Reviewerly/exHarmony huggingface dataset:
Reform of University Physics Experiment Course under the Combination of Big Data and Industrial Internet

Abstract This paper puts forward the combination of big data and university physics experiment teaching based on the combination of big data and industrial internet, and tries to get rid of the traditional teaching mode of university physics experiment and develop innovative experiment teaching, focusing on the application of computer data acquisition and data analysis in the process of university physics experiment teaching. Let our students, like scientists and engineers, open up the relevant concepts in reality in their study, reduce the chance of gross errors in the experiment process, use cloud computing to achieve real-time data feedback, improve the understanding of concepts, increase students’interaction, and promote students’ thinking, thus becoming active participants, allowing students to spend more time in analysis and development. High-level thinking skills.
Facial Recognition with Open Cv

In this digital era, the wide-range implementation of digital authentication techniques has not only impacted the home security systems but also in difference facets of industries. The ever-growing facial recognition technology is now dominated by the digital authentication system with a steady increase in the sophistication of the real time image processing operations, which is more accurate and reliable. The system is more secured by implementing facial recognition as a primary security feature for observing a person in a 3D perspective. In recent times, digital authentication system aims to recognize individuals, payment confirmation, criminal identification etc. In this research work, we have worked with a type of facial recognition system, which is based on “Open-CV” model. Open-CV is a library that is built by intel in 1999. It is a cross platform library, primarily built to work on real time image processing systems, which includes the state-of-the-art computer vision algorithms. The system that we have built can achieve an accuracy of about 85–90% accuracy based on the lighting condition and camera resolution. Based on the database, this system is built by taking still images of the subject, then it trains a model and creates deep neural networks and then for detection it matches the subject in real time and give results based on the match. The system that we have built is quite simple, robust and easy to implement, where it can be used in multiple places for a vast application.
STEM: Secure Token Exchange Mechanisms

With the flooding of a large variety of isolated blockchain solutions into the technological world, one major challenge is to enable efficient interoperable interchain and intrachain exchanges. The dearth of inter-operating among these eclectic tokens is hindering the profits that can be earned by potential investors. The myriads of tokens that are flooding into the blockchain ecosystem need to interoperate amongst each other. This paper proposes a mechanism to provide better atomic intrachain token swaps. Our blockchain solution can assist the exchange of these eclectic heterogeneous tokens securely, using digital signatures and hashed time lock contracts, which reduces the problem of interoperability. This paper presents a solution which is token standard agnostic and provides effective intrinsic smart contracts facilitating token exchange and thus reducing the counterparty risk.
A Method to Estimate Eye Gaze Direction When Wearing Glasses

Eye gaze input system has been used for severe physical disabilities such as ALS patients who are difficult to communicate with others. In conventional eye gaze input systems under natural light, the condition that human wears glasses has not been into account. In this study, we propose a method to estimate eye gaze direction of human who wears glasses. In the method, image processing is conducted as follows; the parts of glasses in eye image after thresholding are identified by conducting labeling processing, and removed them to detect inner corner of the eye, outer corner of the eye, and part of black eye. A three layer neural network is composed to estimate eye gaze direction. In the neural network, the input signals are position coordinates of inner corner of the eye, outer corner of the eye, and center of black eye, and the output signals are the eye gaze directions. To examine the effectiveness of the proposed method, experiments in which human subjects follow a moving target on the screen by eye movement are conducted for both conditions of wearing glasses and without glasses. As the results, it is confirmed that the accuracy of the proposed method for the condition wearing glasses is as good as that without glasses, which shows the effectiveness of the proposed method.
Vietnamese Punctuation Prediction Using Deep Neural Networks

Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly available at https://github.com/BinhMisfit/vietnamese-punctuation-prediction .
Visual Regulation of Differential-Drive Mobile Robots: A Nonadaptive Switching Approach

This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.
Survey of Hardware and Software Design Approaches for Mechatronic Chordophones

Abstract Mechatronic instruments can create complex sounds that computers and digital instruments have yet to offer. As a specific example, we explore mechatronic chordophones, which use strings as a sound generator and offer a wide array of parametric affordances. The purpose of this work is to review recent approaches for chordophone design and construction, and present current, state-of-the-art devices. We explore multiple mechatronic frames and their components, and we analyze the challenges of building and interacting with these machines. Finally, we review software and hardware approaches that have been implemented to enhance these instruments and to bridge technical and creative restrictions.
Multi-Objective Location of Fresh Food E-Commerce Distribution Network Based on Improved NSGA-II Algorithm

