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The article "Some Important Facts about Language Teaching/Learning: Basics and Assumptions" by Dr. Mahmoud M. S. Abdallah explores the foundational elements of language teaching and learning. It emphasizes the importance of language proficiency for personal, social, and professional success, highlighting the cognitive and societal benefits of bilingualism and multilingualism. The article discusses various approaches and methodologies in language teaching, such as Communicative Language Teaching (CLT), which focuses on meaningful communication and interaction. It also addresses the role of teachers as facilitators and the significance of using authentic materials and technology in language instruction. The article examines underlying assumptions in language teaching, such as the importance of grammar instruction and the role of input and output in language acquisition. Additionally, it emphasizes the need for learner autonomy and culturally responsive teaching practices. The article concludes by outlining principles of effective language teaching and addressing challenges and controversies in the field, such as the role of native language, the optimal age for language learning, and the impact of technology. Overall, the article provides a comprehensive guide to understanding the complexities of language teaching and learning, offering valuable insights for educators, policymakers, and language learners.

The landscape of educational research necessitates sophisticated approaches to scholarly communication, demanding researchers to adeptly navigate diverse methodological traditions. This article provides systematic guidelines for academic writing that transcend methodological boundaries, offering practical strategies for crafting clear, ethical, and impactful scholarly work. Emphasizing the importance of structure, style, and scholarly conventions, the article addresses the complexities of quantitative, qualitative, and mixed-methods research. Key guidelines include adherence to ethical standards, clarity and conciseness in communication, and proper citation practices to maintain academic integrity. The article also offers insights into adapting dissertations and theses into journal articles, highlighting the challenges and opportunities for early-career researchers. As educational research evolves, the need for effective academic writing remains paramount, ensuring that valuable insights are shared and built upon within the scholarly community. By fostering excellence in academic writing, researchers can significantly contribute to the advancement of educational theory and practice.

The global status of English as a lingua franca necessitates effective English language teaching (TEFL) to meet the demands of the global economy and social development. This article explores key principles for effective TEFL, grounded in a sophisticated theoretical framework that integrates cognitive, sociocultural, and constructivist perspectives. It emphasizes the importance of understanding learner needs, focusing on meaningful communication, and implementing Communicative Language Teaching (CLT) and Task-Based Language Teaching (TBLT). Differentiated instruction and technology integration are highlighted as essential strategies to cater to diverse learner profiles and enhance engagement. The article also underscores the significance of cultural awareness in language instruction, advocating for the inclusion of cultural elements to prepare students for real-world interactions. Effective assessment practices, combining formative and summative approaches, are crucial for monitoring progress and informing instruction. Continuous professional development (CPD) is emphasized as vital for teachers to remain current and effective, involving action research, participation in professional learning communities, and reflective teaching practices. By adhering to these principles, educators can create dynamic and inclusive classrooms that promote successful language acquisition.

Dr Mahmoud M. S. Abdallah's article, "The New Territory of Educational Research in TESOL/TEFL: What Novice Researchers Should Know", provides a comprehensive guide for novice researchers in Teaching English to Speakers of Other Languages (TESOL) and Teaching English as a Foreign Language (TEFL). It emphasises the importance of research in enhancing teaching practices, informing educational policies, and understanding second language acquisition (SLA). It defines research as a systematic, cyclical process aimed at discovering new knowledge or validating existing knowledge. The article explores various research approaches, including qualitative, quantitative, mixed methods, and action research. Qualitative research is highlighted for its ability to explore human experiences through methods like ethnography, case studies, and discourse analysis. Quantitative research is presented for testing hypotheses and generalising findings through experimental, quasi-experimental, and survey research. Mixed methods research combines both approaches for a holistic understanding, while action research empowers teachers to become researchers in their own classrooms. The article introduces essential research terminologies, such as variables, hypotheses, data, reliability, validity, and generalisability. It outlines the research process, from identifying a problem to writing the report, emphasising the importance of thorough literature reviews, clear research questions, appropriate research designs, data collection and analysis, and findings interpretation. The article encourages TESOL/TEFL educators to embrace research as part of their professional identity, becoming informed consumers and producers of knowledge. It provides criteria for selecting research topics, including relevance, originality, feasibility, personal interest, potential impact, alignment with current educational contexts, and interdisciplinary potential. In conclusion, the article equips novice researchers with the knowledge and tools necessary to engage in rigorous and impactful research, contributing to the continuous improvement of language education.

