Reviewerly/exHarmony huggingface dataset:
Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach

Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning.The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view.The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.
ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets

Supplemental material is available for this article.Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.
New machine learning approaches for real-life human activity recognition using smartphone sensor-based data

In recent years, mainly due to the application of smartphones in this area, research in human activity recognition (HAR) has shown a continuous and steady growth. Thanks to its wide range of sensors, its size, its ease of use, its low price and its applicability in many other fields, it is a highly attractive option for researchers. However, the vast majority of studies carried out so far focus on laboratory settings, outside of a real-life environment. In this work, unlike in other papers, progress was sought on the latter point. To do so, a dataset already published for this purpose was used. This dataset was collected using the sensors of the smartphones of different individuals in their daily life, with almost total freedom. To exploit these data, numerous experiments were carried out with various machine learning techniques and each of them with different hyperparameters. These experiments proved that, in this case, tree-based models, such as Random Forest, outperform the rest. The final result shows an enormous improvement in the accuracy of the best model found to date for this purpose, from 74.39% to 92.97%.
Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network

Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the "GT-DCAE WBC augmentation model". In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the "two-stage DCAE-CNN atypical WBC classification model" (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model's discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.
Incomplete multi-view clustering via kernelized graph learning

A fundamental assumption underpinning the recent progress in multi-view clustering is the full observation of all views, which rarely holds for real-world data as they often suffer from the absence of some instances in individual views. Such incompleteness generally disables the traditional multi-view clustering models in practical applications. This paper proposes a Kernelized Graph-based Incomplete Multi-view Clustering (KGIMC) algorithm to overcome this limitation. The key novelty of our model is that its subtasks, e.g. similarity learning, clustering analysis, and kernel completion, are optimized in a mutual reinforcement manner to achieve an overall optimal clustering result as follows: 1) Similarity learning is directed by clustering analysis to construct a graph with as many connected components just as the number of clusters. 2) The well-constructed similarity graph in the last iteration is employed to guide the process of kernel completion. 3) The updated kernels are in turn used to conduct subsequent similarity learning. We extend our model into multi-kernel settings to alleviate the difficulty of kernel selection. We provide an alternating iterative algorithm to solve KGIMC with convergence guaranteed and complexity analyzed. Extensive experiments are conducted on several popular datasets, and the results demonstrate that KGIMC outperforms the state-of-the-art approaches in general.
Examining the training and education potential of the metaverse: Results from an empirical study of next generation SAFe training

Abstract Restrictions imposed by the COVID‐19 pandemic have forced many to seek alternative means of training and learning, which ended up with increasing investment in the notion of the metaverse. Metaverse is envisioned as the next iteration of the Internet in which the virtual and the real world are blended to materialize a highly immersive experience. Not surprisingly, perhaps, next‐generation training and education systems are concerned with methods to integrate themselves into metaverse environments. In particular, participants are looking for more interactive and flexible training while maintaining a degree of educational content and high quality for their training plans and interactive workflows. In this paper, we conducted research to explore the role of metaverse in employee training. To this end, we utilized a variant of PlaySAFe (i.e., a 3D game) to investigate its metaverse adoption and usage. A qualitative design was adopted, using semistructured interviews to explore practitioners' experiences using the new version of PlaySAFe. After having it played in an industrial setting, we interviewed a group of software practitioners to compare the actual and expected features. This research has explored the pros and cons of using the current technologies for the practical groundwork of SAFe training. Findings from this research suggest that the metaverse holds the potential to deliver improved practical alignment in training and education programs, but that at the present time, practitioners expect more metaverse compatible features.
KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images

Aerial remote sensing image object detection, based on deep learning, is of great significance in geological resource exploration, urban traffic management, and military strategic information. To improve intractable problems in aerial remote sensing image, we propose a high-precision object detection method based on YOLOv5 for aerial remote sensing image. The object detection method is called KCFS-YOLOv5. To obtain the appropriate anchor box, we used the K-means++ algorithm to optimize the initial clustering points. To further enhance the feature extraction and fusion ability of the backbone network, we embedded the Coordinate Attention (CA) in the backbone network of YOLOv5 and introduced the Bidirectional Feature Pyramid Network (BiFPN) in the neck network of conventional YOLOv5. To improve the detection precision of tiny objects, we added a new tiny object detection head based on the conventional YOLOv5. To reduce the deviation between the predicted box and the ground truth box, we used the SIoU Loss function. Finally, we fused and adjusted the above improvement points and obtained high-precision detection method: KCFS-YOLOv5. This detection method was evaluated on three datasets (NWPU VHR-10, RSOD, and UCAS-AOD-CAR). The comparative experiment results demonstrate that our KCFS-YOLOv5 has the highest accuracy for the object detection in aerial remote sensing image.
Secret image sharing scheme with lossless recovery and high efficiency

