Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming Hardware Imperfections in Optical Neural Networks Through a Machine Learning-Driven Self-Correction Mechanism

We developed an optical neural network (ONN) for efficient processing and recognition of 2-dimensional (2D) images, employing a conventional liquid crystal display panel as optical neurons and synapses. This configuration allowed for optical signal outputs proportional to matrix-vector multiplication for 2D image inputs. However, our experimental results revealed a 26.6% decrease in the optical classification accuracy, despite utilizing digitally pre-trained parameters with 100% accuracy for 500 handwritten digits. This decline can be attributed to system imperfections associated with non-ideal functions of optical components and optical alignment. Rather than pursuing an elusive, imperfection-free ONN or attempting to calibrate these defects individually, we addressed these challenges by introducing a self-correction mechanism that utilizes a machine learning algorithm. This approach effectively restored the recognition accuracy and minimized loss of our ONN to levels comparable to the digitally pre-trained model. This study underscores the potential of constructing defect-tolerant hardware in ONNs through the application of machine learning techniques.
Electrostatic Charge Injection for Sustainable Face Mask Reuse: Mechanisms and Performance Enhancement

The COVID-19 pandemic has underscored the necessity for sustainable mask usage and supply during critical periods. While disposable masks effectively curb virus transmission, they pose substantial environmental challenges. We propose a mask reuse strategy involving mask disinfection and recharging to mitigate mask waste pollution and bolster mask availability. We explore three charge reinjection methods to restore filtration performance in disinfected masks, shedding light on their impact on charge levels and filtration efficiency. A method combining corona discharge and surface charge replenishment emerges as the optimal solution, with only a 2.5% decrease in filtration efficiency during an 8-hour testing cycle. This study pioneers effective mask reuse, promoting environmental sustainability and alleviating mask supply pressures.
Experimental Visualization of the LoRaWAN Variable Correlation in Jakarta

This work presents empirical research into the efficacy of LoRaWAN, a wireless communication protocol that enables data transmission over extensive distances while minimizing power consumption and cost. This research aims to understand the correlation between transmission and connectivity within the LoRaWAN framework. Prior research on LoRaWAN has not investigated the influence of factors such as building height above sea level (MASL), elevation coordinate during the driving test, elevation gateway position coordinate point, distance from transmission transmitter to receiver, Signal-to-Noise Ratio (SNR), and Received Signal Strength Indicator (RSSI) on coverage status about environmental complexity. Hence, this study gathered data through a driving test utilizing a GlobalSat tracker with the identification numbers (K02188, K02204, and K02205) to examine these variables’ impact on LoRaWAN performance. The trial results consist of logarithmic and historical data collected at specific gateway locations in Jakarta. The RSSI measurements, which indicate the strength of the signal, varied from -127 dB to 60 dB, with an average Signal-to-Noise Ratio (SNR) of 7 dB. The coverage status is classified into different categories, including Excellent (12.55%), Acceptable (16.57%), Good (19.86%), Poor (23.98%), and Very Poor (27.01%). The correlation variable analysis reveals that the coverage status is highly correlated with the independent variables. Provides useful insight into the areas available for LoRaWAN performance and reliability as a technology that will support digital smart meter deployment in challenging and complex environments. Correlation variables provide information about the elements that influence coverage status.
Matching Words for Out-of-distribution Detection

Deep neural networks often exhibit the overconfidence issue when encountering out-of-distribution (OOD) samples. To address this, leveraging large-scale pre-trained models like CLIP has shown promise. While CLIP has the capability to encode a vast array of interconnected concepts, current OOD detection methods based on it primarily focus on ID categories and a limited set of OOD categories. In this paper, we propose a novel approach that harnesses the power of WordNet to fully exploit the rich knowledge encapsulated within CLIP, resulting in enhanced OOD detection performance. Our methodology involves constructing a word tree that includes both in-distribution (ID) words and a large set of semantically similar OOD words selected from WordNet. By matching a test image with the concepts of the words in the word tree using CLIP, we estimate the probability of the image being classified as either ID or OOD. Furthermore, we introduce a conditional random field model to effectively handle both the parent-child and the sibling-sibling conflicts in the concept matching results. Extensive experiments under various ID/OOD settings demonstrate the effectiveness of our approach and its superiority over state-of-the-art methods.
Practical Verification of Superconducting Feeder Cable on Commercial Rail Lines

