Reviewerly/exHarmony huggingface dataset:
Breast cancer detection by active contour models based on the fusion of a conventional ultrasound image with elasticity and doppler images

Active contour models are an effcient method for breast cancer segmentation on an ultrasound (US) image. There are two types of active contour models, which are parametric active contours (snakes) and geometric or geodesic active contours (level sets). This dissertation studies how the fusion of a conventional ultrasound image with elastography and Doppler images was used to improve the performance of active contours for breast cancer segmentation, especially, snake and level set methods. Snakes have been widely used for breast cancer segmentation. However, the methodscould produce inaccurate results if the seeds are initialized improperly (far from the true boundaries and close to the false boundaries). Therefore, we propose a novel initialization method based on the fusion of a conventional US image with elasticity and Doppler images.The combined information extracted from the three types of images provided better initialization and consequently leaded to better segmentation. The proposed fusion method (FM) has been tested against four state-of-the-art initialization methods on 90 ultrasound images from a database collected by the Thammasat University Hospital of Thailand. The ground truth was hand-drawn by three leading radiologists of the hospital. The reference methods were: center of divergence (CoD), force field segmentation (FFS), Poisson inverse gradient vector flow (PIG), and quasi-automated initialization (QAI). Using a variety of measures, our numerical tests proved the following advantages of the FM. Region-based level sets methods were also extensively applied for breast cancer segmentation on the US images. Regard to the region-based, their segmentation results aremultiple contours. Consequently, the proposed FM method was adapted to enhance their performance for breast cancer detection. The output contours from level sets combined with the useful features from the FM method were used to develop the breast cancer contour classification model. The support vector machine (SVM) was utilized for binary classifying either correct breast cancer contour or non-breast cancer contour. The binary classifier showedeffective results for breast cancer detection. The proposed model has been tested with three well-known region-based level sets on 60 ultrasound images dataset which was selected from the 90 ultrasound images. The verified level set methods were: distance regularized level set evolution (DRLSE), fuzzy clustering and lattice Boltzmann method (FCLBM), and online region-based active contour model (ORACM). From the experimental results, breast cancer contours were detected correctly using the proposed classifier applied on top of the combination of the FM and the verified level set methods.
Neural Network Approaches for Enabling Automatic Target Recognition.

difficult to analyze their internal states. Also, their performance is highly dependent on the training data. Our approach, therefore, is to create feature vectors that contain information that is as meaningful as possible. This paper describes the use of Gabor representations to generate feature vectors that are robust to variations in rotation, scaling, and translation. We are also studying ways to make the system robust to variations in perspective, occlusion, contrast, noise, and background. Gabor filters when used as the receptive field in a hierarchical scheme for feature extraction, offer properties that make them promising for providing the desired robustness to image variations. Unlike other filters, Gabor filters are optimally localized in both the space domain and the frequency domain, so that their space-bandwidth product is minimum. It has been shown that their properties of spatial localization, orientation selectivity, and spatial frequency selectivity make Gabor filters a good model for biological vision. This paper describes experiments in which we demonstrate the ability of the overall recognition system to classify objects in simulated scenes, even though they have undergone variations in orientation scale, and position.
Understanding Safety for Unmanned Aerial Vehicles in Urban Environments.

When Unmanned Aerial Vehicles (UAVs) autonomously operate in urban environments, it is especially important for these systems to behave safely and not harm anybody or anything. However, it is challenging to ensure that these systems behave safely in all possible situations and to clearly define this “safe” behavior for each situation. In this work, we provide a methodology for testing the safe behavior of UAV s while considering their environment with the help of scenario-based testing and search-based techniques. Additionally, we explore two cases throughout the paper: (i) A safety distance is specified, and we can use it for testing. (ii) No safety distance is defined, but we still aim to test the safe behavior of UAV s. In our experiments, we show the effectiveness and applicability of the proposed methods by discovering several safety distance violations and questionable behaviors of the tested UAV for both cases and four scenarios that represent all alternatives to avoid an obstacle.
Effects Of Covid-19 On Education And Its Shift From Face-To-Face To Virtual.

