Reviewerly/exHarmony huggingface dataset:
Experiments on Approaches of Virtualization for Industrial Internet of Things applications

New technologies that comes with recent trends like Internet of Things, Cloud and 5G are promoting new platforms and communication solutions. These trends have also started to impact traditional industrial automation systems, since end customers are starting to expect new services, such as, business intelligence and diagnostic information anywhere at any time. Moreover, the cloud providers lease their infrastructure to be used for deployment of applications using virtualization. Therefore, this paper aims at exploring the possibilities of different virtualization platforms offered by various cloud providers and benchmark the technologies with platforms frequently used within industry. The platform performance was evaluated by conducting experiments with an industrial application, focusing on typical industrial aspect such as latency, jitter and availability. In addition to more industrial focused metrics, the findings in comparison with other related experiments indicate that specific application requirements have an effect on performance. Hence, application specific evaluations may be necessary before taking any decision on where an industrial application may be deployed.
Recognition of Japanese historical text lines by an attention-based encoder-decoder and text line generation

Inspired by the recent successes of attention based encoder-decoder (AED) approach on image captioning, machine translation, we present an AED model as an end-to-end recognition system for recognizing Japanese historical documents. The recognition system has two main modules: a dense convolution neural network for extracting features, and a Long Shor Term Memory (LSTM) decoder integrating with attention model for generating target text. We can train the model end-to-end. The model requires only input text line images and corresponding output characters. Therefore, we don't need annotations for characters and save a lot of time for making annotations. We also present a method to generate artificial text lines to solve the imbalance problem of the current annotated database. The results of experiments on the annotated and artificial databases demonstrate the effectiveness of the text line generation. Our recognition system achieved Character Error Rate of 23.76% and 22.52% by training with and without artificial text lines, respectively. Moreover, our recognition system outperforms the CNN-LSTM system, which achieved the state-of-art results in other document recognition tasks.
Non-Mechanism Model for Superheater Pollution Diagnosis of Waste Incinerator Based on BP Neural Network

Abstract Dust accumulation of boiler heating surface is one of the common problems in power plants, which will affect the unit’s economic and safe operation in serious cases. In this paper, the factors that affect the ash deposition in the superheater of the garbage incinerator are analyzed, and a non-mechanism model for diagnosing the ash deposition pollution in the superheater of the garbage incinerator is established by using 3-layer BP neural network. Parameters such as garbage disposal amount, furnace temperature, primary air flow amount and secondary air flow amount are selected as input vectors, parameters such as secondary superheater inlet smoke temperature and primary superheater inlet temperature are taken as output values, and real-time data of units collected by a power plant DCS system are used as sample sets to train the network after normalization processing. The trained BP network has good effect in superheater pollution diagnosis.
An Improved Robust Adaptive Kalman Filtering Algorithm

Robust adaptive Kalman filter (RAKF) is a method that can not only compensate for the model errors but also resist the measurement outliers. The method reduces the influence of the measurement outliers on the filtering system by reducing the weight of the abnormal observation. After the processing of robust estimation, all the errors are attributed to the inaccuracy of the model. Since the robust estimation is not theoretically quantitative that it can only reduce the observation error to a certain extent, the remaining observation errors are compensated as the model errors, resulting in new estimation errors. To address this problem, a method for detecting the model error is proposed as a trigger condition for RAKF to compensate for the model error, and this constitutes an improved robust adaptive Kalman filter (IRAKF) algorithm. The new algorithm performs the model errors compensation struand then adjust the adaptive factor cter than the original method. The efficacy of the improved algorithm is demonstrated via a traffic running testing with GPS/INS tight integration navigation system. Simulation results indicate that the improved algorithm can effectively reduce the inaccurate adaptive procedure and obtain relatively accurate and stable filtering results.
An Open Source Cloud-Based NoSQL and NewSQL Database Benchmarking Platform for IoT Data

Internet of Things (IoT) is continually expanding, and the information being transmitted through IoT is often in large-scale in both volume and velocity. With its evolution, IoT raises new challenges such as throughput and scalability of software and database working with it. This is the reason that traditional techniques for data management and database operations cannot adopt the new challenges from IoT data. We need an efficient database system that can handle, store, and retrieve continuous, high-speed, and large-volume data, perform various database operations, and generate quick results. Recent developments of database technologies such as NoSQL and NewSQL database provides promising solutions to IoT. This paper proposes an extensible cloud-based open-source benchmarking framework on how these databases could work with IoT data. Using the framework, we compare the performances of VoltDB NewSQL and MongoDB NoSQL database systems on IoT data injection, transactional operations, and analytical operations.
Øresund Space Collective – Kybalion: A cyber-dimensional way to experience music

