Reviewerly/exHarmony huggingface dataset:
Using Classification Algorithms in Data Mining in Diagnosing Breast Cancer

Data mining is the process of extracting useful information from large-scale data in an understandable and logical way. According to the main machine learning techniques of data mining; classification and regression, association rules and cluster analysis. Classification and regression are known as predictive models, and clustering and association rules are known as descriptive models. In this study, the classification method was used. With this method, it is aimed to assign a data set to one of the previously determined different classes. The data set used in the study was obtained from the UCIrvine Machine Learning Repository database. The dataset named “Breast cancer” consists of breast cancer data consisting of 699 samples and 10 features collected by William H. at the University of Wisconsin hospital. The data content includes information about the characteristics of some cells analyzed in the detection of breast cancer, cell division, and whether they are benign or malignant. Upon completion of the study, a classification process is performed by determining whether the targeted person has cancerous or non-cancerous cells. In the study carried out in this context; Data mining analyzes were performed using WEKA and Orange programs, SVM (Support Vector Machine), Random Forest algorithms. Along with the analysis results, a comparison was made on the data set, taking into account the previous studies. It is aimed that the conclusions obtained at the end of the study will guide medical professionals working in this field in the diagnosis of breast cancer.
Aplikasi Penjadwalan Laboratorium Berbasis Android Pada SMK Bina Satria

SMK Bina Satria is one of the schools located in the city of Medan which is tasked with educating the nation's children to have better quality human resources. The laboratory scheduling system is still done conventionally. The schedule for using the laboratory is recorded in a book and then socialized to each teacher concerned. By doing this method, there are still some problems that occur, among others, teachers need a lot of time and energy in obtaining information about the laboratory schedule because they have to come directly to the school. In addition, the admin also has difficulty in conveying laboratory schedule information to the teachers because the admin has to socialize with the teachers one by one. The author conducted this research to design and build an android-based laboratory scheduling application at SMK Bina Satria. With this application, it is hoped that this application can help the SMK Bina Satria admin in processing laboratory schedules and assisting SMK Bina Satria teachers in obtaining laboratory schedule information easily and quickly via an Android smartphone. This application is built using Kodular application and Airtable database.
UAV path planning based on an elite-guided orthogonal diagonalised krill herd algorithm

At present, obstacle avoidance and the shortest path to the destination are the main problems that need to be considered when the UAV (unmanned aerial vehicle) performs its mission. In this paper, an Elite-Guided Orthogonal Diagonalised Krill Herd (EODKH) algorithm is developed to solve the path planning problem of UAV in three-dimensional space with complex terrain. EODKH algorithm divides the krill population into two parts: one part is composed of elite krill, and the evolution of krill population is guided by the experience of elite krill with orthogonal diagonalisation, and the elite krill with higher rank can obtain greater 'respect' in the evolution procedure; the other part evolves according to the original krill herd method, so as to improve the convergence diversity of the whole krill herd. Experimental results show that EODKH is better than other krill herd algorithms in UAV path planning.
Manifolds.jl: An Extensible Julia Framework for Data Analysis on Manifolds

We present the Julia package Manifolds.jl, providing a fast and easy-to-use library of Riemannian manifolds and Lie groups. This package enables working with data defined on a Riemannian manifold, such as the circle, the sphere, symmetric positive definite matrices, or one of the models for hyperbolic spaces. We introduce a common interface, available in ManifoldsBase.jl, with which new manifolds, applications, and algorithms can be implemented. We demonstrate the utility of Manifolds.jl using B\'ezier splines, an optimization task on manifolds, and principal component analysis on nonlinear data. In a benchmark, Manifolds.jl outperforms all comparable packages for low-dimensional manifolds in speed; over Python and Matlab packages, the improvement is often several orders of magnitude, while over C/C++ packages, the improvement is two-fold. For high-dimensional manifolds, it outperforms all packages except for Tensorflow-Riemopt, which is specifically tailored for high-dimensional manifolds.
Measuring Female Facial Beauty by Calculating the Proportions of the Face

