Wasserstein adversarial learning based temporal knowledge graph embedding

Abstract: Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.


EFL Learners' Digitalized Practices in Promoting Vocabulary Learning at the Higher Education Level

Abstract: Technology has become an integral part of education; and language instruction, which is communicative in nature, has a share in the whole unit. The widespread use of portable devices has also transformed language learning through diverse accessible facilities that can alternatively be evaluated in an out-of-school context. This paved the way for self-directed learning practices that enable learners to advance their language skills and vocabulary. This study aimed to investigate these self-directed practices for learning vocabulary in English. 120 EFL learners at diverse departments at a state university in Turkiye participated in the study. For data collection, a survey was employed that interrogated the self-directed vocabulary learning practices of the participants in digital contexts. The results of the study showed that learners use mobile technology for English language practices and adopt different strategies in vocabulary learning supplied by a variety of digital applications and facilities. The results are discussed and related implications are addressed based on the findings.


Role of AR and VR in the Context of Technical Education

Abstract: In recent years, the primary research subjects for improving the lives of future generation youngsters have been excellence and creativity in the existing pedagogical system. Virtual reality plays an important part in introducing new educational ideas. To enhance the quality of teaching in aspects of relevant and interactive learning, the recent trend in educational technology is mostly centered on e - learning and mobile-based learning approaches. Virtual reality is now widely utilized in a variety of fields, including education, healthcare, training, and entertainment. This study paper examines the influence of virtual reality on the pedagogical sector, as well as its current and future.


EFL Learners' Views About the Use of Artificial Intelligence in Giving Corrective Feedback on Writing

Abstract: This chapter presents a qualitative study the aim of which is to explore EFL learners' views about the use of artificial intelligence for giving corrective feedback on their written works. Artificial intelligence use was addressed through one of recent and popular chatbots: ChatGPT. The data were gathered through semi-structured and stimulated-recall interviews, also videorecording of the participants' ChatGPT use for writing feedback. In the interviews, the participants reflected on their individualized language studies, and also having practiced the use of artificial intelligence in receiving corrective feedback on their writing assignments via ChatGPT for five weeks, they expressed their thoughts about this experience. The findings indicated codes and themes about learner characteristics and preferences, and benefits and pitfalls of artificial intelligence use for corrective feedback, which leads to several language teaching implications specifically concerning writing to be proposed in the chapter.


Emerging Tech Analysis of Intellectual Property and Data Ownership in Smart Factories

Abstract: Integrating emerging technologies in smart factories has ushered in a new manufacturing era. These technologies have transformed how data is generated, stored, and utilised, raising critical questions about intellectual property (IP) and data ownership. This study comprehensively analyses the challenges and implications associated with IP and data ownership within the context of smart factories. By reviewing the existing literature and conducting empirical research, this project aims to shed light on the multifaceted issues surrounding these concepts, offering valuable insights for industry professionals and policymakers.


Natural Language Processing Applications in Language Assessment

Abstract: Natural language processing is a subfield of artificial intelligence investigating how computers can be utilised to understand and process natural language text or speech to accomplish useful things in various areas, and it draws on various disciplines, such as computer science, linguistics, and robotics. Natural language processing applications, including automated speech recognition and scoring, have several exciting prospects for language testing and assessment practices. These prospects include addressing practical constraints associated with test administration and scoring, securing standardisation in test delivery, ensuring objectiveness and reliability in scoring procedures, and providing personalised feedback for learning. This chapter focuses on automated speech scoring and its applications in language testing and assessment and discusses how these systems can be employed in assessment contexts. The chapter also discusses the potential benefits and drawbacks of automated speech scoring while focusing on construct-related and practical challenges surrounding such systems.


Fortifying the Digital Forge

Abstract: In our interconnected world, cybersecurity is paramount due to IoT, cloud computing, and automation's impact on manufacturing. This chapter underscores digital manufacturing's dependence on interconnected systems and cloud infrastructure, acknowledging risks like data breaches, IP theft, and operational disruptions. It advocates a comprehensive cybersecurity approach encompassing technical measures, organizational policies, staff training, and incident response. The chapter delves into threats like malware, phishing, ransomware, and supply chain attacks, emphasizing continuous monitoring, threat intelligence, and vulnerability management. Additionally, it explores emerging trends like AI and machine learning in cybersecurity, legacy system security, and collaborative efforts among industry players, government agencies, and cybersecurity experts to safeguard digital manufacturing. This chapter aids manufacturers, security experts, and researchers in building secure systems in our connected digital landscape.


Exploring the Benefits of Virtual Reality (VR) in Manufacturing Training

Abstract: Manufacturing necessitates a delicate balance of productivity and safety, and factory training must reflect this. Leaders must be able to swiftly grow the workforce to guarantee high productivity. Through off-the-line mental repetitions, virtual reality training assists industrial coworkers in acquiring task execution abilities, boosting situational awareness, and comprehending safety standards. Manufacturers are embracing virtual reality training because it has been shown to drastically cut training times, safety issues, and worker time to profitability. By generating engaging, genuine teaching settings, virtual reality (VR) technology revolutionizes human learning by giving hands-on experiences, improving safety, increasing productivity, and lowering costs. Virtual reality (VR) technology enhances hands-on experiences, reliability, efficiency, and affordability by creating immersive, genuine instructional settings. The chapter presents an overview of the major advantages that VR offers in industrial training programs.


Development and Application of Machine Learning Algorithms for Sentiment Analysis in Digital Manufacturing

Abstract: Customer input has increased as digital manufacturing and smart factories advance. However, standard analysis methods struggle to turn this feedback into useful insights. This research study examined the use of machine learning (ML) sentiment analysis algorithms to improve digital manufacturing customer feedback interpretation. Machine learning, sentiment analysis, and digital industrialization theories underpin the research. Sentiment analysis may reveal nuanced consumer feedback insights that traditional methods miss, according to customer experience management and complex data analytics theories. A specially constructed ML system for sentiment analysis was used to real-world customer feedback data from numerous digital manufacturing enterprises in a case study. This method classified feedback sentiment using natural language processing. The program picked up small changes in client emotions that previous methods missed. These findings imply that machine learning-based sentiment analysis improves digital manufacturing customer feedback interpretation.


The Development of an Educational Mobile Application for Malaysian Sign Language

Abstract: In an increasingly connected world, the development of technology has been instrumental in breaking down barriers and fostering communication between diverse groups. One such group that has benefited from technological advancement is the Deaf and hard-of-hearing (D/HH) community, which communicates using sign language. Sign language, a rich and expressive form of communication, communicates visually using the hands, arms, face, and upper body to produce signs. However, there is a lack of teaching and learning resources to assist the deaf community and those interested in learning sign language with the learning process. Therefore, this chapter investigates the development of a mobile application for sign language focusing on Malaysian Sign Language (MSL). The user-friendly interfaces for this application make learning and using sign language more intuitive and accessible to individuals with hearing impairments and those without. This application also provides users with a platform to learn, practice, and improve their sign language knowledge.


Adaptive partitioning and efficient scheduling for distributed DNN training in heterogeneous IoT environment

Abstract: With the increasing proliferation of Internet-of-Things (IoT) devices, it is a growing trend toward training a deep neural network (DNN) model in pipeline parallelism across resource-constraint IoT devices. To ensure the model convergence and accuracy, synchronous pipeline parallelism is usually adopted. However, the synchronous pipeline can incur a long waiting time due to its gradient aggregation of all microbatches. It is urgent for a DNN model to design an adaptive partitioning and efficient scheduling scheme in heterogeneous IoT environment. To address this problem, we propose a policy gradient based model partitioning and scheduling scheme (PG-MPSS) to minimize per-iteration training time. More specifically, we first design a double-network framework to divide and schedule a DNN model. Then, we adopt a policy gradient algorithm to update the double-network parameters, aiming at learning an optimal double-network model. We conduct extensive experiments to compare the DNN training time of the PG-MPSS scheme with that of Dynamic Programming (DP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Average&Greedy (AG) and Proximal Policy Optimization (PPO) five baseline algorithms under different experimental settings. The related experimental results demonstrate that the PG-MPSS scheme can greatly expedite synchronous pipeline training of a DNN model.


