Reviewerly/exHarmony huggingface dataset:
Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

A lump growing in the breast may be referred to as a breast mass related to the tumor.However, not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain, depending on the size and texture of the breast.With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images.This work accomplished breast masses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50 (ResNet50) model along with an adaptive gradient algorithm, adaptive moment estimation, and stochastic gradient descent optimizers, as well as data augmentations and fine-tuning methods, were implemented.In addition, a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy, p-value, test accuracy, area under the curve, sensitivity, precision, F1-score, specificity, and kappa for adaptive gradient algorithm 25%, 75%, 100%, and stochastic gradient descent 100% fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.
Comparative Analysis of Classification Algorithms Applied to Circular Trading Prediction Scenarios

Goods and services trading taxation is the main source of revenue for Brazilian states, therefore its evasion directly affects public services. A particular case of evasion concerns the issuance and use of cold invoices – ones referring to transactions registered at Tax Administration, but which did not actually take place. Following the proposal by Mathews et al. [16], this work reports the application of classic supervised learning algorithms to identify circular trading behaviors involving taxpayers from Brazilian State of Goias, through the analysis of their goods and services trading operations. Experiments showed similar results to the original ones, but pointing to k-Nearest-Neighbours (and not Logistic Regression) as the most accurate technique for this purpose – given Brazilian context's characteristics.
Hand Gesture Recognition for Sign Languages Using 3DCNN for Efficient Detection

Sign Language Recognition aims at providing an efficient and accurate mechanism for the recognition of hand gestures made in sign languages and converting them into text and speech. Sign language is a means of communication using bodily movements, especially using the hands and arms. With sign language recognition methods, dialog communication between the deaf and hearing society can become a reality. In this project, we carry out sign language recognition by building 3D convolutional neural network (3DCNN) models that can perform multi-class prediction on input videos containing hand gestures. On detection of the input, both text and speech are generated and presented as output to the user. In addition to this, we also implement real-time video recognition and continuous sign language recognition for multi-word videos. We present a method for recognition of words in three languages – Tamil Sign Language (TSL), Indian Sign Language (ISL), and American Sign Language (ASL), and outperform state-of-the-art alternatives in with accuracies of 97.5%, 99.75% and 98% respectively.
Cryptanalyzing a Feistel Chaotic Block Cryptosystem Based on Correlation Power Analysis

Chaos-based block encryption algorithms have been proposed for years and their securities have been tested using conventional statistical methodologies, i.e. frequency analysis, avalanche analysis, entropy analysis, etc. However, when those algorithms are carried out on hardware devices, some crucial physical information may leak during the encryption or decryption processes. Feistel chaotic block cryptosystem has been proposed recently and is used for the block ciphers in different applications such as Internet-of-Things. In this work, the security of a Feistel chaotic block cryptographic algorithm is investigated by using Correlation Power Analysis (CPA) when the cryptographic system is in the process of performing encryption. Especially, the cryptosystem is implemented on an Atmel XMEGA128D4 micro controller. The attack points of Feistel chaos-based block encryption algorithm and power mapping model are analyzed in detail, and the procedure of utilizing CPA is given to cryptanalyze its hardware security. In theory, Multiple Samples Correlation Power Analysis (MSCPA) has a higher correct key differentiating level than CPA. However, in practice, if the correct key cannot be recovered by using CPA, the interesting sample points of power trace cannot be found for MSCPA. Experimental results show that the CPA can successfully obtain the secret key of Feistel cryptosystems and interesting sample points of power trace can be found, which laid a foundation of MSCPA to combine multiple samples of power trace. Further, the CPA can hit 100% success rate within [Formula: see text]50 power traces. Overall, this work can help efficiently evaluate the hardware security of Feistel chaotic block cryptosystem.
Modelling of Solar Power Production in Dry and Rainy Seasons Using Some Selected Meteorological Parameters

