Reviewerly/exHarmony huggingface dataset:
PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition

Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method’s simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowess compared to other methodologies.
Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.
A text extraction framework of financial report in traditional format with OpenCV

Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework.
Verifiable quantum protocol for dynamic secure multiparty summation based on homomorphic encryption

Abstract The research of quantum secure multiparty computation is a subject of great importance in modern cryptography. In this study, we construct a veriﬁable quantum protocol for dynamic secure multiparty summation based on the cyclic property of d-level MUBs. Our protocol can realize dynamic parameter update in the aspect of members and secret inputs, improving the practicality of the protocol. Moreover, a veriﬁcation mechanism for result checking by applying ElGamal homomorphic encryption is given, and further enables the detectability of cheating behaviors, making our protocol safer. The security analysis proves the proposed protocol not only can resist a range of typical attacks from outside and inside, but also is secure against dishonest revoked participant attack which has been neglected in previous dynamic quantum summation protocols. From a theoretical perspective, compared with existing summation protocols, the protocol provides better practicability, higher privacy protection, and higher efﬁciency.&amp;#xD;
Image semantic segmentation model based on CBAMUNet

Mainstream image semantic segmentation networks can only extract local features of an image. Their receptive field is limited to the range of convolutional kernel size, which impacts the distinguishability of extracted features by the encoder. This issue leads to inaccurate segmentation, loss of small targets, and other related problems. This paper presents a solution to the aforementioned challenges by proposing the use of a CBAMUNet image semantic segmentation model. The model combines CBAM attention module and UNet En-decoder network. Firstly, the UNet network has been enhanced to eliminate the need for cropping of input images to reduce feature loss. Secondly, the CBAM module has been integrated into its skip connection to enable the UNet network to extract more efficient features and enhance the segmentation effect. And the model uses a new loss function. Data augmentation is also used to improve the generalization ability of the model. Through the comparison experiments with UNet, it is found that CBAMUNet is able to significantly improve the segmentation effect of images, and increase the pixel precision(PA) and the mean intersection over union(mIoU) of the semantic segmentation of images.
Digital Marketing in the Age of Cyber Threats: A Comprehensive Guide to Cybersecurity Practices

As digital marketing strategies become increasingly integrated into business models, unique vulnerabilities to cyber threats make cybersecurity essential. This descriptive study provides a detailed analysis of cybersecurity for digital marketing, which is rapidly evolving with technological advancements and consumer behavior transitions. The integration of tools like Search Engine Optimization, social media, and online advertising and their susceptibility to cyber risks are investigated. Critical cybersecurity threats in digital marketing, including phishing attacks, malware and ransomware, data breaches, and Distributed Denial of Service attacks, are explored, emphasizing their potential impact on business operations, customer trust, and brand reputation. The study further explores best practices in cybersecurity tailored to the digital marketing area, advocating for regular software updates, comprehensive employee training, stringent data encryption protocols, strong password policies, multi-factor authentication, and periodic security audits, highlighting the importance of data backups and adherence to data protection laws in maintaining legal and ethical standards. The role of Artificial Intelligence and Machine Learning is investigated, emphasizing how these technologies enhance cybersecurity measures through proactive threat detection and efficient incident management. Additionally, the study examines the rising consumer concerns and awareness regarding data privacy and security in digital marketing, reflecting how these concerns influence business practices and the increasing demand for transparency and data control among consumers. Necessitating continuous vigilance and adaptation to protect against ever-evolving cyber threats, effective cybersecurity is indispensable in digital marketing for protection against cyber threats and as a vital element in building and maintaining consumer trust and loyalty.
Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

&amp;nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&amp;#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.
Using a fuzzy cognitive map to assess interventions to reduce antimicrobial resistance in a Swedish One Health system context under potential climate change conditions

Abstract Introduction: Antimicrobial resistance (AMR) is a growing One Health crisis that can be impacted by other challenges of sustainable development, such as climate change, but few interventions have been assessed with a systems-wide lens. The objectives of this study were to use a previously defined fuzzy cognitive map (FCM) of the Swedish One Health system to: 1) identify areas in the system to target interventions; and 2) test the potential ability and viability of interventions to reduce AMR under a changing climate. Methods: The FCM, based on participatory modelling workshops and literature scan, was used to assess the sustainability of eight interventions under potential climate change conditions. Network metrics were calculated to describe the system structure and identify highly impactful nodes. Results: The network metrics identified high-leverage nodes including alternative productions systems and good farming practices. None of the scenarios evaluated were able to adequately reduce AMR within the system. Conclusions: Overall, fuzzy cognitive mapping provides an innovative way to analyse the AMR system, identify high-leverage interventions, and examine potential impact of interventions using a broader systems lens.
The development of higher-order thinking skill-based immune system learning tools