Facial attractiveness can be measured by using mathematical ratios provided by investigators in psychology, arts and image analysis. In this paper, we propose a feature-based measuring system for female facial beauty by calculating different proportions from the facial components automatically extracted from images [18]. We achieve a better feature extraction by generalization of features in the human face when obtaining the location of eyes, eyebrows, mouth, nose and chin in a portrait photograph. Using the principles of plastic surgeons and mathematical measurements we train our system to produce grades. We develop a grading algorithm that is based on the relationships between the facial components. This algorithm has been tuned on a set of data and validated by experiments on different people. We observe that the algorithm is able to handle a good range of decision making with the facial components properly extracted.
The impact of interoceptive accuracy and stimulation type on the out-of-body experience

People tend to perceive a virtual body standing in front of them as their own if it is either stroked or moving synchronously with their own real body—the out-of-body experience (OBE). We combined synchrony manipulation with two other factors of theoretical interest: the kind of stimulation, visuotactile stimuli or visuomotor correlations, being synchronised and the interoceptive accuracy (IA) of participants, assessed by means of the heartbeat counting task. Results showed that explicit measures of embodiment were systematically affected by synchrony, and this synchrony effect was more pronounced for visuomotor than for visuotactile conditions. The walking drift was affected by IA: In visuotactile conditions, the synchrony effect was pronounced in individuals with low IA, presumably reflecting a stronger impact of the visual information. In visuomotor conditions, however, the synchrony effect was stronger in individuals with high IA, presumably reflecting a stronger impact of re-afferent information generated by the participants’ own movements.
Digital didactic resources in English language teaching

This paper analyzes the use of didactic resources in teaching the English language, considering the primary source of the Peninsula State University of Santa Elena in Ecuador. A qualitative methodology of descriptive type with a documentary approach was used. Data were presented as scientific support for the research, and interviews with teachers were used as a data collection technique. Among the main results, it was found that most teachers use digital resources to teach their lectures within their workload, but not all do it equally or with the frequency necessary for their effectiveness. In this way, it could be concluded that students respond positively to using technology as the primary education tool in the new educational era fostered by the COVID-19 pandemic, which forced teaching staff to use new resources to maintain an efficient education.
Git Re-Basin: Merging Models modulo Permutation Symmetries

The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -- often variants of stochastic gradient descent -- exhibit surprising effectiveness in fitting large neural networks in practice. We argue that neural network loss landscapes often contain (nearly) a single basin after accounting for all possible permutation symmetries of hidden units a la Entezari et al. 2021. We introduce three algorithms to permute the units of one model to bring them into alignment with a reference model in order to merge the two models in weight space. This transformation produces a functionally equivalent set of weights that lie in an approximately convex basin near the reference model. Experimentally, we demonstrate the single basin phenomenon across a variety of model architectures and datasets, including the first (to our knowledge) demonstration of zero-barrier linear mode connectivity between independently trained ResNet models on CIFAR-10. Additionally, we identify intriguing phenomena relating model width and training time to mode connectivity. Finally, we discuss shortcomings of the linear mode connectivity hypothesis, including a counterexample to the single basin theory.
Ion-native variational ansatz for quantum approximate optimization

Variational quantum algorithms involve training parametrized quantum circuits using a classical coprocessor. An important variational algorithm, designed for combinatorial optimization, is the quantum approximate optimization algorithm. Realization of this algorithm on any modern quantum processor requires either embedding a problem instance into a Hamiltonian or emulating the corresponding propagator by a gate sequence. For a vast range of problem instances this is impossible due to current circuit depth and hardware limitations. Hence we adapt the variational approach---using ion-native Hamiltonians---to create ansatz families that can prepare the ground states of more general problem Hamiltonians. We analytically determine symmetry protected classes that make certain problem instances inaccessible unless this symmetry is broken. We exhaustively search over six qubits and consider up to 20 circuit layers, demonstrating that symmetry can be broken to solve all problem instances of the Sherrington-Kirkpatrick Hamiltonian. Going further, we numerically demonstrate training convergence and levelwise improvement for up to 20 qubits. Specifically these findings widen the class problem instances which might be solved by ion based quantum processors. Generally these results serve as a test bed for quantum approximate optimization approaches based on system native Hamiltonians and symmetry protection.
Multi-task Semi-supervised Learning for Vascular Network Segmentation and Renal Cell Carcinoma Classification