As a well-known secret image sharing scheme, Thien-and-Lin's scheme generates shadow images of reduced size, which is beneficial for storage, transmission, and processing. However, it has some drawbacks, such as lossy recovery, needing extra secure transmission channels. For decades, many improved schemes have been proposed, but none can solve all the drawbacks simultaneously. Therefore, this paper proposes a (k,n)-threshold secret image sharing scheme, trying to solve the drawbacks comprehensively. In the sharing phase, two 8-bit pixels of the secret image are linked as a 16-bit secret value, and all the coefficients of the generating polynomial are used to embed secrets. Then, Shamir's secret sharing is implemented in Galois Field GF(65,537), and the only invalid share 65,536 will be set to 0. In the recovery phase, lossless recovery can be realized through neighborhood consistency calculation. To avoid key transmission, we develop a robust statistical invariance index of the secret image to generate permutation keys. Then Arnold permutation is performed to prevent information leakage. The proposed scheme can realize lossless recovery efficiently, without pixel expansion, complex preprocessing, and extra secure transmission channels. Theoretical analysis and experiments are given to validate the effectiveness of our scheme.
The bias types and drivers of the Furongian Biodiversity Gap

The Furongian Biodiversity Gap (FBG), which is well known as the biodiversity minimum between the Cambrian Explosion (CE) and the Great Ordovician Biodiversification Event (GOBE), has recently been the subject of much research seeking to identify the onset of the GOBE. Whether the FBG is a real evolutionary trend or a bias gap due to insufficient sampling has been controversial. In this study, a systematic investigation of the relationships between fossil sampling intensity, spatial extent, and biodiversity during the Cambrian and Ordovician, using data from the Paleobiology Database (PBDB) is carried out to gain a better understanding of this lowstand in biodiversity. Special focus is placed on the types of bias and the internal and external bias drivers affecting the FBG. The results show that external factors such as a low degree of research interest, socioeconomic influence, and unevenness of research intensity have led to limited fossil sampling and research, which have interacted with internal factors (biological and geological preservation) and led to biases in understanding the FBG. The bias types of the FBG are multi-dimensional. Temporally, there are fewer fossil collections from the Furongian. Geographically, the lack of fossil data at middle and high latitudes results in a low geographical extent and unevenness of the spatial distribution of Furongian fossils. Taxonomically, trilobites and conodonts, which are of high biological importance, are the main studied fossil groups from the Furongian. Lithologically, widely distributed shales deposited in the Furongian and their biotas are not sufficiently studied. Moreover, the above analysis of the FBG based on the PBDB is inherently affected by database bias. The FBG is interpreted as a collection failure which requires more fossil data and methodological innovation for a better and comprehensive interpretation.
A comprehensive evaluation framework for deep model robustness

Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the evaluation and benchmark of model robustness. However, current evaluations usually use simple metrics to study the performance of defenses, which are far from understanding the limitation and weaknesses of these defense methods. Thus, most proposed defenses are quickly shown to be attacked successfully, which results in the “arm race” phenomenon between attack and defense. To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i.e., data and model). Through neuron coverage and data imperceptibility, we use data-oriented metrics to measure the integrity of test examples; by delving into model structure and behavior, we exploit model-oriented metrics to further evaluate robustness in the adversarial setting. To fully demonstrate the effectiveness of our framework, we conduct large-scale experiments on multiple datasets including CIFAR-10, SVHN, and ImageNet using different models and defenses with our open-source platform. Overall, our paper provides a comprehensive evaluation framework, where researchers could conduct comprehensive and fast evaluations using the open-source toolkit, and the analytical results could inspire deeper understanding and further improvement to the model robustness.
Sybil in the Haystack: A Comprehensive Review of Blockchain Consensus Mechanisms in Search of Strong Sybil Attack Resistance

Consensus algorithms are applied in the context of distributed computer systems to improve their fault tolerance. The explosive development of distributed ledger technology following the proposal of ‘Bitcoin’ led to a sharp increase in research activity in this area. Specifically, public and permissionless networks require robust leader selection strategies resistant to Sybil attacks in which malicious attackers present bogus identities to induce byzantine faults. Our goal is to analyse the entire breadth of works in this area systematically, thereby uncovering trends and research directions regarding Sybil attack resistance in today’s blockchain systems to benefit the designs of the future. Through a systematic literature review, we condense an immense set of research records (N = 21,799) to a relevant subset (N = 483). We categorise these mechanisms by their Sybil attack resistance characteristics, leader selection methodology, and incentive scheme. Mechanisms with strong Sybil attack resistance commonly adopt the principles underlying ‘Proof-of-Work’ or ‘Proof-of-Stake’ while mechanisms with limited resistance often use reputation systems or physical world linking. We find that only a few fundamental paradigms exist that can resist Sybil attacks in a permissionless setting but discover numerous innovative mechanisms that can deliver weaker protection in system scenarios with smaller attack surfaces.
CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines

The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies.
Sentiment analysis for customer review: Case study of Traveloka

