Reviewerly/exHarmony huggingface dataset:
"Robot" and "Artificial Intelligence": Concept Demarcation Issues

Purpose. To consider the concepts of “robot” and “artificial intelligence”, conduct a comparative analysis of these concepts, show their differences. Methodology: comparison, analysis, synthesis, hypothesis, induction and deduction. Conclusions. The perfect distinction between the concepts of “robot” and “artificial intelligence” allows us to say that these categories are not only multi-volume, but also semantically do not coincide. Scientific and practical significance. The article analyzes the existing definitions of the concept’s “robot” and “artificial intelligence” in science, and attempts to formulate approaches to the differentiation of the concepts under consideration based on the analysis of existing definitions. The results of the research should give a new impetus to the development of the discussion that is being conducted in scientific and practical circles when regulating relations in the field of artificial intelligence and robotics technologies.
대만의 고등학교 역사 교육 분석과 그 전망 ― 고등학교 역사 수업 요강(課綱)과 교과서를 중심으로 ―

In this article, I discuss the significant changes that history curriculum has undergone at the high school level in Taiwan. First, I examine this branch of curriculum as it existed in pre-democratic Taiwan (between about 1945 and 1995). During those decades, the authoritarian Taiwanese state, ruled with an iron fist by the Kuomintang, established curriculum guidelines that churned out propagandistic textbooks railing against communism and glorifying the Chinese nation. After 1995, some historians in Taiwan argued that the country’s history education should focus more on Taiwan. These arguments set in motion a reformist agenda among educators, but after 2008, with the Kuomintang back in power, the reform stagnated. Chinese nationalists embraced the “Black Box Curriculum,” which sought to root history education in the distant past of Chinese history. Opponents of the Black Box Curriculum movement reflected a growing view that history textbooks should emphasize the due process and the new research results of history. Today, Taiwan’s guidelines for history curriculum focus not on “great statesmen” but on bottom-up history, minority experiences, social movements, progressive ideals, and the methods and subjectivity of practicing history. These new guidelines reflect, in particular, the views of the Taiwanese scholar Wu Rui-ren, which I explore in some detail.
Private Genetic Geneaology Search

Genetic genealogy search has emerged as a powerful technique for identifying individuals by leveraging their genetic information and a genealogical network. The current practice relies on searching within a pre-constructed database containing genetic data from many individuals, and as such exposes those in the database to substantial privacy risks. Motivated by these privacy concerns, we propose a framework of genealogy search that takes into account the amount of privacy exposure. In contrast to the existing static approach of collecting a large amount of genetic data beforehand, we advocate for a new search paradigm whereby genetic samples are accessed in a sequential manner. Our results show that carefully designed sequential search procedures can significantly outperform existing static approaches in terms of the trade-off between cost and privacy exposure. We further characterize the optimal trade-off, and propose a family of search strategies that provably achieve the it over path- and grid-like net- works. Finally, we validate our findings via numerical experiments on both real and synthetic genealogical networks and discuss the policy implications of our results.
VIRTUALIZATION OF DIGITAL MEDIA PRODUCTION IN EDUCATION

As internet speeds become more reliable, consumers are using cloud computer virtualization more than ever to gain access to powerful hardware from anywhere. This also creates new possibilities for educators and educational institutions to leverage high end computing hardware in a scalable and cost-efficient way.This study will consider how the technology can be used for digital media production in education and whether latency limitations make a significant difference. The services used for testing were Paperspace and Parsec. Some latency tests were conducted using NVidia's Reflex Monitor, while others were done using a high-speed camera.In these tests we found that when virtualizing a computer outside of the Philadelphia area, an average of about 66ms of added latency was present. Strong wired connections were used by both the client and host computer. When connecting overseas an average of about 400ms of latency was added. In this scenario the host had a strong wired connection while the client had an average Wi-Fi connection. Finally, when virtualizing a computer on the same network within Drexel University, one using Wi-Fi and the other using a wired connection, there was an added latency of about 20ms. All the results varied depending on which service was used as well as how strong the internet connection was at the time. The greater the average latency, the greater the range in results. These results show that virtualization does not necessarily lead to extreme latency, especially when connecting to a server on the same network as the client. While connecting overseas comes with obvious limitations, the average latency was still useable. The virtualization setup remained a viable solution for anyone without access to a high-power workstation. This also proves that online courses which require access to high end computing can be offered without the need for students to purchase expensive equipment.In conclusion, virtualization options available today provide a substantial amount of flexibility to administrators, faculty, and students. Those who would have previously needed to work in an in-person lab can connect remotely and work from anywhere. They can do this with the same power they would have on a workstation or even more. This can be used to create a computing infrastructure for online or remote students that is both highly scalable as well as cost efficient.
On the Impact of MDP Design for Reinforcement Learning Agents in Resource Management

