Reviewerly/exHarmony huggingface dataset:
Using Evolutionary Algorithms for the Scheduling of Aircrew on Airborne Early Warning and Control System

Equipped with an advanced radar and other electronic systems mounted on its body, Airborne Early Warning and Control System (AWACS) enables the airspace to be monitored from medium to long distances and facilitates effective control of friendly aircraft. To operate the complex equipment and fulfill its critical functions, AWACS has a specialised flight and mission crew, all of whom are extensively trained in their respective roles. For mission accomplishment and effective use of resources, tasks should be scheduled, and individuals should be assigned to missions appropriately. In this paper, we implemented evolutionary algorithms for scheduling aircrew on AWACS and propose a novel approach using Genetic Algorithms (GA) with a special encoding strategy and modified genetic operations tailored to the problem. The objective is to assign aircrew to various AWACS tasks such as flights, simulator sessions, ground training classes and other squadron duties while aiming to maximise combat readiness and minimise operational costs. The presented approach is applied to several test instances consisting notional weekly schedules of Turkish Boeing 737 AEW&amp;C Peace Eagle AWACS Base, generated similar to real-world examples. To test the algorithm and evaluate solution performance, experiments have been conducted on a novel scheduling software called AWACS Crew Scheduling (ACS), developed as a test bed. Computational results reveal that presented GA approach proves to be quite successful in solving the AWACS Crew Scheduling Problem and exhibits superior performance when compared to manual methods.
Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction

Modeling hand-object manipulations is essential for understanding how humans interact with their environment. While of practical importance, estimating the pose of hands and objects during interactions is challenging due to the large mutual occlusions that occur during manipulation. Recent efforts have been directed towards fully-supervised methods that require large amounts of labeled training samples. Collecting 3D ground-truth data for hand-object interactions, however, is costly, tedious, and error-prone. To overcome this challenge we present a method to leverage photometric consistency across time when annotations are only available for a sparse subset of frames in a video. Our model is trained end-to-end on color images to jointly reconstruct hands and objects in 3D by inferring their poses. Given our estimated reconstructions, we differentiably render the optical flow between pairs of adjacent images and use it within the network to warp one frame to another. We then apply a self-supervised photometric loss that relies on the visual consistency between nearby images. We achieve state-of-the-art results on 3D hand-object reconstruction benchmarks and demonstrate that our approach allows us to improve the pose estimation accuracy by leveraging information from neighboring frames in low-data regimes.
Categorical Stochastic Processes and Likelihood

We take a category-theoretic perspective on the relationship between probabilistic modeling and gradient based optimization. We define two extensions of function composition to stochastic process subordination: one based on a co-Kleisli category and one based on the parameterization of a category with a Lawvere theory. We show how these extensions relate to the category of Markov kernels <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="bold">S</mml:mi><mml:mi mathvariant="bold">t</mml:mi><mml:mi mathvariant="bold">o</mml:mi><mml:mi mathvariant="bold">c</mml:mi><mml:mi mathvariant="bold">h</mml:mi></mml:mrow></mml:math> through a pushforward procedure.We extend stochastic processes to parametric statistical models and define a way to compose the likelihood functions of these models. We demonstrate how the maximum likelihood estimation procedure defines a family of identity-on-objects functors from categories of statistical models to the category of supervised learning algorithms <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="bold">L</mml:mi><mml:mi mathvariant="bold">e</mml:mi><mml:mi mathvariant="bold">a</mml:mi><mml:mi mathvariant="bold">r</mml:mi><mml:mi mathvariant="bold">n</mml:mi></mml:mrow></mml:math>.Code to accompany this paper can be found on GitHub (https://github.com/dshieble/Categorical_Stochastic_Processes_and_Likelihood).
Dostroajan: Facial Recognition Based System Input Control Agent