This study will discuss customers’ satisfaction with the services of Traveloka by analyzing how many people are satisfied and unhappy with the services that Traveloka has to offer. This study uses Twitter to acquire all the data we need, focusing only on tweets about Traveloka. The dataset is gathered from Twitter API, which consists of 1200 tweets related to Traveloka. Scikit-learn library is used through python to do the analysis process. This research employs three classification metheods: Support Vector Model (SVM), Logistic Regression, and Na¨ıve Bayes. The steps in this research were data retrieval, transformation, classification training and predicting the test data, and finally, the result analysis. Therefore, this research is looking forward to how most Twitter users feel about the performance of this mobile traveling application. The result shows that SVM has better accuracy in determining the sentiment of tweets about Traveloka.
Student attendance with face recognition (LBPH or CNN): Systematic literature review

Technology growth is speedy, and more and more things can be solved easily with the existence of sophisticated technology. One of them is solving the problem of student attendance at the university. The current attendance system has developed into RFID (Radio Frequency Identification) from the previous manual. However, many things still become obstacles. For example, students who miss their cards cannot take attendance, and the problem of leaving attendance can be cheating. Now, this has developed much technology in the form of face recognition with various algorithms that can be used. The use of face recognition can overcome the previous problem because it only uses faces for attendance. However, the many algorithms for facial recognition make it difficult to determine the best algorithm to implement. The main purpose of this literature review is to compare algorithms suitable for implementation in an environment at universities, especially CNN and LBPH. Based on the literature review, it was found that CNN's accuracy is superior in terms of accuracy compared to LBPH, CNN also produces more stable accuracy if there are external factors that can affect accuracy.
Virtual Reality and Spatial Augmented Reality for Social Inclusion: The “Includiamoci” Project

Extended Reality (XR) technology represents an innovative tool to address the challenges of the present, as it allows for experimentation with new solutions in terms of content creation and its fruition by different types of users. The potential to modulate the experience based on the target audience’s needs and the project’s objectives makes XR suitable for creating new accessibility solutions. The “Includiamoci” project was carried out with the aim of creating workshops on social inclusion through the combination of art and technology. Specifically, the experimentation involved ten young people between the ages of 28 and 50, with cognitive disabilities, who participated in Extended Reality workshops and Art Therapy workshops. In the course of these activities, the outputs obtained were two: a virtual museum, populated by the participants’ works, and a digital set design for a theatrical performance. Through two tests, one on user experience (UX) and one on the degree of well-being, the effectiveness of the entire project was evaluated. In conclusion, the project demonstrated how the adopted solutions were appropriate to the objectives, increasing our knowledge of UX for a target audience with specific user needs and using XR in the context of social inclusion.
How do students perceive educators' digital competence in higher education?

This research aims to contribute to the field of technology in education as the main trend since Covid accelerated digitalization. There are few studies of students' perceptions of digital educators' capabilities following the approach of evidence-based learning. In a survey conducted with 243 respondents using DigComEdu as a valid instrument to measure educators' digital competence (EDC), this research presents an empirical study of the relationship between EDC from students' point of view and their self-perception of learning. The findings demonstrate that 71 % of students consider that the digital competence of their faculty impacts their learning process positively. The study carries out a literature review proposing a model of four elements affecting the student self-perception of learning: 1) educators' digital skills; 2) the use of technology for communication, monitoring, and assessment; 3) educators' engaging in digital ecosystems; and 4) students' data security in the learning process. The results confirm a significant relationship with three of the hypotheses, with no confirmation of the remaining one. These findings have implications for universities institutions and policymakers to assure the digitalization of education drives effectiveness and quality.
MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis

Abstract Today, internet and social media is used by many people, both for communication and for expressing opinions about various topics in many domains of life. Various artificial intelligence technologies-based approaches on analysis of these opinions have emerged natural language processing in the name of different tasks. One of these tasks is Sentiment analysis, which is a popular method aiming the task of analyzing people’s opinions which provides a powerful tool in making decisions for people, companies, governments, and researchers. It is desired to investigate the effect of using multi-layered and different neural networks together on the performance of the model to be developed in the sentiment analysis task. In this study, a new, deep learning-based model was proposed for sentiment analysis on IMDB movie reviews dataset. This model performs sentiment classification on vectorized reviews using two methods of Word2Vec, namely, the Skip Gram and Continuous Bag of Words, in three different vector sizes (100, 200, 300), with the help of 6 Bidirectional Gated Recurrent Units and 2 Convolution layers (MBi-GRUMCONV). In the experiments conducted with the proposed model, the dataset was split into 80%-20% and 70%-30% training-test sets, and 10% of the training splits were used for validation purposes. Accuracy and F1 score criteria were used to evaluate the classification performance. The 95.34% accuracy of the proposed model has outperformed the studies in the literature. As a result of the experiments, it was found that Skip Gram has a better contribution to classification success.