Reviewerly/exHarmony huggingface dataset:
Teaching Model-Based Requirements Engineering to Industry Professionals: An Experience Report

The use of conceptual models to foster requirements engineering has been proposed and evaluated as beneficial for several decades. For instance, goal-oriented requirements engineering or the specification of scenarios are commonly done using conceptual models. Bringing such model-based requirements engineering approaches into industrial practice typically requires industrial training. In this paper, we report lessons learned from a training program for teaching industry professionals model-based requirements engineering. Particularly, we as educators and learners report experiences from designing the training program, conducting the actual training, and applying the instructed material in our day-to-day work. From these findings we provide guidelines for educators designing requirements engineering courses for industry professionals.
DE-GCN: Differential Evolution as an optimization algorithm for Graph Convolutional Networks

Neural networks had impressive results in recent years. Although neural networks only performed using Euclidean data in past decades, many data-sets in the real world have graph structures. This gap led researchers to implement deep learning on graphs. The graph convolutional network (GCN) is one of the graph neural networks. We propose the differential evolutional optimization method as an optimizer for GCN instead of gradient-based methods in this work. Hence the differential evolution algorithm applies for graph convolutional network's training and parameter optimization. The node classification task is a non-convex problem. Therefore DE algorithm is suitable for these kinds of complex problems. Implementing evolutionally algorithms on GCN and parameter optimization are explained and compared with traditional GCN. DE-GCN outperforms and improves the results by powerful local and global searches. It also decreases the training time.
A Multi-Layer Multi-Channel Attentive Network for Gender and Age Recognition

In practical application, the existing gender and age recognition algorithms can’t meet the requirements of both smallsized model and high accuracy simultaneously. Moreover, most models based on CNNs have a even larger size of more than 200M. In this paper a multi-layer multi-channel attentive network based on the idea of divide-and-conquer is proposed. This method uses multi-layer processes in each channel to extract features of different layers and improves accuracy by layer refinement. We use some dynamic parameters to fine- tune each layer to make the model fit better. Each layer uses the same classifier to reduce parameters to make the model smaller. And we import attention mechanisms to increase the ability of the network to use the features. Experiments show that the accuracy of this method is better than several mainstream networks and the size of the model is less than 0.5M, which can be used in mobile terminals well.
Forecasting the amount of rainfall in West Kalimantan using Generalized Space-time Autoregressive model

Abstract The generalized space-time autoregressive or GSTAR is a space-time model which can be used to analyze time-series data in several locations considered to be correlated. In this research, the GSTAR model is applied to forecast the amount of rainfall in West Kalimantan, especially at Sintang Station, Melawi Station, and Ketapang Station. The data used for modeling is data of the amount of rainfall for the period January 2013-December 2017, while the data used for model validation is data for the period January 2018-December 2018. The spatial weights used are uniform weights, inverse distance weights, and normalized cross-correlation weights, and the estimation method used is the ordinary least square or OLS estimation. The best model is selected based on the smallest RMSE (root mean square error). The results showed that all spatial weights gave the same good GSTAR(1:1) model because they had almost the same RMSE value. Thus, this model can be used to forecast the amount of rainfall for the period January 2019-December 2019. The forecast results show that for Sintang Station and Melawi Station, the highest amount of rainfall is estimated to occur in March 2019 and the lowest will occur in August 2019. Meanwhile, for Ketapang Station, the highest amount of rainfall is estimated to occur in January 2019 and the lowest will occur in September 2019.
The Design of the Electromyography Sensor with the Modifying Circuit

Abstract The project is about using the Electromyography (EMG) sensor to display the muscle movement and use connecting circuit to modify the signal from the EMG sensor. The modifying circuit connected to the EMG sensor include the amplifier circuit to amplify the signal, the filter to remove the DC offset and the high frequency noise, the rectifier for the positive swing of the signal and the envelope detector to smooth the signal. The circuit is designed according to the signal amplitude and the frequency spectrum and is simulated on the Multisim and have the output. The actual circuit is built as in the simulation and the signal is from the electrode. The output is in audiovisual and performance of the EMG sensor circuit is demonstrated by experiment. The design of the EMG in the project uses the simple component in electronic circuit with the desired output. The display of the EMG sensor in the project is audiovisual which provides the user with output format both in visual and in audio that allows hearing disability and visual disability users to use the EMG sensor.
Research on Chinese Word Separation Based on Deep Learning

