Reviewerly/exHarmony huggingface dataset:
InstanceSHAP: an instance-based estimation approach for Shapley values

Abstract The growth of artificial intelligence applications requires to find out which explanatory variables mostly contribute to the predictions. Model-agnostic methods, such as SHapley Additive exPlanations (SHAP) can solve this problem: they can determine the contribution of each variable to the predictions of any machine learning model. The SHAP approach requires a background dataset, which usually consists of random instances sampled from the train data. In this paper, we aim to understand the insofar unexplored effect of the background dataset on SHAP and, to this end, we propose a variant of SHAP, InstanceSHAP, that uses instance-based learning to produce a more effective background dataset for binary classification. We exemplify our proposed methods on an application that concerns peer-to-peer lending credit risk assessment. Our experimental results reveal that the proposed model can effectively improve the ordinary SHAP method, leading to Shapley values for the variables that are more concentrated on fewer variables, leading to simpler explanations.
Efficient Conditional Privacy-Preserving Authentication Scheme for Safety Warning System in Edge-Assisted Internet of Things

With the advent of smart cities, the significance of the Internet of Things (IoT) is gaining greater prominence. At the same time, the safety early warning system in the IoT has a significant impact on real-time monitoring and the response to potential risks. Despite the advancements made in edge-assisted IoT deployments, several challenges and constraints persist. Given the potential threat to life posed by safety-related messages, ensuring the authenticity of messages in the edge-assisted IoT safety warning system is crucial. However, considering the identity privacy of devices participating in the edge-assisted Internet of Things system, directly verifying the identity of the sending device is undesirable. To address this issue, in this work, we design a linkable group signature scheme that allows devices to anonymously send safety-related messages to edge nodes, defending against Sybil attacks while ensuring the traceability of malicious device identities. Then, we present a high-efficiency conditional privacy-preserving authentication (CPPA) scheme based on the designed group signatures for the safety warning system in edge-assisted IoT. This scheme effectively protects device identity privacy while providing a reliable authentication mechanism to ensure the credibility and traceability of alert messages. The proposed scheme contributes to the field of safety warning systems in the context of edge-assisted IoT, providing a robust solution for privacy preservation and authentication.
SPGAN-DA: Semantic-Preserved Generative Adversarial Network for Domain Adaptive Remote Sensing Image Semantic Segmentation

Unsupervised domain adaptation for remote sensing semantic segmentation seeks to adapt a model trained on the labeled source domain to the unlabeled target domain. One of the most promising ways is to translate images from the source domain to the target domain to align the spectral information or imaging mode by the generative adversarial network (GAN). However, source-to-target translation often brings bias in the translated images causing limited performance, as semantic information is not well considered in the translation procedure. To overcome this limitation, we present an innovative semantic-preserved generative adversarial network (SPGAN), designed to mitigate the image translation bias and then leverage the translated images as well as unlabeled target images by class distribution alignment (CDA) module to train a domain adaptive semantic segmentation model. The above two stages are coupled together to form a unified framework called SPGAN-DA. Specifically, we first conduct semantic invariant translation from source to target domain, which is achieved by introducing representation-invariant and semantic-preserved constraints to the GAN model. To further narrow the landscape layout gap between the translated and target images, CDA semantic segmentation is proposed. CDA semantic segmentation consists of two aspects. At the model input level, object discrepancy is eliminated by introducing the ClassMix operation. At the model output level, boundary enhancement is proposed to refine the performance of object boundaries. Extensive experiments on three typical remote sensing cross-domain semantic segmentation benchmarks demonstrate the effectiveness and generality of our proposed method, which competes favorably against existing state-of-the-art methods.
End-to-End Historical Handwritten Ethiopic Text Recognition Using Deep Learning

Recognizing handwritten text is a challenging task, especially for scripts with numerous alphabets and symbols. The Ethiopic script has a vast character set and is used for historical documents in typewritten, handwritten, and hand-printed forms. However, despite its importance as an ancient script, optical character recognition research has not given enough attention to Ethiopic text recognition. In recent years, deep learning (DL) has emerged as a powerful technique for recognizing patterns. In this study, a DL approach is used to recognize historical Ethiopic handwritten texts. The recognition model employs an end-to-end strategy enabling sequential feature extraction and efficient recognition. An attention mechanism coupled with a connectionist temporal classification architecture is the core of this recognition model architecture. In addition, there are seven convolutional neural networks and two recurrent neural networks. We increase the training data using data augmentation techniques to address the data scarcity common in deep learning applications. The experiments include an original training dataset of 79,684 historical handwritten images and an augmented dataset of 10,000 images containing Ethiopic texts. The model used for recognition showed promising results. For “Test Set I” which had 6,150 samples, the character error rate (CER) was 17.95%, and for “Test Set II” which had 15,935 samples, the CER was 29.95%. These outcomes indicate that this approach has the potential to improve the recognition of historical handwritten Ethiopic text.
Power Network Optimization: A Quantum Approach

Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. In this paper, we show how to optimize such network costs using a quantum annealing approach. First, we define the Quadratic Unconstrained Binary Optimization (QUBO) problem for network partitioning. To achieve this, we introduce a method to translate inequality constraints with real-valued coefficients into approximate penalty functions. Next, we test the implementation on purely quantum and quantum-classical hybrid architectures. We then solve the problem using the D-Wave hybrid Constrained Quadratic Model (CQM) solver, the D-Wave hybrid Binary Quadratic Model (BQM) solver, as well as classical solvers available on Azure Quantum Cloud. Finally, we find that the value of the objective function obtained with the quantum-classical hybrid solvers is always lower compared to the classical approaches across a range of different problem sizes. This demonstrates that the quantum-classical hybrid methods outperform the classical methods in terms of solution quality.
Autoencoder-Like Knowledge Distillation Network for Anomaly Detection

Anomaly detection is a crucial research field in computer vision with diverse applications in practical scenarios. The common anomaly detection methods employed currently consist of autoencoders, generative adversarial networks, and knowledge distillation (KD) models. However, the teacher and student models in KD might not always yield distinct representations to signify anomalies due to their similar model structure and data flow. This study proposes a novel autoencoder-like KD model based on the attention mechanism for anomaly detection. The pre-trained teacher model incorporates a dual attention module as the encoder, while the student model integrates the same dual attention module as the decoder. The teacher guides the student to learn the feature knowledge of the input image. To connect the teacher-student model, a BottleNeck module is employed, converting the features extracted from the teacher model into more compact latent codes for precise restoration by the student model, thereby achieving anomaly detection. In general, the proposed model exhibits superior performance compared to other existing anomaly detection models on specific datasets. Experimental results demonstrate that the proposed model attains the state-of-the-art (SOTA) performance in anomaly detection on the public dataset MVTec. It achieves an average AUC of 98.2% and 98.0% at sample and pixel levels, respectively.
A Dynamic Optimization-Based Ensemble Learning Method for Traditional Chinese Medicine Named Entity Recognition

The importance of named entity identification in traditional Chinese medicine (TCM) as the basis for supporting downstream tasks is receiving increasing attention. Deep learning-based methods have been widely used for related tasks. However, most current methods do not deal well with two common TCM entity recognition problems: an unbalanced number of entities and sparse entities. To solve these problems, we propose an ensemble learning method based on dynamic optimization. In this study, we first use bidirectional encoder representations from transformers (BERT) to extract word vectors and then further extract features based on BERT-bidirectional long short-term memory (BiLSTM). Then, we dynamically adjust the entity class and fusion weights of ensemble learning according to the entity distribution and sparsity of each batch. Finally, the prediction results are output through the conditional random field (CRF) layer. This approach allows the model to dynamically focus on difficult samples and to improve the update weights of the most beneficial learning tasks. In addition, we introduce a reduction factor to reduce the magnitude of the parameter updates when the entities are sparse. This prevents the model from being unduly disrupted by nonentity information. Therefore, our model can effectively reduce the negative impact of unbalanced numbers of entities and sparse entities. The experimental results show that our model achieves the best results on a publicly available TCM entity recognition dataset and has a faster convergence rate than the baseline model. Compared to the baseline model BERT-BiLSTM-CRF, our method improves the F1-score by 0.56, further demonstrating its effectiveness.
A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy

This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters’ line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language’s efficacy, with the successful identification of a specific set of symbols on various wood species’ sawn surfaces, achieving a mean average precision (mAP) exceeding 90%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.
Edge-Guided Remote-Sensing Image Compression

