Reviewerly/exHarmony huggingface dataset:
The Impact of Blockchain Technology on Internal Auditing in the Financial Sector

Blockchain technology is claimed to lead innovations to many business paradigms by reducing fraud and providing safe transactions. This study examined whether the adoption of blockchain technology will impact their internal audit function (IAF) and the role of IAF in blockchain smart contracts in the financial sector. The study referred to the related literature review and followed the international standards for the professional practice of internal auditing (IPPF). The study found that internal audit function and relative associate with the basics of blockchain technology and reached a continuous audit model with blockchain smart contracts. Considering the significant benefits and also potential challenges of applying blockchain technology in the modern accounting system, the study filled a gap in knowledge on the impact of blockchain on internal auditing.
Securing a Question-Based Multi-factor Authentication System Using LSB Steganography Technique

User authentication is a vital security requirement for most service systems“. Text-based password is commonly used to authenticate the identity of users in most computer systems. The use of a strong password makes it difficult for the user to remember it. Therefore, the password should be saved on an external disk or written on a memo that can be referred to when the user forgets it. Graphical password is one of the authentication solutions used to solve this problem in order to help the user remember the password without it being discovered or broken by password hackers or attackers. This paper proposes a question-based authentication system, which has benefits over both text and graphics passwords along with the use of steganography. The combination of steganography and graphical authentication protects sensitive data exchanged between the user and the server and reduces the risk of passwords being exposed. In the experiment, the peak signal-to-noise ratio and average squared error parameters were calculated to test the effectiveness of the steganography method in the graphical password.
Including temporal changes information to an AI system for breast cancer detection to reduce false positive rate

In breast cancer detection, change in findings throughout time is one of the major biomarkers for the presence of malignancy. Several studies have established the value of comparing mammograms with the ones from previous examinations. Some of them have shown that such comparison decreases the recall rate and increases the biopsy yield of cancer but does not increase the cancer detection rate. This evidence brought us to do the hypotheses that, as for human radiologists, adding temporal context information could be beneficial also for artificial intelligence (AI) systems for breast cancer detection thus improving their specificity which today represents the major limitation for an autonomous use of such AI systems. In this study we carry out a comparison between an AI system for breast cancer detection and an update version of the same system able to integrate the temporal context information.
MRI breast segmentation using unsupervised neural networks for biomechanical models

In multimodal diagnosis for early breast cancer detection, spatial alignment by means of image registration is an important task. We develop patient-specific biomechanical models of the breast, for which one of the challenges is automatic segmentation for magnetic resonance imaging (MRI) of the breast. In this paper, we propose a novel method using unsupervised neural networks with pre-processing and post-processing to enable automatic breast MRI segmentation for three tissue types simultaneously: fatty, glandular, and muscular tissue. Pre-processing aims at facilitating training of the network. The architecture of neural network is a Kanezaki-net extended to 3D and consists of two sub-networks. Post-processing is enhancing the obtained segmentations by removing common errors. 25 datasets of T2 weighted MRI from the Medical University of Vienna have been evaluated qualitatively by two observers while eight datasets have been evaluated quantitatively based on a ground truth annotated by a medical practitioner. As a result of the qualitative evaluation, 22 out of 25 are usable for biomechanical models. Quantitatively, we achieved an average dice coefficient of 0.88 for fatty tissue, 0.5 for glandular tissue, and 0.86 for muscular tissue. The proposed method can serve as a robust method for automatic generation of biomechanical models.
Corrected rank residual constraint model for image denoising

In this paper, a novel image denoising model is proposed, named Corrected Rank Residual Constraint (CRRC). To overcome the drawback that L1 norm penalty yields biased estimators and cannot achieve the best estimation performance, the proposed CRRC incorporates the adaptive correction term with L1 minimization to improve the sparsity of the L1 norm data fidelity term enforced on the rank residual. The existence and uniqueness of solutions of the proposed model are also studied, and the optimization method for solving the model is given. The studies show that the adaptive correction term can not only improve the sparsity of the rank residual but also overcome the over-shrinkage of large singular values. Experimental results demonstrate that the proposed CRRC model outperforms many state-of-the-art methods in both objective and perceptual quality.
Classification of intrinsic subtypes and histological grade for breast cancers by multimodality images

