Reviewerly/exHarmony huggingface dataset:
Deep Learning for Logo Detection: A Survey

Logo detection has gradually become a research hotspot in the field of computer vision and multimedia for its various applications, such as social media monitoring, intelligent transportation, and video advertising recommendation. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, and loss functions have been employed. This article reviews the advance in applying deep learning techniques to logo detection. First, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and their strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection. This study aims better to inform readers about the current state of logo detection and encourage more researchers to get involved in logo detection.
Specifics of doctor’s professional training on the basis of the national medical research center

Successful education of modern students requires great immersion of teachers in a process that is constantly changing, acquiring new properties, and meeting new challenges. So, digital technologies, online learning are already a natural communication environment, and students are motivated for self-development and self-expression. The traditions of the Russian medical school, together with modern trends in the development of education, create the prerequisites for the application of the most successful forms of education that develop the thinking and skills necessary for professional activity. In this article, the authors presented the experience of organizing training under the specialist’s program on the basis of the Department of Neurology and Psychiatry with the clinic of the Institute of Medical Education of the Federal State Budgetary Institution “Almazov National Medical Research Centre” of the Ministry of Health of the Russian Federation. Conclusions are formulated on the basis of the results of questioning teachers and students on the main characteristics of the educational process. The role of interactive and distance technologies is analyzed: online learning methods, video lectures, digital services, and other digital technologies. An analysis of the role of independent education, the most successful forms of knowledge testing, features of the development of communication skills, and the possibilities of mastering the competencies necessary for professional activity are presented. Separately, the interest of students in scientific work, the activity of participation in the student scientific club, the possibility of growth of interested students in professional and pedagogical aspects are analyzed. The presented data illustrated the success of the first experiment on the training of doctors on the basis of a scientific institution.
Neuromorphic regenerative memory optoelectronic oscillator

Neuromorphic spiking information processing based on neuron-like excitable effect has achieved rapid development in recent years due to its advantages such as ultra-high operation speed, programming-free implementation and low power consumption. However, the current physical platforms lack building blocks like compilers, logic gates, and more importantly, data memory. These factors become the shackles to construct a full-physical layer neural network. In this paper, a neuromorphic regenerative memory scheme is proposed based on a time-delayed broadband nonlinear optoelectronic oscillator (OEO), which enables reshaping and regenerating on-off keying encoding sequences. Through biasing the dual-drive Mach-Zehnder electro-optic modulator in the OEO cavity near its minimum transmission point, the OEO can work in excitable regime, where localized states are maintained for robust nonlinear spiking response. Both simulation and experiment are carried out to demonstrate the proposed scheme, where the simulation results and the experimental results fit in with each other. The proposed OEO-based neuromorphic regenerative memory scheme exhibits long-term response ability for short-term excitation, which shows an enormous application potential for high-speed neuromorphic information buffering, optoelectronic interconnection and computing.
Trenggalek dalam Panggung Budaya Masa Lampau di Jawa Bagian Selatan: Ragam dan Karakternya

Trenggalek Regency is one of the regencies of East Java Province located in the southern part of Java Island. Trenggalek Regency is surrounded by Tulungagung, Pacitan, and Ponorogo Regencies which have abundant cultural heritage potential, Trenggalek Regency has the potential for archaeological remains as many as 17 points. Archaeological research in Trenggalek Regency was initiated by the Yogyakarta Archaeological Center in 1983 in the form of surveys or excavations that continued until 2020. The purpose and purpose of writing this article is to know the variety and character of cultural heritage in Trenggalek. This article data was obtained through an archaeological survey in eight sub-districts in Trenggalek Regency in 2020. Based on the results of the study, Trenggalek Regency has a variety and character including the form, distribution of locations, and chronology of its cultural heritage. Archaeological data in the form of moveable and monumental artifacts and features. The dominance of moveable artifact data is in the form of stone piles, stone dies, terracotta pole shell fragments, dakon stones, and Yoni while monumental artefactual data in the form of pillboxes and inscriptions. Chronologically, the discovery of objects suspected of cultural heritage in Trenggalek Regency shows the influence of Hindu-Buddhist until the Islamic Mataram era of the Ngayogyakarta Hadiningrat Sultanate around the XIX century AD. Keywords: Trenggalek; Panggul; yoni; dakon stone; pillbox
THE EFFECTIVENESS OF THE STORY-TELLING METHOD WITH STORY APRONS ON INDONESIAN SPEAKING SKILLS IN EARLY CHILDHOOD

