms have been applied to color-to-thermal natural images and achieved impressive results, they cannot be directly applied to aerial visible-to-infrared image translation due to the substantial differences between natural images and aerial images, including shooting angles, multi-scale targets, and complicated backgrounds. In order to verify the performance of existing image-to-image translation algorithms on aerial scenes as well as advance the development of aerial visible-to-infrared image translation, an Aerial Visible-to-Infrared Image Dataset (AVIID) is created, which is the first specialized dataset for aerial visible-to-infrared image translation and consists of over 3,000 paired visible-infrared images. Over the constructed AVIID, a complete evaluation system is presented to evaluate the generated infrared images from 2 aspects: overall appearance and target quality. In addition, a comprehensive survey of existing image-to-image translation approaches that could be applied to aerial visible-to-infrared image translation is given. We then provide a performance analysis of a set of representative methods under our proposed evaluation system on AVIID, which can serve as baseline results for future work. Finally, we summarize some meaningful conclusions, problems of existing methods, and future research directions to advance state-of-the-art algorithms for aerial visible-to-infrared image translation.
SID-TGAN: A Transformer-Based Generative Adversarial Network for Sonar Image Despeckling

Sonar images are inherently affected by speckle noise, which degrades image quality and hinders image exploitation. Despeckling is an important pre-processing task that aims to remove such noise so as to improve the accuracy of analysis tasks on sonar images. In this paper, we propose a novel transformer-based generative adversarial network named SID-TGAN for sonar image despeckling. In the SID-TGAN framework, transformer and convolutional blocks are used to extract global and local features, which are further integrated into the generator and discriminator networks for feature fusion and enhancement. By leveraging adversarial training, SID-TGAN learns more comprehensive representations of sonar images and shows outstanding performance in speckle denoising. Meanwhile, SID-TGAN introduces a new adversarial loss function that combines image content, local texture style, and global similarity to reduce image distortion and information loss during training. Finally, we compare SID-TGAN with state-of-the-art despeckling methods on one image dataset with synthetic optical noise and four real sonar image datasets. The results show that it achieves significantly better despeckling performance than existing methods on all five datasets.
Multimode character of quantum states released from a superconducting cavity

Quantum state transfer by propagating wave packets of electromagnetic radiation requires tunable couplings between the sending and receiving quantum systems and the propagation channel or waveguide. The highest fidelity of state transfer in experimental demonstrations so far has been in superconducting circuits. Here, the tunability always comes together with nonlinear interactions, arising from the same Josephson junctions that enable the tunability. The resulting nonlinear dynamics correlates the photon number and spatiotemporal degrees of freedom and leads to a multimode output state, for any multiphoton state. In this work, we study as a generic example the release of complex quantum states from a superconducting resonator, employing a flux tunable coupler to engineer and control the release process. We quantify the multimode character of the output state and discuss how to optimize the fidelity of a quantum state transfer process with this in mind.
A Blockchain-Based Solution for Diploma Management in Universities

Diplomas are of particular importance in society, as they serve as official proof of education. Therefore, it is not surprising that counterfeits of such documents have become common. Thus, employers usually have to verify diplomas manually with the issuer. In line with the principles of social responsibility (accountability, transparence, ethical behavior, respect of stakeholders’ interest), ensure inclusive and equitable quality education (SDG 4) become a priority for universities. Blockchain technology creates opportunities to surmount these obstacles because it has revolutionized the way people interact with each other. For that purpose, a total of 147 students from a technical university in Romania answered questionnaires to determine how familiar they are with blockchain concepts and what they think about the implementation of this technology. Further, structured interviews were conducted with education and ICT experts to determine the main advantages and disadvantages, applications, and needs for adopting blockchain technology. Therefore, the objective of this paper is to explore the influence of blockchain on education through the assessment of 69 variables. The final part contains the consequences of the analysis’s findings and potential future advancements.
Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations.
Dial "N" for NXDomain: The Scale, Origin, and Security Implications of DNS Queries to Non-Existent Domains

