Reviewerly/exHarmony huggingface dataset:
Numerical model of mass transfer processes using fractional derivatives

The article describes the scheme of construction and application of finite element method using Grunwald-Letnikov algorithm. Obtained results make it possible to estimate influence of fractional derivative order in terms of time and space on process of gas filtration in porous medium. Numerical ecperification and analysis performed
On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks

In this paper, we have extended the well-established universal approximator theory to neural networks that use the unbounded ReLU activation function and a nonlinear softmax output layer. We have proved that a sufficiently large neural network using the ReLU activation function can approximate any function in $L^1$ up to any arbitrary precision. Moreover, our theoretical results have shown that a large enough neural network using a nonlinear softmax output layer can also approximate any indicator function in $L^1$, which is equivalent to mutually-exclusive class labels in any realistic multiple-class pattern classification problems. To the best of our knowledge, this work is the first theoretical justification for using the softmax output layers in neural networks for pattern classification.
Multi - Class Document Classification: Effective and Systematized Method to Categorize Documents

A large section of World Wide Web is full of Documents, content; Data, Big data, unformatted data, formatted data, unstructured and unorganized data and we need information infrastructure, which is useful and easily accessible as an when required. This research work is combining approach of Natural Language Processing and Machine Learning for content-based classification of documents. Natural Language Processing is used which will divide the problem of understanding entire document at once into smaller chucks and give us only with useful tokens responsible for Feature Extraction, which is machine learning technique to create Feature Set which helps to train classifier to predict label for new document and place it at appropriate location. Machine Learning subset of Artificial Intelligence is enriched with sophisticated algorithms like Support Vector Machine, K – Nearest Neighbor, Naïve Bayes, which works well with many Indian Languages and Foreign Language content’s for classification. This Model is successful in classifying documents with more than 70% of accuracy for major Indian Languages and more than 80% accuracy for English Language.
Dynamic Optimization of Chemical Processes using Symbiotic Organisms Search Algorithm

Chemical process optimization problems are often modeled as dynamic optimization problems (DOPs). Due to the nonlinear, multimodal and multi-dimensional nature of DOPs, efficient solution of DOPs is a very challenging task. In this paper, a new intelligent search algorithm called symbiotic organisms search (SOS) is proposed for tackling the chemical DOPs. SOS mainly mimics three symbiotic relationships in ecosystem, namely mutualism, commensalism and parasitism, to perform global search. In addition, compared with previous intelligent search algorithms, SOS has the advantages of no tuning parameters, easy to implement and high performance. Combined with control vector parameterization, the proposed SOS is applied to solve five chemical DOPs with different levels of complexity. Simulation results show that SOS can achieve solutions with accuracy comparable to those methods in the literature, and thus can be regarded as an effective tool for the chemical DOPs.
Intrinsic Motivation for Encouraging Synergistic Behavior

We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation and multi-agent locomotion tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.
Prezi as an innovative teaching tool for the strengthening of significant learning

The purpose of this research is to determine the implementation of innovative teaching tools, for the strengthening of significant learning, applied by teachers, in the teaching-learning process. By using innovative resources, it allows students to orient and build knowledge in all disciplines, the use of Prezi pursues, that learning is meaningful, the purpose of study is focused on analyzing the use of innovative teaching tools in the teaching process, through observation and survey techniques, in order to improve learning, it was also possible to detect that the origin of the problem is the low use of the application of innovative digital tools that serve as support in the planning of the different subjects in a creative and interactive way in the process.
DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation learning. However, current methods do not yet successfully combine the strengths of these two approaches. Existing representation learning models which rely on latent topological structure such as self-organising maps, exhibit markedly lower clustering performance compared to recent deep clustering methods. To close this performance gap, we (a) present a novel way to fit self-organizing maps with probabilistic cluster assignments (PSOM), (b) propose a new deep architecture for probabilistic clustering (DPSOM) using a VAE, and (c) extend our architecture for time-series clustering (T-DPSOM), which also allows forecasting in the latent space using LSTMs. We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs. On medical time series, we show that T-DPSOM outperforms baseline methods in time series clustering and time series forecasting, while providing interpretable visualizations of patient state trajectories and uncertainty estimation.
Exploring Neural Models for Parsing Natural Language into First-Order Logic

Semantic parsing is the task of obtaining machine-interpretable representations from natural language text. We consider one such formal representation - First-Order Logic (FOL) and explore the capability of neural models in parsing English sentences to FOL. We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula. We improve the standard encoder-decoder model by introducing a variable alignment mechanism that enables it to align variables across predicates in the predicted FOL. We further show the effectiveness of predicting the category of FOL entity - Unary, Binary, Variables and Scoped Entities, at each decoder step as an auxiliary task on improving the consistency of generated FOL. We perform rigorous evaluations and extensive ablations. We also aim to release our code as well as large scale FOL dataset along with models to aid further research in logic-based parsing and inference in NLP.
Treasure Hunter Game Buah Maja Menggunakan Scirra Construct 2