Using high-fidelity image compression makes it possible to transmit remote-sensing images in real-time. Nevertheless, existing lossy remote-sensing image compression (RSIC) methods have some inherent potential issues, including blocking and blurring effects, which are particularly problematic in low-compression-ratio (CR) settings. Although numerous methods have been studied to address the aforementioned issue, the majority of them exploit the prior of local smoothness in images, which usually induces the over-smoothing of regions with noticeable structure (i.e., edges and textures). During this task, we developed an innovative end-to-end framework that enables high-fidelity RSIC while retaining sharp edge and texture information. Initially, we put forth an edge-guided adversarial network (EGA-Net) for simultaneously restoring edge structures and generating texture details. Second, we impose an edge fidelity constraint to direct our network to optimize image content and structural information jointly. In addition, to facilitate this task, we have constructed a large-scale RSIC dataset named NWPU-RS-Compression (NWPU-RSC). This dataset contains over 300000 images of 30 categories, all with a fixed resolution of 600 × 600. Finally, a new quantitative metric for full reference image quality that takes into account signal statistics and the characteristics of the human visual system (HVS) has been developed, which helps evaluate reconstructed remote-sensing images more objectively and accurately. Experimental evidence has demonstrated that the EGA-Net surpasses several representative compression approaches regarding quality metrics on the NWPU-RSC, AID, and ISPR Vaihingen datasets. Code, dataset, and more experimental results can be accessed at https: //github.com/Chenxi1510/Remote-sensing-Image-Compression.
Real-world efficient fall detection: Balancing performance and complexity with FDGA workflow

With the decrease in mobility, falls have become more prevalent and painful for both men and women. Most current approaches for fall detection in daily living employ either simple thresholds or complex networks. However, these methods suffer from an inability to consider the association among data or the disadvantage of excessive complexity in practical use. In this paper, we propose a compromise workflow called FDGA for fall detection that addresses these issues. Our approach first roughly determines the bounding box of the fallen target using the lightweight Fall-ROI extraction, and then further determines the precise skeletal position using a fine top-down skeletal estimator. Additionally, the genetic-algorithm-based classifier minimizes the effect of redundant features. Since no heavy network is required, the proposed workflow maintains an excellent performance-speed trade-off. We validate our approach using two fall detection datasets and a new large-scale fall detection dataset we created. The experimental results demonstrate the efficiency and effectiveness of our approach.
Social Engineering Attacks and Countermeasures

This book chapter examines the increasing danger of social engineering attacks in cybersecurity. These attacks focus on exploiting human vulnerabilities instead of technical weaknesses and target the human element of organizations. The chapter outlines the various types of social engineering attacks such as phishing, pretexting, baiting, and quid pro quo, and explores the strategies employed by social engineers including the creation of urgency, trust, and fear. It also covers countermeasures that can be employed to guard against social engineering attacks, such as education and awareness programs for employees and technical solutions like spam filters and multi-factor authentication. By understanding the threat of social engineering attacks and taking proactive steps to mitigate this risk, individuals and organizations can protect themselves against this growing cybersecurity menace.
Introduction to Dark Web

Darknet is an overlay portion of the Internet network that can only be accessed using specific authorization using distinctively tailored communication protocols. Attackers usually exploit the darknet to threaten several world-wile users with different types of attack/intrusion vectors. In this chapter, we shed the light on the darknet network, concepts, elements, structure, and other aspects of darknet utilization. Specifically, this chapter will extend the elaboration on the darknet, the dark web components, the dark web access methods, the anonymity and confidentiality of the dark web, the dark web crimes, the cyber-attacks on the dark web, the malware on the dark web, the internet governance of dark web, the payment on the dark web, and the impacts of the dark web. This chapter will profound the knowledge of the dark web and provides more insights to readers about darknet attacks, malware, and their counter-measures.
Prediction of Learner Information-Seeking Behavior and Classroom Engagement in the Advent of ChatGPT

Information source accuracy and trustworthiness are vital in undergraduates’ education in terms of how they seek teacher feedback and educational content. Nowadays, the infusion of technologies into education has led to convenient and easy access to learning materials in electronic libraries and web-based platforms or search engines. Undergraduates are expected to make rapid use of ground-breaking technologies to sift for academic information over the internet. With the advent of ChatGPT, it is estimated that undergraduates might rely on it for academic information. The theoretical study gauges the potential impact of ChatGPT on the information-seeking behavior of undergraduates and what it means for classroom engagement. To do this, we utilized the inquiry-based learning theory which emphasizes the active participation of learners in discovering new information. The study is one of the first to contribute to the ongoing discussions on the impact of large language models (LLMs) such as ChatGPT on education.
AI-Enabled Assessment and Feedback Mechanisms for Language Learning

