Reviewerly/exHarmony huggingface dataset:
Perseptron for assessing the students’ task performance in learning a foreign language according to the competencies using bi data

The article presents the Perceptron neural network program developed by the authors, which enables assessing in points the quality of the task performed by students in a foreign language. The theoretical basis for the artificial intelligence systems applied in the learning process has been considered. The studies have shown that the online education market has grown in the world, and the trend marked persists. The trends in use of neural networks in learning have been summarized. Analysts noted that the use of artificial intelligence systems has been becoming more widely used in the learning process. It has been hypothesized and proved that a neural network makes it possible to give a grade for the quality of a control text read by a student according to the requirements of competencies using the BI data resulted from processing with an artificial intelligence system. The algorithm proposed by the authors uses the capabilities of the PyPhox mobile application and the Deductor platform. The algorithm is based on the following scheme. The teacher records a piece of a text into a file on the PhyPhox mobile application; its digitized version is then used as a reference to compare the quality of the assignment done by a Master student. The resulting reference file is exported to XL and then imported into the Deductor for further processing. The students do the same; their control files are paired with the reference, combined in XL and imported into the Deductor for processing. In the final, based on the comparison of the coefficients of correlation between the control and reference files, the Perceptron program assigns a score corresponding to the "excellent," "good," "satisfactory" and "not satisfactory" grades. The neural network formed is trained on data sampling, where they add semester points and competence points. The relevance lies in the fact that the program enables automating the learning process and replacing the teacher's work at the stage of the assignment control according to the competencies, which is of practical importance.
Study of Optimal Text Size Phenomenon in Zipf–Mandelbrot’s Distribution on the Bases of Full and Distorted Texts. Author’s Frequency Characteristics and derivation of Hapax Legomena

This paper explores word-frequency patterns when considering text length, authorship, and random distortion of texts. Through a series of experiments, we determined an optimal text size, a phenomenon that was predicted by George Zipf, which sees a minimal discrepancy between calculated and observed frequencies. A graphic representation allowed a plausible explanation behind the existence of this phenomenon. Working on the assumption that distorted texts might disobey Zipf’s Law, we explored correlations among frequencies and text entirety compared with text distortions. Results reveal the crucial role of text length for maintaining Zipfian distribution: randomly chosen sets of words and fragmentary texts of optimal size still obey Zipf’s Law. Findings show that authorship manifests itself through the author constant, defined as the relative frequency of the most frequent words, which remains constant throughout the works of any given author, including randomly chosen text chunks and fragments of sentences of various sizes.
Spike Sorting Based on Window-Gradient Feature

Spike sorting is difficult when there is high waveforms similarity between different spike or when there is a large number of superimposed spikes in the sample. A new sample optimization method is proposed in the paper, called window-gradient feature. Every spike waveform is segmented into successive fragments in terms of the width σ, and each segment is called a window. Then calculate the gradient change of each window and use the ratio as the new alternative feature of the window. Finally, all window-gradient features of the spike waveforms are used to replace the original waveform features for spike sporting. The method is verified on the simulation data at different SNR. The experimental results show that when using SVM for spike sorting, the optimization effect of the proposed method is better than PCA, especially for data sets data sets with high noise or large sampling waveform similarity.
MODERNIZATION OF FRONT DESK PROJECT (FDP) TRAINING CURRICULUM FOR PUNJAB POLICE

Front Desk Project (FDP) was initiated by the Punjab government to bridge the gap between police and public in 2015.Punjab police was facing too much complains and lack of confidence from public as a friendly service oriented organization.FDP was aimed to bring educated people in police station to deal with the complainants by using latest technology to make it easier to lodge a complaint and First Information Report (FIR).Previously Police manage their records manually but after initiation of FDP, everything was computerized and easy to follow up a case in short period of time.A training program of FDP was devised to train the front desk officers who deal with public.It was initially designed asforty days training based on computer typing skills and law at police training centers.This program consisted of basics of Law, typing skills and motivational lectures.This paper aims to identify the short comings in the training program i.e. traditional curriculum which doesn't match with the job requirement, which requires software training, physiological, behavioral, ethical trainings andalso requires practical exposure.Moreover, exploring the opportunities to propose a system for the transformation of traditional FDP curriculum composed of state-ofthe-art training methods.The proposed FDP curriculum has a capacity to enhance capabilities and real time experience for the trainees.This would also help trainers to automate the course and be helpful in police working and management.
Switching filtering method for interactive systems