The literature review is a pivotal component in educational research studies, serving as a bridge between existing knowledge and the research being undertaken. This article examines the multifaceted role and function of the literature review from a pragmatic perspective, highlighting its significance in advancing knowledge and informing educational practice. A comprehensive literature review establishes the research territory by situating the study within a broader intellectual context, demonstrating the researcher's command of the field, and illuminating the path for their own investigation. It justifies the research undertaking by identifying gaps, inconsistencies, or unanswered questions in the existing literature, thereby establishing the rationale for the study. Moreover, the literature review shapes the research problem and questions by critically evaluating previous studies and refining research focus. It informs methodological choices by showcasing the strengths and weaknesses of different research designs and analytical approaches. Even after data collection and analysis, the literature review provides a framework for interpreting the study's findings, placing them in dialogue with existing knowledge. By embracing a pragmatic lens, researchers can harness the power of the literature review to produce impactful and relevant research that contributes to both theoretical understanding and practical application in educational contexts. The article concludes by emphasizing the dynamic and essential nature of the literature review, which evolves throughout the research process and enhances the relevance, rigour, and potential impact of the study on educational practice and policy.

This article explores the concept of the post-methods era in language teaching, which advocates for moving beyond rigid, prescriptive methodologies. The author argues that no single method can address the diverse needs of all learners and contexts. Instead, teachers should adopt an eclectic and context-sensitive approach, drawing on a range of methods to suit specific situations. Key characteristics of this era include a focus on the learner, the importance of context, authenticity in language use, and the teacher as a reflective practitioner. The article highlights the limitations of traditional methods and the influence of sociolinguistics and psycholinguistics in shaping modern language teaching. Practical implications for teachers include informed eclecticism, needs analysis, materials development, action research, and continuous professional development. While the post-methods approach offers flexibility and adaptability, it also presents challenges, particularly for novice teachers and in systems reliant on traditional methods. The article concludes that ongoing research and dialogue are essential for refining effective language teaching practices in this era.

The field of English Language Teaching (ELT) and Learning is constantly evolving, influenced by advancements in educational theory, technology, and global trends. This article aims to explore several key issues and concepts in ELT, focusing on modern approaches and challenges faced by educators and learners alike. I will examine topics such as contextualised teaching and learning, functional linguistics, web-based new literacies and new literacy practices, situated language learning, lifelong learning, cognitive apprenticeship, online professional development, and the challenges specific to teaching English as a Foreign Language (EFL) in the Egyptian context.

This research study explores the potential of a pedagogical model of self-regulated learning supported with Artificial Intelligence (AI) chatbots to enhance self-expression and reflective writing skills for novice EFL student teachers at Faculty of Education, Assiut University. The study adopted a pre-post quasi-experimental design, that starts with the identification of the necessary self-expression and reflective writing skills for the target participants (50 fresh EFL student teachers at Assiut University who were purposively selected using a screening questionnaire based on their basic IT literacy skills). A pre-test was administered to assess their initial skill levels in self-expression and reflective writing. Then, an intervention was implemented in the form of a pedagogical model designed around the principles of self-regulated learning and situated language learning, which guided the use of AI chatbots (Bing, ChatGPT, and Google Bard). This model was initially piloted on a small sample (n = 10) of EFL student teachers to check validity and reliability and then experimented with the research participants for 8 weeks during the first semester of the academic year 2023/24. Following the intervention, a post-test was conducted to measure the participants' levels of self-expression and reflective writing skills after being exposed to the interventional model, aiming to identify any improvements gained from the intervention. The results indicated a positive effect with noticeable enhancements in the EFL student teachers' skills. This suggests the potential effectiveness of the model in fostering self-expression and reflective writing skills and developing EFL student teachers' general language proficiency and IT literacy. [This paper was published in "Academic Journal of Faculty of Education, Assiut University" v40 n9 p1-50 2024.]

Although the use of social networks, especially Facebook, has become a common practice among educational communities in Egypt, studies have yet to explore the online practices of in-service (language) teachers as part of Continuing Professional Development (hereafter CPD). Therefore, this study investigated English language teachers' informal online professional development that may include some types of language learning for improving their pedagogical content knowledge. It also explored differences between teachers' perceived use of Facebook as a venue of CPD. This study employed a mixed-method design, collecting qualitative and quantitative data via content analysis and a questionnaire. The participants were 180 English-language Egyptian in-service teachers. The qualitative analysis of teacher's posts demonstrates that CPD informal groups, as maintained by Egyptian EFL teachers, display different aspects of professional knowledge. Results showed that the data yielded seven main categories: General Pedagogical Knowledge (PK), Content Knowledge (CK), L2 Pedagogical Content Knowledge, Knowledge of L2 Learners, Knowledge of Educational context and Knowledge of (professional) self and miscellaneous topics. Besides, ANOVA's Welch test indicated significant differences in the means of the sum scores of the questionnaire as well as two sub-domains (PK and CK) among primary, preparatory and secondary teachers in some aspects, such as pronunciation and vocabulary. This study concludes that CPD Facebook groups provide wide options and spaces for continuous learning and informal study for EFL teachers. Pedagogical implications and recommendations for further research were suggested. [This paper was published in "CDELT Occasional Papers in the Development of English Education" v87 2024.]