In this paper, we deployed the multiple linear regression method in developing a solar power output model for solar energy production, where the meteorological parameters are the independent variables.We fitted the model and found that the meteorological variables considered accounted for 94.88% and 99.61% of the power output in both dry and rainy seasons.We observed from the work that the solar panel performs well in all seasons but slightly better in the rainy seasons.This could be attributed to the washing away of dust particles from solar panels by the rain and higher operating temperature different from the specified manufactured temperature of 25˚C.We observed that other factors such as the cloud slightly affect the optimal performance of the system.Panels inclined at an angle of 5˚ (Tilt) and facing south azimuth performs optimally, periodic washing of the surface of solar panels enhances optimal performance.
Cyber threat modeling for protecting the crown jewels in the Financial Services Sector (FSS)

Financial institutions are undergoing the so-called "de-perimeterization." The security model up to today is heavily dependent on "border patrols" focusing mostly on providing a secure perimeter while the internal network is inherently trusted. In the upcoming borderless networks, the focus is shifting to protection of the data itself, considering the full lifecycle or switching toward context-aware defensive strategies also known as zero trust networks. The focus of this work is to critically discuss existing threat modeling methodologies, available and used in the financial services sector (FSS). The objective is to investigate the extent at which existing methodologies cover the different threat actors & events and if they reflect the current threat landscape in the FSS. The investigations are supported by a real-world case study to uncover if any process can reflect the current threat landscape without any customizations or special know-how, and whether the final outcome helps in reaching a secure or compliance state. Through the case study, it is evidenced that by utilizing the IRAM2 methodology resulted in a high ratio of compliance, however, considering the Crown Jewels of a Financial Institution (FI), a secure, as much as possible, state should be the desired outcome.
THE EFFECT OF RECIPROCAL TEACHING METHOD ON STUDENTS’ READING COMPREHENSION

- The objective of the study is to investigate if Reciprocal Teaching Method (RTM) significantly affects students’ reading comprehension. This study was conducted by using experimental research design. The population of the study was the students of the eleventh grade at Madrasah Al Washliyah 12 Perbaungan academic year 2020-2021. The total number of students was 172. By applying cluster random sampling technique, two classes were selected as the sample namely XI IPA 1 and XI IPA 2. There were 36 students in each class. The experimental group was XI IPA 1 taught by employing Reciprocal Teaching Method (RTM) and the control group was XI IPA 2 taught without applying Reciprocal Teaching Method (RTM). The instrument for collecting the data was a multiple choice test consisting of 40 questions of analytical exposition text. The data were analyzed by using t-test formula. It was found that t-observed value was higher than that of t-table (34.21 > 4.11) with degree of freedom df =58 at level significance α= 0.05. It means Ha was accepted and Ho was successfully rejected. Thus, Reciprocal Teaching Method (RTM) significantly affects students’ reading comprehension.Keywords: Reading comprehension, Reciprocal Teaching Method
Enhanced Feature Matching Algorithm for Medical Image Registration

In medical imaging technology, image registration plays a vital role. Image registration is a process of transforming the test image to reference image based on different parameters and features. In traditional feature extraction methods the feature points are unevenly distributed for which the redundant features are more. This results to time consuming process and low registration accuracy. Hence it is a challenging task to match the images with higher accuracy. In the proposed method enhanced and robust image registration algorithm is based on SURF and BRISK feature extraction methods with upgraded GMS and RANSAC algorithm. In this method the keypoints of the medical images are detected through SURF algorithm then features are extracted by BRISK algorithm and false match keypoints are removed through GMS algorithm and improved RANSAC algorithm is used. Experimental results show the efficiency and effectiveness in terms of runtime, accuracy compared to traditional feature extraction matching algorithms which can be implemented in real time for occasion with the requirements of high accuracy.
An Overview of the Maude Strategy Language and its Applications

In the Maude specification language, the behavior of systems is modeled by nondeterministic rewrite rules, whose free application may not always be desirable. Hence, a strategy language has been introduced to control the application of rules at a high level, without the intricacies of metaprogramming. In this paper, we give an overview of the Maude strategy language, its applications, related verification tools, and extensions, illustrated with examples.
Gaussian conditional random fields for classification

