Open Challenges Research Articles

Large language models for whole-learner support: opportunities and challenges.

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the "whole learner" by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Advances in text-guided 3D editing: a survey

In 3D Artificial Intelligence Generated Content (AIGC), compared with generating 3D assets from scratch, editing extant 3D assets satisfies user prompts, allowing the creation of diverse and high-quality 3D assets in a time and labor-saving manner. More recently, text-guided 3D editing that modifies 3D assets guided by text prompts is user-friendly and practical, which evokes a surge in research within this field. In this survey, we comprehensively investigate recent literature on text-guided 3D editing in an attempt to answer two questions: What are the methodologies of existing text-guided 3D editing? How has current progress in text-guided 3D editing gone so far? Specifically, we focus on text-guided 3D editing methods published in the past 4 years, delving deeply into their frameworks and principles. We then present a fundamental taxonomy in terms of the editing strategy, optimization scheme, and 3D representation. Based on the taxonomy, we review recent advances in this field, considering factors such as editing scale, type, granularity, and perspective. In addition, we highlight four applications of text-guided 3D editing, including texturing, style transfer, local editing of scenes, and insertion editing, to exploit further the 3D editing capacities with in-depth comparisons and discussions. Depending on the insights achieved by this survey, we discuss open challenges and future research directions. We hope this survey will help readers gain a deeper understanding of this exciting field and foster further advancements in text-guided 3D editing.

Data Collection and Recharging of Sensor Node by Mobile Sink in Wireless Sensor Network

Abstract: The wireless sensor network (WSN) has limited battery and storage capacity. The main challenge in static sink nodes is the lack of energy and data transfer to the base station (BS). A mobile sink (MS) is an excellent way to handle these difficulties in WSN. The use of an MS solution not only ensures long-term network functionality, but also improves network performance. In this paper, the MS-based data collection and recharging, mobile device is used for data collection from sensor nodes and recharging the sensor nodes throughout the network whenever required. There are two types of MS-based solutions described as (i) Mobile devices for data collection from sensor nodes in the network. (ii) Mobile device for collecting data and recharging sensor nodes from the network. Finally, in this paper, we presented the advantages, disadvantages, and other criteria of both types. Also, a potential framework is presented for data collection and recharging of sensor nodes by MS in WSN. The core contribution of this paper is present the state of art and future roadmap for data collection and recharging of sensor nodes by MS in WSN. The identification of the main open challenges and future direction in this research area are also highlighted and discussed.

Light-driven nitrogen fixation routes for green ammonia production.

The global goal for decarbonization of the energy sector and the chemical industry could become a reality by a massive increase in renewable-based technologies. For this clean energy transition, the versatile green ammonia may play a key role in the future as a fossil-free fertilizer, long-term energy storage medium, chemical feedstock, and clean burning fuel for transportation and decentralized power generation. The high energy-intensive industrial ammonia production has triggered researchers to look for a step change in new synthetic approaches powered by renewable energies. This review provides a comprehensive comparison of light-mediated N2 fixation technologies for green ammonia production, including photocatalytic, photoelectrocatalytic, PV-electrocatalytic and photothermocatalytic routes. Since these approaches are still at laboratory scale, we examine the most recent developments and discuss the open challenges for future improvements. Last, we offer a technoeconomic comparison of current and emerging ammonia production technologies, highlighting costs, barriers, recommendations, and potential opportunities for the real development of the next generation of green ammonia solutions.

Multilinguality and LLOD: A survey across linguistic description levels

Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

Small scale model for predicting transportation-induced particle formation in biotherapeutics

Understanding protein adsorption and aggregation at the air-liquid interfaces of protein solutions is an important open challenge in biopharmaceutical, medical, and biotechnological applications, among others. Proteins, being amphiphilic, adsorb at the surface, partially unfold, and form a viscoelastic film through non-covalent interactions. Mechanical agitation of the surface can break this film up, releasing insoluble protein particles into the solution. These aggregates are usually highly undesirable and even toxic in cases, such as for biopharmaceutical application. Therefore, it is imperative to be able to predict the behavior of such solutions undergoing surface agitation during handling, usually transport or mixing. We apply the findings on the viscoelastic protein film, formed at the air-liquid interface, to the prediction of surface mediated aggregation in selected protein solutions of direct biopharmaceutical relevance. Our broad study of Brewster angle microscopy and aggregation monitoring across multiple size ranges by micro-flow imaging, light scattering, and size exclusion chromatography shows that formation of protein particles is driven by the adsorption rate as compared to the rate of surface turnover and that surface film dynamics in the quiescent phase directly affect aggregation. We demonstrate how these learnings can be directly applied to the design of a novel small scale biopharmaceutical stability study, simulating relevant transport conditions. More generally, we show the impact of adsorption dynamics at the air-liquid interface on the stability of a distinct protein solution, as a general contribution to understanding different colloidal and biological interfacial systems.