With the development of technology, 10,000 songs entered our pockets in the smart devices we carry. However, in the process we have lost the beauty of the connection between the album artwork and music. The use of mixed reality technologies carries the great the great potential to not only bring the magic of album artwork, but also provide new ways to experience both the music and the album art together.
Nationality and Geolocation-Based Profiling in the Dark(Web)

In this paper we are concerned with geolocating the anonymous crowds of Dark Web forums. We do not focus on single users, but on the crowd as a whole. We work in two directions: The first idea is to exploit the time of all posts in the Dark Web forums to build profiles of the visiting crowds and to match the crowd profiles to that of users from known regions. Then, we develop a new dataset to detect the native language of the crowds to support and integrate this match. We assess the effectiveness of our methodology on the standard web and two Dark Web forums with users of known origin, and apply it to three controversial anonymous Dark Web forums. We believe that this work helps the community better understand the Dark Web from a sociological point of view and supports the investigation of authorities when the security of citizens is at stake.
Robot-based 3-D machine vision using circular-feature normal estimation and multi-dimensional image fusion

Reconstruction of 3-D images from 2-D images is an essential problem in vision systems. Circular features represent one of the most common quadratic-curved features, which have been presented for 3-D reconstruction such as machined features in aerospace and electronics industries. The circular features may deviate from their nominal geometric properties due to potential machining errors. In this paper, a robot-based 3-D machine vision method is developed to perform the 3-D reconstruction of machined circular features with the goal of automatically inspecting and reconstructing 3-D objects using machine vision. An innovative algorithm is developed for estimating the normal vector of a circular machined feature on a free-form surface and reconstructing a 3-D surface profile with the machined circular feature using multi-dimensional data fusion. The surface between the projected 2-D circular feature and the identified 3-D surface profilometry is reconstructed via extrapolation by NURBS surface fitting. Some experimental results were successfully demonstrated that the proposed method is both accurate and effective.
Open Cognitive Control System Architecture

In recent years, the automation industry is moving towards Industry 4.0 and cyber-physical systems. There is an increasing demand for smarter plants and plant modules. In this paper, we are proposing an architecture for the open cognitive control system. This will provide the benefits of a control strategy based on the data analysis. With the possibility to connect with the cloud, this will enable easy updates and exchange of task modules that would ensure the system to keep up with the technological advancements. This will also improve the accuracy and lifetime of the automation system. To meet the requirements of flexibility, speed and reliability of the system, the architecture has been based on distributed and concurrent systems design. A detailed description of the architecture along with its challenges have been presented in this paper. A comparison of different implementation techniques for certain blocks has also been discussed.
Life-Changing Digital Education on Environmental Protection and LOHAS

With the rapid development of digital technology, people read in different ways; in particular, picture books can provide the necessary dynamic effects conducive to learning. In collaboration with the Taiwan Sustainable Union and teachers responsible for place-based curriculums, this research takes environmental education as the topic, which draws significant attention, and aims to develop and produce teaching materials about environmental conservation displayed in the form of creative, digital, and dynamic picture e-books. The content of the e-books mainly covers the living environments of "farmers", "frogs", "environmental conservation", and "pesticides". According to the research results, dynamic picture e-books can arouse students' reading interest and improve their attentiveness and understanding, which will facilitate a change of their reading attitudes. Dynamic images can further supplement the meanings of written words, facilitate reflection on environmental conservation, and deepen the understanding of such education, consequently forming wholesome and natural living environments that are free of pesticides. Teachers responsible for place-based curriculums have selected these dynamic picture e-books as teaching materials for local education in schools.
A Hybrid Chaotic Oscillatory Neural Network (HCONN) Based Financial Time Series Prediction System