Vascular network analysis is crucial to define the tumoral architecture and then diagnose the cancer subtype. However, automatic vascular network segmentation from Hematoxylin and Eosin (H &E) staining histopathological images is still a challenge due to the background complexity. Moreover, there is a lack of large manually annotated vascular network databases. In this paper, we propose a method that reduces reliance on labeled data through semi-supervised learning (SSL). Additionally, considering the correlation between tumor classification and vascular segmentation, we propose a multi-task learning (MTL) model that can simultaneously segment the vascular network using SSL and predict the tumor class in a supervised context. This multi-task learning procedure offers an end-to-end machine learning solution to joint vascular network segmentation and tumor classification. Experiments were carried out on a database of histopathological images of renal cell carcinoma (RCC) and then tested on both own RCC and open-source TCGA datasets. The results show that the proposed MTL-SSL model outperforms the conventional supervised-learning segmentation approach.
Formation of Programming Skills among Students of Scientific, Technical and Natural Science Areas of Training Using the Arduino Platform

The problem of improving the educational process of students of scientific, technical and natural science areas of training is one of the relevant aspects of modern pedagogical research. During training, it is necessary to apply new approaches, educational technologies and techniques in order to prepare graduates who are able to independently solve complex scientific, technical and engineering design tasks in the process of modern production. Programming is a universal tool for modeling objects and processes, as well as a powerful tool in developing the thinking of students of scientific, technical and natural science fields of training. The article discusses the issues of formation of programming skills of students based on the use of the Arduino platform. The professional standards are analyzed, the main contradictions of the existing system of training future bachelors in the field of radio electronics and control systems are revealed. The method of organizing effective programming training based on the use of the Arduino platform is presented. The criteria and indicators of the levels of competence formation are given. The results of an experiment on the implementation of the described methodology of teaching programming on the basis of the I.N. Ulyanov Chuvash State University are shown. Using the chi-square criterion, statistical processing of the results of a pedagogical experiment was performed, which convincingly proves that when using the Arduino platform, most students have reached high and advanced levels of programming skills formation.
Saliency Detection via Fusing Color Contrast and Hash Fingerprint

Saliency detection is a technique for automatically extracting regions of interest from the background and has been widely used in the computer vision field. This study proposes a simple and effective saliency detection method combining color contrast and hash fingerprint. In our solution, the input image is segmented into nonoverlapping superpixels, so as to perform the saliency detection at the region level to reduce computational complexity. A background optimization selection is used to construct an accurate background template. Based on this, a saliency map that highlights the whole salient region is obtained by estimating color contrast. Besides, another saliency map that enhances the salient region while restraining the background is also generated through hash fingerprint matching. Ultimately, the final saliency map can be obtained by fusing the two saliency maps. Comparing the performance with other methods, the proposed algorithm works better even in the presence of complex background or very large salient regions.
Термо-ЭДС сульфидов: возможности метода и значение для минералогических исследований (на примере изучения пирита и арсенопирита золоторудных месторождений восточного и южного обрамления Сибирского кратона)

On the example of studying the EMF of pyrrhotite, pyrite, and arsenopyrite from gold deposits of the Yana-Kolyma orogenic belt and the Bodaybo synclinorium, using the local determination method for polished sections, it is shown that this parameter for pyrite and arsenopyrite is an important genetic sign of the their origin. Thermoelectric properties of pyrite and arsenopyrite can be used to estimate not only the temperature of their formation but also the temperature parameters of their metamorphism, as well as to characterize the generation of these minerals. The development of local methods for measuring EMF contributes to the possibility of using them for genetic constructions, especially in the study of gold sulfide-impregnated and lode-veinlet mineralization within structures of different-age orogenic belts.
Adversarial Robustness in Hybrid Quantum-Classical Deep Learning for Botnet DGA Detection

