Reviewerly/exHarmony huggingface dataset:
Create Efficient and Complex Reservoir Computing Architectures with ReservoirPy

Reservoir Computing (RC) is a type of recurrent neural network (RNNs) where learning is restricted to the output weights. RCs are often considered as temporal Support Vector Machines (SVMs) for the way they project inputs onto dynamic non-linear high-dimensional representations. This paradigm, mainly represented by Echo State Networks (ESNs), has been successfully applied on a wide variety of tasks, from time series forecasting to sequence generation. They offer de facto a fast, simple yet efficient way to train RNNs. We present in this paper a library that facilitates the creation of RC architectures, from simplest to most complex, based on the Python scientific stack (NumPy, Scipy). This library offers memory and time efficient implementations for both online and offline training paradigms, such as FORCE learning or parallel ridge regression. The flexibility of the API allows to quickly design ESNs including re-usable and customizable components. It enables to build models such as DeepESNs as well as other advanced architectures with complex connectivity between multiple reservoirs with feedback loops. Extensive documentation and tutorials both for newcomers and experts are provided through GitHub and ReadTheDocs websites. The paper introduces the main concepts supporting the library, illustrated with code examples covering popular RC techniques from the literature. We argue that such flexible dedicated library will ease the creation of more advanced architectures while guarantying their correct implementation and reproducibility across the RC community.
Advances in subspace learning and its applications

Pattern-set matching refers to a class of problems where learning takes place through sets rather than elements. Much used in computer vision, this approach presents robustness to variations such as illumination, intrinsic parameters of the signal capture devices, and pose of the analyzed object. Inspired by applications of subspace analysis, three new collections of methods are presented in this thesis summary: (1) New representations for two-dimensional sets; (2) Shallow networks for image classification; and (3) Tensor data representation by subspaces. New representations are proposed to preserve the spatial structure and maintain a fast processing time. We also introduce a technique to keep temporal structure, even using the principal component analysis, which classically does not model sequences. In shallow networks, we present two convolutional neural networks that do not require backpropagation, employing only subspaces for their convolution filters. These networks present advantages when the training time and hardware resources are scarce. Finally, to handle tensor data, such as videos, we propose methods that employ subspaces for representation in a compact and discriminative way. Our proposed work has been applied in problems other than computer vision, such as representation and classification of bioacoustics and text patterns.
Knowgraph-TT: Knowledge-Graph-Based Transit Time Matching in Semiconductor Supply Chains

The semiconductor supply chain is characterized by a global and complex production network in a competitive market. The time when work at one location ends and can be resumed at another is defined as Transit Time (TT). Therefore, planning Transit Time accurately and minimizing delays is crucial as it is used in the execution system to determine the Available to Promise (ATP) and thus important for daily order confirmation. By determining the ATP, the customer receives a response to the resource availability and a due date to the customer requests. Due to tool inherent differences, we choose semantic integration via Knowledge Graph (KG) to match the planned TT used in the execution system and the actual TT measured in the monitoring tool. KnowGraph-TT thereby serves as a role model for further matching and alignment tasks using KG. It connects actual and planned TT, highlights the gaps via applied queries, and enables an optimized update of planned TT. With our solution, deviations of actual and planned TT can be minimized and confirmations of unrealizable deliverable times are avoided.
Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring

Answer Set Programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver wasp can be improved.
Solving Problems in the Polynomial Hierarchy with ASP(Q)

Answer Set Programming with Quantifiers ASP(Q) is a recent extension of Answer Set Programming (ASP) that allows one to model problems from the entire polynomial hierarchy. Earlier work focused on demonstrating modeling capabilities of ASP(Q). In this paper, we propose a modular ASP(Q) solver that translates a quantified ASP program together with a given data instance into a Quantified Boolean Formula (QBF) to be solved by any QBF solver. We evaluate the performance of our solver on several instances and with different back-end QBF solvers, demonstrating the efficacy of ASP(Q) as a tool for rapid modeling and solving of complex combinatorial problems. The benchmark problems we use include two new ones, Argumentation Coherence and Para-coherent ASP, for which we develop elegant ASP(Q) encodings.
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation

Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feature spatial resolution, which results in a search space consisting of about $$2.78\times 10^{324}$$ possible choices. To handle such a large search space, we leverage differential architecture search methods. However, the architecture parameters searched using existing differential methods need to be discretized, which causes the discretization gap between the architecture parameters found by the differential methods and their discretized version as the final solution for the architecture search. Hence, we relieve the problem of discretization gap from the innovative perspective of solution space regularization. Specifically, a novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. Then, a new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching. In addition, we theoretically show that the optimization of SSR loss is equivalent to the $$L_{0}$$ -norm regularization, which accounts for the improved search-evaluation gap. Comprehensive experiments show that the proposed search scheme can efficiently find an optimal network structure that yields an extremely fast speed (175 FPS) of segmentation with a small model size (1 M) while maintaining comparable accuracy.
Prediction and Analysis of College Sports Test Scores Based on Computational Intelligence

Nowadays, colleges and universities are paying more and more attention to the physical condition of students. Many schools set up physical education courses to exercise students and improve their physical quality. They also conduct physical examinations every semester to test students’ conditions. In order to ensure more accurate sports results, this paper uses optimization of the neural group particle group model method to forecast the physical culture test scores of the investigated students. In addition, to guarantee accuracy the particle swarm optimization neural network model method, we compare the GXD method and the LM method with our method. It has the advantage of high precision, optimal prediction effect, strong versatility, higher recall rate, stronger antinoise performance, and wider application range. The article compares the neural network model method for particle swarm optimization with the GXD way and the LM way to ensure precision the neural network model method for particle swarm optimization.
Publication

While it may not always be desirable to publish knowledge graphs (for example, those that offer a competitive advantage to a company [Noy et al., 2019]), it maybe desirable or even required to publish other knowledge graphs, such as those produced by volunteers [Lehmann et al., 2015, Mahdisoltani et al., 2015, Vrandecic and Krotzsch, 2014], by publicly-funded research [Callahan et al., 2013, Groth et al., 2014, The UniProt Consortium, 2014], or by governmental organizations [Hendler et al., 2012, Shadbolt and O’Hara, 2013]. Publishing refers to making the knowledge graph (or part thereof) accessible to the public, often on the Web. Knowledge graphs published as open data are called open knowledge graphs (discussed in Section 10.1).
The Extraction Studies of Self-driving Awareness Video Semantics Based on Convolution Neural Networks

We propose an extraction method on video object, combined with self-driving automobile camera. Firstly, we use the frame differencing to extract key frame, the modified Video Segmentation Algorithm reduce the computer resource requirements, increase the speed of extracting prospective object. After that, establish an automobile external environment applied for collecting self-driving-aware video to realize the intelligent analysis. Intelligent extract the key frame of prospective object, then use the Convolution Neural Networks to extract feature and analysis behavior on the key frames. To build modeling and realize the detection of self-driving obstacle, road and pedestrian, traffic lights and automobiles etc. processes the ability of learning decision-making behavior of self-driving or auxiliary driving.
The Influence of Teachers’ Management Efficiency and Motivation on College Students’ Academic Achievement under Sustainable Innovation and the Cognition of Social Responsibility after Employment

Teachers’ management effectiveness can reflect the comprehensive ability and effective evaluation of the teaching management objectives of the school and can also reflect the effectiveness management of the school to a certain extent. In the environment of sustainable and innovative development, the rapid development of education has made improving the management efficiency and motivation of university teachers became the key direction of university management. In order to study the impact of teachers’ management efficacy and motivation on college students’ academic achievement and postemployment social responsibility cognition under sustainable innovation, this study compares the impact of teachers in different periods on students and analyzes the changes of teachers in different periods on students. Finally, it is concluded that teachers’ management efficiency and motivation under sustainable innovation can promote students’ employment achievement and social responsibility cognition, improve the teaching quality of colleges and universities, and provide high-quality talents for the country. The educational concept of sustainable innovation is conducive to improving teachers’ independent innovation and realizing the educational function of cultivating students’ personalized development in colleges and universities.
IR Mapping: Intermediate Representation (IR) based Mapping to facilitate Incremental Static Analysis