Success of breast cancer treatment is subject to various factors, including cancer stage and cancer grade. The best treatment is selected based on the characteristic of cancer. It is desirable to predict the cancer characteristics and prognostic factors accurately and promptly by diagnostic imaging. The purpose of the study is to investigate the use of multimodality diagnostic images in predicting breast cancer subtypes to assist diagnosis and treatment planning. In this study, we classify lesions into molecular subtypes and simultaneously predict histological grades and invasiveness of the cancers by mammography and breast ultrasound images. Models with different architectures including single input and multi-input layers with single head and multiple head models are compared. The results indicate that use of multimodality images is more predictive than using single modalities. The automatic subtype classification using multimodality images may support a prompt treatment planning and proper patient care.
Evaluation of an AI system designed for breast cancer detection

Screening programs for the early detection of breast cancer have significantly reduced mortality in women. The limitations of these programmes are primarily due to the use of 2D techniques and the high number of mammograms to be read by radiologists. Artificial Intelligence (AI) systems may lead to new tools to help radiologists read mammograms and classify the examination based on the malignancy of the detected lesions. Several factors related to breast characteristics (thickness and density), technical factors of image acquisition, X-ray system performance and image processing algorithms can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of this work is to analyze the robustness of an AI system for breast cancer detection and its dependence on breast characteristics and technical factors. For this purpose, mammograms from a population-based screening program were scored with the AI system. The AUC (area under the ROC curve) index generated from the scoring ROC curve was 0.92 (CI(95%) = 0.89 - 0.95), demonstrating the robust performance of the AI system. Moreover, the statistical analysis performed showed that the AUC index was independent of breast characteristics, the type of mammographic system and most of the technical parameters considered, demonstrating the effectiveness of the AI system.
Combining neighbourhood component analysis with dictionary learning algorithms to improve the performance of dictionary learning models for geochemical anomaly detection

In geochemical exploration, a geochemical anomaly detection model is usually established to describe the population distribution of geochemical data, and samples that do not conform to the model are identified as geochemical anomalies. Because the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, its performance for mineral exploration targeting is affected to a certain extent. For this reason, neighbourhood component analysis and dictionary learning algorithms were combined to detect geochemical anomalies associated with gold mineralization in the Chengde area in Hebei Province, China. Neighbourhood component analysis was used to transform geochemical data from the input space into the neighbourhood component space to enhance the separability between the geochemical anomalies associated with gold mineralization and the background. Dictionary learning models for geochemical anomaly detection were established in the neighbourhood component space. The performance of the dictionary learning models established in the neighbourhood component space was compared with that of corresponding models established in the input space in geochemical anomaly detection. The results show that the former models are superior to the latter. In addition, there is a strong consistency between the mineral exploration targeting results and metallogenic characteristics of the study area. Therefore, combining neighbourhood component analysis and dictionary learning algorithms can improve the performance of dictionary learning models in geochemical anomaly detection.
Robustness of a U-net model for different image processing types in segmentation of the mammary gland region