The recent progress in Reinforcement Learning applications to Resource Management presents Markov Decision Processes (MDPs) without a deeper analysis of the impacts of design decisions on agent performance. In this paper, we compare and contrast four different MDP variations, discussing their computational requirements and impacts on agent performance by means of an empirical analysis. We conclude by showing that, in our experiments, when using Multi-Layer Perceptrons as approximation function, a compact state representation allows transfer of agents between environments, and that transferred agents have good performance and outperform specialized agents in 80% of the tested scenarios, even without retraining.
ASSESSMENT OF ONLINE LEARNING EFFICIENCY WHEN STUDYING NATURAL SCIENCES IN LOWER SECONDARY SCHOOL IN BULGARIA (10-14 YEAR-OLD STUDENTS)

D. Karagyozova-Dilkova1, Z. Raykova1, S. Petrova1, K. Katsarova2, D. Dimova3, K. Vitlarov4, M. Dakova1, M. Atanasova1, A. Mollov1 1Plovdiv University "Paisii Hilendarski" (BULGARIA) 2Language School "Plovdiv" (BULGARIA) 3III Secondary School - Targovishte (BULGARIA) 4Secondary School "Vasil Levski" - Plovdiv (BULGARIA)
A Chinese character hash function based on strokes for fingerprinting

Character representation in computer systems is the main purpose of character encod- ings, such as Unicode. The representation of Chinese characters in computer systems is a long-standing issue. It is currently still not possible to easily represent, for instance to input, some Chinese characters in computers. In this research, we especially consider the issue of the Chinese characters that are not covered by the conventional encodings. In this paper, in continuation of our previous works on a universal character encoding for such characters, we describe a non-ambiguous hash function for any Chinese character. Unlike conventional approaches, this function is solely based on the character strokes, thus elim- inating any sort of ambiguity. Given its sparsity and low collision rate, the proposed hash function can then be applied to fingerprinting, which can in turn be applied, for instance, to information retrieval. Simplicity and unambiguity are keys to our proposal. This work is then formally evaluated and compared to previous works so as to show its applicability, contribution and to measure its limits.
Joint stereo 3D object detection and implicit surface reconstruction

We present a new learning-based framework S-3D-RCNN that can recover accurate object orientation in SO(3) and simultaneously predict implicit shapes for outdoor rigid objects from stereo RGB images. In contrast to previous studies that map local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric object orientation. This approach features a deep model that transforms perceived intensities to object part coordinates, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. To enable implicit shape estimation, the IGRs are further extended to model visible object surface with a point-based representation and explicitly addresses the unseen surface hallucination problem. Extensive experiments validate the effectiveness of the proposed IGRs and S-3D-RCNN achieves superior 3D scene understanding performance using existing and proposed new metrics on the KITTI benchmark. Code and pre-trained models will be available at this https URL.
Razvoj, pojam i oblici cyber kriminala / Development, Concept and Forms of Cyber Crime

Cybercrime in the broadest sense is any criminal activity that involves a computer, networked device, or network. The specificity and significance of cybercrime is very difficult to express by one definition, due to the great complexity of criminal activities as well as the legal consequences it causes. Due to the ease of access to information and the misuse of social networks, cybercrime could become the most dominant form of crime. One of the motives on the side of the development of this crime is making a big profit. Much more action is being taken internationally than nationally to regulate cybercrime and its manifestations. As a consequence of the mass emergence and development of cybercrime, more effective internal and external data protection is emerging, as well as legal data protection.
Metode Window Shopping untuk Meningkatkan Hasil Belajar Teknik Komputer pada Siswa Kelas VII SMP Negeri 6 Ponorogo

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Grounded Situation Recognition with Transformers