An Intermediate Representation (IR) is a data structure to represent a program. It represents each program entity as an object (IR object), having a unique identification number (ID). Static program analysis tools perform analysis on IRs of the input program and compute analysis information at program points - which are represented as IR objects. The analysis information is stored against their corresponding ID. Performing incremental analysis on evolving systems involve the reuse of analysis information for the unchanged IR objects between versions of a program. However, the IDs changes over the version with the change. This acts as an obstacle to the reuse of analysis information. To overcome this, a one-to-one correspondence between IDs of unchanged IR objects is necessary. We term this correspondence as IR mapping.
Model Driven Architecture In GIS-based DSS for flood management

In the field of hydroinformatics systems, diversity of models has been a big obstacle for model integration during software development. Given the complexity and uncertainty during decision-making process in flood management, which may involves with multiple models, traditional data-driven method no longer fits for system establishment. Therefore, we apply the model-driven architecture (MDA) for hydroinformatics software design. A GIS (Geographic Information System) -based decision support system(DSS) for flood management is developed base on MDA. This DSS is driven by two-dimension hydrodynamic model for flood simulation, and the evacuation model for emergency evacuation planning in the eventuality of flood. With the coupling work of these two models, decision-maker can acquire auxiliary information about flood routing and evacuation scheme.
A study of microseismic first arrival pickup based on image semantic segmentation

Microseismic detection is an important part of geological exploration, and accurate pickup of microseismic first arrival is the basis of microseismic monitoring events. In practical engineering applications, the microseismic monitoring system is located in a complex environment, the signal-to-noise ratio of microseismic signals is low, the manual pickup is time-consuming and labor-intensive, the traditional automatic pickup method cannot balance the pickup effect and efficiency and cannot adapt to noise intensity changes and other problems. To solve the above problems, in order to better understand the properties and characteristics of microseismic signals and improve the rate and accuracy of microseismic first arrival pickup, this paper proposes a microseismic first arrival pickup method based on image semantic segmentation, and uses actual microseismic monitoring data collected from mines for comparison experiments. The experimental results show that the method has high accuracy in locating microseismic events in complex datasets containing multiple microseismic types, and the average pickup error and stability of microseismic first arrival are improved compared with the other network first arrival pickup method, which can improve the efficiency of microseismic monitoring and has practical engineering application value.
A Fast and Robust Maneuvering Target Tracking Method without Markov Assumption

In the traditional methods of maneuvering target tracking, it is necessary to adjust the state transition model in time to match the maneuvering target, which will cause the problems of model decision delay and competition. Besides, the commonly adopted first-order Markov assumption can lead to the loss of information when motion modes are relevant to time. In order to solve these problems, a data-driven algorithm based on LightGBM is proposed in this paper. Maneuvering target tracking is modeled as a non-probabilistic method of direct mapping from sensor measurement to target state, track samples of different motion modes are used for training, and fast online tracking is realized. Comparing it with interacting multiple model (IMM) algorithm in a variety of different scenarios, simulation results show that the proposed method has advantages in accuracy and speed. Finally, the robustness of the algorithm is verified under the compound noise of Cauchy and Gaussian distributions.
Sign language detection using convolutional neural network for teaching and learning application