Data-driven room acoustic modeling via differentiable feedback delay networks with learnable delay lines

Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a feedback delay network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics and outperforms existing methods based on genetic algorithms and analytical FDN design.

A Comprehensive Review on Text Detection and Recognition in Scene Images

Detecting and recognizing text in natural scene images and videos is vital for several real-world applications, such as in the analysis of Crime scene CCTV footage, sports videos, and autonomous driving, to name a few. Therefore, one can expect several challenges, namely, arbitrarily oriented and shaped Text detection and identification in movies and natural environments. Many methods have been developed in the past to address these challenges, including advanced deep-learning models and transformers. Due to several methods available in the literature, it is not so easy to understand the open challenges, applications, directions, scope, limitations, and weaknesses of the methods. Therefore, there is a need to write a survey/review to highlight and discuss the strengths and weaknesses of the developed methods. This survey/review presents different categories of work and discusses their importance, limitations, new challenges, applications, and, finally, directions such that readers can choose appropriate methods and directions to carry out research work in the field of text detection/recognition in the natural scene and videos.

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Survey of AI-driven techniques for ovarian cancer detection: state-of-the-art methods and open challenges

Survey of AI-driven techniques for ovarian cancer detection: state-of-the-art methods and open challenges

Navigating Trust and Safety in Multi-Agent Reinforcement Learning: Attacks, Defenses, and Future Directions

Multi-Agent Reinforcement Learning (MARL) is a key framework for building intelligent systems where multiple agents operate within a shared environment, with applications spanning autonomous driving, robotics, and distributed control systems. However, real-world deployment of MARL brings significant trust and safety challenges, as these systems are susceptible to a range of attacks that can compromise their robustness and reliability. This paper provides a comprehensive review of trust and safety attacks in MARL, categorizing various types of attacks and their implications. We explore existing defense mechanisms designed to mitigate these threats, highlighting their strengths and limitations. Additionally, we identify open challenges that remain unaddressed and propose potential future research directions to enhance the robustness and security of MARL systems.

Noise reducing elements filled with laminated safety glass (LSG) for use along railway lines

The main purpose of noise barriers is to shield the environment from unwanted sound. Along railway lines, noise barriers must also meet strict requirements for structural integrity, usability and fatigue safety. Furthermore, noise barriers should satisfy aesthetic criteria. Transparent noise barriers have less impact on the environment and are more widely accepted by the public. In Germany, PMMA (acrylic glass) is currently the only transparent material approved for railway noise reducing elements. Laminated safety glass (LSG) could be a viable alternative due to its high rigidity and transparency, comparatively low dirt accumulation and high chemical and physical resistance. However, it still needs to be demonstrated that LSG meets the strict requirements for use along railway lines. LSG is a type of laminated glass that has a splitter-binding intermediate layer of plastics. The multi-layered structure presents challenges regarding the proof of usability. MFPA Leipzig GmbH was commissioned by the German Centre for Rail Traffic Research at the Federal Railway Authority to develop and evaluate possible methods to prove the usability of LSG for noise barriers along railway lines. The paper presents the structure of the project, the main findings until now and the open challenges to be addressed.

Non-Abelian Anyon Statistics through ac Conductance of a Majorana Interferometer.

Demonstrating the non-Abelian Ising anyon statistics of Majorana zero modes in a physical platform still represents a major open challenge in physics. We here show that the linear low-frequency charge conductance of a Majorana interferometer containing a floating superconducting island can reveal the topological spin of quantum edge vortices. The latter are associated with chiral Majorana fermion edge modes and represent "flying" Ising anyons. We describe possible device implementations and outline how to detect non-Abelian anyon braiding through ac conductance measurements.