Abstract Financial time series prediction is one of the most complex and challenging problems in both AI and finance engineering. In our research, we proposed a Hybrid Chaotic Oscillatory Neural Network (HCONN) model by replacing the traditional sigmoid-based activation function with chaotic oscillatory activation function, which provides significant performance in the global minimum convergence through the application of Adaptive Moment Estimation optimizer. In addition, by integrating the latest R&amp;D on Quantum Finance Theory (QFT) and its Quantum Price Level (QPL) as the deep features’ extraction, we add the daily 8 nearest QPLs together with the time series price variables as the input of our HCONN. In terms of system implementation, 12 different forex products including the AUDCHF, AUDUSD, CADCHF, EURAUD, EURCHF, EURGBP, EURUSD, GBPAUD, GBPCAD, GBPUSD, USDCAD and USDCHF are used. System performance results reveal that HCONN outperforms other financial models including: Feedforward Backpropagation Neural Network (FFBPN) and Chaotic Oscillatory Neural Network (CONN) in terms of training performance and forecast accuracy.
Computable invariants for curves and surfaces

During the last decade the trend in image analysis has been to shift from axiomatically derived measures into ones that are extracted empirically from data samples. The problem is that accurate geometric data are often unavailable and thus, for proper data augmentation, the research community has resorted yet again to axiomatic construction of invariant measures for curves and surfaces. For some geometric problems, such as shape classification and matching, it is appealing to adopt learning approaches due to their potential accuracy and computational efficiency. Nevertheless, even within the deep learning arena, geometric invariants offer a natural criterion for the learning process. Using geometric invariants one can overcome the need for annotated data and replace it by a purely geometric measure, leading to an unsupervised or a semisupervised learning schemes. Here, we review such constructions that are useful for the geometric analysis of visual information. The measures we explore include the construction of a scale or similarity invariant arc-length for curves and surfaces, an affine invariant one, resulting spectral geometries, and potential signatures that reflect the result of the discrepancies between the corresponding metric spaces. As an example we study novel signatures for surfaces known as the self functional map, which allow us to translate the problem of shape matching into that of computing distances between matrices.
Fisher Loss: A More Discriminative Feature Learning Method in Classification

Convolution neural networks(CNN) and softmax loss(cross entropy) function have been widely used in various classification tasks. It achieves better performance than MSE and other loss. However in most classification tasks, because of the high dimension, excessive noise of input samples, strong coupling of the extracted features, as well as the complicated output-input projection relationship, the features extracted by CNN have large intra-class variance and small inter-class distance, so different class of samples cannot be distinguished well, which will greatly reduce the accuracy of the classification. Aiming at these problems, we combine the Fisher discrimination criterion with the neural network and propose a more discriminative feature learning algorithm, fisher loss. In the experiment, the proposed algorithm is compared with the center loss, island loss, and other algorithms on MNIST datasets with two commonly used network architecture. It is found that our algorithm can better distinguish features and achieve higher classification accuracy.
A Method of Bad Data Identification Based on Improved GSA Algorithm

Accurate and reliable data is very important to the power system. In this paper, based on the analysis of GSA (Gap Statistic Algorithm) data mining technology, an improved clustering method is proposed for the shortcomings of the traditional method. The GSA method is an algorithm for determining the most appropriate number of clusters. The raw data is processed by BP neural network, and the preprocessed results are clustered by an improved clustering algorithm. Then use the elbow criterion to judge the optimal number of clusters. Compared with the original algorithm, it can overcome the defects in calculation speed and recognition accuracy, determine the number of clusters faster, greatly shorten the running time, and successfully identify the bad data in the power system.
The Effects of Emotionally Sound Web-Based Instruction on Performance, Engagement and Retention

This study was designed to investigate the effectiveness of emotionally sound web-based instruction on performance, engagement and retention in learning English grammar. The sample consisted of Form Two students from a secondary school in the Northern region of the Peninsular Malaysia. The data were collected using Post-Test 1, Retention Questionnaire (Post-Test 2) and E-Learning Engagement Instrument (ELEI). The research design for this study is Quasi-experimental with pre-test and post-test design. The data was analyzed by using an independent sample t-test and ANCOVA. On balance, results indicate that the emotionally sound web-based instruction have positive impact on learning. The results revealed that students in WBIe performed better than students in WBIwe based on their academic performance and retention level in English grammar. In terms engagement level, students in both WBIe and WBIwe have a similar score.
Theory of particle swarm optimization: A survey of the power of the swarm’s potential

Abstract This paper presents a survey on different showcases for potential measures on particle swarm optimization (PSO). First, a potential is analyzed to prove convergence to non-optimal points. Second, one can apply a minor modification to PSO to prevent convergence to non-optimal points by using an easy potential measure. Finally, analyzing this potential measure yields a reliable stopping criterion for the modified PSO.
Multimedia Interactive Application for Introduction to Information Technology Course in STMIK STIKOM Bali