Gaussian conditional random fields (GCRF) are a well-known used structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure.In this paper, a Gaussian conditional random fields model for structured binary classification (GCRFBC) is proposed.The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs.The model representation of GCRFBC is extended by latent variables which yield some appealing properties.Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models.In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs.Additionally, two different forms of the algorithm are presented: GCRFBCb (GCRGBC -Bayesian) and GCRF-BCnb (GCRFBC -non Bayesian).The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant.The inference in GCRFBCb is solved by Newton-Cotes formulas for one-dimensional integration.Both models are evaluated on synthetic data.It was shown that both models achieve better prediction performance than unstructured predictors.Furthermore, computational and memory complexity is evaluated.Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail.
Brazilian Veneto (Talian)

Brazilian Veneto is a Romance language spoken by approximately 500,000 people, most of whom live in the southern Brazilian states of Rio Grande do Sul, Santa Catarina and Paraná (Margotti 2004, Pereira 2017). It is also spoken in the southeastern Brazilian states of São Paulo and Espírito Santo (see e.g. Loriato 2019). The language is often referred to by its speakers as Talian (/taˈljaŋ/), meaning ‘Italian’, given the geographical origin of the speakers who developed it, even though it is not a variety of Standard Italian. In this Illustration, I will refer to the language under examination as Talian, following many speakers’ naming preference.
Confidence interval‐based overload avoidance algorithm for virtual machine placement

Abstract Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.
An Improved Adaptive Genetic Algorithm Based on Dynamic Bayesian Network

With the prosperous development of information and science technology, the future modern electronic information warfare will have the characteristics of a high degree of equipment informatization and strong game confrontation. Traditional methods rely too much on experts to extract features. Bayesian network (BN), a graph model based on graph theory and probability statistics, uses machine learning as the core to avoid problems such as excessive human subjectivity. Aiming at the unique structure of the transfer network in which the dynamic Bayesian network (DBN) is responsible for time sequence expansion, a genetic learning algorithm with a two-step strategy and an adaptive mechanism is proposed. This paper proposes a genetic algorithm with a two-step strategy and an adaptive mechanism to realize the adaptation of crossover rate and mutation rate, and uses different strategies for different evolution stages in mutation and crossover operations. The improved adaptive genetic algorithm (IAGA) based on DBN is proposed and implemented. The structure learning comparison of search optimization algorithms verifies the accuracy and convergence efficiency of the improved algorithm.
Construction of a Multimedia-Assisted Teaching System for English Courses in a Multimodal Sensing Environment

With the advent of the information era, education reform has also sounded the call for in-depth reform and development. In order to cope with the development needs of modern society’s economy and political culture, the importance of English, as the mainstream language of international communication in the world today, is also self-evident. In rapid development of science and technology, network multimedia-assisted teaching has also been developed rapidly, while the teaching methods and teaching outcomes of foreign languages have been the focus of people’s attention. The diversified development of students and changes in science and technology has been driving the improvement and construction of teaching systems based on the English curriculum. In today’s multimodal sensing environment, a personalized recommendation algorithm of cluster analysis algorithm + collaborative filtering is introduced to complete the construction of the teaching system, and the superiority of the system is demonstrated by questionnaire survey and performance analysis comparison with teachers and students as the main research targets. The aim is to fully integrate the new media context and multimodal sensing environment in this environment, and to use in this environment, we aim to fully integrate the new media context and multimodal sensing environment and use multimedia technology to assist the construction of the teaching system.
Development of Novel Incremental Question Answering System Using Optimised Deep Belief Network