Aiming at the problem that the mixed noise interference caused by the mixed projection noise system is not accurate and the real-time performance is poor, this article proposes an adaptive system switching filtering method based on Bayesian estimation switching rules. The method chooses joint bilateral filtering and improved adaptive median filtering as the filtering subsystems and selects the sub-filtering system suitable for the noise by switching rules to achieve the purpose of effectively removing noise. The simulation experiment was carried out by the self-developed human–computer interactive projection image system platform. Through the subjective evaluation, objective evaluation, and running time comparison analysis, a better filtering effect was achieved, and the balance between the filtering precision and the real-time performance of the interactive system was well obtained. Therefore, the proposed method can be widely applied to various human–computer interactive image filtering systems.
Hybridization of Migrating Birds Optimization with Simulated Annealing

Migrating Birds Optimization (MBO) algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. Despite its superior performance, one drawback of MBO is its occasional aggressive movement to better solutions while searching the solution space. On the other hand, simulated annealing is a well-established metaheuristic optimization method with a search strategy that is particularly designed to avoid getting stuck at local optima. In this study, we present hybridization of the MBO algorithm with the SA algorithm by embedding the exploration strategy of SA into the MBO, which we call Hybrid MBO. In order to investigate impact of this hybridization, we test Hybrid MBO on 100 Quadratic Assignment Problem (QAP) instances taken from the QAPLIB. Our results show that Hybrid MBO algorithm outperforms MBO in about two-thirds of all the test instances, indicating a significant increase in performance.
Intelligent Task Resource Planning Based on Adaptive Dynamic Resource Composition Model

With the rapid development of information technology, various resources, including computing, network, storage and application, generally present the trend of servitization. In order to plan these resources dynamically in a short time according to the task requirement and allocate them properly, this paper propose an adaptive dynamic resource composition model which can realize fast searching and matching of resources through automatic classification of resources based on type, Hierarchical Task Network (HTN) planning to implement the task planning and resource allocation algorithm to implement the resource allocation. We design a task resource intelligent planning system with a specific scenario to verify our theory and method.
Three-wheel full-wheel robot speed algorithm

This paper introduces the design of the chassis speed algorithm for the Robocon2018 wheeled robot, and designs the motion algorithm of the wheel chassis for the Robocon2018 competition. Establish the coordinate system, through to the robot Chassis movement modelling analysis and based on the actual debugging situation, according to the decomposition and synthesis of vector velocity, the velocity projection value of three omnidirectional wheels is calculated by means of vector dot product, and then the velocity projection value is converted to STM32 PWM output to control the motor. It also proves the effectiveness and feasibility of the velocity algorithm to make the robot move according to the predetermined speed.
A Scheme to Improve Stream Transaction Rates for Real-Time IoT Applications

Due to the recent proliferation of IoT (Internet of Things) devices, a large amount of stream data such as video data or sensor data is transmitted to remote processing computers. The stream transaction rate is one of the main factors to improve the performance of some IoT applications. For instance, in surveillance systems, the probability to catch a moving person increases as the processing computer analyzes video with a higher transaction rate. To improve stream transaction rates, some schemes reduce communication time between a processing computer and stream data sources. They target periodic stream transactions and assume static transmission intervals. However, the communication and transaction time changes dynamically. Therefore, stream transaction rates can be further improved by changing transmission intervals dynamically depending on it. In this paper, we propose a scheme to improve stream transaction rates by changing transmission intervals dynamically. In our proposed scheme, a processing computer sometimes changes transmission intervals to be the same length as the average transaction time. Moreover, our proposed scheme adopts a progressive quality improvement (PQI) approach to reduce communication and transaction time. Our evaluation results revealed that the proposed scheme can achieve a higher stream transaction rate than a conventional scheme with the PQI approach.
Applying GMM-UBM framework for Indonesian forensic speaker verification

A text-dependent speaker verification system has been implemented for forensic application since 2008 when speech recording is allowed as legal evidence in Indonesian court. Due to the laborious procedures of the current system, an automatic text-independent speaker verification system is being developed in order to accelerate the forensic speaker verification process. The new forensic system is developed using a database spoken in Indonesian. The database is built on two scenarios, interview and conversation. This is done to simulate the real forensic condition for speaker verification in Indonesia. The system adopts the very famous Gaussian Mixture Model (GMM) and Universal Background Model (UBM) framework in speaker verification field and using Mel-Frequency Cepstral Coefficients (MFCC) as the speech feature. Along with the application of zero-normalization, the new forensic speaker verification system has successfully reached an Equal Error Rate (EER) of 4.66 % that is a better performance than the previous developed system of Indonesian forensic speaker verification.
Improvement of Self Position Estimation of Electric Wheelchair Combining Multiple Positioning Methods

