Reviewerly/exHarmony huggingface dataset:
Fast Search of Face Recognition Model for a Mobile Device Based on Neural Architecture Comparator

This paper addresses the face recognition task for offline mobile applications. Using AutoML techniques, we propose a novel approach to develop a fast neural network-based facial feature extractor for a concrete device. First, the Once-for-All SuperNet is trained on a large facial dataset. Each device is characterized by its lookup table, which contains the running times of inference in each layer of the SuperNet. An evolutionary search is then used to select the most accurate subnetwork within a limit on the maximum expected latency. We propose training a neural architecture comparator using Gradient Boosted Trees to choose the better subnetwork in this search. Experimental face verification and recognition results demonstrate our proposed approach’s robustness to various facial region positions. Our best model achieves an identification accuracy of 98.7% for the LFW dataset in less than 5 ms on the Qualcomm Snapdragon 865 GPU.
Automatic Urine Sediment Detection and Classification Based on YoloV8

The identification of urine sediment in human urine samples through microscopic images is a critical part of in vitro testing. Currently, automatic urine sediment analyzers are used by doctors to supplement manual examinations. However, the conventional technique of artificial feature extraction used by most analyzers can be labor-intensive and subjectively dependent on the professional's prior knowledge. To overcome these limitations, this work employs YoloV8, a recent version of the Yolo algorithm, to accurately detect and categorize urine particles. In addition, a data-centric strategy has been introduced to address difficulties with missing data, incorrect labeling, and class imbalance. This strategy aims to improve labeling reliability and remove noisy data points. Experimental findings on the dataset show that YOLOv8 has a greater detection accuracy than existing state-of-the-art techniques for detecting eleven different categories of urine sediments. The approach presented in this work outperforms other techniques, yielding a mean average precision (mAP) of 91%. Furthermore, the average detection time of the model is 0.6 microseconds.
Application of nanotechnology in breast cancer screening under obstetrics and gynecology through the use of CNN and ANFIS

Breast cancer is the leading reason of death among women aged 35 to 54. Breast cancer diagnosis still presents significant challenges, and preventing the disease's most severe symptoms requires early detection. The role of nanotechnology in the tumor-treatment has recently attracted a lot of interest. In cancer therapies, nanotechnology plays a major role in the medication distribution process. Nanoparticles have the ability to target tumors. Nanoparticles are favorable and maybe preferable for usage in tumor detection and imaging due to their incredibly small size. Quantum dots, semiconductor crystals with increased labeling and imaging capabilities for cancer cells, are one of the particles that have received the most research attention. The design of the research is cross-sectional and descriptive. From April through September of 2020, data were gathered at the State Hospital. All pregnant women who came to the hospital throughout the first and second trimesters of the research's data collection were included in the study population. 100 pregnant women between the ages of 20 and 40 who had not yet had a mammogram comprised the research sample. 1100 digitized mammography images are included in the dataset, which was obtained from a hospital. Convolutional neural networks (CNN) were used to scan all images, and breast masses and mass comparisons were made using the malignant-benign categorization. The adaptive neuro-fuzzy inference system (ANFIS) then examined all of the data obtained by CNN in order to identify breast cancer early using inputs based on the nine different inputs. The precision of the mechanism used in this technique to determine the ideal radius value is significantly impacted by the radius value. Nine variables that define breast cancer indicators were utilized as inputs to the ANFIS classifier, which was then used to identify breast cancer. The parameters were given the necessary fuzzy functions, and the combined dataset was applied to train the method. Testing was initially performed by 30% of dataset that was later done with the real data obtained from the hospital. The accuracy of the results for 30% data was 84% (specificity =72.7%, sensitivity =86.7%) and the results for the real data was 89.8% (sensitivity =82.3%, specificity =75.9%), respectively.
A label information vector generative zero-shot model for the diagnosis of compound faults

Diagnosis of compound faults remains a challenge owing to the coupling of fault characteristics and the exponential increment of the number of possible fault types. Current compound faults diagnostic methods often require a large number of training data for each type of compound fault. In real-world scenarios, training data of compound faults are usually difficult to acquire and sometimes even inaccessible. In contrast, single fault samples are much easier to obtain. Thus in this paper inspired by the idea of zero-shot learning, we present a novel label information vector generative zero-shot model to identify unknown compound faults, using only single fault samples as the training set. This model comprises several modules, namely label information vector (LIV) definition, feature extractor, and generative adversarial modules, respectively responsible for representing the prior knowledge of specific class labels for the single fault and compound fault, extraction of fault features, and mapping the relationship between the fault features and the fault LIVs. By adversarial training between the samples and LIVs of single faults, the model can generate compound fault features using the compound fault LIVs. Thus the unknown compound faults are identified by measuring the distance between the features extracted from the testing compound fault samples and the generated features from LIVs. The proposed method is evaluated on a self-built experimental platform. The results demonstrate that without any compound fault samples in the training set, the compound fault classification accuracy of the model reaches 78.10%.
Enhancing text representations separately with entity descriptions