Many studies have assessed breast density in clinical practice. However, calculation of breast density requires segmentation of the mammary gland region, and deep learning has only recently been applied. Thus, the robustness of the deep learning model for different image processing types has not yet been reported. We investigated the accuracy of segmentation of the U-net for mammograms made with variousimage processing types. We used 478 mediolateral oblique view mammograms. The mammograms were divided into 390 training images and 88 testing images. The ground truth of the mammary gland region made by mammary experts was used for the training and testing datasets. Four types of image processing (Types 1–4) were applied to the testing images to compare breast density in the segmented mammary gland regions with that of ground truths. The shape agreement between ground truth and the segmented mammary gland region by U-net of Types 1–4 was assessed using the Dice coefficient, and the equivalence or compatibility of breast density with ground truth was assessed by Bland-Altman analysis. The mean Dice coefficients between the ground truth and U-net were 0.952, 0.948, 0.948, and 0.947 for Types 1, 2, 3, and 4, respectively. By Bland-Altman analysis, the equivalence of breast density between ground truth and U-net was confirmed for Types 1 and 2, and compatibility was confirmed for Types 3 and 4. We concluded that the robustness of the U-net for segmenting the mammary gland region was confirmed for different image processing types.
Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings

False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system’s ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.
Gender Prediction Based on Arabic Names with Machine Learning Techniques

The classification based on gender is a popular topic for many machine learning fields, much work has been done on gender classification throughout the years. The same goes when it comes to gender classification based on names, there are many papers for English and Chinese names; still, there has been almost no work done for Arabic so far. Moreover, this paper examines the outcomes of applying six machine learning algorithms (Support Vector Machine, Multinomial Naive Bayes, Bernoulli Naive Bayes, Decision Tree, Random Forrest, and Logistic Regression) and deep learning model (LSTM) for gender prediction on Arabic name. In addition, a dataset is created to investigate the results of these algorithms on Arabic names.
Classification and reviewing of prior screening mammograms from screen-detected breast cancer cases

This study aimed at conducting a review of the prior mammograms of screen-detected breast cancers, found on full-field digital mammograms based on independent double reading with arbitration. The prior mammograms of 607 women diagnosed with breast cancer during routine breast cancer screening were categorized into “Missed”, “Prior Vis”, and “Prior Invis” . The prior mammograms of “Missed” and “Prior Vis” cases showed actionable and non-actionable visible cancer signs, respectively. The “Prior Invis” cases had no overt cancer signs on the prior mammograms. The percentage of cases classified as “Missed”, “Prior Vis”, and “Prior Invis” categories were 25.5%, 21.7%, 52.7%, respectively. The proportion of high-density cases showed no significant differences among the three categories (p-values&lt;0.05). The breakdown of cases into “Missed”, “Prior Vis”, and “Prior Invis” categories did not differ between invasive (488) and in-situ (119) cases. In the invasive category, the progesterone (p-value=0.015) and estrogen (p-value=0.007) positivity and the median ki-67 score (p-value=0.006) differed significantly among the categories with the “Prior Invis” cases exhibiting the highest percentage of hormone receptors negativity. In the invasive cases, the percentage of cancers graded as 3 (i.e., more aggressive) were significantly more in the “Prior Invis” category compared to both “Missed” and “Prior Vis” categories (both p-values&lt;0.05). The status of receptors and breast cancer grade for the in-situ cases did not differ significantly among the three categories. Prior images categorization can predict the aggressiveness of breast cancer. Techniques to better interrogate prior images as shown elsewhere may yield important patient outcomes.
Protection of Traditional Knowledge and its Relevance to National Economic Growth and Development in Nigeria

Nigeria is a country that is richly blessed with abundance of natural resources, good vegetation and fertile soil for cultivation. The country also has diverse ethnic groups, culture and customs. This way of life of the people can be perceived in the activities, traditions and customs that have been handed down to them by their forefathers. Overtime, communities expanded and there was influx of people to the different communities in the country. These people become exposed to this way of life and they benefit from it individually and collectively. This information or knowledge acquired is generally utilized by others without the local or indigenous communities benefitting in any form from their cultural heritage. It is general knowledge that intellectual property protects the activities of the intellect or mental faculty. Could this traditional knowledge (TK) of the people require protection under intellectual property? Would it not be appropriate that these people are recognized as the custodians of the TK? Their consent sought before usage, and benefits arising thereof be given to them on prior agreed terms. This will further strengthen the economic system of the society. Doctrinal research method was used in this work, and reliance was placed on both primary and secondary source of information as the work discussed the need for protection of traditional knowledge (TK), the progress made so far and the challenges to providing proper legal and institutional framework for protection of TK.
Automatic classification and detection of abnormalities in mammograms using deep learning