Currently, a large amount of information is generated every day, and natural language processing techniques can help people to get the information they need quickly. For natural language processing of Chinese, Chinese word separation is a fundamental task in natural language processing. At present, research on Chinese word separation is basically based on machine learning methods, with the disadvantage that a large number of features need to be constructed manually. To address the shortcomings of current Chinese word sorting, this paper first analyzes the common methods and deep learning models for Chinese word sorting, and proposes an improvement scheme based on the Chinese word sorting model Bi LSTM+textbf CRF. And experiments are designed to verify the correctness and superiority of the model proposed in the paper on Chinese word separation on three datasets.
Virtual Loupes: An Augmented Reality Aid for Microsurgery

Microsurgery requires the use of loupes, which are optical elements attached to eyeglass frames that magnify objects such as blood vessels and nerves. To address the inflexibility of loupes, we have developed an augmented reality system that provides virtual loupes with a video see-through headset, allowing for easy-to-use variable zoom in a surgical context. Our prototype solution utilizes a gaze-controlled interface, which allows for the zoom level to be selected in either a discrete or continuous manner. A feasibility study revealed both the potential of virtual loupes and the limitations of current technology to realize this concept.
eGlove: Designing Interactive Fabric Sensor for Enhancing Contact-Based Interactions

We present eGlove, a wearable and low-cost fabric sensor for recognizing a rich context of objects by touching them, including daily necessities, fruits, plants, as well as different body parts. Our sensing approach utilizes Swept frequency Capacitive Sensing (SFCS) to provide consistent sensor readings even when the fabric electrode is under varying deformation and stretching degrees. Our work proposes an easy fabrication method and hardware configuration for prototyping the interactive fabric sensor. We evaluated our system’s classification accuracy through per-user training and found a real-time classification of 96.3%. We also demonstrated novel contextual interactions enabled by our technical approach with several applications.
Learning multiple concepts in description logic through three perspectives

Abstract An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.
Optimization and Research of Urban Traffic Data Based on Knowledge Graph

Knowledge graph can clearly express the relationship between entities and entities, and its integration can more intuitively and clearly allow us to understand and use knowledge units. Since Google first proposed knowledge graphs in 2012, knowledge graphs have attracted more attention in related fields such as industry. As knowledge graphs have attracted more and more attention in recent years, they have developed rapidly in various fields, especially in the medical field and e-commerce fields. However, the application of knowledge graphs to the transportation field is not very extensive. This article is based on the processing of relevant experience articles in the transportation industry to construct a traffic knowledge graph. The construction process is roughly divided into: a. Obtaining traffic knowledge. b. Recognizing traffic entities. c. Obtaining the relationship between traffic entities and entities. d. Traffic knowledge graphing Construct. This article mainly through the research and processing of data in the field of transportation. By building a traffic knowledge graph and using NEO4J graph database to build a database of information related to the traffic field, to integrate the knowledge of the traffic field, users can understand traffic knowledge more intuitively and quickly, and understand the traffic knowledge unit and its related knowledge more clearly unit.
Cross-layer Information Refining Network for Single Image Super-Resolution

Recently, deep learning-based image super-resolution (SR) has made a remarkable progress. However, previous SR methods rarely focus on the correlation between adjacent layers, which leads to underutilization of the information extracted by each convolutional layer. To address this problem, we design a simple and efficient cross-layer information refining network (CIRN) for single image super-resolution. Concretely, we propose the interlaced spatial attention block (ISAB) to measure the correlation between the adjacent layers feature maps and adaptively rescale spatial-wise features for refining the information. Owing to the two stage information propagation strategy, the ISAB can distill the primary information of adjacent layers without introducing too many parameters. Extensive experiments on benchmark datasets illustrate that our method achieves better accuracy than state-of-the-art methods even in 16× scale, specifically it has a better balance between performance and parameters.
Multi-Directional Convolution Networks with Spatial-Temporal Feature Pyramid Module for Action Recognition

