Reviewerly/exHarmony huggingface dataset:
Natural Language Processing Applications in Language Assessment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.