Factors Influencing the Social Help-seeking Behavior of Introductory Programming Students in a Competitive University Environment

Abstract: Collaboration is an important aspect of computing. In a classroom setting, working with others can increase a student’s motivation to attempt more challenges, reduce the difficulty of complicated concepts, and bring about greater overall success. Despite extensive research in other domains, there has been minimal exploration within computing on what impacts a student’s decision to seek social assistance in highly competitive university environments. To understand what affects introductory programming students’ social help-seeking behavior in this context, we conducted 32 semi-structured interviews with students and performed thematic analysis and qualitative coding on the ensuing transcripts. Our qualitative analysis revealed 18 significant factors. We noticed that the decision to seek social help involved a two-fold process: first, the decision to engage in social help-seeking, and subsequently, the decision of who to ask for help. Furthermore, we found that help-seeking in computing is not fundamentally different from other disciplines, although some of the factors were unique to the topic of computing and the specific environment of this study. Factors related to communication style, the type of question being asked, and the school’s cheating policy were central when discussing code, an integral part of computing. Regarding the environment, students repeatedly reported that the competitive major, the explicit and implicit class standards, and feelings of intimidation, among others, influenced them. These findings suggest that understanding both steps and the sociocultural context is important in order to effectively lower the barriers to asking for help.


GLSFF: Global–local specific feature fusion for cross-modality pedestrian re-identification

Abstract: Cross-modality Pedestrian Re-identification (Pedestrian ReID) is an image retrieval technique used to match targets of interest in a library. The main challenge of Pedestrian cm-ReID is that the modality gap between visible and infrared images reduces the recognition effect. To reduce the gap, we propose a novel Pedestrian cm-ReID model called Global–Local Specific Feature Fusion (GLSFF) to integrate the person features extracted by backbone network. It contains the Global Feature Fusion Module (GFFM) and the Local Feature Fusion Module (LFFM). LFFM and GFFM fuse the local specific and global shared features of pedestrians respectively. Fusion features mitigate the gap modality in images and retain discriminative information, such as gait and silhouette. In addition, we propose a joint training method, which combines Heterogeneous Center Loss (HC Loss), Triplet Loss and Cross-Entropy Loss (CE Loss) to accelerate the convergence rate of the model. Extensive experiments were conducted on two popular datasets, SYSU-MM01 and RegDB, and the mAP of GLSFF reached 64.47% and 81.37%, respectively.


EduLGCL: Local-global contrastive learning model for education recommendation

Abstract: Contrastive learning has been used in recommendations to learn user and item representations from sparse and long-tail user-item interaction histories. Recommendations applied in education have several key challenges. Firstly, the frequency of student-course interactions is lower, resulting in limited data. Secondly, existing models rely on multimodal data for multi-objective recommendations and they limit the model’s generalizability. Lastly, it is important to consider the impact of static features on different students. Aiming at addressing these challenges, a novel course recommendation system based on Local-Global Contrastive Learning called as EduLGCL is proposed, in which the mutual information between course features and student features is computed to select course features. After expanding the connections, the relationship between students and courses is described from different perspectives. Multiple graph convolutional operations are performed on the interaction history graph which can connect students and courses and represent student clusters. Through the feature extraction layer, the final student representation is obtained. Extensive experiments were conducted on three public datasets and a course dataset collected from a real-world environment to validate the performance of the EduLGCL. The results demonstrate that EduLGCL outperforms baseline methods, particularly in handling user cold-start problems. Our code is available at https://github.com/mintZYJ/EduLGCL.


Hybrid CNN-transformer based meta-learning approach for personalized image aesthetics assessment

Abstract: Personalized Image Aesthetics Assessment (PIAA) is highly subjective, as people's aesthetic preferences vary greatly. Traditional generic models struggle to capture the unique preferences of each individual, and PIAA often deals with limited samples from individual users. Furthermore, it requires a holistic consideration of diverse visual features in images, including both local and global features. To address these challenges, we propose an innovative network that combines the power of transformer and Convolutional Neural Networks (CNNs) with Meta-Learning for PIAA (TCML-PIAA). Firstly, we leverage both Vision Transformer blocks and CNNs to extract long-term and short-term dependencies, mining richer and heterogeneous aesthetic attributes from these two branches. Secondly, to effectively fuse these distinct features, we introduce an Aesthetic Feature Interaction Module (AFIM), designed to seamlessly integrate the aesthetic features extracted from CNNs and ViT, enabling the interaction and fusion of aesthetic information across different modalities. We also incorporate a Channel-Spatial Attention Module (CSAM), embedding it within both the CNNs and the AFIM to enhance the perception of different regions in images, further exploring the aesthetic cues in images. Experimental results demonstrate that our TCML-PIAA outperforms existing state-of-the-art methods on benchmark databases.


Deformable attention object tracking network based on cross-correlation

Abstract: With the increasing popularity and widespread application of embedded devices, lightweight target tracking networks have emerged as a crucial research direction in the field of computer vision. In this paper, we propose a deformable attention object tracking network based on cross-correlation (DAM-LOTNet), which utilizes the lightweight MobileNetV3 network for efficient feature extraction. Aiming at the problem that lightweight networks cannot better capture target features and morphological changes, deformable attention modules are proposed to generate attention maps along the channel and spatial dimensions. Furthermore, a dual cross-correlation operation is proposed, aiming at fusing channel information and pixel-level information. Thus, the model obtains a more comprehensive feature representation and a more accurate similarity calculation capability while maintaining a lightweight framework. Finally, a feature enhancement network constructed by depthwise separable convolution was added to the classification and regression branches. The designed DAM-LOTNet has 0.552M parameters and 0.121G FLOPs. On the UAV123 dataset, DAM-LOTNet achieves state-of-the-art performance compared to other lightweight object tracking algorithms. Extensive experiments on visual tracking benchmarks (including VOT2018, VOT2019, OTB100, etc.) have demonstrated that DAM-LOTNet has extremely low model FLOPs and parameters compared to common deep learning-based trackers, and is essentially on par with these trackers in terms of performance.


Problems and Prospects of Cryptocurrency Usage in China and Cambodia

Abstract: The transformation of the digital economy has been constantly evolving in the society of the 4.0 industrial revolution, particularly under the governmental policies of developed countries. One of the most popular tools in digital economy activities is cryptocurrency, which has led to a rise in digital awareness among people who are more inclined to live their lives electronically and digitally. The aim of this study is to explore the problems and prospects associated with the usage of cryptocurrency and identify ways to address these issues using the governmental roadmap in China and Cambodia. The methods employed in this study include descriptive, explanatory, and comparative analyses. The results demonstrate that the adoption of cryptocurrency presents both opportunities and threats to the Chinese and Cambodian economies. These opportunities and threats need to be carefully considered and balanced by policymakers and stakeholders. The conclusion drawn from the study is that there is still no official acceptance and recognition of cryptocurrency by the Chinese and Cambodian governments. This is primarily due to the fact that the risks and challenges associated with cryptocurrencies are deemed to be greater than the foreseeable opportunities, making them difficult to manage effectively.