Breast cancer is one of the deadliest diseases. It is affecting majority of women world wide. Computer Aided Diagnosis (CAD) systems can be used to help radiologists in order to examine the initial symptoms. One of the early symptoms is micro-calcifications. Detection of abnormalities is an essential part of treatment in the right direction. Along with detection of abnormalities, the classification of micro-calcification has a vital importance. Timely detection and classification of micro-calcification as malignant or benign can save a lot of women. We have used region based convolutional neural networks and obtained 92.7% mean average precision at training time while at testing time mAP is 89.2%.
Comparison of deep learned and texture features in mammographic mass classification

As deep learning models are increasingly applied in medical diagnostic assistance systems, this raises questions about ones ability to understand and interpret its decision-making process. In this work, using breast lesions from the Optimam Mammography Image Database (OMI-DB), we have explored whether deep learned features have similar predictive information as classical texture features. We trained a deep learning model for mass lesion classification and used Gradient-weighted Class Activation Mapping to produce a representation of deep learned features. Additional, classical texture features (e.g. energy) were extracted. Subsequently, we used the earth mover’s distance to investigate similarities between deep learned and texture features. The comparison identified that texture features such as mean, entropy and auto-correlation showed a strong similarity with the deep learned features and provided an indication of what the deep learning models might have used as information for its classification.
Correlation of thermal performance of ASTER with the hydrothermal alteration zones: the case of Nisyros volcano, Greece

We aim to investigate the relation between advanced spaceborne thermal emission and reflection radiometer (ASTER) and thermal infrared (TIR) data with the hydrothermal alteration zones within the hydrothermal field of the caldera of Nisyros volcano in Greece. This is achieved by utilizing clustering on a dataset of five ASTER L1T TIR images spanning the period 2003 to 2007. Each one of these images is converted to temperature images, coregistered, and vegetation masked, creating a five-band ASTER temperature image. In the sequel, probabilistic clustering hypothesis is applied to the five-band vegetation masked ASTER temperature image, subset to the area of the hydrothermal field. The probabilistic clustering algorithm identifies three “thermal” clusters that exhibit a satisfactory degree of agreement with the active hydrothermal alteration field of Nisyros. Each cluster corresponds to a different degree of hydrothermal alteration zone. We show the existence of a middle temperature zone (cluster), which exhibits significant overlap with the middle hydrothermal alteration zone, along with a high-temperature zone and a low-temperature zone. This temperature transition from high-temperature cluster to the middle and gradually to low-temperature cluster for the time-spanning of the data is significant as the hydrothermal field of Nisyros was very active during this period. This finding coincides temporally and spatially with the continuous N–S trending (Lakki rupture).
Prompted Self-Explanations Improve Learning in Statistics but Not Retention

Background College students often do not retain what they learn in Statistics in order to apply it in Experimental Psychology. Self-explanation, that is, elaborating on what one is trying to learn by asking questions, making inferences, etc., improves learning and may improve retention. Objective The purpose of this study was to determine whether self-explanation was superior to students’ usual study methods specifically for learning some basic concepts in statistics, and, if so, if it was similarly useful for retention a semester after the initial learning. Method We used 199 college students as participants in a randomized, between participant, two-part experiment examining the effects of training by prompting self-explanations as a potential solution to this applied problem. Results The self-explanations that we elicited improved initial learning and were superior to students’ usual study methods, but did not benefit retention. Conclusions Future research on improving the quality of the self-explanations and training with spaced retrieval practice, in order to benefit retention, is suggested. Teaching Implication Self-explanation should be implemented for teaching statistics in order to benefit initial learning. However, teachers should explore other methods to accomplish retention.
Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.