Speed, time and safety are of great importance in many operations conducted today. There are standards such as ISO 27001, ITIL (Information Technologies Infrastructure Library), COBIT (Control Objectives for Information and Related Technology), which are globally recognized not only regarding access to information and the use of information but also information retention. Governmental institutions and many large companies use fingerprint, card reading, iris recognition and facial recognition systems in entrances and exits, regarding the protection of information. The facial recognition system application developed within the scope of this study performs the facial recognition by using Convolutional Neural Networks (CNN), which is one of the deep learning algorithms and restricts the use of your personal computer by people you do not know. In addition to this restriction, it takes a photo of the person who wants to use your personal computer and sends this photo to the mobile phone of the owner of the computer, who was previously defined in the system and informs him/her.Regarding the testing of the face recognition system application FEI (Faculdade de Engenharia Industrial- Faculty of Industrial Engineering) facial database was used. In this facial database, there are 14 different poses of 200 people (one is neutral, one is smiling, one is not smiling, and the others are at different angles). Trials were made to access the system with a total of 2800 photographs and as a result of the trials, success was achieved with a ratio of 76.31% in the worst angle and light and a ratio of 99.15% in the best angle and light.
Reducing computational costs in deep learning on almost linearly separable training data

Previous research in deep learning indicates that iterations of the gradient descent, over separable data converge toward the L2 maximum margin solution. Even in the absence of explicit regularization, the decision boundary still changes even if the classification error on training is equal to zero. This feature of the so-called “implicit regularization” allows gradient methods to use more aggressive learning rates that result in substantial computational savings. However, even if the gradient descent method generalizes well, going toward the optimal solution, the rate of convergence to this solution is much slower than the rate of convergence of a loss function itself with a fixed step size. The present study puts forward the generalized logistic loss function that involves the optimization of hyperparameters, which results in a faster convergence rate while keeping the same regret bound as the gradient descent method. The results of computational experiments on MNIST and Fashion MNIST benchmark datasets for image classification proved the viability of the proposed approach to reducing computational costs and outlined directions for future research.
DEVELOPMENT OF INTERACTIVE MULTIMEDIA TOWARDS ECONOMIC PROBLEMS ON ECONOMIC SUBJECTS FOR SOCIAL SCIENCE CLASS X STUDENTS AT DARMA YUDHA HIGH SCHOOL

This research aims to produce interactive multimedia using Lectora inspire in learning economic using the 4D development model. This work employed a qualitative descriptive method. Interactive multimedia was produced through three stages: definition, design, and development. This research was limited to the development stage. Definition stage comprised the preliminary analysis, curriculum analysis, material analysis, characteristic analysis, and formulation. Meanwhile, the design stage included pre-production from flowchart and storyboard making, media production to final activities: mixing, editing, and finalization. The development stage encompassed the following: (1) Expert appraisal, which was expert validation and revision with a result of very appropriate based on validation of media expert, material expert and language expert, and (2) Developmental testing, consisting of a small-scale tryout with a score of 81.98% (very appropriate). Our proposed system was very appropriate, according to the result of validation by an expert in a small-scale try-out; thus it can be used in economic learning at SMA Darma Yudha.
An Imitation Game for Learning Semantic Parsers from User Interaction

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstration when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstration, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and re-trains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem. Code will be available at https://github.com/sunlab-osu/MISP.
Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons

In many robotics applications, the environment (robots and obstacles) often have very complex geometries. These result in expensive primitive computations such as collision detection which in turn, affect the overall performance of these applications. Approximating the geometry is a common approach to optimize computation. Unlike other applications of geometric approximation where it is applied to one space (usually obstacle space), we approximate both obstacle and free workspace with a set of geometric shape primitives that are completely contained within the space and represent its topology (skeleton). We use these "shape primitive skeletons" to improve collision detection performance in motion planning algorithms. Our results show that the use of shape primitive skeletons improves the performance of standard collision detection methods in motion planning problems by 20–70% in our 2D and 3D test environments regardless of motion planning strategy. We also show how the same shape primitive skeletons can be used with robots of different sizes to improve the performance of collision detection operation.
Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer

Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data. In current dominant approaches, owing to the lack of fine-grained control on the influence from the target style,they are unable to yield desirable output sentences. In this paper, we propose a novel attentional sequence-to-sequence (Seq2seq) model that dynamically exploits the relevance of each output word to the target style for unsupervised style transfer. Specifically, we first pretrain a style classifier, where the relevance of each input word to the original style can be quantified via layer-wise relevance propagation. In a denoising auto-encoding manner, we train an attentional Seq2seq model to reconstruct input sentences and repredict word-level previously-quantified style relevance simultaneously. In this way, this model is endowed with the ability to automatically predict the style relevance of each output word. Then, we equip the decoder of this model with a neural style component to exploit the predicted wordlevel style relevance for better style transfer. Particularly, we fine-tune this model using a carefully-designed objective function involving style transfer, style relevance consistency, content preservation and fluency modeling loss terms. Experimental results show that our proposed model achieves state-of-the-art performance in terms of both transfer accuracy and content preservation.
Computer Musical Creative Work in the Russian Initial Vocational Education System