Recent attempts show that factorizing 3D convolutional filters into separate spatial and temporal components brings impressive improvement in action recognition. However, traditional temporal convolution operating along the temporal dimension will aggregate unrelated features, since the feature maps of fast-moving objects have shifted spatial positions. In this paper, we propose a novel and effective Multi-Directional Convolution (MDConv), which extracts features along different spatial-temporal orientations. Especially, MDConv has the same FLOPs and parameters as the traditional 1D temporal convolution. Also, we propose the Spatial-Temporal Feature Pyramid Module (STFPM) to fuse spatial semantics in different scales in a light-weight way. Our extensive experiments show that the models which integrate with MDConv achieve better accuracy on several large-scale action recognition benchmarks such as Kinetics, AVA and Something-Something V1&V2 datasets.
A Place Beside Satoshi – Scientific Foundations of Blockchain and Cryptocurrency in Business and Economics

Blockchain technology has become an ubiquitous phenomenon. While the topic originated in computer science, the business and economics literature was comparatively slow to pick up on it. To better understand the academic basis, current developments and future research avenues of the discourse, I analyse 467 blockchain and cryptocurrency articles and their 9,672 cited references from the fields of business and economics, which I gather from the Web of Science Core Collection. Five major strands of research are identified through factor analysis. They are reviewed and their interrelation is mapped using social network analysis. I find that research on I) market efficiency and economics and II) asset pricing and valuation is relatively mature and focuses on cryptocurrencies, while research on III) the principles and applications of blockchain technology, IV) transactions and anonymity and V) monetary theory and policy lacks maturity. I point out potential paths for future research and conclude that this young field of research still leaves plenty of room for manoeuvre. A scientific place next to Nakamoto (2008) is still available for existing, emerging and new research streams.
Recognition and Generation of Logically Related Words for Historical Text Data using Reconstruction of Protowords

This article presents new methods for the study of language evolutions which helps researchers and experts. Initially, a method is used to determine if the words are cognate or not. A linguistic information algorithm is proposed to derive cognates from online dictionaries. Later, a dataset is created of similar terms and machine learning techniques are used to focus on spelling in order to classify the cognates. The aligned subsequences are used to identify standards and guidelines for language change in newly created languages mainly to distinguish between non-cognates and cognates which are used for classification algorithms. Next, discriminating cognates and debts give an insight into a language's history and allow a clearer understanding of the linguistic relationship. The task of reconstruction of protowords is to recreate words from its modern daughter languages in an ancient language. The method is based on the regularity of words and use knowledge from many modern languages to build an ensemble method for protoword reconstruction. This method is applied to multiple datasets to improve from the previous dataset accuracies.
Learning Low-Shot Generative Networks for Cross-Domain Data

We tackle a novel problem of learning generators for cross-domain data under a specific scenario of low-shot learning. Basically, given a source domain with sufficient amount of training data, we aim to transfer the knowledge of its generative process to another target domain, which not only has few data samples but also contains the domain shift with respect to the source domain. This problem has great potential in practical use and is different from the well-known image translation task, as the target-domain data can be generated without requiring any source-domain ones and the large data consumption for learning target-domain generator can be alleviated. Built upon a cross-domain dataset where (1) each of the low shots in the target domain has its correspondence in the source and (2) these two domains share the similar content information but different appearance, two approaches are proposed: a Latent-Disentanglement-Orientated model (LaDo) and a Generative-Hierarchy-Oriented (GenHo) model. Our LaDo and GenHo approaches address the problem from different perspectives, where the former relies on learning the disentangled representation composed of domain-invariant content features and domain-specific appearance ones; while the later decomposes the generative process of a generator into two parts for synthesizing the content and appearance sequentially. We perform extensive experiments under various settings of cross-domain data and show the efficacy of our models for generating target-domain data with the abundant content variance as in the source domain, which lead to the favourable performance in comparison to several baselines.
Cognitive Education is a Powerful Weapon of Learning