Non-Existent Domain (NXDomain) is one type of the Domain Name System (DNS) error responses, indicating that the queried domain name does not exist and cannot be resolved. Unfortunately, little research has focused on understanding why and how NXDomain responses are generated, utilized, and exploited. In this paper, we conduct the first comprehensive and systematic study on NXDomain by investigating its scale, origin, and security implications. Utilizing a large-scale passive DNS database, we identify 146,363,745,785 NXDomains queried by DNS users between 2014 and 2022. Within these 146 billion NXDomains, 91 million of them hold historic WHOIS records, of which 5.3 million are identified as malicious domains including about 2.4 million blocklisted domains, 2.8 million DGA (Domain Generation Algorithms) based domains, and 90 thousand squatting domains targeting popular domains. To gain more insights into the usage patterns and security risks of NXDomains, we register 19 carefully selected NXDomains in the DNS database, each of which received more than ten thousand DNS queries per month. We then deploy a honeypot for our registered domains and collect 5,925,311 incoming queries for 6 months, from which we discover that 5,186,858 and 505,238 queries are generated from automated processes and web crawlers, respectively. Finally, we perform extensive traffic analysis on our collected data and reveal that NXDomains can be misused for various purposes, including botnet takeover, malicious file injection, and residue trust exploitation.
Using Gaming Footage as a Source of Internet Latency Information

Keeping track of Internet latency is a classic measurement problem. Open measurement platforms like RIPE Atlas are a great solution, but they also face challenges: preventing network overload that may result from uncontrolled active measurements, and maintaining the involved devices, which are typically contributed by volunteers and non-profit organizations, and tend to lag behind the state of the art in terms of features and performance. We explore gaming footage as a new source of real-time, publicly available, passive latency measurements, which have the potential to complement open measurement platforms. We show that it is feasible to mine this source of information by presenting Tero, a system that continuously downloads gaming footage from the Twitch streaming platform, extracts latency measurements from it, and converts them to latency distributions per geographical location. Our data-sets and source code are publicly available at https://nal-epfl.github.io/tero-project.
A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training

In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms.
Lazy Gatekeepers: A Large-Scale Study on SPF Configuration in the Wild

The Sender Policy Framework (SPF) is a basic mechanism for authorizing the use of domains in email. In combination with other mechanisms, it serves as a cornerstone for protecting users from forged senders. In this paper, we investigate the configuration of SPF across the Internet. To this end, we analyze SPF records from 12 million domains in the wild. Our analysis shows a growing adoption, with 56.5 % of the domains providing SPF records. However, we also uncover notable security issues: First, 2.9 % of the SPF records have errors, undefined content or ineffective rules, undermining the intended protection. Second, we observe a large number of very lax configurations. For example, 34.7 % of the domains allow emails to be sent from over 100 000 IP addresses. We explore the reasons for these loose policies and demonstrate that they facilitate email forgery. As a remedy, we derive recommendations for an adequate configuration and notify all operators of domains with misconfigured SPF records.
Illuminating Router Vendor Diversity Within Providers and Along Network Paths

The Internet architecture has facilitated a multi-party, distributed, and heterogeneous physical infrastructure where routers from different vendors connect and inter-operate via IP. Such vendor heterogeneity can have important security and policy implications. For example, a security vulnerability may be specific to a particular vendor and implementation, and thus will have a disproportionate impact on particular networks and paths if exploited. From a policy perspective, governments are now explicitly banning particular vendors-or have threatened to do so.
Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks

To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0-7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6-2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework.
Ethereum's Proposer-Builder Separation: Promises and Realities

With Ethereum's transition from Proof-of-Work to Proof-of-Stake in September 2022 came another paradigm shift, the Proposer-Builder Separation (PBS) scheme. PBS was introduced to decouple the roles of selecting and ordering transactions in a block (i.e., the builder), from those validating its contents and proposing the block to the network as the new head of the blockchain (i.e., the proposer). In this landscape, proposers are the validators in the Proof-of-Stake consensus protocol, while now relying on specialized block builders for creating blocks with the highest value for the proposer. Additionally, relays act as mediators between builders and proposers. We study PBS adoption and show that the current landscape exhibits significant centralization amongst the builders and relays. Further, we explore whether PBS effectively achieves its intended objectives of enabling hobbyist validators to maximize block profitability and preventing censorship. Our findings reveal that although PBS grants validators the opportunity to access optimized and competitive blocks, it tends to stimulate censorship rather than reduce it. Additionally, we demonstrate that relays do not consistently uphold their commitments and may prove unreliable. Specifically, proposers do not always receive the complete promised value, and the censorship or filtering capabilities pledged by relays exhibit significant gaps.
Flocking to Mastodon: Tracking the Great Twitter Migration