A self-driving electric wheelchair must estimate its own position, identify the travelable area, and determine the travel route. For indoor self-position estimation, the location can be estimated by wireless LAN or beacon. However, for autonomous operation, more accurate position estimation is required. Conventionally, we have used augmented reality (AR) markers with high positioning accuracy as a method for self-position estimation of a wheelchair in a relatively narrow space. However, the positioning error of the angle when the AR marker was recognized from a frontal direction was large, and its improvement was a problem. In this research, we propose a method to correct the errors of positions obtained with AR markers using wheelchair odometry information and object detection. Since estimates using AR markers and odometry information both have errors, we propose a method for correcting the AR marker positioning by odometry information and distance information obtained by object detection. Preliminary experiments showed that using odometry for correction improves the positioning error and allows for stable control of the wheelchair.
Spline-Based Robot Trajectory Generation Using the Dynamic Window Approach

Many path planning systems for mobile robots today rely on a combination of way point navigation and reactive collision avoidance for motion control Generally, such systems are notable to reliably produce smooth and predictable navigation behavior as it is required, e.g., when robots and humans operate in the same environment In contrast, spline-based motion planning techniques can generate smooth goal-directed trajectories But for most robot drives a subsequent control step is required that actually steers the robot along the desired trajectory In this article we propose a control approach that is based on the Dynamic Window Approach(DWA) Classical DWA controls the robot's velocities in order to steer it to a given goal location, avoiding collisions along the way Here, the approach is used to follow a given spline path as close as possible, while collision avoidance is already handled in the spline-based motion planning Thus expensive collision checks can be omitted from the velocity search of the DWA and the gained processing time allows computing velocity controls several time-steps ahead, resulting in improved path following, We demonstrate the benefit of this approach in simulation experiments and show its applicability for a real robot.
Gated Convolutional Encoder-Decoder for Semi-supervised Affect Prediction

Analyzing human reactions from text is an important step towards automated modeling of affective content. The variance in human perceptions and experiences leads to a lack of uniform, well-labeled, ground-truth datasets, hence, limiting the scope of neural supervised learning approaches. Recurrent and convolutional networks are popular for text classification and generation tasks, specifically, where large datasets are available; but are inefficient when dealing with unlabeled corpora. We propose a gated sequence-to-sequence, convolutional-deconvolutional autoencoding (GCNN-DCNN) framework for affect classification with limited labeled data. We show that compared to a vanilla CNN-DCNN network, gated networks improve performance for affect prediction as well as text reconstruction. We present a regression analysis comparing outputs of traditional learning models with information captured by hidden variables in the proposed network. Quantitative evaluation with joint, pre-trained networks, augmented with psycholinguistic features, reports highest accuracies for affect prediction, namely frustration, formality, and politeness in text.
The Limits of Forensic Evidence

Abstract Ten years ago, the US National Academy of Sciences published a report that was critical of the scientific foundations of many forms of forensic evidence. It called for “more and better research”. Ten years on, Significance is publishing a special collection of articles that reflect on the report's impact, the progress that has been made, and the challenges that remain for evaluating and interpreting evidence. First, Brian Tarran highlights the critical role played by statisticians in supporting the development of forensic science
Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter

In the field of visual tracking, discriminative correlation filter (DCF)-based trackers have made remarkable achievements with their high computational efficiency. The crucial challenge that still remains is how to construct qualified samples without boundary effects and redetect occluded targets. In this paper a feature-enhanced discriminative correlation filter (FEDCF) tracker is proposed, which utilizes the color statistical model to strengthen the texture features (like the histograms of oriented gradient of HOG) and uses the spatial-prior function to suppress the boundary effects. Then, improved correlation filters using the enhanced features are built, the optimal functions of which can be effectively solved by Gauss-Seidel iteration. In addition, the average peak-response difference (APRD) is proposed to reflect the degree of target-occlusion according to the target response, and an adaptive Kalman filter is established to support the target redetection. The proposed tracker achieved a success plot performance of 67.8% with 5.1 fps on the standard datasets OTB2013.
Concentration Areas of Sentiment Lexica in the Word Embedding Space