Conceptual Change of ‘Teaching’ among Experienced Teachers after Studying Attentive Teaching

Abstract: One of the obstacles preventing change of teaching methods in schools is teachers’ traditional conceptualizations of ‘teaching’ as transmissive and teacher-centered. The aim of this study was to track changes in experienced teachers’ concept of ‘teaching’, following their exposure to attentive teaching. This is a dialogical method in which the learners represent their concepts in drawings and written explanations, and discuss them with their teacher and peers. Method: This was a multiple-case study. The participants were three teachers who attended an attentive teaching professional development course. They drew ‘teaching’ in the first, sixth, and the last, fifteenth, session, and provided explanations of their drawings. Findings: At the start of the course, they described teaching as a unidirectional process of transmitting knowledge. In the middle, they became more aware of the students as individuals who should be listened to. By the end of the course, teaching was portrayed as multi-directional (and enjoyable), so that all the participants, including the teacher, teach and learn from each other. Conclusions: This study shows that by studying, experiencing, and implementing attentive teaching, it is possible to change experienced teachers’ traditional beliefs without directly challenging them, and that drawings can track the changes’ trajectory.


Regional- to district-scale controls on thermal springs occurrence in South Africa: Insights from investigations of their spatial distribution and their spatial relationships with geological features

Abstract: In the study presented here, the spatial distribution of thermal springs in South Africa was investigated using point distribution analysis, fractal analysis and Fry analysis, and the thermal springs’ spatial relationships with high-heat-producing granitoids (HHPGs), groundwater yield, and structural lineaments were investigated using distance distribution analysis. The purpose of these spatial investigations was to explore the regional- to district-scale geological controls on thermal springs occurrence in South Africa. The results show that regional- to district-scale geological controls on thermal springs occurrence in South Africa involve the presence of or proximity to HHPGs (as heat source control), groundwater yield of ∼2 L/s (as water source control) and intersections of structural lineaments with particular trends (as pathways control). In southern and eastern South Africa, the pathways controls are, respectively, intersections of ENE-trending lineaments with NNW- and NW-trending lineaments and intersections of ENE- and WNW-trending lineaments. This spatial information comprises a conceptual model that can be used as a framework for regional- to district-scale predictive modeling and mapping of the prospectivity (or likelihood) of areas where geothermal resources potentially exist in South Africa.


MTSFBet: A Hand-Gesture-Recognition-Based Identity Authentication Approach for Passive Keyless Entry Against Relay Attack

Abstract: The Passive Keyless Entry and Start system (PKES) has become an essential element of vehicle systems since it allows owners to lock or unlock their properties without having to take out the keys. However, the system suffers from a potential and serious security problem because of a relay attack. This paper introduces dynamic biometrics to resolve such potential problems. Precisely, it opts for hand-gesture and proposes a Multi-Task Siamese Feature-pyramid network with Bidirectional Gated Recurrent Units (MTSFBet) against relay attack. MTSFBet is made up of a Dynamic Biometrics Extraction Module (DBEM) and Multi-Task Operation Module (MTOM). DBEM extracts trajectory and kinematic features from the gesture data of owners, whereas MTOM performs gesture classification and identity authentication using these features. Moreover, we designed a loss function for the identity authentication part. Eventually, based on the dataset collected by mobile devices, we constructed comparative experiments and ablation studies to demonstrate the effectiveness of the method. Our comprehensive model achieves an accuracy of 0.86 in gesture classification and an accuracy of 0.73 in identity authentication. The results showed that our MTSFBet significantly outperforms other comparison methods. The MTSFBet can also be used in any scenario associated with identity authentication based on hand gestures.


Evolutionary Multitasking via Reinforcement Learning

Abstract: Different from traditional evolutionary algorithms (EAs), the multifactorial evolutionary algorithm (MFEA) is proposed to optimize multiple optimization tasks concurrently. Through the knowledge transfer between different tasks, MFEA has been proved to be superior to single-task EAs in the solution quality and convergence speed. Recently, various MFEAs have been developed. Most of them are based on a common model in MFEA, where a fixed knowledge transfer parameter, i.e., random mating probability ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$rmp$</tex-math></inline-formula> ), is used. In addition, a single evolutionary search operator is employed in the whole evolutionary process. However, in this model, the fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$rmp$</tex-math></inline-formula> is difficult to adapt to multiple different tasks. Besides, a single evolutionary search operator may not be suitable for problems with different properties, thus limiting the performance of the algorithm. Based on these considerations, in this article, a reinforcement learning based multifactorial evolutionary algorithm (RLMFEA) is presented. In RLMFEA, it allows different evolutionary search operators to be embedded in MFEA, and each task has a changing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$rmp$</tex-math></inline-formula> that is adaptively adjusted by reinforcement learning. The effectiveness of RLMFEA has been verified on a series of single-objective multitask optimization benchmark functions and a real-world application.


Entanglement Routing Design Over Quantum Networks

Abstract: Quantum networks have emerged as a future platform for quantum information exchange and applications, with promising capabilities far beyond traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet challenging problem. In this paper, we study a quantum entanglement routing problem to simultaneously maximize the number of quantum-user pairs and their expected throughput. Our approach is to formulate the problem as two sequential integer programming problems. We propose efficient entanglement routing algorithms for these two optimization problems and analyze their time complexity and performance bounds. Evaluation results highlight that our approach outperforms existing solutions in both the number of quantum-user pairs served and network throughput.


An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

Abstract: General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients' electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0.7873, recall@10 of 0.9020 and hits@10 of 0.9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.


VR/MR systems integrated with heat transfer simulation for training of thermoforming: A multicriteria decision-making user study

Abstract: The emerging Industry 5.0 paradigm emphasizes the importance of collaboration between machines and human operators in smart factories, thereby preventing full automation and elimination of the skilled workforce. One technology that has been proposed recently as a means of achieving this collaboration, is the Extended Reality (XR), particularly for operators training and upskilling. However, current XR applications in advanced manufacturing sector are rather limited, mainly focusing on maintenance or sequential training. Additionally, most of earlier studies in the field have examined the use of one type of XR environment solely, such as Virtual Reality (VR), Augmented Reality (AR), or Mixed Reality (MR) in a respective application. The present study aims to develop and compare the performance of both MR and VR environments for training operators during the monitor and control of a given thermoforming manufacturing process. To achieve this, Meta Quest 2 (VR headset) and Microsoft HoloLens 2 (MR headset) were utilized in combination with heat transfer numerical simulations of the process. A video training tool was also created and employed as a reference/conventional training method, for comparisons. A user study involving 26 participants (with a mix of prior experience) was conducted, assessing different performance criteria including the usefulness, trust, reliability, user satisfaction, training effectiveness, and overall user preference over each training tool. Results, using statistical analysis along with five Multiple Criteria Decision Making (MCDM) algorithms, collectively indicated that the users preferred MR over VR and video training in the present case study; particularly as MR provided greater presence experience and satisfaction, higher user-friendliness and reliability. A demonstration of the developed applications can be found in the Supplementary Material.


Learnable convolutional attention network for knowledge graph completion

Abstract: Recently, graph convolutional networks (GCNs) and graph attention networks (GATs) have been used extensively in knowledge graph completion (KGC), which aims to solve the incompleteness of knowledge graphs (KGs). However, both GCNs and GATs have limitations in the KGC task, and the best method is analyzing the neighbors of each entity (pre-validating), while this process is prohibitively expensive. Furthermore, relations in KGs have specific semantics and should be considered when aggregating neighbor information (message passing). To address the above limitations, we propose a learnable convolutional attention network for knowledge graph completion named LCA-KGC. LCA-KGC introduces a knowledge graph convolutional attention network using a convolution operation before the attention mechanism to ensure structural information acquisition and avoid redundant information stacking. Furthermore, to complete the autonomous switching of GNNs types and eliminate the necessity of pre-validating the local structure of KGs, LCA-KGC designs a learnable knowledge graph convolutional attention network by comprising three types of GNNs in one learnable formulation. Moreover, a learnable message function is proposed to emphasize relational semantics when aggregating neighbor information. Extensive experiments on standard KG datasets validate the effectiveness of the proposed innovations, and LCA-KGC achieves state-of-the-art (SOTA) performance compared to existing approaches (e.g., compared to SOTA approaches, LCA-KGC improves MRR from 0.360 to 0.372 on FB15k-237 dataset, and Hits@3 from 0.561 to 0.581 on YAGO3-10 dataset).


HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer

Abstract: Abstract Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The proposed method, which is an extension of HSCNet, allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image localization on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined indoor scenes.


A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

Abstract: This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.


Relation fusion propagation network for transductive few-shot learning

Abstract: Previous graph-based meta-learning approaches have explored pairwise feature similarity to learn instance-level relations of samples, however, the gap between the sample relations in feature and label spaces is often ignored. It is empirically observed that instances with different labels may display considerable similarity in visual characteristics, making it challenging to distinguish between them in the feature space. To this end, we propose a dual-branch Relation Fusion Propagation Network (RFPN) for transductive few-shot learning, which explicitly models both feature and label relations across support-query pairs. Specifically, we design a Relation Fusion Block (RFB) to fuse instance-level and class-level relations, thus obtaining more robust fusion-level relations to guide feature propagation. In addition to the feature propagation branch, we encode the label relations and construct a label shortcut branch for label propagation. To alleviate the error accumulation during propagation, which is caused by uncertain pseudo-labels for query samples, we propose a Label Shortcut Mechanism (LSM) to progressively update the sample relations with the initial labels. Our full method is plug-and-play and can be easily applied in existing graph-based approaches for transductive few-shot learning. Extensive experiments demonstrate that our proposed RFPN yields significant improvements over the baselines and achieves promising performance on four popular few-shot classification benchmarks.


Hierarchical variable clustering via copula-based divergence measures between random vectors

Abstract: This article considers rank-invariant clustering of continuous data via copula-based Φ-dependence measures. The general theoretical framework establishes dependence quantification between random vectors (groups of variables), which is used for measuring the similarity between variable clusters in an agglomerative hierarchical procedure afterwards. Special attention is devoted to meta-elliptical copulas, where we present an improved kernel estimator for the density generator and a corresponding bandwidth selector. This allows for non-Gaussian similarities also capturing e.g. tail dependence. Further, a fully non-parametric estimator is considered, enabling cluster detection in contexts where other measures fail. The theory is supported by simulations and a real data example, focusing on cluster analysis of continuous variables.


A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices

Abstract: Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this paper, we propose a Fast Attention Network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency. Specifically, we introduce a clean and parameter-refined attention module to enhance the information exchange between intent and slot, improving semantic accuracy by more than 2%. FAN can be implemented on different encoders and delivers more accurate models at every speed level. Our experiments on the Jetson Nano platform show that FAN inferences fifteen utterances per second with a small accuracy drop, showing its effectiveness and efficiency on edge devices.


Multi-view class incremental learning

Abstract: Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach.


Semantic-Preserving Surgical Video Retrieval With Phase and Behavior Coordinated Hashing

Abstract: Medical professionals rely on surgical video retrieval to discover relevant content within large numbers of videos for surgical education and knowledge transfer. However, the existing retrieval techniques often fail to obtain user-expected results since they ignore valuable semantics in surgical videos. The incorporation of rich semantics into video retrieval is challenging in terms of the hierarchical relationship modeling and coordination between coarse- and fine-grained semantics. To address these issues, this paper proposes a novel semantic-preserving surgical video retrieval (SPSVR) framework, which incorporates surgical phase and behavior semantics using a dual-level hashing module to capture their hierarchical relationship. This module preserves the semantics in binary hash codes by transforming the phase and behavior similarities into high- and low-level similarities in a shared Hamming space. The binary codes are optimized by performing a reconstruction task, a high-level similarity preservation task, and a low-level similarity preservation task, using a coordinated optimization strategy for efficient learning. A self-supervised learning scheme is adopted to capture behavior semantics from video clips so that the indexing of behaviors is unencumbered by fine-grained annotation and recognition. Experiments on four surgical video datasets for two different disciplines demonstrate the robust performance of the proposed framework. In addition, the results of the clinical validation experiments indicate the ability of the proposed method to retrieve the results expected by surgeons. The code can be found at https://github.com/trigger26/SPSVR.


Inter-domain mixup for semi-supervised domain adaptation

Abstract: Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification performance than unsupervised domain adaptation (UDA). However, existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains, resulting in label mismatch in the label space during model testing. This paper presents a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE), to tackle this issue. Firstly, we introduce a cross-domain feature alignment strategy, Inter-domain Mixup, that incorporates label information into model adaptation. Specifically, we employ sample-level and manifold-level data mixing to generate compatible training samples. These newly established samples, combined with reliable and actual label information, display diversity and compatibility across domains, while such extra supervision thus facilitates cross-domain feature alignment and mitigates label mismatch. Additionally, we utilize Neighborhood Expansion to leverage high-confidence pseudo-labeled samples in the target domain, diversifying the label information of the target domain and thereby further increasing the performance of the adaptation model. Accordingly, the proposed approach outperforms existing state-of-the-art methods, achieving significant accuracy improvements on popular SSDA benchmarks, including DomainNet, Office-Home, and Office-31.


Pseudo-label estimation via unsupervised Identity Link Prediction for one-shot person Re-Identification

Abstract: In this paper, we propose an unsupervised identity link prediction (ILP) method for label estimation in one-shot person Re-ID. ILP aims to relax the constraints of labeled samples and group a set of unlabeled pedestrians by their potential identities. The main idea is that the category relationships between pedestrians (nodes) can be inferred from their local context in the feature space. Specifically, an Identity Link Subgraph (ILS) describes the link relationship between nodes and their nearest neighbors, which is constructed by a two-step procedure. Meanwhile, a Dynamic Penalty Module (DPM) is introduced at each ILS construction step to infer which linkage between pairs in the ILS should be pruned to assign higher-quality classification labels. To fully use the accurate identity information in initial labeled samples, we jointly use identity pseudo-labels (which are estimated by adopting the Nearest Neighbors classifier) with classification pseudo-labels for model training. Moreover, we design a Dual-Branch Fusion network (DBF-Net) to optimize the CNN model simultaneously through all (pseudo-)labeled samples. Results on multiple datasets prove that DBF-Net outperforms traditional one-shot Re-ID methods by a large margin.


Simion Zoo: A training workbench for reinforcement learning allowing distributed experimentation

Abstract: Simion Zoo is a Reinforcement Learning (RL) workbench developed for training of novel users that can be deployed over computer farms allowing extensive experimentation over distributed resources. In this paper, we present this software platform and share some of the insights gained during the development. This workbench provides a complete set of tools to design, run, carry out statistical analysis of the results, report preparation, and have qualitative visual assessment of the simulation evolution of continuous RL control experiments. The main features that set apart Simion Zoo from other software packages for introduction to RL experimentation are its easy-to-use GUI, its support for distributed execution including deployment over graphics processing units (GPUs), and the possibility to explore concurrently the RL hyper-parameter space, which is key to successful RL experimentation.


Recursive posterior Cramér–Rao lower bound on Lie groups

Abstract: The recursive posterior Cramér–Rao Lower Bound (CRLB) introduced by Tichavsky et al. (1998) is an efficient tool to estimate the minimum variance of an estimator. Lately, filters and observers on matrix Lie groups have raised interest in control and nonlinear estimation. However, the recursive CRLB applies to variables belonging to the Euclidean space, and a recursive formulation on matrix Lie groups was not introduced yet. Hence, this paper derives a recursive formula of the Fisher information matrix on Lie groups, which reveals to be a natural extension of the information matrix on the Euclidean space. This generic result is applied to nonlinear Gaussian systems on Lie groups and tested on a navigation problem. The proposed recursive CRLB is consistent with state-of-the-art filters and shows a representative behavior with respect to the estimation errors. This paper provides a simple method to recursively compute the minimal variance of an estimator on matrix Lie groups. It enables to assess the behavior of an estimator, which is fundamental to implement robust algorithms.


BiPR-RL: Portrait relighting via bi-directional consistent deep reinforcement learning

Abstract: Portrait relighting via deep reinforcement learning could conduct portrait relighting by sequentially predicting local light editing strokes, simulating image editing by artists using brush strokes. It is a locally effective, scale-invariant and interpretable method, which yields state-of-the-art results in relighting wild portrait images. However, it still has problems due to the lack of supervision information on intermediate processes, which makes the reinforcement learning agent not receive sufficient feedback during the exploration process. This will cause incorrect strokes in the relighting. To further optimize the relighting effect, we take advantage of the fact that the light editing actions are invertible and the inverse actions can generate a backward sequence of states. The backward state sequence also provide useful supervision signals as they convey information about the future states given the action choices. Therefore, we propose to combine the forward and the backward state sequence predictions to improve the learning efficiency for portrait relighting. Using the bi-directional state sequences, we design corresponding bi-directional consistent rewards to guide the model to explore the actions with higher accuracy to maximize the performance of the proposed method. The proposed approach is used for portrait relighting tasks based on both SH-lighting and reference images. The results show that our method has further improved the performance by avoiding wrong strokes. Meanwhile, our method performs better than SOTA methods in producing locally effective relighting images for wild portrait images.


A novel partition strategy for efficient implementation of 3D Cellular Genetic Algorithms

Abstract: Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. Cellular Genetic Algorithms (cGAs) have been competitive at difficult single objective combinatorial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.


Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies

Abstract: This paper presents our solution for the Real Robot Challenge III, aiming to address dexterous robotic manipulation tasks through learning from offline data. In this competition, participants were given two types of datasets for each task: expert and mixed. Each expert dataset is collected by a high-skill policy, whereas the mixed dataset is collected using both expert and non-expert policies. We found that the vanilla behavioural cloning (BC) can learn a very proficient policy with minimal human intervention when trained on expert datasets. Notably, BC outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, when applied to mixed datasets, the performance of BC deteriorates; similarly, the performance of offline RL algorithms is also less than satisfactory. Upon examining the provided datasets, it was apparent that each mixed dataset contained a significant proportion of expert data, which should enable the training of a proficient BC agent. However, the expert data is not labelled in the datasets. As a result, we propose a classifier to identify the pattern of the expert behaviour within a mixed dataset and then utilize it to isolate the expert data. To further boost the BC performance, we take advantage of the geometric symmetry of the arena to augment the training dataset through mathematical transformations. Ultimately, our submission outperformed that of all other participants. Our solution scripts are available on our website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wq13552463699/</uri> Real-Robot-Challenge-III-Winning-Solution.


Behind the Bait: Delving into PhishTank's hidden data

Abstract: Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.


Research on efficient detection network method for remote sensing images based on self attention mechanism

Abstract: Remote sensing images are widely used in aerial drones, satellites, and other fields. However, traditional object detection methods face challenges of low efficiency and accuracy due to the diverse scales, numerous target types, and complex backgrounds of remote sensing images. To address these issues, this paper proposes a remote sensing image detection network based on self-attention, which adaptively learns the relative importance of pixels to achieve more precise and efficient object detection in remote sensing images, meeting the demands of large-scale remote sensing image processing. Firstly, through end-to-end training, the object detection network is optimized as a whole to directly output detection results from the input remote sensing images, eliminating the need for additional intermediate steps. This not only reduces the impact of information loss and inconsistency but also simplifies the entire detection process. Secondly, we integrate the Faster R-CNN architecture as the foundation, combining region extraction and object classification into a unified process. Lastly, we embed self-attention mechanisms at different levels of the Faster R-CNN to progressively extract multi-scale and multi-level feature information, enhancing the network's ability to learn the correlation of information from different positions in the image, automatically capturing the relationships between objects, and improving the accuracy of object detection. This significantly reduces redundant computation, making it more efficient for large-scale remote sensing image processing. Experimental verification demonstrates that this approach outperforms traditional methods in terms of detection accuracy and efficiency, better addressing the particularities of remote sensing images, and providing an efficient and precise solution for aerial drone and satellite image processing. Remote sensing image detection has become one of the research hotspots in the field of remote sensing, holding significant theoretical significance and practical application value.


Geochemical baselines of potentially toxic elements in Brunei-Muara soils, Brunei Darussalam: Towards environmental assessment and risk management

Abstract: We present a comprehensive study on the presence of potentially toxic elements (PTEs) in the topsoil and subsoil of Brunei-Muara District in Brunei Darussalam. Descriptive statistics were employed to analyze the data, and the threshold values for PTEs in both soil layers were determined. Adopting the Tukey's inner fence method, we recommend baselines for PTEs. The study delved into the distribution patterns and potential sources of PTEs in Brunei-Muara, and we identified traffic and agricultural activities as significant contributors to the presence of As, Cr, Cu, and Pb. Industrial and agricultural areas exhibit spatial associations with Mn, Ni and Zn. Cadmium (Cd) was determined to be negligible in Brunei-Muara's soils. Furthermore, geochemical mapping unveiled highly acidic soil conditions in the region, characterized by variations in pH between the topsoil (2.13–8.00) and subsoil (1.89–6.76) layers that generally facilitate mobility of heavy metals. We employed Principal Component Analysis on the log10-transformed data to examine the behavior and mechanisms governing the mobility and retention of PTEs in the soil. This helped us to better characterize the element distribution within the baseline and the recognition of three additional processes enriching the topsoil and subsoil in PTEs. Overall, this study offers valuable insights into the distribution, sources, and behavior of PTEs in the soils of the Brunei-Muara District, which can greatly aid environmental monitoring and management endeavors.


Stable Minimum Solution Calibration Method Using 2-D Laser Radar and Camera

Abstract: Two-dimensional laser radar and cameras are widely used to perceive external environments in unmanned vehicle navigation, target recognition, mobile robot obstacle avoidance, and simultaneous localization and mapping. Herein, the two sensors must be calibrated first to fuse laser data and image. The minimum solution method for calibration is one of the most popular ones but has some shortcomings, such as poor numerical stability and accuracy. Therefore, we present a new stable minimum solution calibration method, which introduces the RP3P algorithm to solve the perspective three-point problem (P3P) and improve the mechanisms of optimal solution selection. First, according to the P3P problem constructed using three checkerboards, the enhanced RP3P algorithm is used to improve the stability of the solution. Second, an error measurement model based on region and boundary constraints is proposed to evaluate the deviation of the multiple solutions to select the optimal solution. Simulation and actual experimental results show that the proposed algorithm can significantly improve the percentage of valid solutions and calibration accuracy compared with the algorithms in the literature. In the simulation experiments, compared with Francisco’s, Hu’s, and Zhou’s methods, the percentage of valid solutions of our method is improved by 7%-33%, 6%-10%, and 5%-9% under different noise levels. Furthermore, the accuracy of the calibration result of our calibration method obtained significant performance improvements.