The article presents a blended dialogic socio-constructivist pedagogy for language learning, emphasizing the importance of both individual and social aspects of learning. It argues that technology alone does not transform pedagogy; rather, it is the approach to technology that matters. The pedagogy combines personal freedom for reflection and individual assignments with social interactions like pair work and group discussions for collaborative knowledge construction. The progression from controlled, face-to-face activities to freer, online ones aids in consolidating new ideas and mastering language skills. The article supports its approach with literature on socio-constructivism, highlighting the synergy between individual constructivism and socio-culturalism. It draws on theories by Vygotsky and Piaget, advocating for a dialogic approach that extends learning beyond the classroom through web-mediated environments. This approach is particularly beneficial for English Language Learners (ELL), moving language learning from isolated mental functioning to real-world communication and fostering a participatory metaphor for language learning. The article concludes that blended learning, which combines web technologies with traditional pedagogies, is crucial for engaging learners in meaningful dialogue and practice.

The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by leveraging their sophisticated natural language understanding capabilities. However, the practical application of LLMs in wireless network orchestration and management remains largely unexplored. Existing literature predominantly offers visionary perspectives without concrete implementations, leaving a significant gap in the field. To address this gap, this paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that uses LLMs to seamlessly orchestrate diverse wireless-specific models from wireless communication communities using their language understanding and generation capabilities. A comprehensive framework is introduced, demonstrating the practical viability of our approach and showcasing how LLMs can be effectively harnessed to optimize dense network operations, manage dynamic environments, and improve overall network performance. NETORCHLLM bridges the theoretical aspirations of prior research with practical, actionable solutions, paving the way for future advancements in integrating generative AI technologies within the wireless communications sector.

Any research endeavour needs to conform with some endorsed research ethics, such as the BERA ethical guidelines for educational research (BERA, 2018). These ethical guidelines can be applied to many research stages and procedures, including dealing with human participants (e.g. getting consent and approvals), data collection and analysis, and using pseudonyms instead of real names in the research report itself. Drawing on British Educational Research Association (BERA, 2018) as an authoritative source in this regard, I will explore how these ethical guidelines apply to educational research in the Arab world, and how they might inform and shape research practices in the light of a chosen experimental research study in language learning (Al-Jarf, 2006) by discussing the key ethical issues and concerns evident in the study, and suggesting alternative ways when needed. This would develop a methodological understanding of the proper conduct of the research process, and hence contribute to the advancement of knowledge in the educational arena. To investigate ethical issues in this paper, focus will be on the following points considering to what extent the researcher considered them: the researcher's qualities and virtues (honesty, objectivity, clarity, etc.), her interaction with participants, tackling previous literature, and research procedures.

The Wigner function and Wigner-Yanase skew information are connected through quantum coherence. States with high skew information often exhibit more pronounced negative regions in their Wigner functions, indicative of quantum interference and non-classical behavior. Thus, the relationship between these two concepts is that states with high quantum coherence tend to display more non-classical features in their Wigner functions. By exploiting this relationship, which manifests as parity symmetry and asymmetry, we analyze parity symmetry and asymmetry in the superposition of two spin coherent states for a spin-$1/2$, as well as for a general spin-$j$. This analysis shows that the preservation of the parity asymmetry, or the violation of the parity symmetry, correlates with an increase in the value of spin $j$. Additionally, we investigate the behavior of parity symmetry and asymmetry of these states subjected to a Gaussian noise channel. Specifically, we examine how this parity symmetry and asymmetry change and identify the points at which parity symmetry is violated in the spin-$1/2$ cat state. Notably, the violation of parity symmetry becomes more pronounced at higher values of the decoherence parameter $s$. Our study shows how the spin value $j$ affects the breaking of parity symmetry in general spin-$j$ cat states that are hit by Gaussian noise.

This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets., Comment: Accepted at Coling2025

In communication complexity the input of a function $f:X\times Y\rightarrow Z$ is distributed between two players Alice and Bob. If Alice knows only $x\in X$ and Bob only $y\in Y$, how much information must Alice and Bob share to be able to elicit the value of $f(x,y)$? Do we need $\ell$ more resources to solve $\ell$ instances of a problem? This question is the direct sum question and has been studied in many computational models. In this paper we focus on the case of 2-party deterministic communication complexity and give a counterexample to the direct sum conjecture in its strongest form. To do so we exhibit a family of functions for which the complexity of solving $\ell$ instances is less than $(1 -\epsilon )\ell$ times the complexity of solving one instance for some small enough $\epsilon>0$. We use a customised method in the analysis of our family of total functions, showing that one can force the alternation of rounds between players. This idea allows us to exploit the integrality of the complexity measure to create an increasing gap between the complexity of solving the instances independently with that of solving them together.

