Reviewerly/exHarmony huggingface dataset:
An Identification and Classification of Thyroid Diseases Using Deep Learning Methodology

The thyroid is one of the most important parts of our body. As part of the endocrine system, this tiny gland in our neck releases thyroid hormone, which is responsible for directing all your metabolic functions which means controlling everything from digestion to conversion to energy. When thyroid dysfunction, it can affect all aspects of our health. Both researchers and doctors face challenges in fighting thyroid disease. In that thyroid disease is a major cause of the emergence of medical diagnostics and prognosis, the beginning of which is a difficult confirmation in medical research. Thyroid hormones are suspected to regulate metabolism. Hyperthyroidism and hypothyroidism are one of the two most common thyroid diseases that release thyroid hormones to regulate the rate of digestion. Early detection of thyroid disease is a major factor in saving many lives. Frequently, visual tests and hand techniques are used for these types of diagnostic thyroid diseases. This manual interpretation of medical images requires the use of time and is highly affected by errors. This work is developed to successfully diagnose and detect the presence of five different thyroid diseases such as Hyperthyroidism, Hypothyroidism, Thyroid cancer, thyroid gland, Thyroiditis and general thyroid screening without the need for several consultations. This leads to predictable disease progression and allows us to take immediate steps to avoid further consequences in an effective and cost-effective way to avoid the human error rate. A web application will also be developed where a scanned image of the inclusion will provide the removal of the most time-consuming thyroid type and patient investment.
Embedding Alignment Methods in Dynamic Networks

In recent years, dynamic graph embedding has attracted a lot of attention due to its usefulness in real-world scenarios. In this paper, we consider discrete-time dynamic graph representation learning, where embeddings are computed for each time window, and then are aggregated to represent the dynamics of a graph. However, independently computed embeddings in consecutive windows suffer from the stochastic nature of representation learning algorithms and are algebraically incomparable. We underline the need for embedding alignment process and provide nine alignment techniques evaluated on real-world datasets in link prediction and graph reconstruction tasks. Our experiments show that alignment of Node2vec embeddings improves the performance of downstream tasks up to 11 pp compared to the not aligned scenario.
Comprehensiveness, Preciseness and Interconnectedness: How to Evaluate International Public Opinion Based on Cross-media Data Mining on the Internet

Abstract With emerging new media technologies, the analytic focus of research on international public opinions has switched from single content form, such as text, audio, image or video into multiple forms of media integrated together in cyberspace or in physical space. The depth and frequency of monitoring international public opinion on topics related to China are improving rapidly. This paper pinpoints the inadequacies of current monitoring activities and puts forward ways to analyze international public opinions on topics related to China based on technologies, such as web indexing, deep learning and data mining. Thematic discovery based on big data analytics, real-time surveillance and monitoring of trending topics, intelligent prediction of popularity, intelligent analysis of emotional valence, etc. can effectively support the establishment of a system using multimedia data to monitor and analyze international public opinion.
Linear regression by quantum amplitude estimation and its extension to convex optimization

Linear regression is a basic and widely used methodology in data analysis. It is known that some quantum algorithms efficiently perform least-squares linear regression of an exponentially large dataset. However, if we obtain values of the regression coefficients as classical data, the complexity of the existing quantum algorithms can be larger than the classical method. This is because it depends strongly on the tolerance error $\ensuremath{\epsilon}$: the best one among the existing proposals is $O({\ensuremath{\epsilon}}^{\ensuremath{-}2})$. In this paper, we propose a quantum algorithm for linear regression, which has a complexity of $O({\ensuremath{\epsilon}}^{\ensuremath{-}1})$ and keeps a logarithmic dependence on the number of data points ${N}_{D}$. In this method, we overcome bottleneck parts in the calculation, which take the form of the sum over data points and therefore have a complexity proportional to ${N}_{D}$, using quantum amplitude estimation, and other parts classically. Additionally, we generalize our method to some class of convex optimization problems.
VR Technology in Physical Education from the Perspective of 5G

This paper studies the application of VR technology (VRT) in physical education (PE) from the perspective of 5G. This paper selects Aerobics in general education of college PE as an example. In the research, we take two classes of freshmen as the research object, and set them as the control class and the experimental class respectively. The control class adopts the traditional PE teaching method, and the experimental class applies VRT to the aerobics teaching on the basis of the traditional PE teaching. At the end of the experiment, we compare and analyze the aerobics skill scores of the two classes and the satisfaction of the two classes. The results show that the total score of the control class is 68 points, the total score of the experimental class is 86 points, the satisfaction of the control class is 80%, and the satisfaction of the experimental class is 96%. It can be seen that VRT applied to aerobics teaching can achieve good teaching effect. The research results of this paper will provide certain reference value for the application of VRT in PE teaching.
Measurements and analysis of acoustic guitars during various stages of their construction

