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This report presents our approach for the IEEE SP Cup 2025: Deepfake Face Detection in the Wild (DFWild-Cup), focusing on detecting deepfakes across diverse datasets. Our methodology employs advanced backbone models, including MaxViT, CoAtNet, and EVA-02, fine-tuned using supervised contrastive loss to enhance feature separation. These models were specifically chosen for their complementary strengths. Integration of convolution layers and strided attention in MaxViT is well-suited for detecting local features. In contrast, hybrid use of convolution and attention mechanisms in CoAtNet effectively captures multi-scale features. Robust pretraining with masked image modeling of EVA-02 excels at capturing global features. After training, we freeze the parameters of these models and train the classification heads. Finally, a majority voting ensemble is employed to combine the predictions from these models, improving robustness and generalization to unseen scenarios. The proposed system addresses the challenges of detecting deepfakes in real-world conditions and achieves a commendable accuracy of 95.83% on the validation dataset., Comment: Technical report for IEEE Signal Processing Cup 2025, 7 pages

Several data source discovery methods take into account the semantic heterogeneity problems by using several Domain Ontologies (DOs). However, most of them impose a topology of mapping links between DOs. DOs and mapping links are available on Internet but with an arbitrary topology. In this paper, we propose a data source Discovery method Adapted to any Mapping links Topology (DAMT) and taking into account semantic problems. Peers using the same DO are grouped in a Virtual Organization (VO) and connected in a Distributed Hash Table (DHT). Lookups within a same VO consists in a classical search in a DHT. Regarding the inter-VO discovery process, we propose an addressing system, based on the existing mapping links between DOs, to interconnect VOs. Furthermore, we adopt a lazy maintenance in order to reduce the number of messages required to update the system due to the dynamicity of peers. The performance analysis of the proposed method shows good results for inter-VO lookup queries. Also, it confirms a significant maintenance cost reduction when peers join and leave the system., Comment: IRIT report research (2014)

Metallic gratings can be used as infrared filters, but their performance is often limited by bandwidth restrictions due to metallic losses. In this work, we propose a metallic groove-slit-groove (GSG) structure that overcomes these limitations by exhibiting a large bandwidth, angularly independent, extraordinary optical transmission. Our design achieves high transmission efficiency in the longwave infrared range, driven by Fano-type resonances created through the interaction between the grooves and the central slit. This mechanism results in a tunable 2 $\mu$m transmission window with high rejection rate. We extend the concept to a two-dimensional GSG array, exhibiting a polarization insensitive 80% transmission window for incident angles up to 50{\deg}, offering significant potential for infrared filtering applications.

Evaluation of join queries is very challenging since they have to deal with an increasing data size. We study the relational join query processing realized by hash tables and we focus on the case of equi join queries. We propose to use a new form of signatures, the algebraic signatures, for fast comparison between values of two attributes in relations participating in an equi join operations. Our technique is efficient especially when the attribute join is a long string. In this paper, we investigate this issue and prove that algebraic signatures combined to known hash join technique constitute an efficient method to accelerate equi join operations. Algebraic signatures allow fast string search. They are descending from the Karp-Rabin signatures. String matching using our algebraic calculus is then several times faster comparing to the fastest known methods, e.g. Boyer Moore.We justify our approach and present an experimental evaluation. We also present a cost analysis for an equi join operation using algebraic signatures. The performance evaluation of our technique shows the improvement of query processing times. We also discuss the reductions of required memory sizes and the disk I/O. The main contribution of this paper is the using of algebraic signatures to accelerate equi join operations especially when the attribute join is a long string and to avoid multiples I/O disk by reduce memory requirement., Comment: Published on April 2008 in IADIS Applied Computing, international association for the development of the information society

The CEEMDAN algorithm is one of the modern methods used in the analysis of non-stationary signals. This research presents a study of the effectiveness of this method in audio source separation to know the limits of its work. It concluded two conditions related to frequencies and amplitudes of mixed signals to be separated by CEEMDAN. The performance of the algorithm in separating noise from speech and separating speech signals from each other is studied. The research reached a conclusion that CEEMDAN can remove some types of noise from speech (speech improvement), and it cannot separate speech signals from each other (cocktail party). Simulation is done using Matlab environment and Noizeus database., Comment: in Arabic language