Several studies have focused on incorporating language models with entity descriptions to facilitate the model with a better understanding of knowledge. Existing methods usually either integrate descriptions in the pre-training stage by designing description-related tasks, or in the fine-tuning stage by directly appending description strings to the original input, this paper falls into the latter group. We separate entity descriptions from the original text and process them by another lighter module. Specifically, we use the original large model to encode the original input, while the lighter module processes the entity descriptions. We also propose a layer-wise fusion strategy to deeply couple the representations of the input and descriptions. To further improve the fusion of the two representations, we explore two auxiliary tasks: the entity-description enhancement task and the entity contrastive task. Experiments on (Open Entity, FIGER, FewRel, TACRED, SST) datasets yield respective improvements of (0.9, 1.4, 0.6, 0.5, 0.3). Utilizing ChatGPT as the description embedding method holds the potential for even more promising results.
Commercial Anti-Smishing Tools and Their Comparative Effectiveness Against Modern Threats

Smishing, also known as SMS phishing, is a type of fraudulent communication in which an attacker disguises SMS communications to deceive a target into providing their sensitive data. Smishing attacks use a variety of tactics; however, they have a similar goal of stealing money or personally identifying information (PII) from a victim. In response to these attacks, a wide variety of anti-smishing tools have been developed to block or filter these communications. Despite this, the number of phishing attacks continue to rise. In this paper, we developed a test bed for measuring the effectiveness of popular anti-smishing tools against fresh smishing attacks. To collect fresh smishing data, we introduce Smishtank.com, a collaborative online resource for reporting and collecting smishing data sets. The SMS messages were validated by a security expert and an in-depth qualitative analysis was performed on the collected messages to provide further insights. To compare tool effectiveness, we experimented with 20 smishing and benign messages across 3 key segments of the SMS messaging delivery ecosystem. Our results revealed significant room for improvement in all 3 areas against our smishing set. Most anti-phishing apps and bulk messaging services didn't filter smishing messages beyond the carrier blocking. The 2 apps that blocked the most smish also blocked 85-100% of benign messages. Finally, while carriers did not block any benign messages, they were only able to reach a 25-35% blocking rate for smishing messages. Our work provides insights into the performance of anti-smishing tools and the roles they play in the message blocking process. This paper would enable the research community and industry to be better informed on the current state of anti-smishing technology on the SMS platform.
Mirror complementary transformer network for RGB‐thermal salient object detection

Abstract Conventional RGB‐T salient object detection treats RGB and thermal modalities equally to locate the common salient regions. However, the authors observed that the rich colour and texture information of the RGB modality makes the objects more prominent compared to the background; and the thermal modality records the temperature difference of the scene, so the objects usually contain clear and continuous edge information. In this work, a novel mirror‐complementary Transformer network (MCNet) is proposed for RGB‐T SOD, which supervise the two modalities separately with a complementary set of saliency labels under a symmetrical structure. Moreover, the attention‐based feature interaction and serial multiscale dilated convolution (SDC)‐based feature fusion modules are introduced to make the two modalities complement and adjust each other flexibly. When one modality fails, the proposed model can still accurately segment the salient regions. To demonstrate the robustness of the proposed model under challenging scenes in real world, the authors build a novel RGB‐T SOD dataset VT723 based on a large public semantic segmentation RGB‐T dataset used in the autonomous driving domain. Extensive experiments on benchmark and VT723 datasets show that the proposed method outperforms state‐of‐the‐art approaches, including CNN‐based and Transformer‐based methods. The code and dataset can be found at https://github.com/jxr326/SwinMCNet .
Parameter optimization of histogram-based local descriptors for facial expression recognition

An important task in automatic facial expression recognition (FER) is to describe facial image features effectively and efficiently. Facial expression descriptors must be robust to variable scales, illumination changes, face view, and noise. This article studies the application of spatially modified local descriptors to extract robust features for facial expressions recognition. The experiments are carried out in two phases: firstly, we motivate the need for face registration by comparing the extraction of features from registered and non-registered faces, and secondly, four local descriptors (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Compound Local Binary Patterns (CLBP), and Weber's Local Descriptor (WLD)) are optimized by finding the best parameter values for their extraction. Our study reveals that face registration is an important step that can improve the recognition rate of FER systems. We also highlight that a suitable parameter selection can increase the performance of existing local descriptors as compared with state-of-the-art approaches.
Fault Prediction of Control Clusters Based on an Improved Arithmetic Optimization Algorithm and BP Neural Network