In this paper, we report that the commercial operation on passengers has begun using superconducting cable for railway. Superconducting materials are expected to have a wide range of applications, including zero-resistance power transmission and magnetic field applications. By making superconducting materials into cables for railway power transmission lines, it is possible to improve regeneration efficiency, reduce power loss, and equalize the load between substations. With the aim of applying superconducting feeder cables to railways, we have so far conducted prototype superconducting cables, cooling tests, and conducting tests. It has been also introduced a superconducting feeding system to commercial lines and successfully conducted train running tests without passengers. Since then, we have been acquiring data under various conditions in the commercial line. Now that it has been obtained technical confirmation, we have been able to obtain government approval to conduct commercial operation on passengers using superconducting cables. And, it has been developed a new 4-cylinder cryocooler to enable commercial operation using a superconducting feeding system. This time, it has been begun the world's first operation using superconducting cables for railway on commercial lines.
Enhancing Synthesized Speech Detection with Dual Attention Using Features Fusion

Automatic Speaker Verification (ASV) is a system based on speech recognition used for identity verification. However, with the continuous improvement of synthetic speech generation technology, the quality of generated speech is also getting higher and higher. This allows some malicious actors to use synthetic speech to deceive, which poses a serious threat to ASV. Therefore, we need to continuously improve the authenticity identification to cope with this challenge. Although many synthetic speech detection algorithms have been proposed, their generalization performance is still not ideal. This means that the performance of ASV systems may suffer in the face of previously unseen synthetic speech attacks. Therefore, researchers need to further explore new methods for detecting synthetic speech attacks and strengthen the robustness of ASV systems against synthetic speech attacks. In order to extract more abundant and reliable speech features, this paper proposes a dual-attention network, specifically combining the features extracted by Wav2vec with traditional speech features Logmel through self-attention, and inputting them into a Resnet network with convolutional block attention module (CBAM). The results have shown that our method has achieved a very competitive performance on the Asvspoof 2021 LA and DF datasets, with a t-DCF of 0.3008 on LA and an EER of 3.9% on DF, indicating good generalization performance.
Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model

Career mobility forecasting and recommendation are important topics in talent management research. While existing models have extensively covered short-term, single-period recommendations and long-term, unconstrained career path suggestions, the user preference-constrained career path optimization problem remains underexplored. This paper addresses the common scenario where individuals have approximate career plans and seek to optimize their career trajectories by incorporating specific user preferences. We develop an exploration space-aware stochastic searching algorithm that incorporates a deep learning-guided searching space determination module and a position transit prediction module. We mathematically demonstrate its strengths in exploring optimal path solutions with fixed components predefined by users. Finally, we empirically validate the superiority of our method using a comprehensive real-world dataset, comparing it against state-of-the-art approaches.
Can the Cans: Determinants of Container Deposit Behavior before and after Introduction of a Container Refund Scheme

Objective: Container deposit schemes are often hailed as a useful avenue to increase consumer recycling rates. Yet, there is little research investigating within-person changes in people’s beliefs and behavior following the implementation of these schemes, or tests of the mechanisms by which such change has occurred. Methods: The current study fills this knowledge gap and assessed container recycling behavior and habits as well as the social cognition factors of attitudes, subjective norms, perceived behavioral control, and intentions in a sample of 90 Queenslanders before the implementation of the container deposit scheme and one and three months post-implementation. Results: Analysis of variance indicated more frequent recycling behavior following the implementation of the scheme, as well as stronger habits, intentions, and perceived behavioral control. Conclusions: Such a concomitant change in behavior, beliefs, and habits provides support for behavior change theory, while also flagging potential targets for strategies that can be paired with container deposit schemes to enhance their efficacy and uptake.
A Practical Clean-Label Backdoor Attack with Limited Information in Vertical Federated Learning

Vertical Federated Learning (VFL) facilitates collaboration on model training among multiple parties, each owning partitioned features of the distributed dataset. Although backdoor attacks have been found as one of the main threats to FL security, research on backdoor attacks in VFL is still in the infant stage. Existing methods for VFL backdoor attacks rely on predicting sample pseudo-labels using approaches such as label inference, which require substantial additional information not readily available in practical FL scenarios. To evaluate the practical vulnerability of VFL to backdoor attacks, we present a target-efficient clean backdoor (TECB) attack for VFL. The TECB approach consists of two phases – i) Clean Backdoor Poisoning (CBP) and Target Gradient Alignment (TGA). In the CBP phase, the adversary trains a backdoor trigger and poisons the model during VFL training. The poisoned model is further fine-tuned in the TGA phase to enhance its efficacy in complex multi-classification tasks. Compared to the existing methods, the proposed TECB achieves a highly effective backdoor attack with very limited information about the target class samples, which is more practical in typical VFL settings. Experimental results verify the superior performance of TECB, achieving above 97% attack success rate (ASR) on three widely used datasets (CIFAR10, CIFAR100, and CINIC-10) with only 0.1% of target labels known, which outperforms the state-of-the-art attack methods. This study uncovers the potential backdoor risks in VFL, enabling the development of secure VFL applications in areas like finance, healthcare, and beyond. Source code is available at: https://github.com/13thDayOLunarMay/TECB-attack
Security Applications of GPUs