This study aims to determine the effectiveness of a Story Telling Method using story aprons on the ability to speak Indonesian in early childhood. Quasi-experimental research to determine the effect of treatment. Sampling using a random sampling technique. The research instrument was a test of Indonesian speaking ability in children which was developed through theoretical studies from several experts, then continued with normality tests, homogeneity tests, and hypothesis testing using the t-test. The results obtained were that the t-value was greater than t -Table thus the Story Telling method with the media of story aprons is effective in improving the ability to speak Indonesian in early childhood. The ability to speak Indonesian well from an early age will affect the child's development in the future.
Spectral Clustering on Multi-aspect Data

The spectral clustering techniques, based on the well-established spectral graph theory, are among the modern and effective clustering approaches. This method involves partitioning an affinity (or co-occurrence) graph into multiple subgroups, each of which can be considered a cluster. In the case of multi-aspect data, the spectral clustering process focuses on identifying a common partition across multiple affinity graphs that correspond to different data views. The underlying procedure entails finding the eigenvectors of the Laplacian graph constructed from the data affinity graph. These acquired eigenvectors form a new low-dimensional space that embeds the latent structure of the graph components or data groups. Consequently, spectral clustering can also be regarded as a dimensionality reduction technique even if its objective is solely to seek the eigenvectors. This chapter provides a review of how spectral clustering techniques are employed on multi-aspect data by exploring various approaches to learning the common eigenvectors among different aspects of the data.
Fabrication of Bioplastics from Sea Weeds

Abstract: This research deals with the production of bio-plastics from sea weeds. The main objectives is to produce bio-plastics from sea weeds (marine algae). The research has got two specific objectives which are to produce plastics that are environmental friendly whereby the plastic produced can be at least decomposed to smaller substance by the living organism and also to check the stability of the bio-plastics produced via mechanical and physical testing. In this study, marine algae specifically brown algae such as sargasum are the raw materials. Other materials required are glycerol, starch, gelatin, and colors. Methods used were observation to identify what type of algae has high content of agar and green algae were most the most efficient type. Experimentation involved the chemical processes to extract agar content from the from the algae by addition of sodium hydroxide to a powder form of algae which was dried and grinded, thereafter the mixture was heated 100 degree centigrade and allowed to cool agar powder was extracted and mixed with starch (as strengthening materials) and glycerol (plasticizer) and heated at 100 degree centigrade to get the tough semi-solid that helps to mould the plastics on a modal vessel
Glapan Weir Irrigation System Optimization Strategy to Increase Agricultural Yield

This study aims to identify the constraints and problems that exist in the irrigation system and to develop an optimization strategy for the Glapan Dam irrigation system in Gubung District, Grobogan Regency. This type of research uses descriptive quantitative. The data collection technique in this study used a questionnaire distributed to key persons who had been determined by purposive sampling. The analysis used is Analytical Hierarchy Process (AHP) then processed using Expert Choice software. The results show that the obstacles and problems that are still faced in the management of the irrigation system in Glapan Weir are the sedimentations of the Tuntang River which causes a reduction in water supply, and the lack of availability of tools such as pumps, sluice gates, and water flow measuring devices. The most prioritized criteria in the strategy of optimizing the irrigation system in Glapan Dam to increase agricultural yields is the money criterion with a weight value of 0.520 and the first priority alternative in the irrigation system optimization strategy to increase agricultural yields is an alternative government fund with a weighted value of 0.211.&#x0D; Penelitian ini bertujuan untuk mengidentifikasi kendala dan permasalahan yang ada pada sistem irigasi serta menyusun strategi optimasi sistem irigasi Bendung Glapan di Kecamatan Gubung, Kabupaten Grobogan. Jenis penelitian ini menggunakan deskriptif kuantitatif. Teknik pengumpulan data dalam penelitian ini menggunakan kuesioner yang dibagikan kepada key person yang telah ditentukan secara purposive sampling. Analisis yang digunakan adalah Analytical Hierarchy Process (AHP) kemudian diolah menggunakan software Expert Choice. Hasil penelitian menunjukkan bahwa kendala dan permasalahan yang masih dihadapi dalam pengelolaan sistem irigasi di Bendung Glapan adalah sedimentasi Sungai Tuntang yang menyebabkan berkurangnya pasokan air, dan kurangnya ketersediaan alat seperti mesin pompa, pintu air, dan alat pengukur debit air. Kriteria yang paling diprioritaskan dalam strategi optimalisasi sistem irigasi di Bendung Glapan untuk meningkatkan hasil pertanian adalah kriteria uang dengan nilai bobot 0,520 dan alternatif prioritas pertama dalam strategi optimalisasi sistem irigasi untuk peningkatan hasil pertanian adalah alternatif dana pemerintah dengan nilai bobot 0,211.
Integrating Nearly-Indistinguishable Quantum Emitters onto a Photonic Interposer