Higher accuracy in cluster failure prediction can ensure the long-term stable operation of cluster systems and effectively alleviate energy losses caused by system failures. Previous works have mostly employed BP neural networks (BPNNs) to predict system faults, but this approach suffers from reduced prediction accuracy due to the inappropriate initialization of weights and thresholds. To address these issues, this paper proposes an improved arithmetic optimization algorithm (AOA) to optimize the initial weights and thresholds in BPNNs. Specifically, we first introduced an improved AOA via multi-subpopulation and comprehensive learning strategies, called MCLAOA. This approach employed multi-subpopulations to effectively alleviate the poor global exploration performance caused by a single elite, and the comprehensive learning strategy enhanced the exploitation performance via information exchange among individuals. More importantly, a nonlinear strategy with a tangent function was designed to ensure a smooth balance and transition between exploration and exploitation. Secondly, the proposed MCLAOA was utilized to optimize the initial weights and thresholds of BPNNs in cluster fault prediction, which could enhance the accuracy of fault prediction models. Finally, the experimental results for 23 benchmark functions, CEC2020 benchmark problems, and two engineering examples demonstrated that the proposed MCLAOA outperformed other swarm intelligence algorithms. For the 23 benchmark functions, it improved the optimal solutions in 16 functions compared to the basic AOA. The proposed fault prediction model achieved comparable performance to other swarm-intelligence-based BPNN models. Compared to basic BPNNs and AOA-BPNNs, the MCLAOA-BPNN showed improvements of 2.0538 and 0.8762 in terms of mean absolute percentage error, respectively.
Researching With, On, In and Through the Postdigital: Accounting for More-Than-Humanness

This chapter engages with the messiness of researching with, on, in, and through the postdigital to explore what this research might entail methodologically and theoretically. I draw on more-than-human sensibilities to untangle three knots. First, how can more nuanced attuning to the co-mingling of humans and technologies enable researchers to move beyond persistent binaries and can such binaries be generative? Second, how do researchers and theorists attune to their own more-than-humanness? Third, how does this attuning contribute to the value, credibility, quality, and politics of postdigital inspired research and accounts of these inquiries? I introduce data from a research project in which more-than-human research sensibilities and methods (e.g., technography, interviewing objects, situated logging, and descriptive anecdoting) were employed to enable participants in this study to notice their own more-than-humanness as they attuned to the digital mediation of their everyday work practices: specifically, the increasing use of automated and assisted decision-making (AADM) in professional work. Data foregrounds several tensions of postdigital research including how these practitioner-researchers attempted to erase, work across, and reify human-digital binaries. Ontological wrestling unfolded as they noticed and questioned their own more-than-humanness to varying degrees: who-what they become with the AI and advanced data systems of their research inquiry. A re-thinking of human-digital practices in more speculative and provisional ways is evident, albeit often layered with caveats.
Mapping and Tracing the Postdigital: Approaches and Parameters of Postdigital Research

The launch of Postdigital Science and Education helped generate a burst of new scholarship about this emerging turn in educational research and theory. Yet, what it means to do postdigital research remains obscure to many. Ongoing debates around definitions, combined with the complexity of analysing digital activity within rich contexts that are also social, material, political, economic, and so on, make it challenging to understand what constitutes postdigital research. Meanings of the postdigital emerge from within the processes of postdigital research. Further, while some individual contributions to postdigital research may be grounded in particular disciplines, we argue that postdigital research, in general, benefits from transdisciplinary knowledge. All of this points to a need for flexibility, and principled, rather than prescriptive, research and scholarship practices. It situates postdigital research in the tradition of compositional and inventive research approaches, and this paper traces that relationship.
Big Bioinformational Education Sciences: New Biodigital Methods and Knowledge Production in Education

Biological sciences increasingly focus on the collection and analysis of bioinformation. In recent years, bioinformational approaches have been developed in research on education, especially through the use of neurotechnologies in educational neuroscience and molecular genomics technologies in educational genomics. In this chapter, educational neuroscience and genomics are conceptualized as an emerging form of bioinformational education science. A postdigital approach is taken to examine the methodologies and knowledge production of bioinformational education science, exploring the methods of the neural and genetic sciences as research assemblages constituted of a variety of situated social, epistemic, material, discursive, and institutional elements that make objects of investigation legible, explainable, and understandable in new ways. The analysis of these research assemblages reveals the organizational arrangements that have taken shape around bioinformational investigation and knowledge production in education; the biodigital methods that are designed and deployed to make new discoveries about the biological aspects of educationally-relevant phenomena; and the particular characteristic forms of bioinformational knowledge they produce. Bioinformational education science represents a particular postdigital imbrication of digital technologies, biological sciences, and educational research, policy, and practice, which is assembling new biodigital objects of attention and analysis that collapse the boundary between the biological and the digital.
Collective Writing: The Continuous Struggle for Meaning-Making