The development of new music technologies necessitates changes in the content of music education. Musical pedagogy is faced with the task of educating creative musicians who are capable of innovative activities and inventive musical thinking. Therefore, primary music education should introduce creative learning technologies aimed at developing students’ creative abilities. This article analyzes the content of students’ musical and creative activities in the field of music computer technologies (MCT) illustrated by the subject “Computer Music Creative Work.” The theoretical and experimental study led us to conclude that this course not only develops creative abilities but also establishes conditions for the comprehensive and harmonious development of students’ personalities.
Integration and the Diplomatic Crisis: Current Realities of the GCC

The article analyzes the integration process in the Gulf Cooperation Council group countries (GCC), which was founded in 1981. Regional economic integration in the GCC has passed through the several traditional stages: the Free trade zone, the Customs Union and the Common market. Certain exceptions remain in the GCC integration, but the countries aim to create a full economic Union by 2025. Currently, the GCC is one of the most advanced and successful integration associations in developing countries. However, the GCC is the only truly functioning integration bloc in the MENA region. The author's analysis confirms that the share of intraregional trade is growing in the GCC, but this share is less high than in other integration blocs. The author emphasizes that the GCC countries have significant success in the world market of goods and capital. The GCC trade turnover is growing steadily, although it depends on fluctuations in world oil prices. The GCC region attracts a lot of foreign investment and technology. This allows them to continue to diversify their national economies. The level and quality of life in the GCC is improving. These and other achievements are largely the result of regional economic integration and cooperation between countries in various areas on the world and regional markets. At the same time, the GCC is one of the most strategically important regions in the world economy and politics. Economic and political processes are influenced by many factors, both internal and external. Therefore, there are many contradictions in the GCC, and sometimes crisis situations are formed. One of them is the current "diplomatic crisis". It began in 2017 and has so far created numerous problems in the region and in the Arab world as a whole. It also has a negative impact on the development and deepening of integration processes. The author analyzed statistical data for 2009-2018 and justified the conclusion that the "diplomatic crisis" damages intraregional trade, reduces the dynamics of macroeconomic indicators, and worsens the position of the GCC countries in the world market of goods and capital. It also creates difficulties for citizens of GCC countries and companies that operate in the region. Now GCC countries should develop a mechanism to de-escalate the crisis, as this is in the economic and political interests of all participants and the Middle East as a whole. The relevance of the research is due to the need to analyze regional economic integration processes in the subregion in the context of growing global instability. The research makes a significant contribution to understanding the nature and features of integration processes in the Middle East.
Initial Reading Lesson through "Dia Tampan" Association Method and Android Photo Editor Media

Technology, as an android smartphone, can be utilized in initial reading lesson.This research discussed the result of initial reading lesson through android photo editor in a smartphone.This research was initiated by numbers of unfinished learners in initial reading lesson.The aims of this research are (1) describing the steps of using photo editor as learning media; (2) discussing the students' responses toward the media; (3) analyzing students' competences in general in initial reading lesson.An individual initial reading test was employed to test their reading competency.The observation of the use of photo editor as a media and students' interest toward it was done.The photo editor media was utilized by editing photos and doodling them as part of the initial reading and writing lesson.Students became more interested in learning through photo editor.Based on the observation result, the level of enthusiasm for the lesson was high.Students' time span in listening to the lesson was also high.In addition, students are able to re-write teachers' words as part of learning process.
The Analysis of Functional Needs on Undergraduate Thesis Information System Management