Current challenges for quantum repeaters using solid-state emitters include incorporating (1) multiple nearly-indistinguishable emitters (2) into an interposer with pho-tonic processing capabilities. We develop a process flow that targets both of these tasks.
Multiparameter Estimation with Two-Qubit Probes in Noisy Channels

This work compares the performance of single- and two-qubit probes for estimating several phase rotations simultaneously under the action of different noisy channels. We compute the quantum limits for this simultaneous estimation using collective and individual measurements by evaluating the Holevo and Nagaoka-Hayashi Cramér-Rao bounds, respectively. Several quantum noise channels are considered, namely the decohering channel, the amplitude damping channel, and the phase damping channel. For each channel, we find the optimal single- and two-qubit probes. Where possible we demonstrate an explicit measurement strategy that saturates the appropriate bound and we investigate how closely the Holevo bound can be approached through collective measurements on multiple copies of the same probe. We find that under the action of the considered channels, two-qubit probes show enhanced parameter estimation capabilities over single-qubit probes for almost all non-identity channels, i.e., the achievable precision with a single-qubit probe degrades faster with increasing exposure to the noisy environment than that of the two-qubit probe. However, in sufficiently noisy channels, we show that it is possible for single-qubit probes to outperform maximally entangled two-qubit probes. This work shows that, in order to reach the ultimate precision limits allowed by quantum mechanics, entanglement is required in both the state preparation and state measurement stages. It is hoped the tutorial-esque nature of this paper will make it easily accessible.
Chatbot Gamified and Automated Management of L2 Learning Process Using Smart Sender Platform

&lt;p style="text-align:justify"&gt;The purpose of this study was to examine the potential impact of integrating the Smart Sender platform to gamify and automate L2 e-classes on students’ motivation, English language proficiency in reading and language use, engagement, and attitudes towards the platform. This experimental research employed a quasi-experimental design, specifically a non-equivalent control group design of a pre-test-post-test type. The study found that the use of chatbot-driven e-classes on the Smart Sender platform increased student motivation, improved their English language proficiency in reading and language usage, and enhanced their engagement within L2 e-classes. Students expressed their appreciation for the ease of use and usefulness of this chatbot-based tool for improving their English language skills. The findings showed that the scores for motivation, engagement, and English language proficiency increased for both the experimental and control groups from pre-test to post-test, with a large effect size observed for the experimental group and a medium effect size observed for the control group. The statistically significant difference in the mean scores between the experimental and control groups indicates the positive impact of incorporating gamified chatbot-driven sessions within L2 e-classes on the Smart Sender platform.&lt;/p&gt;
Analysis of Pedagogical Content Knowledge in Science Teacher Education: A Systematic Review 2011-2021

&lt;p style="text-align:justify"&gt;Pedagogical content knowledge (PCK) consists of a set of understandings, knowledge, skills, and dispositions necessary for effective performance in specific teaching and learning situations. Using Scopus, EBSCO, and Web of Science databases, the study examines the progress of the PCK in science teacher education between 2011 and 2021. In total, 59 articles were reviewed, and 13 were selected according to the inclusion criteria. Among the findings, it stands out that the articles emphasize a series of tools used when teaching applied sciences, such as the use of educational technologies beyond the textbook or the integration of students' thinking. The articles state that PCK transcends subject knowledge and leads to subject knowledge for teaching. Finally, the literature has tried to answer how science teachers use PCK in the classroom, demonstrating strategies and practical value, both of which are vital for the functioning and application of their educational work.&lt;/p&gt;
Liderança em Tempos de Crise: Refl exos da Pandemia de COVID-19 sob a Perspectiva de Gênero