This paper aims to contribute to the adversarial defense research gap in the current state-of-the-art of adversarial machine learning (ML) attacks and defense. More specifically, it contributes to the metric measurement of the robustness of artificial intelligence (AI)/ML models against adversarial example attacks, which currently remains an open question in the cybersecurity domain and to an even greater extent for quantum computing-based AI/ML applications. We propose a new adversarial robustness measurement approach which measures the statistical properties (such as the average of the accuracies and t-test results) from the performance results of quantum ML model experiments involving various adversarial perturbation coefficients (attack strength) values. We argue that our proposed approach is suitable for practical use in realizing a quantum-safe world because, in the current noisy intermediate-scale quantum devices (NISQs) era, quantum noise is complex and challenging to model and therefore complicates the measurement task or benchmarking. The second contribution of our study is the novel hardened hybrid quantum-classical deep learning (DL) model for botnet domain generation algorithm (DGA) detection, employing a model hardening adversarial training technique for mitigating new types of unknown DGA adversaries since new cyberattack approaches from the cyber arms race need to be anticipated. Our analysis shows the vulnerability of the hybrid quantum DL model to adversarial example attacks by as much as a 19% average drop in accuracy. We also found the superior performance of our hardened model obtained average accuracy gains as high as 5.9%. Furthermore, we found that the hybrid quantum-classical DL approach gives the benefit of suppressing the negative impact of quantum noises on the classifier's performance. We demonstrated how to apply our proposed measurement approach in evaluating our novel hybrid quantum DL model and highlighted the adversarial robustness of our model against adversarial example attacks as evidence of the practical implication of our study towards advancing the state of quantum adversarial machine learning research for the quantum-safe world.
Evaluation of a cGAN Model and Random Seed Oversampling on Imbalanced JavaScript Datasets

Malicious JavaScript detection using machine learning models have shown many great results over the years. The main problem is that the dataset used to train the model tends to be imbalanced, as the size of the malicious dataset is far smaller than the benign one. Many of the previous techniques ignore most of the benign samples and focus on training a machine learning model with a balanced dataset. However, real-world data only has a small fraction of malicious JavaScript, making it an imbalanced dataset. This paper proposes a cGAN-based filter model that can quickly classify JavaScript malware using Natural Language Processing (NLP) and oversampling. The feature of the JavaScript file will be converted into vector form and used to train the SVM classifier. Different NLP models and oversampling methods are tested to achieve a high recall score, such as the Doc2Vec and Latent Semantic Indexing (LSI) models. In this paper, a cGAN model will be used to generate new training malicious data based on the original training dataset. We evaluate our models with a dataset of over 30,000 samples obtained from top popular websites, PhishTank, and GitHub. The experimental result shows that the best recall score achieves 0.78 with the LSI model.
A Lens to the Past: Using Site-Specific Augmented Reality for Historical Interpretation

This demo paper presents a locative, site-specific, augmented-reality game for a WW1 historical site. The work constitutes the preliminary results of a research collaboration between two universities, one museum and a developer of serious games. The challenge was to produce an interactive, playful and educational digital experience based on the results of research into national policy and other cultural forces in relation to food and nutrition in Denmark during WW1. The resulting game needed to have a high technology readiness level and be deployed in the historical site for use by visitors. The research collaboration produced several innovations in location-based augmented-reality games for cultural heritage that are likely to be of interest to researchers and developers working in this space: (a) a way to present complex content that requires multiple perspectives in a geolocatable double structure; (b) the use of a visual aesthetic that resonates highly with the historical period in question in order to encourage reflection that relates the past to the present; (c) an approach to adding supplementary historical information in a fashion that aims not to overload the player with information during the play experience. While we have yet to evaluate the work through a user trial, this demo paper presents our design motivations and solutions that arose from the collaboration and the complex historical material.
Entropy–Based Diversification Approach for Bio–Computing Methods

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.