Teaching lower school mathematic could be easy for everyone. For teaching in the situation that cannot speak, using sign language is the answer especially someone that have infected with vocal cord infection or critical spasmodic dysphonia or maybe disable people. However, the situation could be difficult, when the sign language is not understandable by the audience. Thus, the purpose of this research is to design a sign language detection scheme for teaching and learning activity. In this research, the image of hand gestures from teacher or presenter will be taken by using a web camera for the system to anticipate and display the image's name. This proposed scheme will detects hand movements and convert it be meaningful information. As a result, it show the model can be the most consistent in term of accuracy and loss compared to others method. Furthermore, the proposed algorithm is expected to contribute the body of knowledge and the society.
Comparative analysis of time series prediction model for forecasting COVID-19 trend

&lt;span&gt;The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and &lt;a name="_Hlk111113162"&gt;&lt;/a&gt;long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values. The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).&lt;/span&gt;
Relationship between Urban Landscape Design and Ecological Environment Protection in the Application of Environmental Protection and Renewable New Energy Materials

With the economic growth in recent years, urban construction has also developed rapidly. In developing countries, the pursuit of rapid development has also brought about huge energy consumption, energy shortage, and environmental pollution. It is urgent to find and develop renewable energy. In order to understand the role of renewable energy materials in urban landscape design and ecological environment protection, replace nonrenewable materials with renewable energy materials, and reduce urban pollution, we analyze the available energy for buildings in urban areas and optimize the allocation of various available resources. Energy, improve energy efficiency, so as to save energy, protect the environment, and coordinate the development of energy, economy, and environment. Renewable energy is also known as alternative energy, sustainable energy, or nontraditional energy. It is energy that can be obtained from nature and can be replenished naturally. Experimental results prove that the energy required for traditional material consumption is much higher than the consumption of renewable energy, and its range is more than 50%. Renewable energy can replace the role of nonrenewable energy. This article hopes to stimulate more relevant architects to think about this subject through the research and analysis of renewable energy.
Aplikasi Pencari Tempat Magang Berbasis Android Menggunakan Metode Agile Scrum

Internship is a process that is an obligation to be carried out in the academic process at the Telkom Institute of Technology Purwokerto, however with various media sources used in finding internships, students have problems in finding suitable internships. This study aims to apply Agile Scrum and Simple Additive Weighting in making applications to find a suitable internship place. Agile Scrum is used as a method of continuous application development, Simple Additive Weighting is used as a method in determining recommendations based on existing input. Application testing is carried out using Black Box Testing which tests the success of the function in carrying out its scenario and User Acceptance Test which is used as a benchmark in the suitability of the application with user needs. The results of this study are an android application in the form of an apk that was tested using Black Box Testing and got valid results in all scenarios and had a score of 87% on the User Acceptance Test
Quantitative Evaluation and Empirical Analysis of Educational Policies for Ethnic Minorities in Border Areas Based on the PMC Index Model

China is a multiethnic country, and the border areas are the gathering places of ethnic minorities. Strengthening the ethnic unity education in the border areas is an important measure to maintain the social stability and national security in the border areas of China. Colleges and universities in the border areas are the cradle of cultivating outstanding ethnic minority cadres, and colleges and universities in the border areas shoulder an important historical mission. In recent years, many domestic experts and scholars began to try to use the PMC index model for policy evaluation. Chinese scholars also have a deeper understanding of the standards of policy evaluation and put forward policy evaluation standards that are more in line with the actual needs of China’s public policy evaluation research. This paper takes the education policy text of the border ethnic minorities as the research object, takes the policy evaluation as the research perspective, constructs the transformation policy text evaluation model according to Ruiz Estrada’s PMC index model construction method, defines the calculation method and rating method, draws the PMC surface of each sample, and makes a comparative analysis of the PMC index. According to the comparative analysis results of PMC index and the scanning results of policy text, this paper puts forward three optimization strategies for the education policy of border ethnic minorities.
Introduction: Microsoft Power Apps

Microsoft Power Apps is a trending, less-code and no-code business application development platform that comes with a suite of apps, services, and connectors. Its data platform provides a rapid development environment for building custom business applications that are fit for any business need. With the introduction of no-code, less-code app development, anyone can quickly build and share low-code apps with Microsoft Power Apps. The benefits of using Microsoft Power Apps are as follows: