Reviewerly/exHarmony huggingface dataset:
Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection

Fraud detection is one of the core issues of loan risk control, which aims to detect fraudulent loan applications and safeguard the property of both individuals and organizations. Because of its close relevance to the security of financial operations, fraud detection has received widespread attention from industry. In recent years, with the rapid development of artificial intelligence technology, an automatic feature engineering method that can help to generate features has been applied to fraud detection with good results. However, in car loan fraud detection, the existing methods do not satisfy the requirements because of overreliance on behavioral features. To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. Experiments are developed based on actual car loan credit database to evaluate the performance of the proposed scheme. Compared with traditional automatic feature engineering methods, the number of features and training time are reduced by 92.5% and 54.3%, respectively, whereas accuracy is improved by 23%. The experiment demonstrates that our scheme effectively improved the existing automatic feature engineering car loan fraud detection methods.
An efficient CNN based algorithm for detecting melanoma cancer regions in H&amp;E-stained images

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.
Mathematical framework for describing multipartite entanglement in terms of rows or columns of coefficient matrices

In this paper, we develop a mathematical framework for describing entanglement quantitatively and qualitatively for multipartite qudit states in terms of rows or columns of coefficient matrices. More specifically, we propose an entanglement measure and separability criteria based on rows or columns of coefficient matrices. This entanglement measure has an explicit mathematical expression by means of exterior products of all pairs of rows or columns in coefficient matrices. It is introduced via our result that the [Formula: see text]-concurrence coincides with the entanglement measure based on two-by-two minors of coefficient matrices. Depending on our entanglement measure, we obtain the separability criteria and maximal entanglement criteria in terms of rows or columns of coefficient matrices. Our conclusions show that just like every two-by-two minor in a coefficient matrix of a multipartite pure state, every pair of rows or columns can also exhibit its entanglement properties, and thus can be viewed as its smallest entanglement contribution unit too. The great merit of our entanglement measure and separability criteria is two-fold. First, they are very practical and convenient for computation compared to other methods. Second, they have clear geometric interpretations.
Access Control Scheme Supporting Attribute Revocation in Cloud Computing

To break the data barrier of the information island and explore the value of data in the past few years, it has become a trend of uploading data to the cloud by data owners for data sharing. At the same time, they also hope that the uploaded data can still be controlled, which makes access control of cloud data become an intractable problem. As a famous cryptographic technology, ciphertext policy-based attribute encryption (CP-ABE) not only assures data confidentiality but implements fine-grained access control. However, the actual application of CP-ABE has its inherent challenge in attribute revocation. To address this challenge, we proposed an access control solution supporting attribute revocation in cloud computing. Unlike previous attribute revocation schemes, to solve the problem of excessive attribute revocation overhead, we use symmetric encryption technology to encrypt the plaintext data firstly, and then, encrypting the symmetric key by utilizing public-key encryption technology according to the access structure, so that only the key ciphertext is necessary to update when the attributes are revoked, which reduces the spending of ciphertext update to a great degree. The comparative analysis demonstrates that our solution is reasonably efficient and more secure to support attribute revocation and access control after data sharing.
Grayscale-inversion and rotation invariant image description using local ternary derivative pattern with dominant structure encoding

Local ternary pattern (LTP) is sensitive to inverse grayscale changes and its split encoding limits its descriptive ability. In this paper, we investigate these problems and present an enhanced LTP descriptor called local ternary derivative pattern with dominant structure encoding (DLTDP). Firstly, we propose a local ternary derivative pattern (LTDP) operator which encodes the neighboring relationship in the magnitude response space of Gaussian derivative filters. In LTDP, adaptive ternary quantization is performed on the magnitude responses of Gaussian derivative filters to achieve grayscale-inversion and rotation invariance. An extended non-split ternary encoding scheme is developed to obtain compact yet discriminative LTDP codes. Secondly, to complement the magnitude responses used in LTDP, we leverage a dominant structure measure to additionally encode each pixel in the original filter response space as well as the input image space. Finally, we integrate all the generated codes to construct joint histogram features as the DLTDP descriptor. Extensive experiments on four benchmark databases (i.e., Outex, CUReT, KTH-TIPS and DTD) demonstrate the superiority of our DLTDP descriptor over state-of-the-art LBP and LTP variants for texture classification under (linear or nonlinear) grayscale inversion and image rotation.
Benchmarking Human Performance for Visual Search of Aerial Images

Aerial images are frequently used in geospatial analysis to inform responses to crises and disasters but can pose unique challenges for visual search when they contain low resolution, degraded information about color, and small object sizes. Aerial image analysis is often performed by humans, but machine learning approaches are being developed to complement manual analysis. To date, however, relatively little work has explored how humans perform visual search on these tasks, and understanding this could ultimately help enable human-machine teaming. We designed a set of studies to understand what features of an aerial image make visual search difficult for humans and what strategies humans use when performing these tasks. Across two experiments, we tested human performance on a counting task with a series of aerial images and examined the influence of features such as target size, location, color, clarity, and number of targets on accuracy and search strategies. Both experiments presented trials consisting of an aerial satellite image; participants were asked to find all instances of a search template in the image. Target size was consistently a significant predictor of performance, influencing not only accuracy of selections but the order in which participants selected target instances in the trial. Experiment 2 demonstrated that the clarity of the target instance and the match between the color of the search template and the color of the target instance also predicted accuracy. Furthermore, color also predicted the order of selecting instances in the trial. These experiments establish not only a benchmark of typical human performance on visual search of aerial images but also identify several features that can influence the task difficulty level for humans. These results have implications for understanding human visual search on real-world tasks and when humans may benefit from automated approaches.
VR Neuro Game: a Virtual Reality Game to Support Neuroanatomy Teaching and Learning

Using virtual environments (VEs) is a safer and cost-effective alternative to training people in different contexts. Immersive Virtual Reality (VR), combined with game aspects, have the potential to improve the user experience in the VE by increasing realism, engagement, and motivation. In education, there is a consensus about its contribution in the teaching and learning processes. However, in Brazil, only a few research groups have developed projects that involve the development of VR for education, due to technical difficulties and high costs. This manuscript presents the development and evaluation of an VR serious game to support the learning of neuroanatomy, an extended version of our previously published paper at the SVR 2020. In this version, we present the results of four different user studies, involving 57 participants, which suggest that the VR Neuro Game is easy to use, even by inexperienced subjects in VR, and is potentially useful for teaching and learning processes, both by individual students and groups of students and also in remote learning. In addition, the game was considered fun and did not cause discomfort. When comparing the knowledge test performance before and immediately after the experiments, we found significant difference only for the virtual condition. The same could be observed for retention results.
Exercise Behavior Prediction and Injury Assessment Based on Swarm Intelligence Algorithm

The biggest view of the whole world on science and technology and sports is that science and technology and sports both represent national strength. At present, the integration of sports and science and technology has not reached a certain height, especially in the prediction of sports behavior and injury assessment, and the investment in science and technology is still lacking. This leads to a high number of injuries caused by sports every year. However, swarm intelligence algorithm has made few breakthrough achievements in the past few years, and the combination of sports behavior and swarm intelligence algorithm can just solve this problem. It is very important to choose the algorithm for predicting and assessing sports behavior. We should choose an efficient algorithm with high stability, high convergence speed, and optimization ability. In this paper, the IPSGWO algorithm is proposed to realize this application. IPSGWO algorithm is based on the GWO algorithm, with appropriate strategies and ideas, to maximize the improvement. In this paper, the convergence curve of PSO, GWO, and IPSGWO is tested to determine whether the IPSGWO algorithm has more stable and higher performance, and the simulation experiment is used to determine whether the IPSGWO algorithm is suitable for prediction and injury assessment compared with the other two. From the experimental results, the IPSGWO algorithm does have higher performance; because of this, it is more accurate for prediction and injury assessment.
Sensitive Data Exposure: Data Forwarding and Storage on Cloud Environment

Sensitive data has become an essential part of life today. With the increase in sensitive data, the importance of maintaining its confidentiality and integrity has increased. One of the solutions became to store this data in the cloud. But the risk of revealing this data still exists. This is because the rate of attack, leakage and loss of this data has become a serious matter. The importance of sensitive data in our current era is considered our oil, as it is very important in several uses in statistical analyzes and other important matters that help the authorities to know the type of people and their interests, and when publishing this information it is important to know what information should be available and What information should not appear or be used on the sites. In this paper, discuss this issue, which is one of the most important security issues that is sensitive data exposure. We touched on this research and the techniques used to reduce these risks to the data stored in the cloud. Mention the types of sensitive data and the types of attacks that may affect these data, and mention the points of weakness, and then the methods of protecting this data.
A Robust video watermarking approach based on QR code

Video watermarking embeds copyright mark called watermark in video frame to prove the ownership of video copyright. Compared with more mature image watermarking algorithms, video watermarking algorithms require higher robustness. This paper encodes the watermark into QR code and makes full use of the high fault tolerance of QR code, proposes a watermark generating and decoding strategy based on the characteristics of QR code, which improves the robustness of the watermarking algorithm. The experimental results show that the algorithm is more robust than the algorithm using random binary string or scrambling QR code as watermark.
Emotion Detection and Analysis from Facial Image using Distance between Coordinates Feature

Facial expression recognition has long been established as a subject of continuous research in various fields. In this study, feature extraction was conducted by calculating the distance between facial landmarks in an image. The extracted features of the relationship between each landmark and analysis were used to classify five facial expressions. We increased the data and label reliability based on our labeling work with multiple observers. Additionally, faces were recognized from the original data, and landmark coordinates were extracted and used as features. A genetic algorithm was used to select features that were relatively more helpful for classification. We performed facial recognition classification and analysis using the method proposed in this study, which showed the validity and effectiveness of the proposed method.
Fine-Grained and Traceable Key Delegation for Ciphertext-Policy Attribute-Based Encryption

Permission delegation is an important research issue in access control.It allows a user to delegate some of his permissions to others to reduce his workload, or enables others to complete some tasks on his behalf when he is unavailable to do so.As an ideal solution for controlling read access on outsourced data objects on the cloud, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has attracted much attention.Some existing CP-ABE schemes handle the read permission delegation through the delegation of the user's private key to others.Still, these schemes lack the further consideration of granularity and traceability of the permission delegation.To this end, this article proposes a flexible and fine-grained CP-ABE key delegation approach that supports white-box traceability.In this approach, the key delegator first examines the relations between the data objects, read permission thereof that he intends to delegate, and the attributes associated with the access policies of these data objects.Then he chooses a minimal attribute set from his attributes according to the principle of least privilege.He constructs the delegation key with the minimal attribute set.Thus, we can achieve the shortest delegation key and minimize the time of key delegation under the premise of guaranteeing the delegator's access control requirement.The Key Generation Center (KGC) then embeds the delegatee's identity into the key to trace the route of the delegation key.Our approach prevents the delegatee from combining his existing key with the new delegation key to access unauthorized data objects.Theoretical analysis and test results show that our approach helps the KGC transfer some of its burdensome key generation tasks to regular users (delegators) to accommodate more users.
Blockchain and IoT - Challenges and Its Role in Security: A Brief Overview

Versatility is the main intrinsic characteristic of Blockchain, making this technology applicable to a great variety of fields such as Agriculture, Healthcare, Artificial Intelligence, Smart Cities, and many others, all of them within the Internet of Things environment and aimed to its integration. This paper presents a detailed review of IoT fields in which Blockchain is currently being applied, the challenges they face towards their integration, and the current role of Blockchain concerning the security environment. The authors used inclusion and exclusion criteria, and as a result, 164 articles ranging from 2018 to 2021 were utilized to fulfill this objective. Additionally, the authors found 11 environments where Blockchain is being integrated. Moreover, a quantitative study was carried out to determine which of the challenges are the focus of attention.
Myth In The Structure Of Mass Consciousness Of The Digital Era Society

Mass consciousness is a relevant subject for science.The task of studying the connection between mass consciousness and myth retains its importance.In recent decades, these concepts have been at the center of philosophical discussion, which is due to the novelty of interpretations and new scope of application of these concepts.The problem of the impact of the process of informatization of society actualizes this task.An essential feature of setting such a task is the consideration of the specifics of mythologization of mass consciousness.The dynamics of information technology is accompanied by the emergence of new aspects of it, which, in turn, leads to the specifics of the connection of mass consciousness and myth.Myth and mythmaking are the characteristics of both the information and communication space and the modern mass consciousness.The field of myth-making extends from the practice of interpretation of concrete facts to the model of being.By problematizing reality, mass consciousness forces its bearer to reckon with what is non-obvious but imaginable in it.Imagination reveals the content of existence, which is always greater than the everyday (myth).Myth-making is a way of influencing reality.Creating its own model of the world, myth in the mass consciousness gives reality a meaning, demonstrating its ability to be an ideal motivating force of social behavior.Socio-philosophical analysis of the genetic, functional features of mass consciousness and myth allow us to conclude that the nature of mass consciousness is myth-consciousness.
Improved N-Best Extraction with an Evaluation on Language Data

Abstract We show that a previously proposed algorithm for the N-best trees problem can be made more efficient by changing how it arranges and explores the search space. Given an integer N and a weighted tree automaton (wta) M over the tropical semiring, the algorithm computes N trees of minimal weight with respect to M. Compared with the original algorithm, the modifications increase the laziness of the evaluation strategy, which makes the new algorithm asymptotically more efficient than its predecessor. The algorithm is implemented in the software Betty, and compared to the state-of-the-art algorithm for extracting the N best runs, implemented in the software toolkit Tiburon. The data sets used in the experiments are wtas resulting from real-world natural language processing tasks, as well as artificially created wtas with varying degrees of nondeterminism. We find that Betty outperforms Tiburon on all tested data sets with respect to running time, while Tiburon seems to be the more memory-efficient choice.
MEAL: Meta enhanced Entropy-driven Adversarial Learning for Optic Disc and Cup Segmentation

Accurate segmentation of optic disc (OD) and optic cup (OC) can assist the effective and efficient diagnosis of glaucoma. The domain shift caused by cross-domain data, however, affect the performance of a well-trained model on new datasets from different domain. In order to overcome this problem, we propose a domain adaption model based OD and OC segmentation called Meta enhanced Entropy-driven Adversarial Learning (MEAL). Our segmentation network consists of a meta-enhanced block (MEB) to enhance the adaptability of high-level features, and an attention-based multi-feature fusion (AMF) module for attentive integration of multi-level feature representations. For the optimization, an adversarial cost function driven by entropy map is used to improve the adaptability of the framework. Evaluations and ablation studies on two public fundus image datasets demonstrate the effectiveness of our model, and outstanding performance over other domain adaption methods in comparison.
Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.
Lived experience of teaching interns during the COVID-19 Pandemic

The COVID-19 pandemic wreaked havoc on the educational landscape. As part of a state university's learning continuity plan, a flexible delivery system was implemented. This descriptive phenomenological study aims to investigate, through unstructured online interviews, the online teaching experience of ten (10) interns. Rigor was established following Lincoln and Guba’s (1985) Trustworthiness Framework. Using Colaizzi's (1979) thematic analysis, emergent themes were identified. The interns revealed their reached and unmet expectations, as well as the challenges in delivering their lessons and managing their classes. Nevertheless, they acknowledged the presence of a solid support system. They maintained a strong cognitive-social-teaching presence. They also displayed a growth mindset, resilience, integrity, and persistence in overcoming the challenges. Despite the difficulties they faced, they experienced successes and had realizations during their online teaching internship. Moving forward, the teaching interns made some useful suggestions for improving the teaching internship program's policies and practices.