The Introduction to Information Technology course in STMIK STIKOM Bali basically uses the teacher-centered teaching method, where the teacher is the main role in the class and the students listen and focus to the teacher. Some of the classroom activities are speech, classroom discussion, demonstration, and experimentation. This type of teaching method most of the time can be boring for the students. The students need additional media that is more enjoyable for learning. A multimedia interactive application is one of the answers, while it offers not only enjoyment but also flexibility. With this application, the students can play it anywhere and anytime as they wish, with embedded educational games inside, that they also can play. This research is of type Education Research and Development (R&D), where the model is developed and adapted based on Borg&Gall, Allessi&Trollip, and Dick&Carey model. There are four important stages that are followed while developing the application: (1) preliminary study, (2) product design, (3) product development, and (4) product evaluation. The evaluation stage then resulting a conclusion that the developed application software is worth to be used as a learning media for the students in STMIK STIKOM Bali. Moreover, the difference of effect size in pre-test and post-test is 22, which means that the application software is very effective in increasing the learning outcome of the students.
Finite Difference Method Based Super-Diffusive Model for Benign Brain Tumor Segmentation

Brain tumor segmentation in early stage or benign stage is a complex and challenging task. It involves the process of identifying early stage brain tumor cells among normal healthy cells which have approximately similar characteristics or low variation to its surrounding tissues. Many methods have been proposed for benign brain tumor segmentation but most of the methods fails to detect such low variation data. In the present manuscript, fractional model for benign brain tumor segmentation is proposed. The model has higher sensitivity towards low differential data due to arbitrary order of derivative. This arbitrary order of derivative has been exploited to segment brain tumor in early stage. The results obtained have been compared with existing popular methods and it has been found that proposed method is superior among them in brain tumor segmentation in early stage.
Using Graph Embedding to Improve Requirements Traceability Recovery

Information retrieval (IR) is widely used in automatically requirements traceability recovery. Corresponding approaches are built based on textual similarity, that is, the higher the similarity, the higher possibility of artifacts related. A common work of many IR-based techniques is to remove false positive links in the candidate links to achieve higher accuracy. In fact, traceability links can be recovered by different kinds of information, not only the textual information. In our study, we propose to recover more traceability links by exploring both textual features and structural information. Specifically, we use combined IR techniques to process the textual information of the software artifacts, and extract the structural information from the source code, establishing corresponding code relationship graphs. We then incorporate such structural information into the traceability recovery analysis by using graph embedding. The results show that combined IR techniques and using graph embedding technology to process structural information can improve the recovery traceability.
Atmosphere channel for “last mile problem” in quantum communication

The work is devoted to the creation of a telescopic transceiver system that organizes an atmospheric point-to-point communication channel, and its use for quantum communication at sideband frequencies as the “last mile” for data protection in a geographically distributed data centre
Evaluating an Adaptive Web Traffic Routing Method for the Cloud

The low maintenance requirement, capacity scalability, and pay-as-you-go properties of cloud computing are attractive for the virtualized deployment of diverse web services. Web traffic is typically handled by multiple server mirrors that are spatially dispersed to satisfy the expectations of a large number of worldwide users. Since the energy consumption of each server depends on its workload, the use of web routing opens the possibility of reducing operational costs through the exploitation of the regional and temporal differences in energy pricing at the mirroring sites. On the downside, the shared nature of the cloud and the network brings potential latency issues that could impact the quality of service of many applications. In this paper, we report on experimental results obtained from a web service system that uses learning automata, a reinforcement learning approach to make dynamic routing decisions based on a cost and quality-of-service criteria in the cloud. The experiments were conducted using a network of 24 nodes running in the CloudLab with time-varying energy prices that were modeled from real data.
Efficient Binocular Stereo Matching Based on Sad and Improved Census Transformation

Binocular stereo matching aims to obtain disparities from two very close views. Existing stereo matching methods may cause false matching when there are much image noise and disparity discontinuities. This paper proposes a novel binocular stereo matching algorithm based on SAD and improved Census transformation. We first perform improved Census transformation, and then we get the matching costs by combining SAD and improved Census transformation. Finally we cluster the matching costs and calculate the disparities. To generate better disparities, we further propose the improved bilateral and selective filters to enhance the accuracy of disparities. Experimental results show that our binocular stereo matching can produce more accurate and complete disparities, and it works well in complex scenes with irregular shapes and more objects, thus it has wide applications in stereoscopic image processing.