To optimize the distribution network location of fresh food e-commerce, based on the traditional location model, customer satisfaction, and the loss function of fresh goods were defined, the multi-objective location model of the fresh food e-commerce distribution network was established to get the moderate balance between customer satisfaction and the cost of the distribution. Combined with multi-population optimization algorithms, an improved Non-dominated sorting genetic algorithm-II (NSGA-II) was proposed. Multiple populations are introduced to search at the same time, and an elite population is formed to co-evolution, improving the population diversity and the search performance of global search and local search. The simulation results show that the improved NSGA-II algorithm can effectively obtain the Pareto optimal solution of the model and provide a comprehensive decision for the distribution network location of the fresh food e-commerce enterprises.
Toward a Distinguishing Approach for Improving the Apriori Algorithm

Association rule mining, one of the most important branches of data mining, which focused on detecting frequent patterns of itemsets. Apriori is the first algorithm proposed for association rule mining. This algorithm has the best response and can detect all frequent itemsets from transaction databases. Apriori is of time complexity order two to the power n at worst case, n is the number of items in the database. At each step, the database is scanned to detect frequent itemsets. As a result, this algorithm has a very large response time for large databases. There are two ways to reduce the response time of this algorithm. First, prune the itemsets which candidate for checking. Second, reduce the dimension of the database. We used the second solution and reduce the dimension of the database considering that if a set is frequent, all of its subsets are frequent with more frequencies in the database. In the proposed algorithm, database scanned one time, and then frequent itemsets are detected by the reduced database. Our algorithm improved an apriori response time. To evaluate the algorithm, precision and recall measures have been used. According to the experimental in most cases, the algorithm can provide precision and recall above ninety percent.
SF-KCCA: Sample Factoring Induced Kernel Canonical Correlation Analysis

The Canonical Correlation analysis (CCA), such as linear CCA and Kernel Canonical Correlation Analysis (KCCA) are efficient methods for dimensionality reduction (DR). In this paper, a method of sample factoring induced KCCA is proposed. Different from traditional KCCA method, sample factors are introduced to impose penalties on the sample spaces to suppress the effect of corrupt data samples. By using a sample factoring strategies: cosine similarity metrics, the relationships between data samples and the principal projections are iteratively learned in order to obtain better correlation projections. By this way, the authentic and corrupt data samples can be discriminated and the impact of the corrupt data samples can be suppressed. Extensive experiments conducted on face image datasets, such as Yale, AR, show our approach has better classification and DR performance than that of linear CCA and KCCA, especially in noisy datasets.
An improved merging method for Gaussian mixture probability hypothesis density filter

The random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) filter is a promising and efficient suboptimal approximation for the multi-target Bayes filter. However, the GM-PHD filter is unable to track nearby targets caused by the improper position distribution of target-originated measurements. Aiming at the problem, a multi-target GM-PHD filter with an improved component merging method is proposed. Based on a proposed adaptive threshold-based component similarity measure scheme, the improved component merging method is able to avoid incorrect fusion of the components of targets in close proximity and optimize the target components within the target posterior intensity. Experimental results illustrate that the proposed algorithm not only can achieve better estimation accuracy in terms of the target states and its number but also has high computation efficiency when compared against the related GM-PHD-based filters.
The Application of Improved Hybrid Particle Swarm Optimization Algorithm in Job Shop Scheduling Problem

An improved hybrid particle swarm optimization algorithm based on adaptive inertia weight is proposed for job shop scheduling problem(JSSP). In order to overcome the shortcomings of the standard particle swarm optimization algorithm(PSO) in the optimization process, such as slow iteration speed, low precision and easy to fall into local optimal, simulated annealing algorithm(SA) and PSO are combined(SAPSO); In order to balance the global search ability and local search ability of particles, the adaptive weight of each particle is dynamically adjusted in this paper. The improved hybrid algorithm utilizes the probabilistic mutation capability of SA, which can accept both the good solution and the bad solution with a certain probability when accepting the new solution, and can get rid of the local optimal solution of the algorithm, which not only improves the flexibility and diversity of the algorithm, but also improves the diversity of particles. Adding adaptive weight to improve inertia weight in PSO makes it possible to dynamically adjust parameter factors according to fitness. Finally, the feasibility and efficiency of the improved algorithm are verified by the standard examples of JSSP.
Probability κ-means Clustering for Neural Network Architecture

Cluster analysis indices are aimed at classifying the elements used to estimate the quality of the categories based on their similarity. It is a challenging task because, with the same data set, there may be many partitions that fit natural groupings of a given data set. Applications of clustering can include pattern recognition, image analysis and information retrieval. We propose an approach based on the κ-means concept that clustering centers more often have a higher density than their neighbors: then we use a probability κ-means algorithm to achieve fuzzy clustering in continuous form over a relatively large distance from other points with higher densities. Further, in order to follow the mainstream neural network architecture, we define a favorable activation function and corresponding loss function for the clustering iteration. Our method come from the basis of a clustering procedure in which the number of clusters arises intuitively and clusters are achieved regardless of the high dimensions. We demonstrate the result of complete algorithm on several clustering test cases.
A Neural Network Based Text Classification with Attention Mechanism

Text classification is a basic task in Natural Language Processing field. As neural networks gains great breakthroughs in computer vision and speech recognition, neural networks based model, such as convolutional neural networks and recurrent neural networks, is also proved to be powerful in many Natural Language Processing tasks compared to traditional approaches. Based on recurrent neural networks with attention mechanism, a new model incorporated enhanced text representation by means of convolutional neural networks is proposed to deal with text classification task. The experimental results show that the proposed model gains higher accuracy compared to the common attention based recurrent neural networks model.
A Response to Rosalind Barber’s Critique of the Word Adjacency Method for Authorship Attribution

The Word Adjacency Network (WAN) method of authorship attribution was introduced in a series of papers by the present authors between 2015 and 2018. ANQ has published two critiques of the method (B...
SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation

Gender classification is a demographic attribute that found an increasing amount of applications particularly in human-computer interaction, security access control and biometrics. The purpose of this paper is to investigate the feasibility of using time series for gender classification. After transforming input depth images of faces into one-dimensional vector using Peano-Hilbert space filling curve, a dimensionality reduction step is performed to convert this vector to a sequence of alphabets using Symbolic Aggregate approXimation (SAX). With FRGCv2 dataset, standard 10-fold cross-validation experiments are performed using quadratic kernel for SVM classifier. The results have shown that the proposed approach can reach a correct classification rate higher than 96%.
Athena

Scholarly search systems greatly aid the deep understanding of scholarly data and facilitate the research activities of scholars for scientific studies. Though a number of such systems have been developed, most of them either support rankings of limited search of entities or provide only basic ranking metrics. These existing systems also mainly adopt RDBMSs as their storage such that the linked feature of scholarly data is not fully exploited. In this study, we design and develop a novel scholarly search system Athena. (1) It supports four types of scholarly entity searches: articles, authors, venues and affiliations, and is equipped with five ranking metrics, including three traditional metrics and two comprehensive importance ranking metrics. (2) It also provides profiling of scholarly entities. (3) It further utilizes a graph storage to directly leverage the linked feature for speeding up the processing of complex queries. We demonstrate the advantages of Athena at scholarly search, profiling, graph storage and ranking quality.
Simulating the Evolution of Ancient Fortified Cities

Abstract Ancient cities and castles are ubiquitous cultural heritage structures all over Europe, and countless digital creations (e.g. movies and games) use them for storytelling. However, they got little or no attention in the computer graphics literature. This paper aims to close the gap between historical and geometrical modelling, by presenting a framework that allows the forward and inverse design of ancient city (e.g. castles and walled cities) evolution along history. The main component is an interactive loop that cycles over a number of years simulating the evolution of a city. The user can define events, such as battles, city growth, wall creations or expansions, or any other historical event. Firstly, cities (or castles) and their walls are created, and, later on, expanded to encompass civil or strategic facilities to protect. In our framework, battle simulations are used to detect weaknesses and strengthen them, evolving to accommodate to developments in offensive weaponry. We conducted both forward and inverse design tests on three different scenarios: the city of Carcassone (France), the city of Gerunda (Spain) and the Ciutadella in ancient Barcelona. All the results have been validated by historians who helped fine‐tune the different parameters involved in the simulations.
Analysis of the Thematic Overlap Between Library and Information Science and Other Subdisciplines of the Social Communication and Media Sciences in Poland

Purpose/Thesis: The recent decision to join three previously separate disciplines – library and information science, media studies, and cognition and social communication science, into a single discipline of social communication and media sciences prompted the author to investigate if joining of these disciplines according to the compulsory categorization published by the OECD, is supported by an overlap in their fields of research, or by a similarity in their methods of conducting it.Approach/Methods: An analysis of the review articles devoted to the research fields of all three disciplines, and of the information regarding the research interests of the journals affiliated with them, as published on the journals’ websites, allowed the author to establish their thematic scope. The results of this analysis were compared with bibliographic data and sets of keywords found in the affiliated journals. The comparison relied on an analysis of citations, and of coexistence of specialized terms.Results and conclusions: The analysis of the review articles suggested that the basic research fields of library and information science and of the media studies and cognition and social communication science are aligned and complement each other. This conclusion was further supported by the analysis of the guidelines for the potential contributors provided on the websites of the investigated journals. However, the analysis of the bibliographic data and of the keyword sets gave an entirely different idea of the relation between the studied disciplines, indicating that there is no significant thematic overlap between them. Nevertheless, this might be due to the quality of this particular data sample, and to the methods’ susceptibility to data disruption.Originality/Value: The article proves that there is an overlap between library and information science, and the social communication and media sciences. Furthermore, it shows the limits of the citation method and of the specialized terms coexistence method, resulting from the practices of the authors and the editorial teams of some of the journals discussed. The article shows that all quantitative studies of the state of scholarship in a given discipline in Poland must be conducted with great care, and their results should not be the only basis for conclusions.
Filter-Based Information-Theoretic Feature Selection

Feature subset selection methods aim at identifying the smallest subset of features that maximize generalization performance, while preserving the true nature of the joint data distribution. In classification tasks, this is tantamount to finding an optimal subset of features relevant to the target class. A distinctive family of feature selection methods use a distance metric to identify relevant features, even under high feature interaction, by looking at the local class distribution. In this study we present EBFS: a new algorithm that is inspired by Relieff and uses an entropy-based metric to discover relevant features. Results on UCI data-sets show the effectiveness of our approach when compared to other filter-based feature selection methods.
Detecting Sensitive Content in Spoken Language

Spoken language can include sensitive topics including profanity, insults, political and offensive speech. In order to engage in contextually appropriate conversations, it is essential for voice services such as Alexa, Google Assistant, Siri, etc. to detect sensitive topics in the conversations and react appropriately. A simple approach to detect sensitive topics is to use regular expression or keyword based rules. However, keyword based rules have several drawbacks: (1) coverage (recall) depends on the exhaustiveness of the keywords, and (2) rules do not scale and generalize well even for minor variations of the keywords. Machine learning (ML) approaches provide the potential benefit of generalization, but require large volumes of training data, which is difficult to obtain for sparse data problems. This paper describes: (1) a ML based solution that uses training data (2.1M dataset), obtained from synthetic generation and semi-supervised learning techniques, to detect sensitive content in spoken language; and (2) the results of evaluating its performance on several million test instances of live utterances. The results show that our ML models have very high precision (>>90%). Moreover, in spite of relying on synthetic training data, the ML models are able to generalize beyond the training data to identify significantly higher amounts (~2x for Logistic Regression, and ~4x-6x for a Neural Network models such as Bi-LSTM and CNN) of the test stream as sensitive in comparison to a baseline approach using the training data (~1 Million examples) as rules. We are able to train our models with very few manual annotations. The percentage share of sensitive examples in our training dataset from synthetic generation using templates and manual annotations are 98.04% and 1.96%, respectively. The percentage share of non-sensitive examples in our training dataset from synthetic generation using templates, automated labeling via semi-supervised techniques, and manual annotations are 15.35%, 83.75%, and 0.90%, respectively. The neural network models (Bi-LSTM and CNN) also use lower memory footprint (22.5% lower than baseline and 80% lower than Logistic Regression) while giving improved accuracy.