This chapter examines the integration of artificial intelligence (AI) in language learning assessment and feedback. It highlights limitations of traditional models before exploring AI-driven innovations in automated scoring, speech recognition, multimodal analytics and adaptive testing. The ensuing pedagogical transformation is discussed. AI feedback mechanisms including automated writing evaluation, intelligent tutoring systems, conversational agents, affective computing and learning analytics dashboards are analyzed. Benefits are presented alongside challenges regarding ethics, overreliance on technology and transparency. Case studies provide examples across educational contexts. The future outlook considers emerging innovations, increased accessibility, research gaps, policies and human-AI partnerships. The conclusion emphasizes responsible, human-centric integration of AI to enhance pedagogy and learner experience.
Augmented Reality and Student Learning: Analysis of Mental Models of Salt Hydrolysis at SMAN 51 Jakarta, Indonesia

This study aimed to ascertain students' mental models while learning about salt hydrolysis through augmented reality (AR). The study comprised 36 participants from Public High School 51 in Jakarta. A descriptive qualitative approach was adopted for this research, employing various data collection methods such as written drawings, interviews, classroom observations, teacher notes, student worksheets, and final tests. In categorizing students' mental models, three main types emerged: scientific, synthetic, and initial mental models. The findings revealed that 7.20% of students fell into the initial mental model category, 53.90% exhibited synthetic mental models, and 38.90% demonstrated scientific mental models. Notably, incorporating AR into salt hydrolysis learning predominantly influenced the development of synthetic mental models. The study's results also indicated that the utilization of AR positively enhanced students' spatial abilities in understanding submicroscopic representations.
Attribute Learning with Knowledge Enhanced Partial Annotations

Under limited annotation cost, large-scale attribute learning datasets only contain partial labels for each image. The conventional methods treat the un-annotated attributes as negative or ignore their loss without considering the associated knowledge. In this paper, we present a knowledge enhanced selective loss for partially labeled attribute learning. Given a visual instance, we investigate the object-attribute co-occurrence as internal knowledge to subdivide the unannotated attributes into feasible and infeasible sets. Based on that, we can enhance the model to focus on the learning of feasible un-annotated attributes and remove the distraction from the infeasible ones. Besides the internal knowledge, we adopt external knowledge to excavate the unseen object-attribute pairs. Experimental results show that our proposed loss can achieve state-of-the-art performance on the newly cleaned VAW2 dataset that contains 170,407 instances, 1763 objects, and 591 attributes. The code and VAW2 dataset are available at https://github.com/GriffinLiang/seal.
Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing

Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, many dehazing networks aim to enhance performance by adopting intricate network architectures, complicating training, inference, and deployment procedures. This study proposes an end-to-end U-Net dehazing network model with recursive gated convolution and attention mechanisms to improve performance while maintaining a lean network structure. In our approach, we leverage an improved recursive gated convolution mechanism to substitute the original U-Net's convolution blocks with residual blocks and apply the SK fusion module to revamp the skip connection method. We designate this novel U-Net variant as the Dehaze Recursive Gated U-Net (DRGNet). Comprehensive testing across public datasets demonstrates the DRGNet's superior performance in dehazing quality, detail retrieval, and objective evaluation metrics. Ablation studies further confirm the effectiveness of the key design elements.
Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. In this article, we focus on achieving accurate 2D object detection in urban autonomous driving scenarios. Considering the occlusion issues of using a single sensor from a single viewpoint, as well as the limitations of current vision-based approaches in bad weather conditions, we propose a novel multi-modal sensor fusion network called LRVFNet. This network effectively combines data from LiDAR, mmWave radar, and visual sensors through a deep multi-scale attention-based architecture. LRVFNet comprises three modules: a backbone responsible for generating distinct features from various sensor modalities, a feature fusion module utilizing the attention mechanism to fuse multi-modal features, and a pyramid module for object reasoning at different scales. By effectively fusing complementary information from multi-modal sensory data, LRVFNet enhances accuracy and robustness in 2D object detection. Extensive evaluations have been conducted on the public VOD dataset and the Flow dataset. The experimental results demonstrate the superior performance of our proposed LRVFNet compared to state-of-the-art baseline methods.