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings., Comment: 14 pages, 6 figures, 10 tables

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to various machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95\%. Additionally, we obtained high accuracy from Support Vector Machine (99.79\%), Random Forest (99.73\%), and Naive Bayes (95.93\%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development., Comment: I am requesting the withdrawal of my paper due to critical issues identified in the methodology/results that may impact its accuracy and reliability. I also plan to make substantial revisions that go beyond minor corrections

We performed transverse-field muon spin relaxation/rotation (TF-{\mu}SR) on a high-entropy-type (HE-type) superconductor AgInSnPbBiTe5. The emergence of bulk superconducting states was confirmed from magnetic susceptibility, specific heat, and {\mu}SR. The superconducting gap 2{\Delta}(0) estimated from {\mu}SR was clearly larger than that expected from conventional weak-coupling phonon-mediated model, suggesting the strong-coupling nature of superconductivity. In addition, a long penetration depth of 3.21(7) {\mu}m was obtained. The strong-coupling nature of superconductivity and the long penetration depth are similar to the trends observed in the other HE-type superconductors (HE alloys and transition-metal zirconides), which may be universal feature of HE-type superconductors., Comment: 14 pages, 4 figures, Supplementary information

Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL).

Non-invasive methods for diagnosing mental health conditions, such as speech analysis, offer promising potential in modern medicine. Recent advancements in machine learning, particularly speech foundation models, have shown significant promise in detecting mental health states by capturing diverse features. This study investigates which pretext tasks in these models best transfer to mental health detection and examines how different model layers encode features relevant to mental health conditions. We also probed the optimal length of audio segments and the best pooling strategies to improve detection accuracy. Using the Callyope-GP and Androids datasets, we evaluated the models' effectiveness across different languages and speech tasks, aiming to enhance the generalizability of speech-based mental health diagnostics. Our approach achieved SOTA scores in depression detection on the Androids dataset., Comment: 6 pages, 4 figures

Large Language Models (LLMs) are the cornerstones of modern artificial intelligence systems. This paper introduces Juhaina, a Arabic-English bilingual LLM specifically designed to align with the values and preferences of Arabic speakers. Juhaina inherently supports advanced functionalities such as instruction following, open-ended question answering, information provisioning, and text processing. Our model contains 9.24 billion parameters and is trained on a context window of up to 8,192 tokens. This paper details the creation process of Juhaina and provides an extensive empirical evaluation. Furthermore, we identify the limitations of widely-adopted Open Arabic LLM Leaderboard (OALL) and propose a new evaluation benchmark, CamelEval. Our findings demonstrate that Juhaina surpasses existing LLMs of comparable sizes, such as the Llama and Gemma families, in generating helpful responses in Arabic, providing factually accurate information about the region, and understanding nuanced cultural aspects. We aspire for Juhaina to democratize cutting-edge AI technologies, serving over 400 million Arabic speakers by offering LLMs that not only communicate in their language but also comprehend their culture. We publicly release all models on Huggingface \url{https://huggingface.co/elmrc}.

Deep learning has achieved outstanding accuracy in medical image segmentation, particularly for objects like organs or tumors with smooth boundaries or large sizes. Whereas, it encounters significant difficulties with objects that have zigzag boundaries or are small in size, leading to a notable decrease in segmentation effectiveness. In this context, using a loss function that incorporates smoothness and volume information into a model's predictions offers a promising solution to these shortcomings. In this work, we introduce an Adaptive Focal Loss (A-FL) function designed to mitigate class imbalance by down-weighting the loss for easy examples that results in up-weighting the loss for hard examples and giving greater emphasis to challenging examples, such as small and irregularly shaped objects. The proposed A-FL involves dynamically adjusting a focusing parameter based on an object's surface smoothness, size information, and adjusting the class balancing parameter based on the ratio of targeted area to total area in an image. We evaluated the performance of the A-FL using ResNet50-encoded U-Net architecture on the Picai 2022 and BraTS 2018 datasets. On the Picai 2022 dataset, the A-FL achieved an Intersection over Union (IoU) of 0.696 and a Dice Similarity Coefficient (DSC) of 0.769, outperforming the regular Focal Loss (FL) by 5.5% and 5.4% respectively. It also surpassed the best baseline Dice-Focal by 2.0% and 1.2%. On the BraTS 2018 dataset, A-FL achieved an IoU of 0.883 and a DSC of 0.931. The comparative studies show that the proposed A-FL function surpasses conventional methods, including Dice Loss, Focal Loss, and their hybrid variants, in IoU, DSC, Sensitivity, and Specificity metrics. This work highlights A-FL's potential to improve deep learning models for segmenting clinically significant regions in medical images, leading to more precise and reliable diagnostic tools., Comment: 15 pages, 4 figures

Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement and interpret. However, in practice, exploration is largely performed by stochastic policies. First-order Bayesian Optimization (BO) methods offer a principled way of performing exploration using deterministic policies. This is done through a learned probabilistic model of the objective function and its gradient. Nonetheless, such approaches treat policy search as a black-box problem, and thus, neglect the reinforcement learning nature of the problem. In this work, we leverage the performance difference lemma to introduce a novel mean function for the probabilistic model. This results in augmenting BO methods with the action-value function. Hence, we call our method Augmented Bayesian Search~(ABS). Interestingly, this new mean function enhances the posterior gradient with the deterministic policy gradient, effectively bridging the gap between BO and policy gradient methods. The resulting algorithm combines the convenience of the direct policy search with the scalability of reinforcement learning. We validate ABS on high-dimensional locomotion problems and demonstrate competitive performance compared to existing direct policy search schemes., Comment: Accepted to the International Conference on Learning Representations (ICLR) 2024

Recently, huge uniaxial negative thermal expansion (NTE) along a $c$-axis has been observed in transition-metal ($Tr$) zirconides $Tr$Zr$_{2}$ with a tetragonal CuAl$_{2}$-type structure. In a recent study on FeZr$_{2}$ [M. Xu et al., Nat. Commun. 14, 4439 (2023)], the importance of optical phonons to the emergence of the $c$-axis NTE in FeZr$_{2}$ has been proposed. In this study, the physical properties of $Tr$Zr$_{2}$ ($Tr$ = Fe and Co$_{1-x}$Ni$_{x}$) have been studied by specific heat, sound velocity measurements, and theoretical phonon calculations to discuss the importance of optical phonons to the emergence of the $c$-axis NTE in CoZr$_{2}$ and FeZr$_{2}$. From analyses of lattice specific heat, we found that Ni substitution results in a systematic decrease in oscillator strength for the Einstein modes with 8.74 meV (CoZr$_{2}$). From phonon calculations, the low-energy optical phonon branches at the $\Gamma$ point were observed for CoZr$_{2}$ and FeZr$_{2}$ with $c$-axis NTE, but not in NiZr$_{2}$ with positive thermal expansion. The enhancement of phonon density of states near the above-mentioned optical phonon energy in CoZr$_{2}$ and FeZr$_{2}$ is consistent with the specific heat analyses. We propose the importance of the low-energy optical phonons to the emergence of the $c$-axis NTE in TrZr$_{2}$., Comment: 14 pages, 9 figures, and 3 tables

Requirement traceability is the process of identifying the inter-dependencies between requirements. It poses a significant challenge when conducted manually, especially when dealing with requirements at various levels of abstraction. In this work, we propose a novel approach to automate the task of linking high-level business requirements with more technical system requirements. The proposed approach begins by representing each requirement using a Bag of-Words (BOW) model combined with the Term Frequency-Inverse Document Frequency (TF-IDF) scoring function. Then, we suggested an enhanced cosine similarity that uses recent advances in word embedding representation to correct traditional cosine similarity function limitations. To evaluate the effectiveness of our approach, we conducted experiments on three well-known datasets: COEST, WARC(NFR), and WARC(FRS). The results demonstrate that our approach significantly improves efficiency compared to existing methods. We achieved better results with an increase of approximately 18.4% in one of the datasets, as measured by the F2 score.