This chapter is a summary of philosophy, theory, and practice arising from collective writing experiments conducted between 2016 and 2022 in the community associated with the Editors' Collective and more than 20 scholarly journals. The main body of the chapter summarises the community's insights into the many faces of collective writing. Appendix 1 presents the workflow of the chapter's development. Appendix 2 lists approximately 100 collectively written scholarly articles published between 2016 and 2023. Collective writing is a continuous struggle for meaning-making, and our research insights merely represent one milestone in this struggle. Collective writing can be designed in many different ways, and our workflow merely shows one possible design that we found useful. There are many more collectively written scholarly articles and chapters than we could gather, and our reading list merely offers sources that the co-authors could think of. While our research insights and our attempts at synthesis are inevitably incomplete, 'Collective Writing: The Continuous Struggle for Meaning-Making' is a tiny theoretical steppingstone and a useful overview of sources for those interested in theory and practice of collective writing.
Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience

The fields of medicine and neuroscience often face challenges in obtaining a sufficient amount of diverse data for training machine learning models. Data augmentation can alleviate this issue by artificially synthesizing new data from existing data. Generative adversarial networks (GANs) provide a promising approach for data augmentation in the context of images and biomarkers. GANs can synthesize high-quality, diverse, and realistic data that can supplement real data in the training process. This study provides an overview of the use of GANs for data augmentation in medicine and neuroscience. The strengths and weaknesses of various GAN models, including deep convolutional GANs (DCGANs) and Wasserstein GANs (WGANs), are discussed. This study also explores the challenges and ways to address them when using GANs for data augmentation in the field of medicine and neuroscience. Future works on this topic are also discussed.
Non-ambiguous and simplified quantum teleportation protocol

Abstract In this study, a new version of the quantum teleportation protocol is presented, which does not require a Bell state measurement (BSM) module on the sender side (Alice), a unitary transform to reconstruct the teleported state on the receiver side (Bob), neither a disambiguation process through two classic bits that travel through a classic disambiguation channel located between sender and receiver. The corresponding theoretical deduction of the protocol, as well as the experimental verification of its operation for several examples of qubits through implementation on an optical table, complete the present study. Both the theoretical and experimental outcomes show a marked superiority in the performance of the new protocol over the original version, with more simplicity and lower implementation costs, and identical fidelity in its most complete version.
Evaluation Method of Japanese Teaching Effect Based on Feature Offset Compensation

Abstract This study applies big data processing technology and parallel computing methods to assess the teaching effect of Japanese in the Flipped Classroom (FC) and task-based teaching mode. We propose a model for evaluating the Japanese teaching effect in this mode using feature offset compensation. We employ distributed mining of association rules to detect the teaching effect and extract ontology information and association rules related to the distribution of Japanese teaching effect in the FC and task-based teaching mode. Furthermore, we construct a fuzzy decision-making model for evaluating the teaching effect. The joint information entropy characteristic value of the teaching effect deviation is calculated, and the extracted characteristic quantity is classified and identified using feature deviation compensation and the C-means clustering method. Based on the classification and identification results, we achieve accurate evaluation of the Japanese teaching effect in the FC and task-based teaching mode. Our simulation results demonstrate high precision rates and confidence levels in the evaluation of Japanese teaching effect in this mode.
Post‐quantum identity‐based authenticated multiple key agreement protocol

Abstract Authenticated multiple key agreement (AMKA) protocols provide participants with multiple session keys after one round of authentication. Many schemes use Diffie–Hellman or authenticated key agreement schemes that rely on hard integer factorizations that are vulnerable to quantum algorithms. Lattice cryptography provides quantum resistance to authenticated key agreement protocols, but the certificate always incurs excessive public key infrastructure management overhead. Thus, a lightweight lattice‐based secure system is needed that removes this overhead. To answer this need, we provide a two‐party lattice‐ and identity‐based AMKA scheme based on bilateral short integer or computational bilateral inhomogeneous small integer solutions, and we provide a security proof based on the random oracle model. Compared with existing AMKA protocols, our new protocol has higher efficiency and stronger security.