Vibration measurements were taken at various stages of the construction of three steel-string acoustic guitars. The construction stages include: the unaltered top plate, braced top plate at multiple steps during the brace carving, glued guitar body before and after sanding, and the completed guitar. Measurements were taken with a laser Doppler vibrometer and force hammer to analyze the mode frequencies, damping, and amplitudes, and track how they evolve. The motivation is to investigate which building steps create the most significant vibrational changes and gain insight into the significance of each change. This knowledge can be used to help instrument builders know at which stage they can make specific alterations, and how much variation they can expect to see during the remainder of the construction.
Integration of IoT and Blockchain Technology for Smart Cities

In this modern era of the digital revolution, urban development is growing rapidly and many cities around the world are continuing to develop in many areas be its health sector, government sector, education sector and so on. But there is no denial that information and technology (IT) is one of the main contributors to this development. With the growing demand for smart devices such as smartphones along with various internet services, there is no doubt that communication has been made extremely easy for everyone. IoT is one of the emerging technology that can contribute to a smart city. It is seen that many IoT-based systems are currently used in various sectors from healthcare to parking systems and is proved to be efficient. But IoT suffers from security issues and is vulnerable to attacks. Blockchain on the other hand is a technology that is secured by design. It uses cryptography techniques to secure the data and is meant to be a trusted technology in a trust-less environment. In this paper, we discuss the existing systems that integrate these two technologies and mention the challenges faced when such systems are deployed in a working environment.
Machine Learning Algorithms for Predicting the Spread of Covid‒19 in Indonesia

Coronavirus 2019 or Covid-19 is a major problem for health, and it is a global pandemic that has to be controlled. Covid-19 spread so fast to 196 countries, including Indonesia. The government has to study the pattern and predict its spread in order to make policies that will be implemented to tackle the spread of some of the existing data. Therefore this research was conducted as a precautionary measure against the Covid-19 pandemic by predicting the rate of spread of Covid-19. The application of the machine learning method by combining the k-means clustering algorithm in determining the cluster, k-nearest neighbor for prediction and Iterative Dichotomiser (ID3) for mapping patterns is expected to be able to predict the level of spread of Covid-19 in Indonesia with an accuracy rate of 90%.
Perlustration on Mobile Forensics Tools

Nowadays many people store more information in cellphones rather than do on their computer which leads to the increase of crimes taking place in mobile. This is also because of extensive use of mobile devices. People refer to store more information in mobile than in computer. Therefore,scope of forensics in mobile has increased in last few years as compared to computers. In this paper , we have carried out a survey on tools and techniques which are in practice for mobile forensics. The survey is the amalgamation of the in practice technical methods and discussion of some of the tools for specific operating systems. Some challenges faced by practitioners while performing forensics has also been enlisted.
Predicting Risk of Mortality in Pediatric ICU Based on Ensemble Step-Wise Feature Selection

Background. Prediction of mortality risk in intensive care units (ICU) is an important task. Data-driven methods such as scoring systems, machine learning methods, and deep learning methods have been investigated for a long time. However, few data-driven methods are specially developed for pediatric ICU. In this paper, we aim to amend this gap-build a simple yet effective linear machine learning model from a number of hand-crafted features for mortality prediction in pediatric ICU.Methods. We use a recently released publicly available pediatric ICU dataset named pediatric intensive care (PIC) from Children's Hospital of Zhejiang University School of Medicine in China. Unlike previous sophisticated machine learning methods, we want our method to keep simple that can be easily understood by clinical staffs. Thus, an ensemble step-wise feature ranking and selection method is proposed to select a small subset of effective features from the entire feature set. A logistic regression classifier is built upon selected features for mortality prediction.Results. The final predictive linear model with 11 features achieves a 0.7531 ROC-AUC score on the hold-out test set, which is comparable with a logistic regression classifier using all 397 features (0.7610 ROC-AUC score) and is higher than the existing well known pediatric mortality risk scorer PRISM III (0.6895 ROC-AUC score).Conclusions. Our method improves feature ranking and selection by utilizing an ensemble method while keeping a simple linear form of the predictive model and therefore achieves better generalizability and performance on mortality prediction in pediatric ICU.
Case studies on using natural language processing techniques in customer relationship management software