Despite the recent advances in software security hardening techniques, vulner-abilities can always be exploited if the attackers are really determined. Regardless the protection enabled, successful exploitation can always be achieved, even though admittedly, today, it is much harder than it was in the past. Since securing software is still under ongoing research, the community investigates detection methods in order to protect software. Three of the most promising such methods are monitoring the (i) network, (ii) the filesystem, and (iii) the host memory, for possible exploitation. Whenever a malicious operation is detected then the monitor should be able to terminate it and/or alert the administrator. In this chapter, we explore how to utilize the highly parallel capabilities of modern commodity graphics processing units (GPUs) in order to improve the performance of different security tools operating at the network, storage, and memory level, and how they can offload the CPU whenever possible. Our results show that modern GPUs can be very efficient and highly effective at accelerating the pattern matching operations of network intrusion detection systems and antivirus tools, as well as for monitoring the integrity of the base computing systems.
A Systematic Review of the Management of Knee Osteoarthritis by Proximal Fibular Osteotomy in the Indian Population

This systematic review aims to assess the management of knee osteoarthritis through proximal fibular osteotomy (PFO) in the Indian population by synthesizing data from various prospective cohort and interventional studies. We seek to provide an overview of the effectiveness and safety of PFO as a treatment modality and offer insights into its potential implications for clinical practice in India. A systematic search strategy was employed, targeting multiple medical databases to identify relevant studies published from 2018 to 2023. Inclusion criteria encompassed studies involving Indian patients with medial compartment knee osteoarthritis and varus deformity who underwent PFO. Data were extracted and evaluated according to the Newcastle-Ottawa Scale for observational studies. Eight studies were included in this review, each displaying varying designs, patient populations, and follow-up duration. The findings consistently indicated that PFO improved pain, knee function, and radiological outcomes, such as knee joint space and tibio-femoral angles. These improvements were generally sustained over several months to a year. The available evidence underscores the potential of PFO as a promising intervention for managing knee osteoarthritis in the Indian population, particularly in patients with medial compartment involvement and varus deformity. While these results are promising, the limitations inherent in the current literature, including study design variations and small sample sizes, necessitate further research with more extensive and diverse patient populations. This systematic review provides valuable insights for healthcare professionals and researchers, highlighting the need for more rigorous investigations and supporting the consideration of PFO as a viable treatment option for knee osteoarthritis in India.
Characterizing Postural Stability in a Quasi-Static Sitting Position among Individuals with Sensorimotor Impairments Following Spinal Cord Injury

The objective of this study was to determine a minimum data set of postural measures to characterize seated stability in individuals with spinal cord injury (SCI) by computing 39 Center-Of-Pressure (COP) measures routinely investigated in standing posture. Two short-sitting positions on an instrumented seat with the feet resting on force plates were compared between 14 individuals with SCI and 14 healthy controls: 1) with both hands on their thighs and 2) with both upper extremities flexed at 70o and abducted at 45o. The correlations between all COP measures for the resultant, anteroposterior and mediolateral components were also computed. Differences in seated stability were observed between individuals with SCI and healthy controls, irrespective of the tasks. More precisely, the bilateral hand support was confirmed to be an effective strategy to compensate for anterior instability in individuals with SCI. As anticipated, time domain distance and frequency domain measures revealed complementary information. Distance and area COP measures were highly correlated with each other (i.e., redundant information) but were not correlated with frequency and hybrid measures. For both groups (between-task comparisons), the most discriminative uncorrelated measures were related to frequency parameters (i.e., independent information). Overall, our analyses revealed that a minimal data set of postural measures should include mean distance, mean velocity, centroidal frequency, median power frequency and frequency dispersion. These measures should be reported for all directional components whenever applicable, as both anteroposterior and mediolateral activities independently contribute to the resultant COP outcome measures.
Plant leaf diseases classification using Deep Learning

As the world’s population expands, securing global food security has taken on greater importance. The early detection and prevention of plant diseases, which can have catastrophic impacts on agricultural yields and food supplies, is one of the biggest challenges facing farmers. The ability to automatically identify and categorize plant diseases based on visual signs has been made possible in recent years thanks to advancements in computer vision and deep learning.This article examines the most recent deep learning models and image processing techniques for computer vision-based plant disease classification.The Xception and GoogleNet networks are linked to Global Average Pooling, and then the outputs are combined and passed to the classification phase to give a single neural network. The suggested network’s total average accuracy across all classes is 98.76%.
Diagnosing and Managing Uveitis Associated with Immune Checkpoint Inhibitors: A Review