Deep reinforcement learning agents are prone to goal misalignments. The black-box nature of their policies hinders the detection and correction of such misalignments, and the trust necessary for real-world deployment. So far, solutions learning interpretable policies are inefficient or require many human priors. We propose INTERPRETER, a fast distillation method producing INTerpretable Editable tRee Programs for ReinforcEmenT lEaRning. We empirically demonstrate that INTERPRETER compact tree programs match oracles across a diverse set of sequential decision tasks and evaluate the impact of our design choices on interpretability and performances. We show that our policies can be interpreted and edited to correct misalignments on Atari games and to explain real farming strategies.

Magnetic flux trapping in field-cooled (FC) Sn-Pb solders has been recently studied because of the observation of nonvolatile magneto-thermal switching [H. Arima et al., Commun. Mater. 5, 34 (2024)] and anomalous magnetic field-temperature (H-T) phase diagrams [T. Murakami et al., AIP Adv. 13, 125008 (2023)]. In this paper, we investigate the origin of the anomalously low specific heat (C) in Sn10-Pb90 and Sn45-Pb55 solders after FC at H = 1500 Oe. We show that the FC solders exhibit self-heating possibly caused by the flux flow during the reduction of trapped fluxes when heating the sample during the C measurements. The T dependence of T rise clearly exhibits unexpectedly large values when the low-C states are observed. In addition, the cause of the transition-like behavior in C-T of FC solders are explained by local heating during H control and flux-jump phenomena., Comment: 19 pages, 7 figures, Supplemental data. arXiv admin note: text overlap with arXiv:2403.01719

Formation of vortices is a typical phenomenon of type-II superconductors under magnetic fields (H). In contrast, type-I superconductors do not host vortices because of their Meissner state. As rare cases, vortices have been observed in intermediate states of type-I superconductors; in addition, the recent observation of type-II superconductivity with vortices in a (originally type-I) Pb crystal film at extremely low temperature (T) under H has opened new pathway to study vortex physics. However, thermodynamic characteristics of such type-II-like superconducting states with vortices in originally type-I element superconductors have not been detected because of the lack of bulk example. In this study, we investigated superconducting states of phase-separated Sn-Pb solders using specific heat and magnetization to reveal magnetic-flux-trapping mechanisms. Here, we show that Sn islands in the Sn-Pb solders exhibit type-II-like superconducting states with vortices when the solders host extremely high magnetic fluxes after field cooling. Furthermore, with increasing T, the amount of trapped flux decreases, and the driving force of the magnetic flux changes from type-II superconducting states of Sn to supercurrent of Pb regions surrounding the Sn islands. The field-cooled Sn-Pb solders are rich in physics of bulk vortex formation and anomalously enhanced supercurrent in element (originally type-I) superconductors., Comment: We found a problem on the analysis of specific heat data because of possible self-heating of the measured solders when flux reduction. We will resubmit the data after careful analyses

The classical Hertzian contact model establishes a monotonic correlation between contact force and area. Here, we showed that the interplay between local friction and structural instability can deliberately lead to unconventional contact behavior when a soft elastic shell comes into contact with a flat surface. The deviation from Hertzian contact first arises from bending within the contact area, followed by the second transition induced by buckling, resulting in a notable decrease in the contact area despite increased contact force. Friction delays both transitions and introduces hysteresis during unloading. However, a high amount of friction suppresses both buckling and dissipation. Different contact regimes are discussed in terms of rolling and sliding mechanisms, providing insights for tailoring contact behaviors in soft shells.

Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behaviour of agents in autonomous intelligent systems with human values. However, the current literature is limited to the incorporation of effective norms for single-value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms and decentralised reasoning to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them., Comment: Accepted in MODeM 2023 Workshop at ECAI 2023. arXiv admin note: substantial text overlap with arXiv:2305.07366

The UK continues to face a huge adult-literacy challenge, with a sizeable percentage of the UK adult population having very low literacy skills. In addition to highlighting the literacy challenge in the UK, this report shows these individuals experience negative impacts on personal relationships, wellbeing, health, and education, as well as a greater risk of unemployment or being in low-paid work. In addition to many people struggling with low literacy skills, the report also showed that there are also many negative perceptions around, and attitudes towards, literacy. If more action is not taken to tackle low literacy in adults, there is the risk of perpetuating and reinforcing an intergenerational cycle of disadvantage.