Reviewerly/exHarmony huggingface dataset:
Cartesian Difference Categories: Extended Report

Cartesian differential categories are categories equipped with a differential combinator which axiomatizes the directional derivative. Important models of Cartesian differential categories include classical differential calculus of smooth functions and categorical models of the differential lambda-calculus. However, Cartesian differential categories cannot account for other interesting notions of differentiation such as the calculus of finite differences or the Boolean differential calculus. On the other hand, change action models have been shown to capture these examples as well as more exotic examples of differentiation. However, change action models are very general and do not share the nice properties of a Cartesian differential category. In this paper, we introduce Cartesian difference categories as a bridge between Cartesian differential categories and change action models. We show that every Cartesian differential category is a Cartesian difference category, and how certain well-behaved change action models are Cartesian difference categories. In particular, Cartesian difference categories model both the differential calculus of smooth functions and the calculus of finite differences. Furthermore, every Cartesian difference category comes equipped with a tangent bundle monad whose Kleisli category is again a Cartesian difference category.
Implementasi Algoritma Dijkstra Pada Game Pacman

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations
Active Loop Programming for Adaptive Systems

We describe a new approach to adaptive system construction, based on our belief that there are no one-way functions in biology (for example, no sensor is a one-way input device, and no effector is a one-way output device).We choose to mimic the fact that all biological systems have many active processing loops running at all times (at various different time and space scales), and all of them both produce and consume data.We wanted to see how far this notion can carry us towards highly adaptive computational systems, in combination with computational reflection and certain other biological principles of organization.We show that it carries us surprisingly far, by describing a system architecture that uses it as a fundamental organizing principle.We define what active loop programming is, show how it provides enormous flexibility in a software-intensive system, and show how it can be implemented with Wrappings.
Research on Model Correction of Turbofan Engine Based on Quantum-behaved Particle Swarm Optimization

Abstract In order to solve the problem that the calculation result of mathematical model and the measured data of the whole machine deviate greatly due to the conditional simplification in the modeling process and engine component difference, an engine model correction method based on Quantum-behaved Particle Swarm Optimization(QPSO) is proposed. And the engine model calculation results are compared with the measured data of the whole machine performance. The results show that using the QPSO to modify the engine model can significantly improve the accuracy of the model. Before the correction, comparing the performance calculation result of the engine model with the measured data, the maximum error reaches 4.84%. After the correction, the accuracy of the model is greatly improved, and the maximum error is only 0.966%. The correction effect is good.
The Temptation of Simplicity in the Era of Transit

The author pays a special attention to classical model of university education as a system of knowledge transfer that is connected with the hope of increasing the effectiveness of teaching philosophy in the format of the history of ideas, that in the transition from a literacy of the classical era to the application of modern information technologies is associated with a number of persistent thinking stereotypes. The author points out that the temptation to simplicity of abstract introduction into competences, including critical reflection, excludes the use of modern discourse in the philosophical analysis of modernity. The author comes to conclusion that modernity itself as an area of scientific research, the integration of disciplinary approaches and educational programs remains on the other side of the conceptualization.
Leggett, C - Biographical entry - Index to the AMA archive

Leggett, C - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Machine learning for asymmetric catalysis

Organic ChemistryCatalysts can introduce asymmetry in the outcome of chemical reactions, favoring one mirror-image product over another. Many of the most effective catalysts for this application were optimized through trial and error, but more recently, parameterization and systematic analysis have played an increasing role. Singh et al. now showcase the predictive power of machine learning applied to the ligands used for asymmetric hydrogenation. A random forest algorithm trained on several different families of chiral binaphthyl phosphorus compounds predicted selectivity in hydrogenation of alkenes and imines with a root-mean-square error of just over 8%.Proc. Natl. Acad. Sci. U.S.A. 117 , 1339 (2020).
Knowledge Management Dalam Pemilihan Teknologi Administrasi P3M Politeknik Negeri Cilacap

Application of technology is expected to simplify administrative work management. Cilacap State Polytechnic has P3M unit in charge of managing research and community service conducted by lecturers. Intellectual capital in P3M units that are easily lost, internal communication problems in P3M units that can inhibit knowledge and work sharing and the occurrence of errors’ duplication in P3M archive activities are some obstacles that occur during data management process. The technology application in the concept of knowledge management by considering the number of participants and the duration of the conversation is needed, therefore it can be concluded that the technology which can be used is presentation, computer share, chat, email, application. Applications made are suited to the needs of the data management application in the P3M unit. Successful application of technology can use the concept of ACKM and is applied in tertiary institutions.
Ultra High Fidelity Image Compression with $\ell_\infty$-constrained Encoding and Deep Decoding