Grounded Situation Recognition (GSR) is the task that not only classifies a salient action (verb), but also predicts entities (nouns) associated with semantic roles and their locations in the given image. Inspired by the remarkable success of Transformers in vision tasks, we propose a GSR model based on a Transformer encoder-decoder architecture. The attention mechanism of our model enables accurate verb classification by capturing high-level semantic feature of an image effectively, and allows the model to flexibly deal with the complicated and image-dependent relations between entities for improved noun classification and localization. Our model is the first Transformer architecture for GSR, and achieves the state of the art in every evaluation metric on the SWiG benchmark. Our code is available at https://github.com/jhcho99/gsrtr .
Correcting Face Distortion in Wide-Angle Videos

Video blogs and selfies are popular social media formats, which are often captured by wide-angle cameras to show human subjects and expanded background. Unfortunately, due to perspective projection, faces near corners and edges exhibit apparent distortions that stretch and squish the facial features, resulting in poor video quality. In this work, we present a video warping algorithm to correct these distortions. Our key idea is to apply stereographic projection locally on the facial regions. We formulate a mesh warp problem using spatial-temporal energy minimization and minimize background deformation using a line-preservation term to maintain the straight edges in the background. To address temporal coherency, we constrain the temporal smoothness on the warping meshes and facial trajectories through the latent variables. For performance evaluation, we develop a wide-angle video dataset with a wide range of focal lengths. The user study shows that 83.9% of users prefer our algorithm over other alternatives based on perspective projection.
Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA), exploring sentiment polarity of aspect-given sentence, is a fine-grained task in the field of nature language processing. Previously researches typically tend to predict polarity based on the meaning of aspect and opinions. However, those approaches mainly focus on considering relations implicitly at the word level, ignore the historical impact of other positional words when the aspect appears in a certain position. Therefore, we propose a Position-based Contributive Embeddings (PosCE) to highlight the historical reference to special position aspect. Contribution of each positional words to the polarity is similar to the process of fairly distributing gains to several actors working in coalition (game theory). Therefore, we quote from the method of Shapley Value and finally gain PosCE to enhance the aspect-based representation for ABSA task. Furthermore, the PosCE can also be used for improving performances on multimodal ABSA task. Extensive experiments on both text and text-audio level using SemEval dataset show that the mainstream models advance performance in accuracy and F1 (increase 2.82% and 4.21% on average respectively) by applying our PosCE.
Personalized local search using ontology based user profile category model

The personalized local mobile search aims at finding the right on the spot information that is most relevant to the user's requests. It is implemented as a mobile application where the user can access nearby places based on his/ her current location. In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. The user profile based personalization approach can be applied to enhance the power of mobile local search for local spots and contributes to a significant convenience in location-based mobile searching. The system takes the user information such as personal, health, entertainment and choice of preference and these parameters are passed to Google Maps API key for personalized query processing. As a result, the user will get prominent services rather than closing one.
Accelerating non-LTE synthesis and inversions with graph networks

Context: The computational cost of fast non-LTE synthesis is one of the challenges that limits the development of 2D and 3D inversion codes. It also makes the interpretation of observations of lines formed in the chromosphere and transition region a slow and computationally costly process, which limits the inference of the physical properties on rather small fields of view. Having access to a fast way of computing the deviation from the LTE regime through the departure coefficients could largely alleviate this problem. Aims: We propose to build and train a graph network that quickly predicts the atomic level populations without solving the non-LTE problem. Methods: We find an optimal architecture for the graph network for predicting the departure coefficients of the levels of an atom from the physical conditions of a model atmosphere. A suitable dataset with a representative sample of potential model atmospheres is used for training. This dataset has been computed using existing non-LTE synthesis codes. Results: The graph network has been integrated into existing synthesis and inversion codes for the particular case of \caii. We demonstrate orders of magnitude gain in computing speed. We analyze the generalization capabilities of the graph network and demonstrate that it produces good predicted departure coefficients for unseen models. We implement this approach in \hazel and show how the inversions nicely compare with those obtained with standard non-LTE inversion codes. Our approximate method opens up the possibility of extracting physical information from the chromosphere on large fields-of-view with time evolution. This allows us to understand better this region of the Sun, where large spatial and temporal scales are crucial.
Persepsi Siswa terhadap Pembelajaran Bahasa Indonesia Berbasis Daring di SMAN 1 Pasuruan