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on Grand Challenge using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC., Comment: For HNTS-MRG 2024 volume of Lecture Notes in Computer Science

The application of Virtual Reality Environments (VRE) has been gaining momentum as a relatively new tool to assist with mitigating various difficulties including abstractness of concepts, lack of user engagement, perception of disconnection from other users. A VRE may offer both synchronous and asynchronous experiences, in addition to an immersive environment which promotes users' engagement. Past research has shown that, in general, VRE do improve the experiences they try to enhance in many aspects of human activity. Terms like immersiveness and 3D representation of real life objects and environments are, as it appears, the two most obvious positive effects of Virtual Reality (VR) applications. However, despite these benefits it does not come without challenges. The main three concepts/challenges are the spatial design, the collaboration interaction between its members and the VRE, and the audio and video fidelity. Each of the three includes a number of other components that should be addressed for the total experience to be fine-tuned. These include mutual embodiment and shared perspectives, teleportation, gestural interaction, symmetric and asymmetric collaboration, physical and virtual co-location, inventory, and time and spatial synchronization. This paper comprises a survey of the literature, that identifies and explains the features introduced and the challenges involved with the VREs, and furthermore provides various interesting future research directions., Comment: 22 pages, 10 figures, Appendix with a table

In this paper, we explore the construction of Planar Maximally Entangled (PME) states from phase states. PME states form a class of $n$-partite states in which any subset of adjacent particles whose size is less than or equal to half the total number of particles is in a fully entangled state. This property is essential to ensuring the robustness and stability of PME states in various quantum information applications. We introduce phase states for a set of so-called noninteracting $n$ particles and describe their corresponding separable density matrices. These phase states, although individually separable, serve as a starting point for the generation of entangled states when subjected to unitary dynamics. Using this method, we suggest a way to make complex multi-qubit states by watching how unconnected phase states change over time with a certain unitary interaction operator. In addition, we show how to derive PME states from these intricate phase states for two-, three-, four-, and K-qubit systems. This method of constructing PME states is particularly relevant for applications in fields such as quantum teleportation, quantum secret sharing, and quantum error correction, where multiparty entanglement plays a central role in the efficiency of the protocols.

This letter proposes decision-directed semi-blind channel estimation for massive multiple-input multiple-output low-Earth-orbit satellite communications. Two semi-blind estimators are proposed. The first utilizes detected data symbols in addition to pilot symbols. The second, a modified semi-blind estimator, is specially designed to mitigate the channel-aging effect caused by the highly dynamic nature of low-Earth-orbit satellite communication channels -- an issue that adversely impacts the performance of pilot-based estimators. Consequently, this modified estimator outperforms an optimal pilot-based estimator in terms of normalized mean square error and achieves symbol error rate performance comparable to that of a Genie-aided (perfectly known channel) detector. The trade-offs between the proposed estimators are also examined.

This analysis focuses on probing the lepton portal dark matter using Monte Carlo simulated samples from electron-positron collisions at the International Linear Collider (ILC) of 500 GeV center of mass energy with an integrated luminosity of 1000 fb$^{-1}$. The study examines a benchmark scenario where the dark matter is a scalar particle produced as a daughter particle of the vector-like lepton. The signal topology consists of missing transverse energy and dilepton. If no new physics is discovered,the study sets 95\% confidence level exclusion limits on the mass of vector-like leptons.

Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although recent advances in large language models (LLMs) offer the potential for more flexible interactions, their lack of controllability and transparency poses significant challenges in sensitive areas like psychotherapy. In this work, we explored how aligning LLMs with expert-crafted scripts can enhance psychotherapeutic chatbot performance. Our comparative study showed that LLMs aligned with expert-crafted scripts through prompting and fine-tuning significantly outperformed both pure LLMs and rule-based chatbots, achieving a more effective balance between dialogue flexibility and adherence to therapeutic principles. Building on findings, we proposed ``Script-Strategy Aligned Generation (SSAG)'', a flexible alignment approach that reduces reliance on fully scripted content while enhancing LLMs' therapeutic adherence and controllability. In a 10-day field study, SSAG demonstrated performance comparable to full script alignment and outperformed rule-based chatbots, empirically supporting SSAG as an efficient approach for aligning LLMs with domain expertise. Our work advances LLM applications in psychotherapy by providing a controllable, adaptable, and scalable solution for digital interventions, reducing reliance on expert effort. It also provides a collaborative framework for domain experts and developers to efficiently build expertise-aligned chatbots, broadening access to psychotherapy and behavioral interventions.

Entanglement serves as a core resource for quantum information technologies, including applications in quantum cryptography, quantum metrology, and quantum communication. In this study, we give a unifying description of the stationary bipartite and tripartite entanglement in a coupled optomechanical ring cavity comprising photon and phonon modes. We numerically analyze the stationary entanglement between the optical mode and each mechanical mode, as well as between the two mechanical modes, using the logarithmic negativity. Our results demonstrate that mechanical entanglement between the two mechanical modes is highly dependent on the optical normalized detuning and the mechanical coupling strength, with entanglement maximized within specific detuning intervals and increased coupling broadening the effective range. Furthermore, we study the entanglement's sensitivity to temperature, noting that higher coupling strengths can sustain entanglement at elevated temperatures. The study also reveals that the entanglement between the mechanical mode and the optical mode is enhanced with increasing laser power, but is similarly susceptible to thermal noise. Additionally, we explore tripartite entanglement through the minimum residual contangle, highlighting its dependence on detuning, temperature, and laser power. Our findings underscore the importance of parameter control in optimizing entanglement for quantum information processing applications.