In many professional fields, such as medicine, remote sensing and sciences, users often demand image compression methods to be mathematically lossless. But lossless image coding has a rather low compression ratio (around 2:1 for natural images). The only known technique to achieve significant compression while meeting the stringent fidelity requirements is the methodology of $\ell_\infty$-constrained coding that was developed and standardized in nineties. We make a major progress in $\ell_\infty$-constrained image coding after two decades, by developing a novel CNN-based soft $\ell_\infty$-constrained decoding method. The new method repairs compression defects by using a restoration CNN called the $\ell_\infty\mbox{-SDNet}$ to map a conventionally decoded image to the latent image. A unique strength of the $\ell_\infty\mbox{-SDNet}$ is its ability to enforce a tight error bound on a per pixel basis. As such, no small distinctive structures of the original image can be dropped or distorted, even if they are statistical outliers that are otherwise sacrificed by mainstream CNN restoration methods. More importantly, this research ushers in a new image compression system of $\ell_\infty$-constrained encoding and deep soft decoding ($\ell_\infty\mbox{-ED}^2$). The $\ell_\infty \mbox{-ED}^2$ approach beats the best of existing lossy image compression methods (e.g., BPG, WebP, etc.) not only in $\ell_\infty$ but also in $\ell_2$ error metric and perceptual quality, for bit rates near the threshold of perceptually transparent reconstruction. Operationally, the new compression system is practical, with a low-complexity real-time encoder and a cascade decoder consisting of a fast initial decoder and an optional CNN soft decoder.
Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos

The recent development of commodity 360$^{\circ}$ cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. However, research in omnidirectional video analysis has lagged behind the hardware advances. In this work, we address the important problem of action recognition in top-view 360$^{\circ}$ videos. Due to the wide filed-of-view, 360$^{\circ}$ videos usually capture multiple people performing actions at the same time. Furthermore, the appearance of people are deformed. The proposed framework first transforms omnidirectional videos into panoramic videos, then it extracts spatial-temporal features using region-based 3D CNNs for action recognition. We propose a weakly-supervised method based on multi-instance multi-label learning, which trains the model to recognize and localize multiple actions in a video using only video-level action labels as supervision. We perform experiments to quantitatively validate the efficacy of the proposed method and qualitatively demonstrate action localization results. To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.
ITENSITY OF USE MEDIA RELATIONS INTERACTIVE (COMPUTER GAME ONLINE) MOTIVATION AND LEARNING

Interactive media can cause negative things for teenagers, including affect motivation and aggressive behavior. Subjects were studenst of SMP-1 range, 11-14 years of age, male gender. The experience of playing computer games online ± 1 year, high school parent education, parental income ≤ Rp. 1.500.000,- / month, student rank 1-10 in the classroom and the subject of 40 people. Data analysis  technique used is the product moment correlation analysis technique. Measuring instruments used ini this research is by using questionnaires. Data collection procedures done in three phases: preparation, data collection phase, the normality test to determine whether the normal distribution of data, linearity test to find  out the degree of relationship between the independent variables on the dependent variable and statistical analysis using product moment correlation analysis technique. The survey results revealed that the intensity of use computer games online contributes very significantly to the tendency of aggressive behavior by the coefficient r xy= 0.725 where p <0.01 means that the higher the intensity of use of computer games the higher the aggressive behavior and vice versa. Then the intensity of the use of computer games contributed r x y = -0.545 on motivation to learn and vice versa. The researchers suggest is the need to increase the understanding and knowledge of the benefits from the use of computer games online, the need to give attention the teenager against her social behavior that teenagers are not the wrong way. The need for watchdogs against teenagers in the use of interactive media. Key Words: Intensity of use media interaktif, Computer games, motivation to learn, aggressive behavior
Electronic secret voting system using decentralized technologies developing principles