The article considers cognitive education, its types and technologies. Cognitive technologies allow not only influencing human behavior, but also to predict and analyze information.
Analysis on solid protection system of industrial control network security in intelligent factory

This paper focuses on the typical business scenario of intelligent factory, it includes the manufacturing process, carries out hierarchical security protection, forms a full coverage industrial control security protection network, completes multi-means industrial control security direct protection, at the same time, it utilizes big data analysis, dynamically analyzes the network security situation, completes security early warning, realizes indirect protection, and finally builds a self sensing and self-adjusting industrial network security protection system It provides a reliable reference for the development of intelligent manufacturing industry.
Feature Extraction Using Neural Networks for Vietnamese Text Classification

In this paper, a feature extraction method based on neural networks for Vietnamese text classification is developed. The Vietnamese online news documents are initially preprocessed to transform to lower-case documents as well as remove punctuation and stop-words. Then, the tokenization applying the combination of uni-gram and bi-gram models is conducted to generate a list of tokens for each document. As a result, polyphonic linguistic problems in Vietnamese language are diminished. The statistical term frequency - inverse document frequency model is employed to represent the lists of tokens as real value vectors. Instead of applying conventional feature selection algorithms, neural networks are conducted for dimensionality reduction. Therefore, not only the size of the term frequency - inverse document frequency vectors are reduced, but also distinctive feature vectors are created for text classification tasks. Support vector machines are utilized in the classification step. The results attained in the experiment have shown that our work using neural networks for the feature extraction outperforms other traditional methods.
DeepPear: Deep Pose Estimation and Action Recognition

Human action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a large-scale dataset for human action recognition.
Identifying environmental issues on the prehistoric societies in history learning

Abstract Internalizing environmental values requires a multidisciplinary approach. Therefore, almost all aspects have an interest in environmental problems, including history education. This paper aims to identify environmental issues that can be incorporated into history learning. One of the topics that are nearly related to ecological matters is the prehistoric topic. Here, people still have a high dependence on nature. As part of historical material, prehistoric times are very relevant to introduce environmental issues to students in the classroom. This paper uses an analysis of the historical education curriculum and textbooks used. This paper intends to find the relevance of history learning in the cultivation of environmental care among students. This paper will discuss strategies for internalizing the environmental values in historical learning, especially in prehistory discussion. This paper is expected to be able to contribute to how historical education plays a role in strengthening the character of caring for the environment.
Research on the New Capability Structure of University Accounting Talents under the Background of Artificial Intelligence and Blockchain

The widespread application of blockchain technology and artificial intelligence in the field of accounting will replace many manual tasks, which will have a great impact on the training of accounting professionals in colleges and universities, mainly focused on training skilled personnel. Therefore, it is of great significance to study the impact of blockchain and artificial intelligence on the accounting professionals in universities and their corresponding ability structure. Defining the application stage of artificial intelligence and blockchain in the field of finance and accounting from the perspective of technology implementation, we can clarify the functional characteristics of artificial intelligence and blockchain in this field; By analyzing the development process of artificial intelligence and blockchain in the field of finance and accounting with non-technical factors, it can be judged that it has an achievable development stage. And on this basis, the author put forward specific suggestions for the ability structure training of future accounting professionals.
GPU Accelerated Speech Recognition

Speech recognition technology is one of the quickly developing advanced technologies of engineering. It has various applications in different zones and offers potential points of interest. There is a correlation to pre-requirement for speech recognition and machine learning which is that grammar classification and a method for extraction phonemes from utterances are required. For Speech architecture, three models are utilized in speech recognition to do the preparing like a phonetic dictionary, acoustic model, language model. There are four stages of the recognition process: analysis, feature extraction, modeling and matching. Deficiency factors were brought forward which is the dataset threshold and feature extracting method. The higher dataset produces a lower word error rate (WER) which gives more accuracy in the recognition process.