This review aims to provide an understanding of the diagnostic and therapeutic challenges of uveitis associated with immune checkpoint inhibitors (ICI). In the wake of these molecules being increasingly employed as a treatment against different cancers, cases of uveitis post-ICI therapy have also been increasingly reported in the literature, warranting an extensive exploration of the clinical presentations, risk factors, and pathophysiological mechanisms of ICI-induced uveitis. This review further provides an understanding of the association between ICIs and uveitis, and assesses the efficacy of current diagnostic tools, underscoring the need for advanced techniques to enable early detection and accurate assessment. Further, it investigates the therapeutic strategies for ICI-related uveitis, weighing the benefits and limitations of existing treatment regimens, and discussing current challenges and emerging therapies in the context of their potential efficacy and side effects. Through an overview of the short-term and long-term outcomes, this article suggests recommendations and emphasizes the importance of multidisciplinary collaboration between ophthalmologists and oncologists. Finally, the review highlights promising avenues for future research and development in the field, potentially informing transformative approaches in the ocular assessment of patients under immunotherapy and the management of uveitis following ICI therapy.
Variation de la fonction $L$ $p$-adique par isogénie

Soit p un nombre premier impair. Soit W une representation padique de Gal(Q/Q), c'est-a-dire un QP-espace vectoriel de dimension finie sur lequel Gal(Q/Q) agit de maniere continue. Soit Q~ la ZP-extension cyclotomique de Q de groupe de Galois I' sur Q. Choisissons un reseau L de W stable par Gal(Q/Q). Greenberg construit a partir de ces donnees un module d'Iwasawa sur l'algebre Zp[[I']] (dont nous rappellerons la construction). Si on fait l'hypothese qu'il est de ZP[[I']]-torsion, ce qui est une conjecture sous des hypotheses raisonnables, on definit (a une unite pres) un element fL de ZP[[I']] que l'on appelle sa serie caracteristique. Soit μ(L) l'ordre de la plus grande puissance de p divisant fL, c'est-a-dire le μ-invariant de fLNous etudions ici la variation de μ(L) et done de JL lorsque L varie (W etant fixe). Cela nous permettra ulterieurement de comparer cette variation avec celle de periodes p-adiques et pourra etre utile pour formuler une conjecture raisonnable liant les series caracteristiques aux fonctions L p-adiques interpolant des valeurs speciales de fonctions L. Dans le cas des representations p-adiques attachees a une variete abelienne ayant bonne reduction ordinaire en toute place au dessus de p, cette etude a ete faite dans [3] et dans [4]. Nous reprenons ici les methodes de [3] utilisant les theoremes classiques de dualite locale de Tate et globale de Poitou et Tate.
Unreliable Pixel Contrast Based on von Mises–Fisher Distribution for Semi-Supervised SAR Segmentation

The unique visual properties and the huge size of synthetic aperture radar (SAR) images pose challenges in labeling data. The scarcity of labeled data limits the training of SAR image segmentation networks. To address this issue, semi-supervised methods are used to train the network. However, traditional semi-supervised algorithms, such as Mean Teacher, often generate erroneous pseudo-labels that carry over to subsequent training epochs, leading to overfitting and affecting network performance. This overfitting stems from an overreliance on unreliable pixels in Mean Teacher. In this study, an enhanced approach is proposed called unreliable pixel contrast (UPCo), where a von Mises–Fisher (vMF) distribution is applied to constrain unreliable pixels in feature space. We augment the segmentation network with a feature output header for pixel-level contrastive learning in UPCo. Moreover, to minimize the computational effort during the training phase, hard sample selection (HSS) and negative object nonuniform selection (NONS) strategies are designed to facilitate contrastive learning. The proposed UPCo was evaluated on two large-scene SAR images and demonstrated its superiority over other comparative algorithms, achieving more optimal performance in semi-supervised segmentation of SAR images.
Une approche opérationnelle pour la production de modèles 3D urbains

3D city models production is a difficult and expensive problem. Automatic processes in the production lines seem to be necessary to produce 3D city models at reasonable costs. This paper presents a 3D reconstruction software platform, based on several algorithms developed in the MATIS laboratory, which deals with aerial or space images and cadastral maps. Due to landscape complexities and disparities, the choice of a unique and efficient production way is not possible. In our approach, the operator manages his strategy and combines as he wants the different algorithm modules. Step by step a standard scenario seems to be identified. Tests have been carried out with two different input data contexts: first, using IGN digital camera images, and then, with Pleiades-HR simulations.
SDN/NFV Security Challenges and Proposed Architecture

Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) have revolutionized network management and service delivery. However, their dynamic and virtualized nature introduces new security challenges. In this scientific article, we propose a comprehensive security architecture that combines both SDN and NFV to address the evolving threat landscape. We discuss key security considerations, architectural components, and highlight the importance of integrating security measures at each layer. The proposed architecture aims to provide a robust security framework for SDN and NFV deployments, enhancing network resilience and protecting critical assets.