The paper reveals the prospects of using electronic trust services, in particular electronic signature services, for building voting systems. The advantages of such systems are described, as well as problematic issues related to the implementation of remote voting systems in practice. The most famous existing electronic voting protocols based on classical principles are analyzed. Their shortcomings and difficulties in implementation are highlighted. We present new principles for building an electronic voting system. In particular, the paper proposes a two-level architecture of a decentralized electronic voting system, which consists of a decentralized public key infrastructure that combines trusted certification authorities and a decentralized voting system that integrates the distributed representative offices of an electronic voting agency. Also, an adapted protocol of electronic voting is proposed. Unlike the analyzed protocols, the new approach is based on a decentralized public key infrastructure. The paper shows how, using DLT technology, it is possible to ensure that the necessary requirements for system stability are met without introducing significant redundancy into the interaction protocol of all interested parties. Deploying a decentralized electronic voting system based on a decentralized public key infrastructure allows voters to use existing key pairs. The proposed electronic voting protocol is a modification of the Nurmi-Salom-Santin protocol without the need for special bodies to form voter lists. It also allows preserving the anonymity of voters without the use of blind signatures.
Структура деонтологической готовности будущих педагогов к взаимодействию с дезадаптированными подростками

STRUCTURE OF DEONTOLOGICAL READINESS OF FUTURE TEACHERSTO INTERACTION WITH DISADAPTIVE ADOLESCENTSI. KARPOVICH
COLLOCATION IN THE MOVIE ENTITLED COCO BY MOLINA AND MATTHEW ALDRICH

Abstract This research is concerned with collocation in the movie subtitle Coco by Molina and Matthew Aldrich. This research is a descriptive qualitative study. It is supposed to identify the English collocation found in Coco movie; to classify the types of collocation found in Coco movie; and to discuss the meanings of collocation found in Coco movie. In data collection, the research employed triangulation that presented three different techniques, namely documentation, observation and interview. Based on the findings, it is shown that there were 134data of grammatical collocations found in the movie, which consisted of 7 types of grammatical collocations. They were 1 datum of noun+preposition combination, 2 data of noun+to+infinitive, 1 datum of noun+that+clause, 7 data of preposition+noun, 3 data of adjective+preposition combinations, 4 data of predicate adjective+to+infinitive, and 116 data of collocation verb patterns. Keywords: Collocation, Types of Collocation, Subtitle, Classify.
THE PROBLEM OF CONTROL WHILE IMPLEMENTING ORGANIZATIONAL CHANGES AT THE UNIVERSITIES

The nature of educational services of higher education has been considered and a number of factors, that need to be taken into account when assessing the productivity and effectiveness of changes from the point of view of different stakeholders, taking into account the mechanism of coordination of interests, has been considered. One of the results of the study is the classification of forms of control depending on the degree of realization of the stakeholders’ interests. The analysis of existing methods for assessing the effectiveness of organizational changes in terms of their applicability to organizational changes in universities also has been made. It has been concluded, that the organization’s ability to achieve its goals depends on the degree of mutual integration and consistency of all elements of the considered comprehensive change management mechanism at the university.
Aligning the Pretraining and Finetuning Objectives of Language Models

We demonstrate that explicitly aligning the pretraining objectives to the finetuning objectives in language model training significantly improves the finetuning task performance and reduces the minimum amount of finetuning examples required. The performance margin gained from objective alignment allows us to build language models with smaller sizes for tasks with less available training data. We provide empirical evidence of these claims by applying objective alignment to concept-of-interest tagging and acronym detection tasks. We found that, with objective alignment, our 768 by 3 and 512 by 3 transformer language models can reach accuracy of 83.9%/82.5% for concept-of-interest tagging and 73.8%/70.2% for acronym detection using only 200 finetuning examples per task, outperforming the 768 by 3 model pretrained without objective alignment by +4.8%/+3.4% and +9.9%/+6.3%. We name finetuning small language models in the presence of hundreds of training examples or less "Few Example learning". In practice, Few Example Learning enabled by objective alignment not only saves human labeling costs, but also makes it possible to leverage language models in more real-time applications.
Pixel-wise Conditioned Generative Adversarial Networks for Image Synthesis and Completion