How can we use a text corpus stored in a customer relationship management (CRM) database for data mining and segmentation? To answer this question, we inherited the state of the art methods commonly used in natural language processing (NLP) literature, such as word embeddings, and deep learning literature, such as recurrent neural networks (RNN). We used the text notes from a CRM system taken by customer representatives of an internet ads consultancy agency between 2009 and 2020. We trained word embeddings by using the corresponding text corpus and showed that these word embeddings could be used directly for data mining and used in RNN architectures, which are deep learning frameworks built with long short-term memory (LSTM) units, for more comprehensive segmentation objectives. The obtained results prove that we can use structured text data populated in a CRM to mine valuable information. Hence, any CRM can be equipped with useful NLP features once we correctly built the problem definitions and conveniently implement the solution methods.
Systems security assurance as (micro) publishing

Markup technologies are very general purpose, as reflects their generality of conception. They become interesting as well as useful as they are applied to accomplish goals in the real world. Since principles of generic declarative markup were first applied to accomplishing publishing-related goals in information management, design and application, 25 or 40 years ago, they have repeatedly demonstrated both their generality – they really do work – and their demand for applicability. Get one thing wrong, or leave it out, and the effort sits on a shelf. Design and deploy it carefully and sensitively, and even an inexpensive initiative can pay dividends for years. These systems become sustainable in the context of the sustainable operations of which they are a part. Decades of experience have shown us how to use declarative markup to sustain publishing operations. Now we have to deal with similar problems of information description, management, reuse across contexts, referencing, tracing, and authentication, only at even larger scales than before, both in size and complexity. This paper proposes some lessons and insights we can bring from our experience with publishing technologies, and suggests how they might be applicable in the growing domain of systems security assurance.
Pengembangan Aplikasi Hebat Fluida Berbasis Android Untuk SMA Kelas XI Pada Materi Fluida Statis

The rapid development of technology and information has not been fully utilized in education system. There needs the development of Interactive learning media that can increase students' learning motivation. The aims of this study is to develop learning media for android-based Hebat Fluida applications for high school class XI on static fluid material. This study uses the Research and development (R&amp;D) method and the research model used is the DDD-E (Decide, Design, Develop, Evaluate) development model. This research was conducted with a feasibility test which has 3 aspects, the material aspect getting a percentage of 83,75%, the media aspect getting a percentage of 90%, and the language aspect getting a percentage of 85%. It can be concluded that the average percentage of the feasibility test of the media is 86,25%, which means it is very valid and suitable for use Hebat Fluida as a learning medium.
Counting plane cubic curves over finite fields with a prescribed number of rational intersection points

For each integer $$k \in [0,9]$$ , we count the number of plane cubic curves defined over a finite field $${{\mathbb {F}}}_q$$ that do not share a common component and intersect in exactly $$k\ {{\mathbb {F}}}_q$$ -rational points. We set this up as a problem about a weight enumerator of a certain projective Reed–Muller code. The main inputs to the proof include counting pairs of cubic curves that do share a common component, counting configurations of points that fail to impose independent conditions on cubics, and a variation of the MacWilliams theorem from coding theory.
Enhancing Deep Neural Network Saliency Visualizations With Gradual Extrapolation

In this paper, an enhancement technique for the class activation mapping methods such as gradient-weighted class activation maps or excitation backpropagation is proposed to present the visual explanations of decisions from convolutional neural network-based models. The proposed idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output. Instead of producing a coarse localization map that highlights the important predictive regions in the image, the proposed method outputs the specific shape that most contributes to the model output. Thus, the proposed method improves the accuracy of saliency maps. The effect has been achieved by the gradual propagation of the crude map obtained in the deep layer through all preceding layers with respect to their activations. In validation tests conducted on a selected set of images, the faithfulness, interpretability, and applicability of the method are evaluated. The proposed technique significantly improves the localization detection of the neural networks' attention at low additional computational costs. Furthermore, the proposed method is applicable to a variety deep neural network models. The code for the method can be found at https://github.com/szandala/gradual-extrapolation
Линзовый корректор волнового фронта для изучения плоских поверхностей

The method of manufacturing and the results of studies of a lens corrector that converts a spherical diverging front into a plane one and is intended for studying flat surfaces as part of an interferometer with a diffraction comparison wave is described. A feature of the corrector is the use of an aspherical convex surface with a maximum deviation of ∼200 μm from the nearest sphere. The first experimental results are presented, indicating the prospects for using ion-beam processing to improve the quality of the wavefront. After the procedure of ion-beam processing, the aberrations over the entire aperture of the corrector decreased by more than 4 times and amounted to the parameter of the height difference PV = 207 nm (∼λ/3) and RMS = 19.2 nm (∼λ /33). On an area with a diameter of 80%, the aberrations fell to the nanometer level: PV = 65 nm (∼λ/10) and RMS = 8.3 nm (∼λ/76).
An Efficient Liver Tumor Detection using Machine Learning