Considering the COVID-19 pandemic, the study of how leaders act in times of crisis is essential to understand their impacts on society, as well as on ways of leading, which brings to light examples of how leaders manage the pandemic situation and how much leaders’ gender infl uence this. The purpose of this article is to carry out a theoretical-conceptual discussion about leadership during the COVID-19 pandemic from a gender perspective, analyzing how the consequences of the pandemic can help question prevailing models of leadership. This study analyzes the performance of leaders during the coronavirus crisis and the phenomena that involve the exercise of leadership, discussing how the search for gender equity in positions of power contributes to the post-pandemic world. It is urgent that we re-signify the concept of leadership and its surrounding culture, which requires studies that analyze exclusion phenomena and that consider the scarcity of women and minority groups in leadership positions .Keywords: Leadership, COVID-19, crisis, gender, women.
A Prior-Guided Generative Adversarial Net for Semantically Strict Ultrasound Images Augmentation

Deep learning-based image generation methods can alleviate the class imbalance in training ultrasound image classification models. However, one of the problems faced by the existing image generation methods on ultrasound images is the unreasonable semantics of generated images due to the lack of corresponding constraints on the generator. Recently, image attribute editing methods have gradually matured, aiming to manipulate images with desired semantic rationality attributes while preserving other details. Therefore, this paper proposes to accomplish the generation task by attribute editing to constrain the rational anatomical structure of the generated images. Nevertheless, due to a small discrepancy in lesion-healthy tissue distribution in the ultrasound image, the current attribute editing models prematurely judge the original attribute that has been manipulated to the target attribute, so the target image usually contains some original attribute features. Therefore, a Prior-Guided Generative Adversarial Net (PGedGAN) based on the image attribute editing technology to guide complete attributes manipulation is proposed in this paper. The prior includes two parts: 1) Location Prior that constrains the position of the attribute editing by dividing the foreground and background, and 2) Content Prior that constrains the complete manipulation of the original attribute by minimizing the smoothness of the target attribute region and the space distance between semantic features of the target sample and the original image simultaneously. The experiments prove the effectiveness of our method on downstream classification tasks and image attribute editing that includes image quality, attribute editing rationality, and attribute manipulation completeness.
A Multi-granularity Decision Fusion Method Based on Category Hierarchy

The increasing complexity of convolutional neural networks (CNNs) has greatly improved the accuracy of image classification. However, there are unbalanced visual similarities between image categories, which results in different difficulties in classification. The existing solutions construct hierarchical structures according to image labels and use hierarchical information of categories to improve the performance of the network by constructing tree classifiers, but there is a problem of error transmission. In this paper, a multi-granularity decision fusion method based on category hierarchy is proposed. The two-level hierarchical structure of the image label is constructed according to the traditional algorithm, and the accuracy of image classification is improved through the decision fusion method of coarse and fine granularity, which effectively avoids the inter-level transmission of error. Real public data sets are used for verification. Experimental results show that, compared with the original convolutional neural network, the proposed method achieves higher classification results on CIFAR-10, CIFAR-100, and SVHM data sets.
Metal Oxide Classification Based on SVM

Oxides are classified into various oxides according to their constituent elements, among which there are binary and quaternary oxides. Binary oxides are widely used in industry, and quaternary oxides have great potential in this aspect of electrode materials. This paper focuses on a support vector machine based classification method for oxides, redefining the method for describing metal oxides in order to facilitate computation, and using principal component analysis to reduce the dimensionality of the data, evaluating the classification results using a variety of metrics, and comparing the classification results of multiple kernel functions and the number of principal components to select the best algorithm. The results show that the support vector machine with the number of principal components of 4 and a Gaussian kernel function has better classification results and is important for the identification of oxides.
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography

Explainability of machine learning methods is of fundamental importance in healthcare to calibrate trust. A large branch of explainable machine learning uses tools linked to the Shapley value, which have nonetheless been found difficult to interpret and potentially misleading. Taking multiclass classification as a reference task, we argue that a critical issue in these methods is that they disregard the structure of the model outputs. We develop the Categorical Shapley value as a theoretically-grounded method to explain the output of multiclass classifiers, in terms of transition (or flipping) probabilities across classes. We demonstrate on a case study composed of three example scenarios for pneumonia detection and subtyping using X-ray images.
Addressing hate speech through education

Addressing hate speech through education requires a multisectoral, whole-of-society, approach that relies on strategies to mitigate and minimize the impacts on targeted individuals and groups at every level of formal, non-formal and informal education – from early childhood through higher education to lifelong learning.