Spatial variability and hydro/geochemical profiling of the elemental composition of mineral deposits and drip water from caves using unsupervised chemometric modelling

Abstract: Caves are complex systems characterized by high variability in trace element signatures that simple geochemical analysis cannot detect since useful information hidden in the dataset may be lost. Sufficiently robust unsupervised statistical approaches may provide these significant signatures for cave hydro/geochemical profiling. The present study focused primarily on quantifying the relevant contributions of elements and their relationships to the variability of the hydro/geochemical profiles of speleothems, sediments and drip water within a cave using Principal Component Analysis (PCA) after Varimax rotation and two-way joining analysis as a clustering method. This prospective study was conducted on the Urșilor (Bears') show cave (Romania). The contents of Ca, Mg, Sr, Al, P, S, Fe and Zn in the mineral deposits were determined via X-ray fluorescence, while the content of these elements in drip water was determined via inductively coupled plasma–optical emission/mass spectrometry. The contents of HCO3−, Cl−, F−, NO3−, and SO42−; pH; electrical conductivity; and organic and inorganic carbon fractions were also determined. In the case of speleothems, the determinations were performed on randomly selected spots, reflecting the composition at various growth times. The PCA revealed that the geochemical profile of speleothems can be described by four principal components (PCs), represented by 71% for stalactites and 82/92% by three PCs for stalagmites/sediments. The strong signatures of Ca, Mg/Ca, K, and detrital elements (Al, Si, P, S) in stalagmites/sediments in proportions of 41/46%; K, Fe and Zn 30/32%; and Sr and Sr/Ca 11/15% were retained by PCA. The geochemical profile of the stalactites was described by Mg (35%), P, Zn, and Sr (21%); Al and Si (15%); and S, Ca and Mg/Ca (10%). Reducing the dimensionality of the data matrix to alkaline earth elements, two PCs described 95% of the mineral deposits, including the strong signatures of Mg/Ca and Sr/Ca, exceeding 50% of the variability in the first PC. Stalactites and stalagmites were primarily grouped based on their geochemical profile and less so on location, yielding two clusters/four subclusters of stalagmites. The PCA plot did not show a separation between stalactites and stalagmites, as their geochemical profiles differ significantly only in terms of Fe concentration. The hydrochemical profile of drip water was described by 4 PCs at a percentage of 95%: (i) alkaline elements, alkaline earth elements, anions and organic carbon and (ii) trace elements and DOC. The profile of Ca2+–HCO3− imprint drip water was described by two PCs at proportions of 66% (Ca, Mg, Na, K, Mg/Ca, Sr/Ca, Ba/Ca ratios, HCO3− and organic carbon) and 74% by trace elements (Co, Ni, Ti, Fe, Mn, V and Si). The transition trace elements have very complex chemical behaviour and appear in drip water as Ca-/Si-rich inorganic/organic colloids and simple ions. Although complementary to the PCA, the two-way joining analysis in a heatmap did not offer relevant information in the description of the hydro/geochemical profile of the karst system compared to PCA. A study of a farmed calcite sample highlighted its difference in composition compared with stalagmites in terms of Mg, Si, K, Zn, Sr and Sr/Ca. Future development could include the application of PCA to seasonally collected farmed calcite and drip water samples to obtain hydro/geochemical profiles of stalagmites in the making to determine the seasonal leaching of metals from bedrock/soil by drip water, inclusion in stalagmites, and seasonal growth. The practical significance of this paper for trace element research is of great concern because it allows these recorded indicators to be connected to the ancient climate and environment of stalagmites.


Double-Layer InSb Photonic Crystal Structure for Enhanced Nonreciprocal OTS and Its Potential in Extending the Measurement Range of Bivariate Sensors

Abstract: In this paper, the dependence of optical Tamm state (OTS) to the refractive index of the interface medium is used to form nonreciprocal absorption peaks after introducing the double InSb layers on both sides of the structure. And compared to the introduction of a single layer, the energy coupling between the double InSb layers is stronger, which greatly improves the absorption performance. The enhancement of the incident angle can significantly regulate the polarization separation of the absorption peak in different directions, and the degree of nonreciprocity will also be affected by temperature and magnetic field. In the opposite direction, the relevant absorption peaks show a differentiated linear range for refractive indexes and incident angles, thereby increasing the measurement range (MR) of dual variable detection. For positive incidence, the MR, sensitivity (S) and detection limit (DL) of refractive index detection are 0.52 THz/RIU, 2.6-3.4 RIU, and 5.5×10 -4 RIU, respectively. The corresponding indicators of angle measurement are successively 0.0074 THz/degree, 15-40 degrees, and 0.039 degrees. For negative incidence, the performance indexes of refractive index measurement are 0.81 THz/RIU, 2.1-2.6 RIU, and 3.25×10 -4 RIU in turn. The angle detection characteristics are 0.01 THz/degree, 40-76 degrees, and 0.047 degrees. By comparison, the MR of the refractive index sensor is extended by at least 62.5%, while that of the angle sensor rises by 69.4%. We hope that the proposed design is beneficial to the improvement of sensor performances.


A Low-Complexity Air-Digit-Writing Recognition Method Based on Adaptive Trajectory Learning Using MIMO Radar

Abstract: Touchless hand gesture recognition is one of the hottest areas of the human-computer interaction. In this paper, we propose a low-complexity air-digit-writing recognition method based on adaptive trajectory learning using a frequency modulated continuous wave multiple-input-multiple-output (MIMO) radar. The core idea of the proposed method is the adaptive learning trajectory, which includes the rough trajectory estimation, the effective trajectory interception, and the fine trajectory processing. Firstly, the rough trajectory estimation is sequentially conducted by the clutter suppression, range object detection, direction of arrival estimation, angle object detection and centroid estimation. This could reduce the effective feature dimension and further the complexity of the following network and real-time system. Secondly, to remove the interference of redundant actions, the effective trajectory interception employs the proposed double adaptive thresholds gradient valley search approach to detect the start and end of the hand gestures. Thirdly, the fine trajectory processing is performed by the position-shift, normalization and resampling to eliminate the differences of hand gestures, including the position, size and speed, and improve the generalization ability and accuracy. Moreover, in order to further reduce the computational complexity, a single-layer long short-term memory network is constructed for identification. Finally, the method is deployed on a commonly used micro unit control to realize real-time system. The experimental results show that the proposed method can achieve an average accuracy of 99.6% in train and test spilt test, 98.24% in leave one subject out test, 97.4% in real-time test on trained people, and 97% in real-time test on untrained people.


Dual-stream pyramid and attention network for stereo matching

Abstract: In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet.


MTT-27 Autonomous and Connected Systems: Report for 2021–2022 [MTT-S Society News]

Abstract: Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.


A hierarchical probabilistic underwater image enhancement model with reinforcement tuning

Abstract: Underwater Image Enhancement (UIE) is a challenging problem due to the complex underwater environment. Traditional UIE methods can hardly adapt to various underwater environments. Deep learning-based UIE methods are more powerful but often rely on a large deal of real-world underwater images with distortion-free reference images. This gives rise to two issues: First, the reference images are highly uncertain because the ground-truth images cannot be are captured directly in underwater environment. Second, learning-based methods may lack generalization ability for diverse underwater environments. To tackle these issues, we propose HPUIE-RL, a hierarchical probabilistic UIE model facilitated by reinforcement learning. This model integrates UNet with hierarchical probabilistic modules to produce various enhanced candidate images that reflect the uncertainty of the enhancement. Then, a reinforcement learning fine-tuning framework is designed to fine-tune the pretrained model in an unsupervised manner, which responds to the dynamic underwater environment. Experiments on real-world datasets from diverse underwater environments demonstrate that our HPUIE-RL model outperforms state-of-the-art UIE methods regarding visual and quantitative performance and generalizability.