An analysis of the current situation due to the Covid 19 pandemic and its impact on face-to-face education has changed to a virtual education model. Education has shifted to a virtual education model. The planning of a hybrid planning for a hybrid educational model has not been successful and only one educational model should be between the two current models, the face-to-face model and the virtual model.virtual model. The Covid-19 pandemic has changed the way of educating and has led to a virtual model where the old models no longer have a place in the models no longer have a place in the education of the future. There are controversies about how students should be educated in the future, while the future seems to have arrived with new educational transformed with new educational options that will be around for a long time, and not just for the duration of the and not just for the duration of the pandemic.
NOVEL SATYANING ATI (TINJAUAN SOSIOLOGI SASTRA)

This study aims to determine the narrative structure and social problems in the novel Satyaning Ati . The data in this study were sourced from the novel Satyaning Ati by I Komang Alit Juliartha which was collected using reading, observing, copying and collecting techniques according to the required classification. After the data were collected, the data were analyzed using qualitative descriptive techniques. The result of this research is that the novel Satyaning Ati is composed of a narrative structure including a synopsis, theme, character and characterization, plot, setting and message. The social problems contained in the novel include poverty, family disharmony, social inequality, homosexuality and environmental pollution. Hopefully, through this research, the government will be more sensitive and able to find solutions to social problems that are reflected in the novels that are real in the field. Keywords: novel of Satyaning Ati , sociology of literature
Flexible and Explainable Solutions for Multi-Agent Path Finding Problems.

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. The real-world applications of MAPF require flexibility (e.g., solving variations of MAPF) as well as explainability. In this study, both of these challenges are addressed and some flexible and explainable solutions for MAPF and its variants are introduced.
Latviešu valodas apguvēju korpuss lietojumā: teorētisks un metodoloģisks ieskats

Several text corpora have been created in Latvia, including learner corpora. One of the latest projects is the Latvian Language Learner Corpus (LaVA), which contains the works of international students studying in Latvian higher education institutions who are learning Latvian as a foreign language. The texts are morphologically tagged automatically, and learner errors are tagged manually. A sufficient scope of publications is available, which provides the theoretical basis for the creation of Latvian language learner corpora; however, there is a lack of studies or practical methodological guidelines concerning the opportunities for their application, and there is little data about the use of text corpora in language acquisition. The aim of this study is to explain from the theoretical perspective for what purposes learner corpus data may be used, as well as to illustrate the methodological groundwork with examples from the LaVA corpus. Analysis of theoretical literature has demonstrated the functions and meaning of learner corpora in research, and experience with the use of corpora in acquiring a foreign language has been analysed. Examples of the use of the LaVA corpus as a didactic resource have been prepared using Corpus Linguistics methods. The study was conducted within the state research programme project “The Latvian Language”. After studying the functions of learner corpora from the theoretical perspective, it was concluded that the target audience of the LaVA corpus mainly includes teachers of Latvian as a foreign language (LATS), authors of teaching materials, as well as Latvian language learners. To facilitate the use of the LaVA corpus, it is important to have basic knowledge of Corpus Linguistics, an understanding of the theory of language, as well as an understanding of foreign language teaching methodology. LATS teachers can use the LaVA corpus data in the creation of curricula and teaching materials, in the preparation of language proficiency tests, etc. Using the inductive approach in language acquisition, language learners can also become language researchers, can analyse the errors of other learners, etc. Undeniably, the LaVA corpus can be used in broader linguistic research, for example, in contrastive interlanguage analysis, comparing the data of language learners with the data of native speakers or the data of different groups of language learners.
Salt&amp;Pepper: Spice up Security Behavior with Cognitive Triggers

This paper presents an interdisciplinary approach to improve impact factors on Information Security Behavior (ISB) considering various aspects from human behavior research. According to the Fogg Behavior Model (FBM) human behavior is a product of three factors: motivation, ability and triggers. We use this model as basis to derive potential influences on ISB. The FBM is combined with Dupree's User Clustering (DUC) to identify target group-specific potentials for behavior changes. In this work, our focus is to raise the factor motivation. As motivational triggers we focus on cognitive biases. In order to show the feasibility of the proposed interdisciplinary approach, the findings went into the design and implementation of a game-based prototype called Salt&Pepper.
Moving beyond downloads and views when assessing digital repositories

The institutional repository has become another type of information collection that is managed by information professionals. Repositories are versatile platforms that can be used to collect scholarly work; publish and preserve data; or used as a publication platform for archival and library-based materials. This chapter presents an approach for developing a systematic repository assessment programme that can be used in a variety of repository environments or settings. The chapter begins by briefly describing different types of libraries and includes a short history of repository development. It then explores definitions of downloads, views, and use, common terms used in relation to repository evaluation. The chapter concludes with a step-by-step description of how to use nested mixed methods to develop an institutionally personalised repository assessment programme.
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning

The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning. Existing theoretical analysis shows that PATE consistently learns any VC-classes in the realizable setting, but falls short in explaining its success in more general cases where the error rate of the optimal classifier is bounded away from zero. We fill in this gap by introducing the Tsybakov Noise Condition (TNC) and establish stronger and more interpretable learning bounds. These bounds provide new insights into when PATE works and improve over existing results even in the narrower realizable setting. We also investigate the compelling idea of using active learning for saving privacy budget. The novel components in the proofs include a more refined analysis of the majority voting classifier -- which could be of independent interest -- and an observation that the synthetic student learning problem is nearly realizable by construction under the Tsybakov noise condition.
Analyzing multiword term formation as a means to facilitate translation

In specialized language, multiword terms (MWTs) are one of the most frequent term types. Although MWTs are highly relevant to conceptual systems and specialized knowledge transmission, they are not simple to analyze. This chapter presents a corpus-based analysis of a set of English MWTs in the domain of wind energy. The results obtained explain the conceptual development of these terms and highlight the usefulness of this process for translation. This analysis first disambiguated the structure of these MWTs. The components of each term were then assigned semantic categories, and their internal semantic relations were made explicit. This revealed the microcontext of each MWT, which not only explains their formation and productivity but also facilitates their translation.
Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning.

Identifying string theory vacua with desired physical properties at low energies requires searching through high-dimensional solution spaces - collectively referred to as the string landscape. We highlight that this search problem is amenable to reinforcement learning and genetic algorithms. In the context of flux vacua, we are able to reveal novel features (suggesting previously unidentified symmetries) in the string theory solutions required for properties such as the string coupling. In order to identify these features robustly, we combine results from both search methods, which we argue is imperative for reducing sampling bias.
Intrinsic concise General Relativity and possible generalization for all interactions

This paper proposes a minimal autoportant intrinsic formulation of General Relativity and field equations that could govern all interactions.
Retos psicopedagógicos para estudiantes universitarios frente al uso de las nuevas tecnologías. Una apuesta necesaria

Este articulo de reflexion realiza aportes al alrededor de los posibles retos psicopedagogicos que la educacion superior, incluyendo la de caracter virtual, demanda frente a sus estudiantes y entorno al uso de las nuevas tecnologias. Para ello resaltan la manera como el docente sigue siendo un agente del cambio en la formacion universitaria, sin desconocer que el estudiante es el protagonista activo de su propio aprendizaje y el llamado a apoyar dichos retos. La universidad debe garantizar la interaccion, la cultura empatica y el trabajo colaborativo entre los actores educativos. Las implicaciones que tienen las tecnologias en la vocacion humana y social, coinciden con los postulados de la educacion virtual, generando desafios a la pedagogia en cuanto a una propuesta de la psicopedagogia frente a las TIC..Palabras clave: Psicopedagogia, educacion, tecnologias, pedagogia y TIC.  ABSTRACTThis reflection article makes contributions to the possible psychopedagogical challenges that higher education, including the virtual nature, demands from its students and around the use of new technologies. For this, they highlight the way in which the teacher continues to be an agent of change in university education, without ignoring that the student is the active protagonist of their own learning and the call to support these challenges. The university must guarantee interaction, empathic culture and collaborative work among educational actors. The implications that technologies have on the human and social vocation coincide with the postulates of virtual education, generating challenges to pedagogy in terms of a proposal of psychopedagogy in the face of ICT.Keywords: Psychopedagogy, education, technologies, pedagogy and ICT.
Pengembangan Sistem Informasi Pengelolaan Judul Skripsi dan Tugas Akhir dengan Fitur Deteksi Kemiripan Menggunakan Algoritma Winnowing

The development of information technology has a major impact on almost all aspects of human life, such as in the field of education. Complex college academic activities require an information system that can assist in its management. Skripsi management is part of the university's academic activities that aim to manage data from the process of implementing skripsi activities. Submission of skripsi titles using printed proposals, finding information about skripsi title data stored at universities is relatively difficult, the process of comparing titles based on the examiner's knowledge of existing title data is a problem that must be minimized. A skripsi title submission system is designed that applies the winnowing algorithm in it. With this system, title proposals can be submitted online, there is a data search feature for skripsi titles, the system can provide recommendations for acceptance of the proposed title based on the comparison of the percentage of similarity with the percentage limit of the percentage of accepted similarity that has been determined in the system. The results of the algorithm test show that the winnowing algorithm can be used in calculating the word similarity level by looking for fingerprint similarities in the sentences being compared
Research on Obstacle Avoidance of Mobile Robot Based on Improved Artificial Potential Field Method