Generative Adversarial Networks (GANs) have proven successful for unsupervised image generation. Several works have extended GANs to image inpainting by conditioning the generation with parts of the image to be reconstructed. Despite their success, these methods have limitations in settings where only a small subset of the image pixels is known beforehand. In this paper we investigate the effectiveness of conditioning GANs when very few pixel values are provided. We propose a modelling framework which results in adding an explicit cost term to the GAN objective function to enforce pixel-wise conditioning. We investigate the influence of this regularization term on the quality of the generated images and the fulfillment of the given pixel constraints. Using the recent PacGAN technique, we ensure that we keep diversity in the generated samples. Conducted experiments on FashionMNIST show that the regularization term effectively controls the trade-off between quality of the generated images and the conditioning. Experimental evaluation on the CIFAR-10 and CelebA datasets evidences that our method achieves accurate results both visually and quantitatively in term of Frechet Inception Distance, while still enforcing the pixel conditioning. We also evaluate our method on a texture image generation task using fully-convolutional networks. As a final contribution, we apply the method to a classical geological simulation application.
Student/Teacher Advising through Reward Augmentation

Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem. This is useful when one wishes to change the architecture or learning algorithm of an agent (so that the new knowledge need not be built "from scratch"), when new agents are frequently introduced to the environment with no knowledge, or when an agent must adapt to similar but different problems. Great progress has been made in the agent-to-agent case using the Teacher/Student framework proposed by (Torrey and Taylor 2013). However, that approach requires that learning from a teacher be treated differently from learning in every other reinforcement learning context. In this paper, I propose a method which allows the teacher/student framework to be applied in a way that fits directly and naturally into the more general reinforcement learning framework by integrating the teacher feedback into the reward signal received by the learning agent. I show that this approach can significantly improve the rate of learning for an agent playing a one-player stochastic game; I give examples of potential pitfalls of the approach; and I propose further areas of research building on this framework.
PKM Diseminasi Informasi Kesehatan Bahaya Penyalahgunaan Napza Pada Remaja Di SMPN 16 Kelas 8 Kota Kupang Provinsi NTT

Community Partnership Program for Dissemination of Health Information on Drug Use Hazards In adolescents, especially students of Kupang 16 Public Middle School in the form of lectures, simulations, demonstrations and visualization posters of Narcotics, Psychotropic and Addictive Substances. One of the problems The location was chosen as one of the locations of community service based on the results of field observations explaining the need for health dissemination at this school and there has never been any health dissemination activity involving public health faculty lecturers. Besides providing information and developments in health science and technology in handling health problems, including one of them to prevent drug abuse among adolescents, especially in junior high school students. This program method is to provide 1) planning and design of activities to be carried out. 2) supply of tool materials according to the planned requirements. 3) preparation for implementation. 4) implementation of activities. 5) banner installation and application tools. 6) Submission of material and drug socialization as a way to prevent drug abuse among adolescents. 7) frequently asked questions, suggestions, implementers and partners. 9) monitoring of activities by the FKM Undana monitoring team. 10) evaluation of Community Partnership Program activities by the activity implementing team and partners. 11) reporting the activities of the Community Partnership Program as the responsibility of the activities of the Community Partnership Program. This program output can provide added value knowledge and information. Specifically, this service activity is expected to be able to provide goals to direct the target (students of SMPN 16) for take precautionary measures against opportunities for health problems in adolescents, increase knowledge and understanding of adolescent health, pmpowering teenage students so that preventive actions can be implemented well, supporting government programs in maintaining and improving the health status of adolescents, identifying the dangers of drug abuse that can be experienced at adolescence, provide information and outreach about the dangers of drug abuse that can be experienced at adolescence, convey information and outreach about the sources or causes of the dangers of drug abuse, provide an understanding of the impact of the dangers of drug abuse on its growth and development in adolescence, provide solutions in the form of preventive measures and appropriate handling of the dangers of drug abuse that can occur in adolescence, and provide motivation to keep guarding against drug abuse that can occur at the age of adolescence.
Duality of Width and Depth of Neural Networks.

While classic studies proved that wide networks allow universal approximation, recent research and successes of deep learning demonstrate the power of the network depth. Based on a symmetric consideration, we investigate if the design of artificial neural networks should have a directional preference, and what the mechanism of interaction is between the width and depth of a network. We address this fundamental question by establishing a quasi-equivalence between the width and depth of ReLU networks. Specifically, we formulate a transformation from an arbitrary ReLU network to a wide network and a deep network for either regression or classification so that an essentially same capability of the original network can be implemented. That is, a deep regression/classification ReLU network has a wide equivalent, and vice versa, subject to an arbitrarily small error. Interestingly, the quasi-equivalence between wide and deep classification ReLU networks is a data-driven version of the De Morgan law.