A low-noise, high-SNR and large-dynamic-range balanced homodyne detector for broadband squeezed light measurement

Abstract: Squeezing-based quantum metrology has been extensively applied in the field of precision measurement. Performance characteristics of squeezed light are obtained by a balanced homodyne detector. Here, we report a large dynamic range balanced homodyne detector with low-noise and high-signal-to-noise-ratio (SNR) for broadband squeezed light measurement. We perform a comprehensive theoretical analysis of the SNR in a wide bandwidth and experimentally analyze the important printed circuit board (PCB) layout and electronic components. Consequently, in the 100 MHz frequency band, the SNR and common mode rejection ratio (CMRR) rise over 12.6 dB and 55 dB, respectively, and the electronic noise is suppressed below −90 dB. The directly observed squeezing level at 1550 nm with the specified detector is 11.8 dB at 2 MHz and 3.1 dB at 100 MHz.


Efficient number theoretic transform accelerator for CRYSTALS-Kyber

Abstract: The national institute of standards and technology (NIST) has presented its draft of the module-lattice-based key-encapsulation mechanism standard (MLBKEMS), choosing cryptographic suite for algebraic lattices (CRYSTALS)- Kyber as the base encryption. Existing hardware implementations of modern cryptography will need to process the new standard efficiently. The primary process in CRYSTALS-Kyber key-encapsulation mechanism (KEM) is the number theoretic transform (NTT), which requires heavy computing power. This paper contributes an efficient hardware accelerator for NTT and inverse NTT (INTT) by CRYSTAL-Kyber parameters. The proposed design utilizes the K-RED algorithm for reducing polynomial multiplication. It also incorporates the BrentKung method for efficient modular addition and subtraction operation with an address generator to control the sequences of computation. On the Xilinx Artix 7 field programmable gate array (FPGA), our design achieves 262 MHz clock speed, utilizing only 1405 LUTs.


Navigation of mobile robots using neural networks and genetic algorithms

Abstract: The navigation of robots has been a subject of widespread interest over the last few decades. In the previous years, traditional methods based on mathematical equations were used, and there has been an evolution towards the use of methods based on artificial intelligence. Two of which have been used in this work: neural networks and genetic algorithms. Neural networks are used as a machine learning model to teach the robot to move from any starting point to a goal, avoiding obstacles along the way. However, this model needs an algorithm to learn how to carry out this activity, which is what the genetic algorithm will be used for. Furthermore, this method of navigation will be compared with the traditional method based on potential fields, where it can be observed how this new method based on artificial intelligence improves and solves some typical problems of the old methods, such as the tendency to get stuck in local minima.


Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Patient characteristics: MGUS.


Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Performance evaluation of MoSaicNet and AwareNet deep learning models: A, The ROC curves and AUC values of the MoSaicNet superpixel classifier. The values in brackets indicate the 95% CI. B, Two-dimensional mapping of superpixels using MoSaicNet learned 200-dimensional features after dimensionality reduction by UMAP. C, The ROC curves and AUC values of single-cell classifier model on separately held test data. The values in brackets indicate the 95% CI. D, UMAP features visualization of deep learned features by AwareNet single-cell classifier CNN. E and F, Validation of AwareNet model using correlation of density of CD8+ (E) and CD4+ (F) cells in panel 1 and panel 2.


Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Patient characteristics: paired diagnostic and posttreatment samples.


Abstract: Patient characteristics: MGUS.


Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract:
Abstract
Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning–based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques.
Significance:
Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.


Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Studying bone physiology using MoSaicNet. A, Proportion of different compartments of BM trephine digital images. B–E, One stacked bar represents a sample. Box plots showing the difference in %bone between samples from NDMM and posttreatment (B), MGUS and NDMM (C), different age groups (median age = 58.0 years; D), and gender groups (E). F, Scatter plot showing the number of bone superpixels in 17 clusters from MGUS, NDMM, and posttreatment samples. The size of the dots represents the percentage of superpixels. The color represents the number of slides in each cluster. G, Correlation of percentage of superpixels in each cluster between different patient groups. A point represents a cluster. H, Scatter plot of slide-level heterogeneity of bone features measured by features variance (Supplementary Materials and Methods). A point represents a patient/slide. I and J, Box plots showing differences in bone density heterogeneity between NDMM and posttreatment (I), and between MGUS and NDMM (J). K–N, Box plots showing the difference in bone thickness between samples from NDMM and posttreatment (K), MGUS and NDMM (L), and different age groups (median age = 58.0 years; M) and gender (N).


Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Supplementary methods, tables and figures.


Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Overview of computational deep learning and image processing pipelines for BM MIHC images: A, MoSaicNet pipeline. The polygons (black) indicate superpixels. MoSaicNet dissects a tissue section into bone, blood, fat, and cellular tissue regions (Supplementary Materials and Methods). B, AwareNet for attention-based cell detection and classification (Supplementary Materials and Methods). The attention image pixel values were generated from the abundance of cell types. An attention image was applied to the objective function during model parameter optimization to regularize the algorithm by assigning high attention to rare cell types. The cell detection algorithm generates a cell probability map. A postprocessing algorithm was developed to find the cell nucleus center, (x, y) location, from the probability map (Supplementary Materials and Methods). A patch centered on each cell was extracted and fed to deep learning (DL)-based classifier to infer its class. C, Spatial and morphologic analysis of BM trephine samples. Bone texture and structural heterogeneity were investigated using an autoencoder-based machine learning method (Supplementary Materials and Methods). We used spatial proximity analysis to study the spatial relations of cells. r, radius. Cell density refers to the number of cells per unit of tissue area.


Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: Computational methods for bone thickness analysis and cell infiltration patterns: A, Image analysis to estimate bone thickness (Supplementary Materials and Methods). Using the same BM sample image as Fig. 1A, the bone segmentation (ii) is an output of MoSaicNet (Supplementary Materials and Methods), and each bone is displayed in a different color. The color bar shows the pixel intensity of the image in iii and iv. The pixel intensity on the skeleton indicates half of the bone thickness (Supplementary Materials and Methods). B, Cell infiltration pattern analysis using NND and the null hypothesis of CSR (Supplementary Materials and Methods). Z < −1.96, Z > 1.96, and −1.96 ≤ Z ≤ 1.96 indicate a clustered, dispersed, and random distribution of observed cells, respectively. std, standard deviation; μ, mean NND of CSR.


Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

Abstract: A and B, Spatial neighborhood of immune and tumor cells (A and B) and between MGUS and NDMM (B). The P* indicate P values after multiple testing correction using the BH method. The points represent the mean and the bars are 95% CIs, indicating uncertainty. C, Sample images showing an increased number of BLIMP1+ cells in the neighborhood with CD8+ on NDMM samples (NDMM example shown here is the same image as Fig. 5L) compared with MGUS samples. D–I, Clustered or dispersed pattern of immune and tumor cells in BM trephine sample. Box plots showing the difference in NND and Z score between NDMM and posttreatment for CD8+ cells (D), BLIMP1+ cells (E), and FOXP3−CD4+ cells (F). Box plots showing the difference in NND and Z score between NDMM and MGUS for CD8+ cells (G) and BLIMP1+ cells (H), and between male and female for BLIMP1+ cells (I). The unit of NND is μmol/L. The Z score shows the significance of the difference between the NND distribution for a given cell type from a complete spatial random distribution and the observed NND (Supplementary Materials and Methods).


Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

Abstract: White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.