In order to solve the problem of autonomous obstacle avoidance of mobile robots in static and dynamic environments,on the basis of analyzing the deficiencies of the classic artificial potential field method,by improving the repulsive potential field function and adjusting the gravitational component on the coordinate axis,the problems of target unreachable and local minimum in the obstacle avoidance process are solved respectively.At the same time,considering the impact of the speed of obstacles on the obstacle avoidance of the mobile robot,the relative speed between the two is added to the potential field function,so that the mobile robot can autonomously avoid obstacles in a dynamic environment. Finally,a simulation experiment is conducted on the MATLAB platform. The experimental results show that the mobile robot can avoid obstacles in static and dynamic environments and reach the target position,which verifies the feasibility of the improved algorithm.
Blockchain based Confidentiality and Integrity Preserving Scheme for Enhancing E-commerce Security

In recent years, Electronic Commerce (E-commerce) applications are attracting many users and merchants to conduct their daily business online which includes payment of bills, online banking, buying tickets and purchasing goods etc. E-commerce transaction security is a major concern for E-commerce websites along with its customers. The basic requirements for any E-commerce transaction are privacy, authentication, integrity and non-repudiation. In this paper, a transaction processing system (TPS) for E-commerce by using a Blockchain technology, zero-knowledge proof and modified elliptic curve cryptography encryption is proposed. Also a denial of service attack detection model for the E-commerce system is proposed which take care of the DoS attack during E-commerce transactions.
On Comparison of Different Image Segmentation Techniques

Nowadays, image processing is mostly denoted to the processing of digital images. Image processing has two branches: improving images and machine vision. Improving images are the methods that use blurring and increase contrast to improve the visual quality of images and ensure that they are displayed correctly in the destination environment (such as a printer or monitor), while machine vision deals with methods that can be used to mean and understand the content of images to be used in robotics. Image segmentation is one of the greatest significant steps on digital image processing. Image segmentation is separation of picture pixels into separate zones that are identical or as closely correlated as possible in terms of brightness, texture, or color. Image segmentation has lots of applications in many image processing tasks, such as image therapy, machine vision, image compression, object detection. This paper, will provide comprehensive review about different image segmentation techniques. We will compare each of the existing methods with each other and will investigate the positive potentials and drawbacks of each of these methods.
The Effects of Media Literacy Education and its Influence on Digital Citizenship: Focusing on CMF Education Programs in Korea

PurposeThis study aims to measure the effects of media literacy education through the social science method and find out the impact of media literacy education on digital citizenship in Korea and its implications for the development of democracy.Design/methodology/approachThis study used an independent t-test to analyze relativeness between media literacy education and media literacy factors. We also adopted an independent t-test to investigate media literacy influence on digital citizenship. Furthermore, we found out age differences using ‘the one-way analysis of variance (the one-way ANOVA)’.FindingsWe have found four media literacy factors, MC, CT, C&P, R&R, have been significantly affected by media literacy education. We have also pulled out three digital citizenship factors, IPA, TS, and CP, involved in media literacy education. Moreover, we have analyzed our survey by different age groups. The lower the age, the higher the TS. CP was highest among those from 30 to 59, followed by those under the 30s.
MICNet: A Hybrid Model Based on Inception Network and CNN for Automatic Modulation Classification

In recent years, the increasingly complex electromagnetic environment requires more accurate and rapid modulation methods. With the continuous development and application of deep learning, more and more methods and structures are proposed to solve the problem. In this letter, we propose an efficient architecture for an automatic modulation classification (AMC) based on inception network and CNN. We combine the improved inception modules with convolutional neural network (CNN) to obtain a mixed network structure (MICNet), which has fewer parameters and higher accuracy. In the experiments, MICNet reaches the overall 24-modulation classification rate of 97.54% at 28 dB SNR on the well-known DeepSig dataset. In addition, MICNet can be used for classification modulation recognition on computers with less hardware configuration.