&#x0D; &#x0D; &#x0D; &#x0D; Learning tools can assist teachers in facilitating achievement during the learning process. At that time, the learning tools utilized still relied upon the pre-existing 2013 curriculum, lacking the incorporation of higher-order thinking Skills (HOTS) indicators for critical thinking skills, particularly in the context of immune system material. Therefore, research was conducted to create a learning tool immunity that was based on HOTS. The objectives of this study were as follows: 1) To analyze the requirement of HOTS-based biology learning tools in the immune system material for class XI in SMA/MA; 2) To design the product development for HOTS-based biology learning tools in class XI immune system material in SMA/MA; 3) To validating HOTS-based biology learning tool products in class XI immune system material in SMA/MA. This study employed the ADDIE (Analysis, Design, Development, Implementation, Evaluation) a model for instructional design. However, this research was conducted only up to the development stage of the ADDIE model. The sample comprised five experts and three practitioners, namely biology learning experts, lecturers’ teachers, and language experts. The purposive sampling technique was employed in this research. The instruments used included product validation sheets and interview sheets. Data collection methods involved distributing product validation sheets and conducting interviews with validators. This research produces a HOTS-based immunity learning tool product which generally falls into the very valid category with a percentage of 94.56%. It means that the development product is valid for use in biology learning at the SMA/MA level.&#x0D; &#x0D; &#x0D; &#x0D;
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

Learning Electronic Health Records (EHRs) representation is a preeminent yet under-discovered research topic. It benefits various clinical decision support applications, e.g., medication outcome prediction or patient similarity search. Current approaches focus on task-specific label supervision on vectorized sequential EHR, which is not applicable to large-scale unsupervised scenarios. Recently, contrastive learning has shown great success in self-supervised representation learning problems. However, complex temporality often degrades the performance. We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems. Unlike the state-of-the-art, we do not change the graph structure to construct augmented views. Instead, we use Kernel Subspace Augmentation to embed nodes into two geometrically different manifold views. The entire framework is trained by contrasting nodes and graph representations on those two manifold views through the commonly used contrastive objectives. Empirically, using publicly available benchmark EHR datasets, our approach yields performance on clinical downstream tasks that exceeds the state-of-the-art. Theoretically, the variation in distance metrics naturally creates different views as data augmentation without changing graph structures. Practically, our method is non-ad hoc and confirms superior performance on commonly used graph benchmark datasets.
Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

To improve soldiers’ combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> smart, and information <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm’s motion costs.
Robust Testing of AI Language Model Resiliency with Novel Adversarial Prompts

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.
Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases

Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations.AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment.Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance.This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.
Learning noisy transition matrix using a neural network

In the field of deep learning, it is crucial to know the accurate distribution of dataset. However, to obtain a high quality of dataset using traditional methods is prone to be both costly and inefficient. Using deep learning methods to estimate the noisy transition matrix provides a feasible way, as the result of its essential function of learning to denote the relationship between clean labels and noisy labels and building a statistically consistent classifier. The major difficulty of learning the noisy transition matrix stems from the unavailability of the distribution of clean data and noisy data. In this paper, we propose a practical and convenient method to study a combination of augumentation and a novel loss function, only leveraging the already known clean labels to aid in learning the noisy transition matrix in the whole dataset. Finally, Through the experiment, the result demonstrates a superior performance and generalization capabilities of the proposed method.
A comprehensive analysis of gesture recognition systems: Advancements, challenges, and future direct

Gesture recognition emerges as a potent avenue for human-computer interaction, harnessing mathematical algorithms to interpret gestures. It promises to surpass text-based or graphical interfaces, enabling touchless device control through simple gestures. Our review of 7 papers encompassing various fields and methods underscores its diverse applications. Challenges persist, such as distinguishing genuine user intent from accidental actions amid environmental interference. Creating a universal EMG pattern recognition model demands intricate individual pre-training. Sensor-based gesture recognition grapples with real-world dynamics, necessitating adaptable models that discern user intent from non-intent actions. Addressing these gaps holds the key. Adaptable models and personalized approaches can enhance robustness and accuracy across applications, surmounting challenges in the gesture interaction technology realm.