Majalengka is one of the regencies located in West Java Province. With the diversity of cultures and tourist attractions in Majalengka, the author intends to make a game that can accommodate several aspects of Majalengka so that it can be better recognized by the public, especially the Majalengka community itself. This research aims to introduce Majalengka to the outside community and the Majalengka community itself, through Majalengka icons and their natural wealth in the form of legendary Gincu and Maja Fruit. The system development method used is a development method using the MDLC (Multimedia Development Life Cycle) method. Questionnaires were given to people who had tried playing the Treasure Hunter Buah Maja, a questionnaire was made aimed at finding out the people's response to the game. With a result of 77.5%, the community stated when they first tried the application to be easy to use. 80.8% of the people expressed interest in using the Treasure Hunter Buah Maja game. And as much as 85% of the people stated that this game was quite entertaining.
The Application of a Mixed Teaching Model to the Academic English Teaching for Graduate Students at Inner Mongolia University

This paper explores the application of a mixed teaching model of Task-Based Learning (TBL), Collaborative-Inquiry Model (CIM) and MOOCs to the academic English teaching for graduate students at Inner Mongolia University, China.Teaching tasks of eight units are assigned for students to learn through the online course on MOOC in advance.Graduate students are grouped to work and cooperate with one another to complete the presentations of their learning results.A five-year teaching practice indicates that 1) graduate students appear more active in class than students taught in traditional class; 2) the writing quality of abstracts is greatly improved through the cooperative learning pattern; 3) students show more satisfaction for the course than ever before regarding the teacher's help for their academic activities.The application of the new teaching model has not only benefited the Inner Mongolia University (IMU) students, but also set up a pioneering example in teaching reform for graduate students and provided valuable experiences for other universities in ethnic areas.
Algorithm for tailoring a quadratic lattice with a local squeezed reservoir to stabilize generic chiral states with nonlocal entanglement

We demonstrate a new approach to the generation of custom entangled many-body states through reservoir engineering, using the symmetry properties of bosonic lattice systems coupled to a local squeezed reservoir. We outline an algorithm where, beginning with a desired set of squeezing correlations, one uses the symmetry to constrain the Hamiltonian and find a lattice configuration which stabilizes a pure steady state realizing these correlations. We demonstrate how to use this process to stabilize two unique pure states with non-local correlations that could be useful for quantum information applications. First, we show how drive a square lattice into a product state of entangled quadruplets of sites. Second, using a bisected system, we generate a steady state where local measurements in one half of the lattice herald a pure delocalized state in the second half.
Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5

Blood donation is a process of taking blood from donors that is declared feasible, in terms of various factors including age, weight, blood pressure, hemoglobin levels, and donor status which are taken into consideration during the feasibility test. This study was conducted to find the most appropriate method with high accuracy and Area Under Curve (AUC) values using 3710 blood donor datasets from the Bekasi City PMI, processed using the Naïve Bayes algorithm method, K-Nearest Neighbors and Decision Tree C4.5. The analysis shows that the Decision Tree C4.5 algorithm shows higher accuracy of 93.83% compared to Naïve Bayes algorithm which shows an accuracy value of 85.15% and the K-Nearest Neighbors algorithm with an accuracy value of 84.10%. In addition to these values, Decision Tree C4.5 is also visually superior where the Decision Tree has an output model tree that shows attribute relationships and has an AUC value of 0.978, Naïve Bayes with an AUC value of 0.927 and K-Nearest Neighbors with an AUC value of 0.816.
The contexts for education on computer and information literacy and computational thinking

The International Computer and Information Literacy Study (ICILS) 2018 collected and reported information relating to the national contexts in which computer and information literacy (CIL) and computational thinking (CT) are developed for grade eight students. This information illustrates the range of national approaches regarding curriculum, assessment policy and program support regarding these areas and can support interpretation of data gathered from students, teachers, and schools in ICILS. National research coordinators (NRCs) coordinated responses to detailed questions about characteristics of the educational system, plans and policies for using ICT in education and about how such policies are implemented in schools. Additionally NRCs authored respective country profiles which describe the overarching goals and direction for the educational system in participating countries, detailing how the curriculum relating to the use of ICT in education is developed, implemented, and assessed, highlighting many similarities but key differences across participating ICILS 2018 countries. In almost all countries, schools had at least some autonomy with most aspects of school policies, with private schools typically having a greater degree of autonomy. A wide range of differences across participating countries exist, both in relation to information and communication technology (ICT) infrastructure availability and economic characteristics. Although the formulation of plans and policies supporting the use of ICT in education differed across countries, there was a high degree of similarity in the content related to improving student learning, ICT resources, methods to support student learning, and the priorities for the use of ICT. While many countries had explicit or implicit recognition of different CIL aspects in their national curriculum, aspects of CT were less frequently included. Countries had very different approaches to the development of teachers’ capacity to use ICT, but generally provided a large degree of support for teacher access to ICT-based professional development. Reports from school principals and ICT coordinators provide a contrasting profile of differences across participating ICILS countries in terms of school resourcing, policies, and priorities.
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability

Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process. Such intermittent client availability would seriously deteriorate the performance of the classical Federated Averaging algorithm (FedAvg for short). Thus, we propose a simple distributed non-convex optimization algorithm, called Federated Latest Averaging (FedLaAvg for short), which leverages the latest gradients of all clients, even when the clients are not available, to jointly update the global model in each iteration. Our theoretical analysis shows that FedLaAvg attains the convergence rate of $O(E^{1/2}/(N^{1/4} T^{1/2}))$, achieving a sublinear speedup with respect to the total number of clients. We implement FedLaAvg along with several baselines and evaluate them over the benchmarking MNIST and Sentiment140 datasets. The evaluation results demonstrate that FedLaAvg achieves more stable training than FedAvg in both convex and non-convex settings and indeed reaches a sublinear speedup.
Meta-heuristic techniques for path planning: recent trends and advancements

Path planning is a propitious research domain with extensive application areas. It is the procedure to construct a collision-free path from specified source to destination point. Earlier, classical techniques were widely implemented to solve path planning problems. Classical techniques are very easy to implement but they are time-consuming and are not effective in case of uncertainties. But meta-heuristic techniques have the ability even to perform in an approximate and uncertain environment. This makes the use of meta-heuristic techniques in a more focused manner for the optimal path planning research. This paper presents the Overview, recent trends and advancement from year 2001 to 2017 in the field of optimal path planning using meta-heuristic techniques. During the study, different meta-heuristic algorithms are analysed and classified into three categories: swarm-based meta-heuristic techniques, other than swarm-based techniques and combinational meta-heuristic techniques. In addition, basic understanding and applicability of specific algorithms for path planning are also discussed along with its strengths and downsides.
Development and Efficiency Assessment of New Methods on Rapid Assessment of Toxicity in Environmental Monitoring

The transformation of hazardous pollutants in the environment usually results in formation of hard-to-identify compounds that may have a significant impact on ecosystems. They are difficult to identify by conventional methods of quantitative chemical analysis, but their detrimental effects can be detected by biological methods. The goal of this study was to develop novel methodological approaches to the express assessment of the state of the environment as well as toxicity of its components in the process of biological monitoring at hazardous industrial facilities. The article presents the results of comprehensive toxicity assessment of environmental objects using originally developed system of express methods. The objects of research were water fleas (Daphnia magna Straus and Ceriodaphnia affinis Lilljeborg), infusoria (Paramecium caudatum), preparation of lyophilized fluorescent bacteria based on recombinant Escherichia coli strain M-17 (Ecolum), algae (Chlorella vulgaris Beijer, Scenedesmus quadricauda (Turp.) Breb.), and mammalian gonads. The original methods for determining toxicity by changing the motor activity of water fleas at elevated temperatures of the studied environment are described. The description of the following original utility models is presented: “Method of bioassay for water samples and device for its implementation”, “Device for express assessment of natural environment quality” and “Device for comprehensive assessment of natural environment quality”, confirmed by the patents of the Russian Federation. Their use may significantly reduce the cost of ecotoxicological studies in terms of time and money expenditures. The effectiveness of the system of express methods was established during a biological monitoring of a sanitary protection zone of the Balakovo NPP. Toxicity of the samples of bottom sediments, soil, as well as water samples from the cooler pond and the adjacent water area of the Saratov reservoir were identified. The bioassay results were matched by both conventional methods’ results and originally developed system of express methods.
An Improved Virtual Sample Generation Technology based on Mega Trend Diffusion

The problem of small sample size is a major and severe problem in the era of big data. At present, the common solution to the problem of small samples is to expand the number of samples by using virtual sample generation (VSG) technology. But there are still many problems such as rationality, accuracy and practical application. Based on this, this paper proposes an improved VSG technology. At first, a region expansion technique based on Euclidean distance of sub-region is proposed, which extends the input sample space and output sample space. Then, in the extended region, the virtual input samples are generated equidistantly. The virtual output samples are generated by the mapping model and the virtual input samples, and the virtual output samples are deleted by combining the output sample space. Next, a random weight neural network (RWNN) is used to generate virtual input and output samples based on its hidden layer. Finally, these true and virtual samples are mixed to produce the final modeling. The validity and rationality of the VSG technology are validated by benchmark data set.
A Simple General Approach to Balance Task Difficulty in Multi-Task Learning

In multi-task learning, difficulty levels of different tasks are varying. There are many works to handle this situation and we classify them into five categories, including the direct sum approach, the weighted sum approach, the maximum approach, the curriculum learning approach, and the multi-objective optimization approach. Those approaches have their own limitations, for example, using manually designed rules to update task weights, non-smooth objective function, and failing to incorporate other functions than training losses. In this paper, to alleviate those limitations, we propose a Balanced Multi-Task Learning (BMTL) framework. Different from existing studies which rely on task weighting, the BMTL framework proposes to transform the training loss of each task to balance difficulty levels among tasks based on an intuitive idea that tasks with larger training losses will receive more attention during the optimization procedure. We analyze the transformation function and derive necessary conditions. The proposed BMTL framework is very simple and it can be combined with most multi-task learning models. Empirical studies show the state-of-the-art performance of the proposed BMTL framework.