Evolution of Unmanned Surface Vehicle Path Planning: A Comprehensive Review of Basic, Responsive, and Advanced Strategic Pathfinders

Unmanned Surface Vehicles (USVs) are rapidly becoming mission-indispensable for a variety of naval operations, from search and rescue to environmental monitoring and surveillance. Path planning lies at the heart of the operational effectiveness of USVs, since it represents the key technology required to enable the vehicle to transit the unpredictable dynamics of the marine environment in an efficient and safe way. The paper develops a critical review of the most recent advances in USV path planning and a novel classification of algorithms according to operational complexity: Basic Pathfinders, Responsive Pathfinders, and Advanced Strategic Pathfinders. Each category can adapt to different requirements, from environmental predictability to the desired degree of human intervention, and from stable and controlled environments to highly dynamic and unpredictable conditions. The review includes current methodologies and points out the state-of-the-art algorithmic approaches in their experimental validations and real-time applications. Particular attention is paid to the description of experimental setups and navigational scenarios showing the realistic impact of these technologies. Moreover, this paper goes through the key, open challenges in the field and hints at the research direction to leverage in order to enhance the robustness and adaptability of path planning algorithms. This paper, by offering a critical analysis of the current state-of-the-art, lays down the foundation of future USV path planning algorithms.

Toward Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges

Toward Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges

Conference Report: Challenging Construction Grammar: New Insights from Morphology (8–12 April 2024; Monte Verità, Switzerland)

As reflected in its title, this workshop, which took place from the eighth to the twelfth of April 2024 at Monte Verità, Ascona, Switzerland, focused on constructional perspectives in their application to morphology (Hoffmann & Trousdale 2013). As Construction Grammar has become an important theoretical framework within linguistics, it has been applied in morphological studies from both synchronic (e.g., Booij 2010; Jackendoff & Audring 2020) and diachronic perspectives (Hartmann 2019; Van Goethem & Norde 2020). Nevertheless, Construction Morphology is a comparatively young branch of Construction Grammar that poses many open questions and challenges. The questions that were asked within this workshop relate to the connection between morphology and Construction Grammar as a framework, the handling of phenomena situated at the interface of morphology and syntax, and how phenomena that are specific for the scope of morphology can be captured from a constructional point of view. To address this issue, young and early career researchers were invited to present their current work. These contributions were complemented by the contributions of the invited plenary speakers Muriel Norde (Humboldt University of Berlin), Francesca Masini (University of Bologna), Livio Gaeta (University of Turin), Kristel Van Goethem (University of Louvain) and Steffen Höder (University of Kiel). The workshop was organised by Elena Smirnova (University of Neuchâtel), Martin Hilpert (University of Neuchâtel) and Jenny Audring (University of Leiden), and made possible with the generous support of SCF (Congressi Stefano Franscini, https://csf.ethz.ch), the SNF (Swiss National Science Foundation, Programm Scientific Exchanges, Grant Nr IZSEZ0_221843) and the FLSH (Faculty of Arts and Humanities) of the University of Neuchâtel.

Fuzzing drones for anomaly detection: A systematic literature review

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one commonly used technique for finding bugs and vulnerabilities in software programs and protocols. This article reviews various approaches where fuzzing is applied to detect bugs and vulnerabilities in UAVs. Our goal is to assess the current state-of-the-art fuzzing approaches for UAVs, which are yet to be explored in the literature. We identified open challenges that call for further research to improve the current state-of-the-art.

Struck-out handwritten word detection and restoration for automatic descriptive answer evaluation

Unlike objective type evaluation, descriptive answer evaluation is challenging due to unpredictable answers and free writing style of answers. Because of these, descriptive answer evaluation has received special attention from many researchers. Automatic answer evaluation is useful for the following situations. It can avoid human intervention for marking, eliminates bias marking and most important is that it can save huge manpower. To develop an efficient and accurate system, there are several open challenges. One such open challenge is cleaning the document, which includes struck-out words removal and restoring the struck-out words. In this paper, we have proposed a system for struck-out handwritten word detection and restoration for automatic descriptive answer evaluation. The work has two stages. In the first stage, we explore the combination of ResNet50 and the diagonal line (principal and secondary diagonal lines) segmentation module for detecting words and then classifying struck-out words using a classification network. In the second stage, we explore the combination of U-Net as a backbone and Bi-LSTM for predicting pixels that represent actual text information of the struck-out words based on the relationship between sequences of pixels for restoration. Experimental results on our dataset and standard datasets show that the proposed model is impressive for struck-out word detection and restoration. A comparative study with the state-of-the-art methods shows that the proposed approach outperforms the existing models in terms of struck-out word detection and restoration.

Adaptation in Edge Computing: A Review on Design Principles and Research Challenges

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings.