Reviewerly/exHarmony huggingface dataset:
Learning Similarity-Preserving Meta-Embedding for Text Mining

Publicly available pre-trained word embeddings are rich sources for turning critical high-dimensional representations of huge text data repositories into meaningful compact vectors essential for text mining applications. With many of such pre-trained embedding sources available, each faces limitations in the appropriateness of their language use for the downstream text-mining tasks. Meta-embeddings aim to tackle this ambiguity challenge by fusing multiple embedding sources into one feature space. However, current meta-embedding methods assume vocabularies across sources are similar or even identical; which unfortunately stands in sharp contrast to the fact that many sources barely overlap. Further, these methods encode a meta-embedding for each word by reconstructing its actual embedding values (word-encoder), while valuable information of relationships (distances) among words within each source are not directly considered. In this work, we instead propose a novel relation-encoder learning approach called Similarity-Preserving Meta-Embedding (SimME) that directly integrates word-pair relationships from partially overlapping embedding sources. SimME embeds words such that their similarities are learned from those observed in multiple pre-trained sources. To handle relations between words that are not present in all sources, we introduce maskout, a new loss term, that steers the learning selectively to the sources containing said relations. SimME consistently outperforms state-of-the-art methods by 10% on average and with up to 20% across several core metrics in 4 popular mining tasks on 23 datasets.
Automatic Punctuation Restoration for corpus in Traditional Chinese Language using Deep Learning

The Automatic Speech Recognition (ASR) technique has already been applied to several chat apps, allowing people to orally input messages instead of typing words by hand. Meanwhile, ASR techniques have also been used in the transcription of meeting minutes from audio records. However, there exist two main reasons such that ASR systems are not suitable for some formal situations: wrong words caused by erroneous recognition and lacking punctuation marks, which degrade the readability and might express wrong meaning. In our work, we expect to set up a model to automatically restore punctuation marks for the corpus generated by ASR systems; however, since lacking such labeled data for our ASR corpus, we train and test our model totally on the corresponding transcript data. This research focuses on automatic punctuation restoration for traditional Chinese language corpus using neural network model. Our results show that the bidirectional Gated Recurrent Unit (GRU) with attention mechanism outperforms other models on our punctuation restoration task when the amount of the training data is limited.
Raven paradox: problem and solution given on the basis of Aristotle’s logic

The paper is devoted to the solution of two well-known paradoxes of inductive logic: Hempel’s and Goodman’s, which the science has not solved unambiguously yet. The results of this research can be used in any natural science, but they are especially relevant for areas where there is an emphasis placed on environmental friendliness and sustainable development. The central problem of this research is the problem of limits of applying classical logic laws. The problem is solved by method of reduction of logical laws to those cases where they, according to Aristotle, act faultlessly, and refusal of their recognition in the cases where their action is questionable. The aim of the paper is to demonstrate the solution of both problems within Aristotle’s logic. In that regard, the following results are received: common faults of previous solutions of Hempel’s paradox, consisting in ignoring any of its parties, are revealed; the original nature of Goodman’s paradox, consisting in wrong interpreting “inductive confirmation” criteria is opened; two methods of forming and assessing the subject volumes of statements are revealed: analytical and synthetical ones; it is proved that the theses treated in Hempel’s paradox as equivalent ones are not always so, but only on condition of their subjects’ reality and of their subject volumes’ identity; it is established that the conditions of the statement equivalence correspond to the limits of applying logic laws in Aristotle’s interpretation.
Cybertraining – A Development by Using a Holonic Control Structure

School reform is process that develops from within, but under the pressure of external influences. Externally, social demands and the increasing pressure of technologies and research that find a field of application in education are acting. From within can act the desire for modernization, the accumulated competence. The article combines the two forms of pressure in the form of solutions already implemented in the CyberTrainer™service ( www.cybertrainer.online ), a professional service to assist learning processes. It is justified, for the beginning, the need to integrate information technology in the school concept. Subsequently, the set of requirements of the specification is built and the details of developing and implementation of the solution are provided. The chosen path is to treat the school as a second-order cyber system and to build a holonic control system. In this system, students and instructor are holons. The main features provided by the control system are the student’s autonomy and the high degree of cooperation during the learning process. The effects of applying the solution indicate a sketch of the change of school idea: the teacher becomes an instructor, the student becomes more responsible and involved in his work, and the school activity more and more efficient and adaptable.
A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule

A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.
Developing Community Information Group Website to Improve Digital Literacy

Abstract Community Information Group is a public service institution as a community information center at Biting Ponorogo. Dissemination of information to the public is often an obstacle due to limited communication media. Then, word of mouth is often inaccurate, causing miscommunication. This research aims to develop a website as a community information center. As well as being a communication and information medium, this website development also aims to improve community digital literacy. This research used the System Development Life Cycle method. This study succeeded in developing a website for the Biting Community Information Group. The feedback from Biting Community Information Group peoples expected that these websites could be developed as a medium of communication and information for the community. This website can be accessed via a search engine (https://kimbiting.com/). The next research can measure the effectiveness of the Biting Community Information Group website as the communication media. Besides, future research also can develop another communication medium based on information and communication technology.
An Image Dehazing Algorithm Based on Adaptive Correction and Red Dark Channel Prior

The dark channel prior defogging algorithm is one of the most representative image defogging algorithms. However, the restored images have problems such as halo effect, color distortion and so on. In view of the above problems, we proposes a method based on the red channel prior algorithm to estimate the transmission maps of different color channels. After defogging, it will reduce the brightness and contrast of the image, resulting in the loss of detail information. Therefore, this paper uses gamma function on the defogged image to performs color compensation and optimazes the detailed information of the image. The algorithm proposed in this article is used in the contrast experiment with other defogging algorithms, and the experimental results are evaluated objectively based on the information entropy and other parameters. Through experimental comparison, our algorithm compared to the dark channel prior defogging algorithm to obtain a clearer image defogging image, and the subjective vision is more consistent with the real scene.
Discussion on Teacher-student Factors of Influencing Factors of Sergeant Vocational and Technical Education

As individual participants at both ends of teaching activities, the subjective factors of teachers and students play a very important role in the development of Sergeant vocational and technical education. Effectively solving the problems existing in both teaching activities and seizing the opportunity for the development of college education reform is one of the current priorities of Sergeant Vocational and Technical Education institutions.
Common Methods of Image Panoptic Segmentation Based on Deep Learning

In recent years, with the rapid development of deep learning technology and its wide application in the field of computer vision, various image understanding tasks including semantic segmentation and instance segmentation have made great progress, and people's further demand for image understanding has spawned a more comprehensive task image panoptic segmentation. Image panoptic segmentation can be seen as the combination of semantic segmentation and instance segmentation. For uncountable object categories (called stuff), the pixel category is distinguished. For countable object categories (called things), not only the semantic category of the target is recognized, but also each instance is distinguished. This task can provide more comprehensive scene information, and can be widely used in the understanding of various natural scenes. This paper investigate the commonly used panoptic segmentation methods, including the basic shared feature extraction method, the information combination method between semantic segmentation and instance segmentation sub-tasks, and the learnable method to remove the overlap between instances. This paper also summarize the commonly used panoptic segmentation datasets and the evaluation metrics, then the experimental performance evaluation results of various methods on commonly used datasets are showed. Finally, this paper summarize the general direction of panoptic segmentation, and predict the future research direction.
Stereoscopic Dataset from A Video Game: Detecting Converged Axes and Perspective Distortions in S3D Videos

This paper presents a method for generating stereoscopic or multi-angle video frames using a computer game (Grand Theft Auto V). We developed a mod that captures synthetic frames allows us to create geometric distortions like those that occur in a real video. These distortions are the main cause of viewer discomfort when watching 3D movies. Datasets generated in this way can aid in solving problems related to machine-learning-based assessment of stereoscopic- or multi-angle-video quality. We trained a convolutional neural network to evaluate perspective distortions and converged camera axes in stereoscopic video, then tested it on real 3D movies. The neural network discovered multiple examples of these distortions.
Image Enhancement by Recursive Correction of its Average Brightness

This article discusses improving the efficiency of image and video enhancements in real-time apps. The aim of this work is to increase the efficiency of improving image quality in automatic mode by transforming image intensity using adaptive piecewise-linear stretching. To this end, we propose two new approaches to adaptive transforming image intensity, which are based on the recursive mean-separate contrast stretching (RMSCS) and the recursive correction of average image brightness (RCAB). These approaches are based on the assumption that the best for visual perception is the normalized image in which the average brightness is equal to the middle of the brightness scale. The RMSCS and RCAB techniques provide a more effective improvement in the image quality, a more even distribution of contrast of objects in the image, increasing its overall contrast, and reducing the risk of distortions and artifacts. The RMSCS and RCAB techniques work in a fully automatic mode, are efficient, easy to implement, computationally low-cost, and are designed to normalize and enhance video in real-time apps.
An Empirical Study on Smart Steel Stockyard Management System through Character Recognition on Steel Materials using CNN

Abstract In shipyards wherein large ships and plants are constructed, the timely supply of steel is a critical factor that can decrease the production costs and shorten the construction period. However, the standard size of the steel materials utilized in shipyards exceeds at least 10m, and the weight of the steel is heavy; therefore, it is difficult to readjust the steel once it is deployed. Moreover, if any part of the production plan is changed, the steel that has been deployed according to the existing plan must be relocated. However, when cumbersome, heavy steel is vertically stacked in several layers, it is difficult for a shipyard worker to directly identify the desired steel’s location. Therefore, it is not easy to spot the location of the steel to be found. In cases such as this, one attempts to recognize characters on the steel’s surface using OCR, but it is difficult to apply this to an actual system as the recognition rate is only about 50 to 70% on average. Therefore, through this study, we intend to develop a model that recognizes characters printed on the steel’s surface using CNN and to check whether it can be employed in a smart steel stockyard management system.
A New Noise Generating Method Based on Gaussian Sampling for Privacy Preservation

Centralised machine learning brings in side effect pertaining to privacy preservation, most of machine learning methods prone to using the frameworks without privacy protection, as current methods for privacy preservation will slow down model training and testing. In order to resolve this problem, we develop a new noise generating method based on information entropy by using differential privacy for betterment the privacy protection which owns the architecture of federated machine learning. Our experiments unveil that this solution effectively preserves privacy in the vein of centralized federated learning. The gained accuracy is promising which has a room to be uplifted.
Quantum Fisher Information of Two-Level Atomic System under the Influence of Thermal Field, Intrinsic Decoherence, Stark Effect and Kerr-Like Medium

In this paper, we have proposed the numerical calculations to study the quantum entanglement (QE) of moving two-level atom interacting with a coherent and the thermal field influenced by intrinsic decoherence (ID), Kerr medium (non-linear) and the Stark effect.The wave function of the complete system interacting with a coherent and the thermal field is calculated numerically affected by ID, Kerr (non-linear) and Stark effects.It has been seen that the Stark, Kerr, ID and the thermal environment have a significant effect during the time evolution of the quantum system.Quantum Fisher information (QFI) and QE decrease as the value of the ID parameter is increased in the thermal field without the atomic movement.It is seen that QFI and von Neumann entropy (VNE) show an opposite and periodic response in the presence of atomic motion.The non-linear Kerr medium has a more prominent and significant effect on the QE as the value of the Kerr parameter is decreased.At smaller values of the non-linear Kerr parameter, the VNE increases, however, QFI decreases, so QFI and VNE have a monotonic connection with one another.As the value of the Kerr parameter is increased, the effect of non-linear Kerr doesn't stay critical on both QFI and QE.However, a periodic response of QE is seen because of the atomic movement which becomes modest under natural impacts.Moreover, it has been seen that QFI and QE rot soon at the smaller values of the Stark parameter.However, as the value of the Stark parameter is increased, the QFI and QE show periodic response even when the atomic movement is absent.
Ultralow-power instant-on photon-pair counting and photon-entanglement analysis

Abstract The latest breakthroughs in quantum technologies, such as satellite quantum communications, present new challenges, imposing stringent restrictions on weight, size, and power consumption of quantum information systems. Here, we show that nonlinear and quantum optics provides powerful resources to confront these challenges by offering attractive solutions for photon-pair counting and quantum-entanglement detection. We demonstrate a low-cost, readily miniaturizable photon-pair counting module, which consumes less than 100 μ Ah during a sub-10 ms power-on/off measurement cycle, thus providing a meaningful performance as a promising component for satellite quantum technologies.
Closed Itemset based Sensitive Pattern Hiding for Improved Data Utility and Scalability

Frequent itemset mining is used to extract interesting associations and correlations between the itemsets present in transactional datasets. The frequently appearing patterns are used for various business decision making policies, for instance to increase co-purchase of products, price optimization, cross promotion etc. However, there are some sensitive patterns present in datasets that can reveal individual or organisation's specific confidential information that they would not prefer to be known since it can cause them huge social and monetary loss. Privacy Preserving Data Mining (PPDM) approaches are used to hide these sensitive patterns with maintaining the utility of the data. Heuristics-based PPDM approaches are widely adopted sensitive pattern hiding approaches due to their simplicity and lesser computational time as compared to the border-based and exact approaches. However, these approaches causes high side effects concerning the quality of datasets. In this paper, two heuristics-based algorithms, Removal of Closed Sensitive Itemsets with Maximum Support (MaxRCSI) and Removal of Closed Sensitive Itemsets with Minimum Support (MinRCSI), are proposed. In these algorithms, data sanitization is performed over closed sensitive itemsets to improve the utility of sanitized data. The proposed algorithms are parallelized on Spark parallel computing framework to deal with the massive amount of data i.e. big data. Experiments performed on real and synthetic datasets show that the proposed algorithms preserve the privacy of datasets with substantially better utility as compared to the traditional algorithms with less execution time.
Random Subspace-Based Hybridized SVM Classification for High-Dimensional Data

In the modern world of significant advances in technology along with the need to handle massive amount of data in the field of computation biology, microarray gene expression analysis has posed significant challenges to the state-of-the-art data mining techniques. These types of data generally deal with a large number of dimensions and are known as high-dimensional data. Classification is one of the widely used data mining techniques which is being used in diverse range of applications like credit card fraud detection, recognizing cancerous cell, in retail sector, etc. The high dimensions have rendered many existing classification techniques impractical. From the information collected by extensive studies, it is found that highest number of misclassified objects lie beside the hyperplane by which the classes are separated. We have proposed an efficient SVM classifier which hybridizes outlier detection method with SVM, and random forest classifier is used as an auxiliary classifier. This approach significantly improves the accuracy of the existing SVM classifier.
Android-Based National University Logistic Information System Using Sdlc Model Waterfall Method: Android-Based National University Logistic Information System Using Sdlc Model Waterfall Method

The utilization of computerized information systems is very important, especially in higher education. The existence of a computerized information system makes a process of processing important data that can be arranged and stored properly and makes it easier to find data when needed. Included in the logistics department of the National University which still needs the support of a system that is able to facilitate the processing of data entry and exit of goods contained in the National University logistics warehouse. The purpose of this research is to be able to alleviate the work on logistics in terms of streamlining the use of time, energy, and costs incurred in recording and making logistical reports. Then an android-based logistics information system will be built using the information system approach method used in making and developing applications, namely the system development life cycle (SDLC) method using the waterfall model. The construction of this system was made using sublime text 3 and MySQL as its database. This system will be able to manage the data of incoming and outgoing goods and also perform data searches on a mobile basis using an Android smartphone. With the creation of a logistics information system at the Android-based National University, it is hoped that it can help and speed up the work in processing logistical data that will be used as material for making reports.
Language Identification using Stacked Convolutional Neural Network (SCNN)

The Automatic Speech Reorganization (ASR) system has a wide range of industrial and practical applications. The Language Identification (LID) is the first step of the ASR and utilized in various applications. LID system is used to classify the spoken language from an audio signal. The automatic language detection provides correctly parsed speech utterance and grammar rules to the speech recognition process. In this work, we proposed an image domain LID system instead of an audio domain. A Stacked Convolutional Neural Network (SCNN) is used on the extracted spectrogram images of an audio signal. The features are extracted from the image domain signal and classified based on their sampling rate and frequency. The feature extraction and language detection are performed by the SCNN method. The performance evaluation of the proposed method is done based on accuracy, precision, and recall parameters. The language identified by SCNN for the EU speech dataset is more accurate than the CNN-RNN approach. The SCNN based LID model can be used for the large scale dataset and implemented on the long uttered audio signal.