In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet the specific needs of various AI services. The complexity of optimizing and automating S2L surpasses that of L2S due to intricate AI services' requirements, such as handling uncontrollable parameters, learning in adversarial conditions, and achieving long-term performance goals. This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L. Indeed, we choose two candidate slicing agents, namely the Exploration and Exploitation (EXP3) and Deep Q-Network (DQN) from the Online Convex Optimization (OCO) and Deep Reinforcement Learning (DRL) frameworks, and compare them. Our evaluation involves a series of carefully designed experiments that offer valuable insights into the strengths and limitations of EXP3 and DQN in slicing for AI services, thereby contributing to the advancement of zero-touch network capabilities., Comment: 9 pages, 2 figures and 2 tables magazine paper

Multi-Object Tracking (MOT) poses significant challenges in computer vision. Despite its wide application in robotics, autonomous driving, and smart manufacturing, there is limited literature addressing the specific challenges of running MOT on embedded devices. State-of-the-art MOT trackers designed for high-end GPUs often experience low processing rates (<11fps) when deployed on embedded devices. Existing MOT frameworks for embedded devices proposed strategies such as fusing the detector model with the feature embedding model to reduce inference latency or combining different trackers to improve tracking accuracy, but tend to compromise one for the other. This paper introduces HopTrack, a real-time multi-object tracking system tailored for embedded devices. Our system employs a novel discretized static and dynamic matching approach along with an innovative content-aware dynamic sampling technique to enhance tracking accuracy while meeting the real-time requirement. Compared with the best high-end GPU modified baseline Byte (Embed) and the best existing baseline on embedded devices MobileNet-JDE, HopTrack achieves a processing speed of up to 39.29 fps on NVIDIA AGX Xavier with a multi-object tracking accuracy (MOTA) of up to 63.12% on the MOT16 benchmark, outperforming both counterparts by 2.15% and 4.82%, respectively. Additionally, the accuracy improvement is coupled with the reduction in energy consumption (20.8%), power (5%), and memory usage (8%), which are crucial resources on embedded devices. HopTrack is also detector agnostic allowing the flexibility of plug-and-play.

We present some regularity results on the gradient of the weak or entropic-renormalized solution $u$ to the homogeneous Dirichlet problem for the quasilinear equations of the form \begin{equation*}\label{p-laplacian_eq} -{\rm div~}(|\nabla u|^{p-2}\nabla u)+V(x;u)=f, \end{equation*} where $\Omega$ is a bounded smooth domain of $\mathbb R^n$, $V$ is a nonlinear potential and $f$ belongs to non-standard spaces like Lorentz-Zygmund spaces. Moreover, we collect some well-known and new results for identifying some interpolation spaces and enrich some contents with details.

We present high precision measurements of the radiative heat transfer of a glass microsphere immersed in a thermal bath in vacuum facing three different planar substrates (SiO2, SiC and Au), which exhibit very different optical behaviors in the infrared region. Using a thermoresistive probe on a cantilever, we show the non-monotonic behavior of the radiative flux between the microsphere and its environment when the microsphere is brought closer to the substrate in the far-field to near-field transition regime. We demonstrate that this unexpected behavior is related to the singularities of dressed emission mechanisms in this three-body system sphere-substrate-bath with respect to the separation distance., Comment: 5 pages, 3 figures + Supplementary

When a magnetic field is applied in the perpendicular direction to a metallic disk under the action of a radial bias voltage, a tangential electric current superimposes to the radial current due to the presence of the Lorentz force which acts on electrons. Here we introduce a thermal analog of this Corbino effect in many-body systems made of nonreciprocal bodies which interact by exchanging photons in near-field regime. In systems out of thermal equilibrium with a radial temperature gradient, we demonstrate that the Poynting field in the Corbino geometry is bent in presence of an external magnetic field, giving rise to a tangential heat flux. This thermomagnetic effect could find applications in the field of thermal management and energy conversion at nanoscale., Comment: 5 pages, 4 figures

We prove the asymptotic stability of the high speed solitary waves to the Benjamin equation. This is done by establishing a Liouville property for the nonlinear evolution of the Benjamin equation around these solitary waves. To do this, inspired by Kenig-Martel-Robbiano 2011, we make use of the KdV limit of the Benjamin equation together with known rigidity property of the KdV flow. The main difficulties are linked to the presence of the Hilbert transform, that is a non-local operator, as well as the non-positivity of the quadratic part of the energy in the case $ \gamma<0$ which is the physical case.

Kinetic inductance travelling-wave parametric amplifiers (KTWPAs) are emerging as core components in many applications where wideband cryogenic rf amplification at or near the quantum limit of added noise is critical. What is unique about these thin film superconducting devices and what makes them suitable for a wide range of applications is their ability to simultaneously provide large dynamic range and quantum-limited amplification of single photon to 100,000s of photon signals. However, despite the promising performance of co-planar NbTiN thin-film KTWPAs, the original promise of a "simple" single-layer metal fabrication has encountered roadblocks and their broader adoption has been hindered by low fabrication yield. In this work, we present a novel post-lithography correction technique that eliminates short circuits, significantly improving yield and enabling reliable wafer-scale production. Using automated image acquisition, error analysis, and correction, we successfully fabricated operational KTWPAs on high-resistivity silicon, achieving > 10 dB between 2-4 GHz. This approach paves the way for the scaling up manufacturing of KTWPAs, positioning them for widespread deployment in quantum technologies.

A transverse radiative heat flux induced by the gradient of spin angular momentum of photons in non-reciprocal systems is predicted. This thermal analog of the inverse spin Hall effect is analyzed in magneto-optical networks exhibiting C4 symmetry, under the action of spatially variable external magnetic fields. This finding opens new avenues for thermal management and energy conversion with non-reciprocal systems through a localized and dynamic control of the spin angular momentum of light.

The escalating frequency of intrusions in networked systems has spurred the exploration of new research avenues in devising artificial intelligence (AI) techniques for intrusion detection systems (IDS). Various AI techniques have been used to automate network intrusion detection tasks, yet each model possesses distinct strengths and weaknesses. Selecting the optimal model for a given dataset can pose a challenge, necessitating the exploration of ensemble methods to enhance generalization and applicability in network intrusion detection. This paper addresses this gap by conducting a comprehensive evaluation of diverse individual models and both simple and advanced ensemble methods for network IDS. We introduce an ensemble learning framework tailored for assessing individual models and ensemble methods in network intrusion detection tasks. Our framework encompasses the loading of input datasets, training of individual models and ensemble methods, and the generation of evaluation metrics. Furthermore, we incorporate all features across individual models and ensemble techniques. The study presents results for our framework, encompassing 14 methods, including various bagging, stacking, blending, and boosting techniques applied to multiple base learners such as decision trees, neural networks, and among others. We evaluate the framework using two distinct network intrusion datasets, RoEduNet-SIMARGL2021 and CICIDS-2017, each possessing unique characteristics. Additionally, we categorize AI models based on their performances on our evaluation metrics and via their confusion matrices. Our assessment demonstrates the efficacy of learning across most setups explored in this study. Furthermore, we contribute to the community by releasing our source codes, providing a foundational ensemble learning framework for network intrusion detection.

The rapid advancement of autonomous vehicle (AV) technology has introduced significant challenges in ensuring transportation security and reliability. Traditional AI models for anomaly detection in AVs are often opaque, posing difficulties in understanding and trusting their decision making processes. This paper proposes a novel feature ensemble framework that integrates multiple Explainable AI (XAI) methods: SHAP, LIME, and DALEX with various AI models to enhance both anomaly detection and interpretability. By fusing top features identified by these XAI methods across six diverse AI models (Decision Trees, Random Forests, Deep Neural Networks, K Nearest Neighbors, Support Vector Machines, and AdaBoost), the framework creates a robust and comprehensive set of features critical for detecting anomalies. These feature sets, produced by our feature ensemble framework, are evaluated using independent classifiers (CatBoost, Logistic Regression, and LightGBM) to ensure unbiased performance. We evaluated our feature ensemble approach on two popular autonomous driving datasets (VeReMi and Sensor) datasets. Our feature ensemble technique demonstrates improved accuracy, robustness, and transparency of AI models, contributing to safer and more trustworthy autonomous driving systems., Comment: 31 pages, 4 figures (including the subfigures)

Explainability and evaluation of AI models are crucial parts of the security of modern intrusion detection systems (IDS) in the network security field, yet they are lacking. Accordingly, feature selection is essential for such parts in IDS because it identifies the most paramount features, enhancing attack detection and its description. In this work, we tackle the feature selection problem for IDS by suggesting new ways of applying eXplainable AI (XAI) methods for this problem. We identify the crucial attributes originated by distinct AI methods in tandem with the novel five attribute selection methods. We then compare many state-of-the-art feature selection strategies with our XAI-based feature selection methods, showing that most AI models perform better when using the XAI-based approach proposed in this work. By providing novel feature selection techniques and establishing the foundation for several XAI-based strategies, this research aids security analysts in the AI decision-making reasoning of IDS by providing them with a better grasp of critical intrusion traits. Furthermore, we make the source codes available so that the community may develop additional models on top of our foundational XAI-based feature selection framework., Comment: 24 pages, 4 figures