Undergraduate thesis is a scientific paper that students must complete to get a bachelor’s degree. During the completion of the thesis, many students face difficulties in determining research topics, supervision, and administration processes so that they cannot graduate on time. This study aims to analyze the functional needs of the undergraduate thesis information system management that is needed by both students and administrators. This system serves to accelerate the thesis preparation process that is useful for students, supervisors, and thesis administration managers. The procedure of the study was conducted by compiling matrix planning and data collection. As the data source, this study involved 20 students who were writing their undergraduate thesis and 10 alumni who have completed their theses. This sample was selected with the criteria of students or alumni who have been involved in the undergarduate thesis business process. In the process of taking samples reseachers used a purposive sampling method. In addition, difficulties in managing the thesis were also explored by supervisors and study program management. Furthermore, the data were analyzed through PIECES (Performance, Information, Economy, Control, Efficiency, Service). This study produces a list of functional needs on the thesis management system that can encourage students to graduate on time, in which universities can implement this system and socialize it to users.
FORECASTING OF AGRICULTURAL PRODUCTION RESULTS IN SOUTH DENPASAR USING QUADRATIC TREND METHOD BASED GIS

Agriculture is an activity of exploiting natural resources performed by humans to meet their needs, especially as staple food. Agricultural activities can produce foodstuffs, raw materials for industry, energy and also can balance the natural ecosystems. People may have forgotten the function of agriculture for several factors. Mentioning one, the population growth, with the result that land conversion is done to housing. Another, an economic concern that being a farmer does not guarantee the future, so that people have turned to other livelihoods that are considered promising, and other factors. Based on these facts that there has been a decline in agricultural land from 2011-2014 in Denpasar area, especially South Denpasar District, which can affect the amount of agricultural production as staple food. Total production of staple crops, especially rice plants, has decreased significantly in 2014.It became great concern to government and people in finding a solution. Due to lack of information on the impact of agricultural land conversion that affects the production, so that food needs are not met. Based on those problems, it is necessary to forecast the number of agricultural production using Quadratic Trend Method based on GIS (Geographic Information System) application using ArcView software. The results of this forecast that are implemented with a digital map can be used as a reference to the government in making decisions and create people awareness how important it is to preserve agriculture.
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform

We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained generation. To borrow advantages from different models, we implement strategies for cross-model integration, from the token probability level to the latent space level. An interface to external knowledge is provided via a module that retrieves on-the-fly relevant knowledge from passages on the web or any document collection. A user interface for local development, remote webpage access, and a RESTful API are provided to make it simple for users to build their own demos.
A Minimum Energy Filter for Distributed Multirobot Localisation

We present a new approach to the cooperative localisation problem by applying the theory of minimum energy filtering. We consider the problem of estimating the pose of a group of mobile robots in an environment where robots can perceive fixed landmarks and neighbouring robots as well as share information with others over a communication channel. Whereas the vast majority of the existing literature applies some variant of a Kalman Filter, we derive a set of filter equations for the global state estimate based on the principle of minimum energy filtering. We show how the filter equations can be decoupled and the calculations distributed among the robots in the network without requiring a central processing node. Finally, we provide a demonstration of the filter's performance in simulation.
The technology of automation of the preparation of the educational programs of university in accordance with the requirements of the Federal State Educational Standards of Higher Education 3++

The article discusses the experience of automation developing educational programs of university (EdProgram). The analysis of the results of the project of software creating to preparе components of EdProgram according to the requirements of the Federal State Educational Standards of Higher Education of the latest generation (FSES 3+) at the Bashkir State Agrarian University is given. The developed software product is created by extension technology of 1C:Enterprise 8.3 platform and expands the functionality of 1C:University PROF information system.The article actualizes the need to use automation tools in modern conditions when developing and updating the components of the EdPrograms are based on the Federal State Educational Standards of Higher Education. The proposed approach allows you to take into account the requirements of professional standards, as well as the requirements of employers (labor market) for graduates. The names of indicators of achievement of professional competencies are the names of the labor functions of the professional standards, the implementation of which requires the graduate to perform certain labor activities (skills) of the professional standards, acquisition relevant knowledge and skills as learning outcomes in the disciplines and practices of the higher education and vocational education and training institute of higher education, including those formulated as specific employer requirements.The proposed approach in the field of creating an automated technology for the preparation of EdProgram components on the basis of professional standards in accordance with the Federal State Educational Standards of Higher Education 3++ can be used by any university operating the 1C:University of PROF.