The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised both praise and concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we analyze the migration of 136,009 users from Twitter to Mastodon. We inspect the impact that this has on the wider Mastodon ecosystem, particularly in terms of user-driven pressure towards centralization. We further explore factors that influence users to migrate, highlighting the effect of users' social networks. Finally, we inspect the behavior of individual users, showing how they utilize both Twitter and Mastodon in parallel. We find a clear difference in the topics discussed on the two platforms. This leads us to build classifiers to explore if migration is predictable. Through feature analysis, we find that the content of tweets as well as the number of URLs, the number of likes, and the length of tweets are effective metrics for the prediction of user migration.
Simulating gauge theories with variational quantum eigensolvers in superconducting microwave cavities

Quantum-enhanced computing methods are promising candidates to solve currently intractable problems. We consider here a variational quantum eigensolver (VQE), that delegates costly state preparations and measurements to quantum hardware, while classical optimization techniques guide the quantum hardware to create a desired target state. In this work, we propose a bosonic VQE using superconducting microwave cavities, overcoming the typical restriction of a small Hilbert space when the VQE is qubit based. The considered platform allows for strong nonlinearities between photon modes, which are highly customisable and can be tuned in situ, i.e. during running experiments. Our proposal hence allows for the realization of a wide range of bosonic ansatz states, and is therefore especially useful when simulating models involving degrees of freedom that cannot be simply mapped to qubits, such as gauge theories, that include components which require infinite-dimensional Hilbert spaces. We thus propose to experimentally apply this bosonic VQE to the U(1) Higgs model including a topological term, which in general introduces a sign problem in the model, making it intractable with conventional Monte Carlo methods.
Quantization-Aware Training for Mixed Precision Photonic Neural Networks

Neuromorphic photonics is among the most promising approaches for Deep Learning (DL) hardware accelerators with recent state-of-the-art mixed precision configurations lowering even further the energy consumption while increasing simultaneously the computation rate. To this end, we propose a novel stochastic mixed precision quantization-aware training scheme oriented to Photonic Neural Networks (PNNs) that gradually lowers the bit resolution of layers based on their position and the observed tolerance to quantization noise. The effectiveness of the proposed method is demonstrated on an image classification task applying a ResNet8 architecture and a financial time series forecasting applying a Recurrent Neural Network (RNN) using two photonic configurations.
Classical Machine Learning and Transformer Models for Offensive and Abusive Language Classification on Dziri Language

Offensive and abusive language identification is a challenging task, especially for low-resource languages such as Dziri, a dialect of Algerian Arabic. In this paper, we propose two approaches to enhance the performance of this task for Dziri. The first approach fine-tunes several pre-trained transformer models, including DziriBERT (a BERT-based model trained on Dziri corpus), AraBERT (a BERT-based model trained on Arabic corpus), BERT-base model (a BERT-based model trained on English corpus), and XLM-Roberta (a multilingual BERT-based model trained on 100 languages). The second approach applies a tailored preprocessing pipeline for the Dziri dialect, optimized chi-2 feature selection, and optimized SVM hyper-parameters using the Bayesian optimization. The evaluation demonstrates that our approach substantially improves the identification score compared to baseline classifiers. However, DziriBERT still outperforms our approaches by 5.83 % and 1.17 % of F -score in binary and ternary classification, respectively. The code to reproduce the results is available at: https://github.com/Bouchenemehdi24/Off-Abus-Dziri.
Federated Learning with Partial Gradients Over-the-Air

We develop a theoretical framework to study the training of federated learning models with partial gradients via over-the-air computing. The system consists of an edge server and multiple clients, aiming to collaboratively minimize a global loss function. The clients conduct local training and upload the intermediate parameters (e.g. the gradients) by analog transmissions. Specifically, each client modulates the entries of its local gradient onto a set of common orthogonal waveforms and sends out the signal simultaneously to the edge server; owing to the limited number of orthogonal waveforms, only a subset of the parameters can be selected for uploading during each round of communication. On the server side, it passes the received analog signal to a bank of match filters and obtains a noisy partial gradient vector. The server then uses this partial gradient to update the global parameter and feeds the new model back to all the clients for another round of local training. We derive the convergence rate of such a model training algorithm. We also conduct experiments to investigate the effects of different masking schemes on the convergence performance. The findings advance the understanding of over-the-air federated learning and provide useful insights for system designs.