This paper presents the foundational framework and initial findings of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, designed to address the need for comprehensive Arabic language resources aligned with diverse readability levels. Inspired by the Taha/Arabi21 readability reference, BAREC aims to provide a standardized reference for assessing sentence-level Arabic text readability across 19 distinct levels, ranging in targets from kindergarten to postgraduate comprehension. Our ultimate goal with BAREC is to create a comprehensive and balanced corpus that represents a wide range of genres, topics, and regional variations through a multifaceted approach combining manual annotation with AI-driven tools. This paper focuses on our meticulous annotation guidelines, demonstrated through the analysis of 10,631 sentences/phrases (113,651 words). The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 79.9%, reflecting a high level of substantial agreement. We also report competitive results for benchmarking automatic readability assessment. We will make the BAREC corpus and guidelines openly accessible to support Arabic language research and education., Comment: 16 pages, 3 figures

Large language models (LLMs) hold significant promise in advancing network management and orchestration in 6G and beyond networks. However, existing LLMs are limited in domain-specific knowledge and their ability to handle multi-modal sensory data, which is critical for real-time situational awareness in dynamic wireless environments. This paper addresses this gap by introducing ENWAR, an ENvironment-aWARe retrieval augmented generation-empowered multi-modal LLM framework. ENWAR seamlessly integrates multi-modal sensory inputs to perceive, interpret, and cognitively process complex wireless environments to provide human-interpretable situational awareness. ENWAR is evaluated on the GPS, LiDAR, and camera modality combinations of DeepSense6G dataset with state-of-the-art LLMs such as Mistral-7b/8x7b and LLaMa3.1-8/70/405b. Compared to general and often superficial environmental descriptions of these vanilla LLMs, ENWAR delivers richer spatial analysis, accurately identifies positions, analyzes obstacles, and assesses line-of-sight between vehicles. Results show that ENWAR achieves key performance indicators of up to 70% relevancy, 55% context recall, 80% correctness, and 86% faithfulness, demonstrating its efficacy in multi-modal perception and interpretation.

The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have focused on optimizing model inference but have overlooked a critical question: is it necessary to perform user model inference for every ad request in large-scale social networks? To address this question and these challenges, we first analyze user access patterns at Meta and find that most user model inferences occur within a short timeframe. T his observation reveals a triangular relationship among model complexity, embedding freshness, and service SLAs. Building on this insight, we designed, implemented, and evaluated ERCache, an efficient and robust caching framework for large-scale user representations in ads recommendation systems on social networks. ERCache categorizes cache into direct and failover types and applies customized settings and eviction policies for each model, effectively balancing model complexity, embedding freshness, and service SLAs, even considering the staleness introduced by caching. ERCache has been deployed at Meta for over six months, supporting more than 30 ranking models while efficiently conserving computational resources and complying with service SLA requirements.

A tensor-based formula for solving the linear system involving Kronecker sum is proposed. It is shown that the linear system involving Kronecker sum of matrices is directly related to the matrix and tensor forms of Sylvester equation. The new tensor-based formula indicates that a Sylvester tensor equation has a unique solution if the sum of spectra of the matrices does not contain zero. As an application, we solve the discretized Poisson equation on a rectangular domain and Dirichlet boundary conditions. In 2d, we show that both the new formula and the fast Poisson solver based on diagonalization have the same cost $\mathcal {O}(n^{3/2})$. In 3d, the new formula has the cost $\mathcal {O}(n^{4/3})$ while cyclic reduction and successive over-relaxation methods have a complexity of $\mathcal {O}(n^{3/2})$.

This study investigates the effectiveness of implementing a flipped classroom strategy/model in teaching a Computer-Assisted Language Learning (CALL) course to senior EFL student teachers. Characterized by pre-class online content delivery and in-person interactive activities, the flipped classroom strategy was explored in terms of its impact on students' acquisition and application of technological terms in language teaching/learning contexts. Using a quasi-experimental design, two randomly selected groups of senior EFL student teachers studying a CALL course at Assiut University underwent pre and post-tests to assess the influence of the flipped classroom on technological terms proficiency. The 100 participants were randomly assigned to an experimental group (n = 50) exposed to the flipped classroom model and a control group (n = 50) following traditional methods. Post-test results indicated superior performance in the experimental group, highlighting the positive influence of the flipped classroom. A questionnaire revealed favourable perceptions in the experimental group, emphasising its efficacy in enhancing engagement, motivation, autonomy, collaboration, and content comprehension within the CALL course. Additionally, semi-structured interviews with a subset of the experimental group (n = 15) identified specific benefits, including deeper engagement with CALL content, facilitated collaborative learning, and increased motivation and confidence in using technological terms. This study concludes that the flipped classroom is a promising strategy for senior EFL student teachers in the CALL course, offering practical implications and recommendations for its successful integration. [This paper was published in "Journal of Faculty of Education - Assiut University" v40 n2 p1-60 2024.]