&#x0D; &#x0D; &#x0D; &#x0D; The Covid-19 pandemic has had a major impact on all aspects of education around the world. This pandemic shattered the previously established education system. Face-to-face offline learning in schools has been forced to close and disrupt the learning of more than 1.6 billion students worldwide. Learning in droves has turned to using online-based technology, including learning Indonesian in Indonesia. Students are forced to follow these fundamental changes for the sake of the continuity of the learning process. This study attempts to describe students' perceptions of online-based Indonesian language learning in one of the Indonesian schools, SMAN 1 Pasuruan.&#x0D; &#x0D; &#x0D; &#x0D;
Self-Improving Voronoi Construction for a Hidden Mixture of Product Distributions

We propose a self-improving algorithm for computing Voronoi diagrams under a given convex distance function with constant description complexity. The $n$ input points are drawn from a hidden mixture of product distributions; we are only given an upper bound $m = o(\sqrt{n})$ on the number of distributions in the mixture, and the property that for each distribution, an input instance is drawn from it with a probability of $\Omega(1/n)$. For any $\varepsilon \in (0,1)$, after spending $O\bigl(mn\log^{O(1)} (mn) + m^{\varepsilon} n^{1+\varepsilon}\log(mn)\bigr)$ time in a training phase, our algorithm achieves an $O\bigl(\frac{1}{\varepsilon}n\log m + \frac{1}{\varepsilon}n2^{O(\log^* n)} + \frac{1}{\varepsilon}H\bigr)$ expected running time with probability at least $1 - O(1/n)$, where $H$ is the entropy of the distribution of the Voronoi diagram output. The expectation is taken over the input distribution and the randomized decisions of the algorithm. For the Euclidean metric, the expected running time improves to $O\bigl(\frac{1}{\varepsilon}n\log m + \frac{1}{\varepsilon}H\bigr)$.
University teacher competency assessment indicators

The article discusses the urgent problems of the educational process in higher education caused by crisis phenomena in pedagogy as the main way of personality development.One of the ways to overcome the crisis is the formation of the worldview of the teacher, substantiates the content of his intellectual, cognitive, communication, research competencies.
Long-tailed Recognition by Learning from Latent Categories

In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training. However, due to the limited training images for tail classes, the diversity of tail class images is still restricted, which results in poor feature representations. In this work, we hypothesize that common latent features among the head and tail classes can be used to give better feature representation. Motivated by this, we introduce a Latent Categories based long-tail Recognition (LCReg) method. Specifically, we propose to learn a set of class-agnostic latent features shared among the head and tail classes. Then, we implicitly enrich the training sample diversity via applying semantic data augmentation to the latent features. Extensive experiments on five long-tailed image recognition datasets demonstrate that our proposed LCReg is able to significantly outperform previous methods and achieve state-of-the-art results.
FEATURES OF THE DIGITALIZATION OF THE EDUCATIONAL SPHERE: STATE OF THE ISSUE

O. Sheredekina , A. Pyatnitsky , N. Dmitrieva , O. Mikhailova Peter the Great St.Petersburg Polytechnic University (RUSSIAN FEDERATION)
GSU Library Research Guides: PACE Client-Based Consulting Resources: Research Resources

Recommended resources for students to use on PACE consulting projects How to find research articles, news articles, data and other information
Generating meta-learning tasks to evolve parametric loss for classification learning

The field of meta-learning has seen a dramatic rise in interest in recent years. In existing meta-learning approaches, learning tasks for training meta-models are usually collected from public datasets, which brings the difficulty of obtaining a sufficient number of meta-learning tasks with a large amount of training data. In this paper, we propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data. The loss is represented by a deep neural network, called meta-loss network (MLN). To train the MLN, we construct a large number of classification learning tasks through randomly generating training data, validation data, and corresponding ground-truth linear classifier. Our approach has two advantages. First, sufficient meta-learning tasks with large number of training data can be obtained easily. Second, the ground-truth classifier is given, so that the difference between the learned classifier and the ground-truth model can be measured to reflect the performance of MLN more precisely than validation accuracy. Based on this difference, we apply the evolutionary strategy algorithm to find out the optimal MLN. The resultant MLN not only leads to satisfactory learning effects on generated linear classifier learning tasks for testing, but also behaves very well on generated nonlinear classifier learning tasks and various public classification tasks. Our MLN stably surpass cross-entropy (CE) and mean square error (MSE) in testing accuracy and generalization ability. These results illustrate the possibility of achieving satisfactory meta-learning effects using generated learning tasks.