Liver Cancer is among the most commonly diagnosed diseases in today’s modern era. Liver tumor segmentation is a fundamental task to perform early diagnosis and recommend a treatment. Manual segmentation is the traditional approach to achieve the required results but it has always been a time-consuming process. There are some anomalies like ambiguous gray level color ranges similar to other neighboring organs, irregular tumor shapes, and various uneven tumor sizes which are overlooked. Due to these reasons, some semi-automated and even fully automated techniques have been put forward. However, the advancement of machine learning has been very accommodating for addressing this issue. In this paper, we propose an unsupervised machine learning technique combined with a supervised mechanism that accurately performs liver tumor segmentation. We perform clustering on our collected dataset and extract LBP features as well as HOG features from these clusters. Furthermore, we perform classification which is based on these extracted features using KNN. Furthermore, we have compared our results with two classifiers namely SVM and Ensemble to achieve a better understanding. Our proposed technique outperformed existing techniques and showed encouraging results when compared to other methods.
A new approach of classical Hill Cipher in public key cryptography

Secure message transformation promote the creation of various cryptography systems to enable re???ceivers to interpret the transformed information. In the present age of information technology, the secure transfer of information is the main study of most agencies. In this study, a particular symmetric cryptography system, Hill Cipher method, is enhanced with the help of encryption and decryption algorithms of the asymmetric RSA cryptography system to avoid certain problems. Previous results show that the original Hill algorithms are still insufficient because of their weakness to known plaintext attack, and a modification of the Hill Cipher cryptography system is therefore presented to increase its invulnerability. The enhancement focuses on implementing the RSA algorithms over the Hill Cipher to increase its security efficiency. The suggested method relies on the security of the RSA and Hill Cipher cryptosystems to find the private decryption keys, and thus is much more secure and powerful than both methods applied separately. Also secure and dynamic generation of the Hill Cipher matrix instead of using static matrix are proposed. This new approach is composed of a public key cryptosystem that has a shared secret key (between participants only), public key (announced to all), and two private keys (for each person). Therefore, this new modification can increase the invulnerability of the Hill Cipher against future attacks during transmission of information between agencies in this age of information technology
Universal Singlet‐Triplet Qubits Implemented Near the Transverse Sweet Spot

Abstract The key to realizing fault‐tolerant quantum computation for singlet‐triplet (ST) qubits in semiconductor double quantum dot (DQD) is to operate both the single‐ and two‐qubit gates with high fidelity. The feasible way includes operating the qubit near the transverse sweet spot (TSS) to reduce the leading order of the noise, as well as adopting the proper pulse sequences which are immune to noise. The single‐qubit gates can be achieved by introducing an AC drive on the detuning near the TSS. The large dipole moment of the DQDs at the TSS has enabled strong coupling between the qubits and the cavity resonator, which leads to two‐qubit entangling gates. When operating in the proper region and applying modest pulse sequences, both single‐ and two‐qubit gates have fidelity higher than 99%. The results in this paper suggest that taking advantage of the appropriate pulse sequences near the TSS can be effective to obtain high‐fidelity ST qubits.
AUBER: Automated BERT regularization

How can we effectively regularize BERT? Although BERT proves its effectiveness in various NLP tasks, it often overfits when there are only a small number of training instances. A promising direction to regularize BERT is based on pruning its attention heads with a proxy score for head importance. However, these methods are usually suboptimal since they resort to arbitrarily determined numbers of attention heads to be pruned and do not directly aim for the performance enhancement. In order to overcome such a limitation, we propose AUBER, an automated BERT regularization method, that leverages reinforcement learning to automatically prune the proper attention heads from BERT. We also minimize the model complexity and the action search space by proposing a low-dimensional state representation and dually-greedy approach for training. Experimental results show that AUBER outperforms existing pruning methods by achieving up to 9.58% better performance. In addition, the ablation study demonstrates the effectiveness of design choices for AUBER.
Characters Recognition based on CNN-RNN architecture and Metaheuristic

Convolutional neural networks (CNN) are composed of multiple convolutional layers and a fully connected layer(s) (FC). In most of CNN models, the memory needed only for the weights of FC layers exceeds the total required by the rest of the layers. Consequently, for decreasing memory size needed and the acceleration of the inference, it obvious to focus on the an FC layer optimization method. In this paper, we propose a hybrid neural network architecture to perform image classification that combines CNN and the recurrent neural networks (RNN) to deal with the presented problem. To do so, a pretrained CNN model is used for features extraction (without FC Layers), then plugged into a parallel architecture of a RNN. In this work the Hopfield is considered. The obtained results on the Noisy MNIST Dataset have exceeded the state of the art for this problem.