Question answering system is a more eminent research area because of its vast usage in recent years, which can be modelled to solve the deep learning-related limitations. More number of research works have been presented in this question answering field, where most of the systems adopt deep learning as the major contribution. Question answering system focusses on satisfying the users in getting relevant answers regarding a certain question in natural language. This paper presents the incremental question answering system using optimised deep learning. The proposed model covers two-step feature extraction, feature dimension reduction, and deep learning-based classification. From the benchmark dataset collected from a public source, the initial process is to extract the features using word-to-vector. Further, Principle Component Analysis (PCA) is adopted for reducing the dimension of the feature vector. These dimension-reduced features are used for incremental question answering systems by the Optimised Deep Neural Network (O-DNN). Here, the testing weight of the DNN is updated by the Modified Deer Hunting Optimisation Algorithm (M-DHOA) for handling the incremental data. Various implementation details in the algorithms produce better results, which shows the superior performance of the proposed method over existing systems.
Hand Activity Recognition From Automatic Estimated Egocentric Skeletons Combining Slow Fast and Graphical Neural Networks

In this paper, we present an unified framework for understanding hand action from the first-person video. The proposed framework composes two main components. The first component estimates three-dimensional (3D) hand joints from RGB images. Two network structures derived from the baseline HopeNet network are proposed: convolutional neural networks (CNNs) which are traditional multi-layer CNN and CNN combining with GraphCNN to perform 3D hand pose estimation, without the use of GraphUNet as in baseline HopeNet method. The second component of the framework recognizes hand action from skeleton stream. We first deploy two recent advanced neuronal networks that are PA-ResGCN and Double-feature Double-motion (DDNet). To focus more on the hand pose changes, we improve DDNet with two normalization strategies of the hand joints. Finally, we fuse PA-ResGCN with our improved DDNet to still boost the recognition performance. We evaluate our proposed methods on First-Person Hand Action Benchmark dataset. Experiments show that our model for 3D hand joints estimation achieves the best precision (36.6 mm). Our hand joint normalization strategies improve the original DDNet from 0.71% to 4.05% of accuracy with the ground-truth hand pose while the improvement is significantly larger (from 2.96% to 10.98%) with the estimated hand pose. The late fusion schemes outperform different state-of-the-art methods for the hand action recognition with the highest accuracy of 86.67%. These experimental results show potential and extendable possibilities for developing practical first-person vision applications.
Semantic speckle: an auto-located speckle pattern for DIC measurement

Digital image correlation (DIC) has been widely used in both experimental mechanics and engineering fields. The matching algorithm of the DIC method usually requires surfaces containing a random speckle pattern as a deformation information carrier. The speckle pattern plays an irreplaceable role in DIC, which has led to extensive research on it. However, most previous research had always focused on the fabrication and computational performance of the speckle, ignoring the value of intentionally defining the meaning of speckle in design. In this study, we describe a novel, to the best of our knowledge, speckle pattern named semantic speckle. It is a digital speckle composed of several different speckle patterns with similar characteristics. Based on the deep-learning method and matching algorithm, the central location of the semantic part in the overall speckle image can be obtained automatically. Through the intentional definition of the semantic part, it can be possible to calibrate the camera parameters and correct the external parameters of the DIC systems.
Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing

The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention. To quickly respond to the large-scale task, we propose an Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing (DRNPH) to achieve cross-modal retrieval in the common Hamming space, which has the advantages of storage and efficiency. To fulfill the nearest neighbor search in the Hamming space, we demand to reconstruct both the original intra- and inter-modal neighbor matrix according to the binary feature vectors. Thus, we can compute the neighbor relationship among different modal samples directly based on the Hamming distances. Furthermore, the cross-modal pair-wise similarity preserving constraint requires the similar sample pair have an identical Hamming distance to the anchor. Therefore, the similar sample pairs own the same binary code, and they have minimal Hamming distances. Unfortunately, the pair-wise similarity preserving constraint may lead to an imbalanced code problem. Therefore, we propose the cross-modal triplet relative similarity preserving constraint, which demands the Hamming distances of similar pairs should be less than those of dissimilar pairs to distinguish the samples’ ranking orders in the retrieval results. Moreover, a large similarity marginal can boost the algorithm’s noise robustness. We conduct the cross-modal retrieval comparative experiments and ablation study on two public datasets, MIRFlickr and NUS-WIDE, respectively. The experimental results show that DRNPH outperforms the state-of-the-art approaches in various image-text retrieval scenarios, and all three proposed constraints are necessary and effective for boosting cross-modal retrieval performance.