Sentiment lexicons play an important role in opinion mining systems and cognitive linguistics. Previous work aimed mostly at creating sentiment lexicons, but not thorough research into their fundamental properties. In this paper the arrangement of sentiment lexica in the multidimensional space of distributed word representations is studied. A hypothesis on the existence of sentiment lexica concentration areas is introduced and it is tested on the basis of the joint analysis of the distribution of sentiment words and general lexica. The results of the test allow to confirm the proposed hypothesis and discover the words which more than 80% of the sentiment lexica is concentrated around.
Inference New Knowledge Using Sparql Construct Query

SPARQL Protocol and RDF Query Language (SPARQL) is the Semantic Web based query language which extracts and manipulate data stored in the RDF datasets. SPARQL is the W3C recommendation which allow users to get what they need rather than how a particular dataset is organized. SELECT query is the most popular and widely used SPARQL query which extracts the required information from the RDF datasets. However, the topic of this study is the SPARQL Construct query, which returns an RDF graph rather than returning a table of resulted values. The SELECT query can only be used to fetch the data which are already stored in our datasets and does not infer/create new knowledge. In our work, we have tried to employ the SPARQL Construct queries to demonstrate how new information can be inferred based on the already existed data stored in our ontology, which otherwise are possible via (Semantic Web) rules.
Dempster‐Shafer theory‐based hierarchical saliency detection

Abstract This article presents a new saliency detection method based on a hierarchical structure. In order to better capture the key elements in an image, we first construct a three‐layer structure by applying a merging strategy to the image. For different layers, we exploit different methods to detect salient regions. After obtaining initial saliency maps of five layers, we construct a Dempster‐Shafer model to fuse and refine these saliency maps. In the Dempster‐Shafer model, the saliency value of upper layers can provide guidance for saliency detection of lower layers. Finally, we fuse the saliency map of each layer to obtain the final result. Abundant experimental results on two benchmark data sets demonstrate that our method outperforms most of 12 state‐of‐the‐art methods in terms of three popular evaluation criteria, that is, the P ‐ R curve, the F ‐measure, and the mean absolute error.
Using the SPAR Ontology Network to Represent the Scientific Production of a University: A Case Study

Research is commonly used to measure the prestige of universities. Currently, many universities register some of their scientific production (such as thesis, articles) in open access repositories using technologies like DSpace or ePrints. Likewise, scientific production is available in different and overlapping databases such as Scopus, Web of Science, Google Scholar, Crossref. Connecting these datasets, with the application of ontologies, will potentially increase their value and help discover interesting relationships amongst them. The present study aims to use the SPAR Ontology Network, which allows representing scholarly publishing, in order to check whether it is possible to represent the scientific production of universities. For that, we propose competency questions with the purpose of measuring scientific production. Likewise, we obtained data from Scopus regarding an Ecuadorian university (University of Guayaquil) and transformed it to RDF using the SPAR Ontology Network and other well-known ontologies to build it semantically. We used SPARQL queries to answer the competency questions. We concluded that the SPAR Ontology Network is a wide-ranging solution for scholarly publishing. Nevertheless, it is necessary to build an extension to one of their ontologies to provide a complete representation of a university’s scientific production.
Maximizing Gain over Flexible Attributes in Peer to Peer Marketplaces

Peer to peer marketplaces enable transactional exchange of services directly between people. In such platforms, those providing a service are faced with various choices. For example in travel peer to peer marketplaces, although some amenities (attributes) in a property are fixed, others are relatively flexible and can be provided without significant effort. Providing an attribute is usually associated with a cost. Naturally, different sets of attributes may have a different "gains" for a service provider. Consequently, given a limited budget, deciding which attributes to offer is challenging. In this paper, we formally introduce and define the problem of Gain Maximization over Flexible Attributes (GMFA) and study its complexity. We provide a practically efficient exact algorithm to the GMFA problem that can handle any monotonic gain function. Since the users of the peer to peer marketplaces may not have access to any extra information other than existing tuples in the database, as the next part of our contribution, we introduce the notion of frequent-item based count (FBC), which utilizes nothing but the database itself. We conduct a comprehensive experimental evaluation on real data from AirBnB and a case study that confirm the efficiency and practicality of our proposal.
Establishment and Implementation of Quality Management Standard System for Aviation Industry

In this paper, firstly, the principles, ideas and methods for the construction of quality management standards system in China’s aviation industry have been proposed based on the current situation. Secondly, system diagram of quality management standards has been established and a detailed list of standards has been compiled. In the end, a few suggestions for the follow-up implementation have been made accordingly.