LeBenchmark 2.0: A standardized, replicable and enhanced framework for self-supervised representations of French speech

Abstract: Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related tasks are now being approached with pre-trained models. This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 h of heterogeneous speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community, and an evaluation protocol made of six downstream tasks to complement existing benchmarks. LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for speech with the investigation of frozen versus fine-tuned downstream models, task-agnostic versus task-specific pre-trained models as well as a discussion on the carbon footprint of large-scale model training. Overall, the newly introduced models trained on 14,000 h of French speech outperform multilingual and previous LeBenchmark SSL models across the benchmark but also required up to four times more energy for pre-training.


Comparing translation accuracy in Belt and Road Malaysia children’s literature: Malay and Chinese native speakers vs ChatGPT

Abstract: The study investigates the translation processes of human and artificial intelligence translators in comparison. Human translators consist of a Chinese native speaker and belt and road translators. Different versions of artificial intelligence translators comprise ChatGPT 3.5 and ChatGPT 4.0. The research methodology employed is a keyword detection technique. One human translator and one translator powered by artificial intelligence achieved the highest scores in keyword detection, according to the results. Human translators continue to be indispensable in the field of translation, particularly in the translation of literary works. However, the research group is optimistic that artificial intelligence will soon be able to resolve this issue.


Simulation of a physical reservoir made of a Ag2S islands network

Abstract: Abstract Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.


Zip Codes with High Pediatric Asthma Hospital Encounters are Areas with Increased Social Needs – Lessons From Geo-Mapping Technology

Abstract: Social determinants of health (SDoH) factors are associated with increased asthma risk. We aimed to utilize Geo-mapping techniques to identify hotspot zip codes in Detroit with the most pediatric hospital encounters for asthma and to explore the associated SDoH factors within these areas.


Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps

Abstract: This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app’s setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.


Digitalization-growth nexus: some empirical evidence from four regions with a Muslim majority

Abstract: Purpose- The purpose of this study is to investigate whether regional variations exist in the development of the digital economy and how they impact growth in the four global regions where Muslims predominate. Methodology- The study employs empirical examination of 48 countries with Muslim majority divided in four regions (Middle East and North Africa region, Europe and Eurasia region, South Asia, East Asia region and the Pacific and Africa region) between 2000 to 2021. The study employs Kao Residual Cointegration Test, and the Long-run Valuation of Fully Modified Ordinary Least Squares (FMOLS/DOLS-Dynamic). A certain number of specific variables in the econometric model will be employed to measure the level of digitization on economic growth, such as: Digital economy infrastructure proxied by Individuals using the internet, Digital economic openness proxied by ICT product exports and Digital technology competitiveness proxied by Research and Development as share of GDP. Findings- The analysis reveals that despite the fact that the digital economy made a positive contribution to economic growth in both Sub-Saharan Africa and Europe and Euroasia, the impact on these regions is less than that on the Middle East and North Africa, South Asia, East Asia, and the Pacific countries due to the underdeveloped infrastructure of the digital economy The least effect of digitalization on growth was found in Sub-Saharan African countries with low incomes.. Conclusion- Based upon the analysis, it may be concluded that digitalization can considerably boost economic growth, but its benefits may differ depending on how developed a nation is. There is a clear geographical imbalance in the development of the digital economy across 48 nations with a majority of Muslims .To increase overall GDP growth, those countries need to look into policies that will help increase ICT and the digital economy use. Keywords: Digital economy, economic growth, ICT, the Muslim Word, panel cointegration model. JEL Codes: O40, O47, E22.


USER MANAGEMENT SYSTEM USING ADMIN PANEL

Abstract: This project aims at creating a simple user management system that is required by every website where multiple users can login. This let new user registration , login & logout for every user. A User Management System implemented with JavaScript and PHP is a vital component of web applications that facilitates user registration, authentication, authorization, and profile management. This abstract outlines the key features and components of such a system, focusing on the synergy between JavaScript and PHP to deliver a secure, interactive, and efficient user experience. The system allows users to create accounts, log in securely, and manage their profiles while administrators can oversee and administer user accounts.Robust security measures, including data validation and protection against common web vulnerabilities, ensure the integrity of user data and the application's overall security. The system empowers web applications to efficiently manage and serve a diverse user base, ensuring a seamless and secure user experience.


Usability of blood morphology in diagnostics of ovarian cancer

Abstract: Introduction: It has been reported that the levels of neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) might be correlated with ovarian cancer (OC) staging and useful in predicting OC histotype. Information on these parameters is usually obtained during routine diagnostics.


Improve exploration in deep reinforcement learning for UAV path planning using state and action entropy

Abstract: Abstract Despite being a widely adopted development framework for unmanned aerial vehicle (UAV), deep reinforcement learning is often considered sample inefficient. Particularly, UAV struggles to fully explore the state and action space in environments with sparse rewards. While some exploration algorithms have been proposed to overcome the challenge of sparse rewards, they are not specifically tailored for UAV platform. Consequently, applying those algorithms to UAV path planning may lead to problems such as unstable training processes and neglect of action space comprehension, possibly causing negative impacts on the path planning results. To address the problem of sparse rewards in UAV path planning, we propose an information-theoretic exploration algorithm, Entropy Explorer (EE) , specifically for UAV platform. The proposed EE generates intrinsic rewards based on state entropy and action entropy to compensate for the scarcity of extrinsic rewards. To further improve sampling efficiency, a framework integrating EE and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is proposed. Finally, the TD3-EE algorithm is tested in AirSim and compared against benchmarking algorithms. The simulation outcomes manifest that TD3-EE effectively stimulates the UAV to comprehensively explore both state and action spaces, thereby attaining superior performance compared to the benchmark algorithms in the realm of path planning.


Techniques Toward Quantum Computing System Scaling

Abstract: It is expected that a useful quantum computing (QC) system would require millions of qubits within one system. The state-of-the-arts QC systems contain hundreds of qubits, which is still several orders of magnitude away from the ultimate goal. To scale the QC system, namely, to increase the number of qubits and QC circuit layers, and also to improve the performance such as fidelity, robustness, etc., we need to develop scalable techniques including high-density qubit devices, ultra-low power qubit interfacing circuits, 3D interconnection and packaging etc. This article will review the recent reported device, circuit and system-level techniques that contribute to the QC system scaling and may ultimately lead to a fully functional quantum computer.


How to Support ML End-User Programmers through a Conversational Agent

Abstract: Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.


AIGC-Assisted Instructional Design for the Self-Regulated Learning Course on "Implementing 2D Animation for the Ziyun Temple Stories"

Abstract: The instructional design effectively utilizes generative AI to assist in the self-regulated learning course "2D Animation Implementation of Chiayi Temple Stories". It adopts 2D animation and the PPVIO teaching model, leveraging generative AI to create 2D animation scripts and 2D animations. This enhances and sharpens students' storytelling, 2D animation production, and the ability to use generative AI. It also promotes social care, communication, collaboration, and practical application skills. Within the domain of animation graphics, tools like Midjourney and other image-generating AIs are used, with animations completed using the Cartoon Animator 5 software. In this teaching design, teachers play a guiding and mentoring role, leading students to ask more precise prompts to make good use of ChatGPT, and guiding students to overcome the setbacks of self-regulated learning. With the assistance of AI generation tools, students can not only achieve cross-disciplinary learning objectives but, more importantly, reduce the time needed to learn drawing tools. This allows students, even those not specializing in IT, to enhance their digital capabilities in a shorter period. Two suggestions are put forth. Firstly, in the creative section, although the use of AI generation tools is convenient, once technical barriers are removed, the educational content should elevate the level of students' professional creation, such as emphasizing artistic styles. Secondly, regarding self-regulated learning, self-regulated learning should not be pursued for its own sake, nor should learning be for the mere act of learning. It's crucial to stress self-monitoring, self-regulation, and self-discipline to truly grasp the essence of self-regulated learning.