Unmanned Aerial Vehicles (UAVs) are becoming more popular in various sectors, offering many benefits, yet introducing significant challenges to privacy and safety. This paper investigates state-of-the-art solutions for detecting and tracking quadrotor UAVs to address these concerns. Cutting-edge deep learning models, specifically the YOLOv5 and YOLOv8 series, are evaluated for their performance in identifying UAVs accurately and quickly. Additionally, robust tracking systems, BoT-SORT and Byte Track, are integrated to ensure reliable monitoring even under challenging conditions. Our tests on the DUT dataset reveal that while YOLOv5 models generally outperform YOLOv8 in detection accuracy, the YOLOv8 models excel in recognizing less distinct objects, demonstrating their adaptability and advanced capabilities. Furthermore, BoT-SORT demonstrated superior performance over Byte Track, achieving higher IoU and lower center error in most cases, indicating more accurate and stable tracking. Code: https://github.com/zmanaa/UAV_detection_and_tracking Tracking demo: https://drive.google.com/file/d/1pe6HC5kQrgTbA2QrjvMN-yjaZyWeAvDT/view?usp=sharing

This work proposes a strategy for organising quadrotors around a payload to enable hovering without external stimuli, together with a MATLAB software for modelling the dynamics of a quadrotor-payload system. Based on geometric concepts, the proposed design keeps the payload and system centre of mass aligned. Hovering tests that are successful confirm the method's efficiency. Moreover, the algorithm is improved to take thrust capacities and propeller distances into account, calculating the minimum number of quadrotors needed for hovering. The algorithm's effectiveness is demonstrated by numerical examples, which reveal that larger quadrotors may require fewer units while smaller ones give greater flexibility. Our code can be found at: \href{https://github.com/Hosnooo/Swarm-Slung-Payload}{https://github.com/Hosnooo/Swarm-Slung-Payload}, Comment: accepted for publication at AIAA SCITECH 2025

Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one of the most common types of such questions. In this paper, we introduce TEMPAMBIQA, a manually annotated temporally ambiguous QA dataset consisting of 8,162 open-domain questions derived from existing datasets. Our annotations focus on capturing temporal ambiguity to study the task of detecting temporally ambiguous questions. We propose a novel approach by using diverse search strategies based on disambiguated versions of the questions. We also introduce and test non-search, competitive baselines for detecting temporal ambiguity using zero-shot and few-shot approaches., Comment: Accepted at EMNLP 2024 Findings

Time spent by academics on research quality assessment might be reduced if automated approaches can help. Whilst citation-based indicators have been extensively developed and evaluated for this, they have substantial limitations and Large Language Models (LLMs) like ChatGPT provide an alternative approach. This article assesses whether ChatGPT 4o-mini can be used to estimate the quality of journal articles across academia. It samples up to 200 articles from all 34 Units of Assessment (UoAs) in the UK's Research Excellence Framework (REF) 2021, comparing ChatGPT scores with departmental average scores. There was an almost universally positive Spearman correlation between ChatGPT scores and departmental averages, varying between 0.08 (Philosophy) and 0.78 (Psychology, Psychiatry and Neuroscience), except for Clinical Medicine (rho=-0.12). Although other explanations are possible, especially because REF score profiles are public, the results suggest that LLMs can provide reasonable research quality estimates in most areas of science, and particularly the physical and health sciences and engineering, even before citation data is available. Nevertheless, ChatGPT assessments seem to be more positive for most health and physical sciences than for other fields, a concern for multidisciplinary assessments, and the ChatGPT scores are only based on titles and abstracts, so cannot be research evaluations.

Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrieving relevant documents from a corpus like Wikipedia, whereas the latter uses generative models such as Large Language Models (LLMs) to generate the context. In this paper, we introduce a novel context preparation approach called HINTQA, which employs Automatic Hint Generation (HG) techniques. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question rather than generating relevant context. We evaluate our approach across three QA datasets including TriviaQA, NaturalQuestions, and Web Questions, examining how the number and order of hints impact performance. Our findings show that the HINTQA surpasses both retrieval-based and generation-based approaches. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts., Comment: Accepted at EMNLP 2024

Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data, a process essential to a multitude of applications such as enriching training datasets, anomaly detection, and risk management in finance. Given the success of Generative Adversarial Networks in classical image generation, the development of its quantum versions has been actively conducted. However, existing implementations on quantum computers often face significant challenges, such as scalability and training convergence issues. To address these issues, we propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder. Although it was initially designed for image generation, the LatentQGAN approach holds potential for broader application across various practical data generation tasks. Experimental outcomes on both classical simulators and noisy intermediate scale quantum computers have demonstrated significant performance enhancements over existing quantum methods, alongside a significant reduction in quantum resources overhead., Comment: This paper was accepted for publication on the 10th IEEE World Forum on Internet of Things (IEEE WFIoT2024), in the session SS - QIoT-1: Special Session - Quantum Internet of Things (QIoT)-1, November 10th, from 14:00 to 15:30 EST