Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Classification Algorithms Used for Software Defect Prediction

The software quality and reliability are being the major issues while developing software, and as in the software project management, the major cost overruns are due to the deployment of bug prone software at the customer side. Mainly, warehouses are used for maintaining the databases regarding fixing the defects. In this research work, six different classification techniques were being selected based on previous researches which showed better results while developing efficient predictive model. The classification techniques being considered were Naive Bayes, multilayer perceptron, random forest, support vector machine, PART and simple CART, and these various techniques were being measured on various performance measure included as correctly classified instances or accuracy, recall, precision, ROC, F-measure, etc. The experimental analysis used the WEKA environmental tool with calibration setting of tenfolds cross-validation which was being applied on PROMISE repository datasets AR1, AR3, AR4, AR5 and AR6 so as to measure the performance of various algorithms on parameters like accuracy or correctly classified instances, recall, F-measure, precision and ROC measure values, and finally, the overall performance was being evaluated. For the all over performance evaluation, the method of average and correlation was being used. The findings suggest that support vector machine classification algorithm was the best and most promising when applied on software defect prediction supported by the highest average accuracy measure of 87.22%. Similarly, other measures like recall, F-measure, precision have higher values of 0.872, 0.847 and 0.857, respectively.
Extracting possessions from text: Experiments and error analysis

Abstract This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions and assign temporal anchors indicating when a possession relation holds between the possessor and possessee. We work with intra-sentential possessor and possessees that satisfy lexical and syntactic constraints. We experiment with traditional classifiers and neural networks to automate the task. In addition, we analyze the factors that help to determine possession existence and possession type and common errors made by the best performing classifiers. Experimental results show that determining possession existence relies on the entire sentence, whereas determining possession type primarily relies on the verb, possessor and possessee.
Organization of Educational Practice of Students of “Land Management and Cadastres” Training Area

The research objective is as follows: to improve organization of educational practice of students - future specialists in the sphere of land management and real estate cadastre in terms of optimization of educational outcomes in practical training (readiness for professional activity, formation of professional competences, competent presentation of outcomes, etc.). The article considers the problem of organizing educational practice of students of the “Land Management and Cadastres” training area; justifies the role of educational practice in training specialists in the sphere of land management and real estate cadastre; examines new legal requirements for practical training outcomes; reveals a possibility for cooperation in organizing educational practice by the example of an electronic educational resource. Scientific originality of the study lies in the fact that the researcher identifies the role of practice and practical training in students’ professional development, proposes a model of an educational practice program for students of the “Land Management and Cadastres” training area, justifies efficiency of using electronic educational resources while organizing educational practice under the conditions of education digitalization. The conclusion is made that students’ conscious perception of educational practice, understanding its goals, tasks, outcomes is achieved under the condition of equal participation of educational and profiled organizations in formulation of educational practice tasks.
The Estimators of Vector Autoregressive Moving Avarege Model VARMA of Lower Order: VARMA (0,1), ARMA (1,0), VARMA (1,1), VARMA (1,2), VARMA (2,1), VARMA (2,2) with Forecasting

Abstract This research includes analyzing the relationship between two financial time series, which is a series of global monthly Silver price in dollar and global monthly gold price in dollar, a Vector Autoregressive Moving Avarege VARMA Model was used to analysis this relationship, for the period from January 2016 to December 2019, it is 48 monthly value, where the data has been transferred to get the Stationarity, Diekey Fuller test for the Stationarity was conducted, lower order VARMA were estimated, the best order for The model was determined through a standard Akaike information AIC, Akaike information corrected AICC, Hannan-Quinn criterion HQC, Schwarz Bayesian criterion SBC, Final prediction error criterion FPE, It turns out that the best model is VARMA(0,1), according to all standards, Some tests were conducted such as Portmanteau test, Jarque - Bera test, Autoregressive Conditional Heteroscdastic ARCH test to residuals for the selected model, with forecasting for the VARMA(0,1) model for the period from Jan 2020 to Jan 2021, It is 12 monthly value, It turns out that there is an increase in volatility for the two price forecasting series with increase in forecasting period. The simulation has been applied using three samples sizes, It turns out that the model is appropriate when the sample size (50), the result has been computed through the SAS. Program.
Research on multi-dimensional end-to-end phrase recognition algorithm based on background knowledge

Currently, the deep end-to-end methods based on supervised learning are widely used in entity recognition and dependency analysis. However, this method has two problems: first of all, it cannot incorporate background knowledge; secondly, it cannot recognize the nested features of multiple granularities in natural language. To solve the problems, we proposed the phrase-window-based annotation rules, and designed the Multi-Dimensional End-to-End Model (MDM). The annotation rules use the phrase as the minimal unit, divide the sentences into seven categories of nestable phrases, and then tag the syntactic dependencies between the phrases. MDM not only supports incorporating background knowledge, and can also recognize both the categories of the nestable phrases in the sentences and the syntactic dependencies between the phrases. The experiment results show that the annotation rules can be used easily without ambiguity; and the MDM is more suitable for processing the multi-granular and diverse features of sentences than the traditional end-to-end algorithms. After incorporating the background knowledge in the experiment on Chinese Phrase Window Dataset (CPWD), the algorithm improved the accuracy of the phrase recognition by more than one percent over the tradition end-to-end methods with the POS dimensions. Also, we applied this algorithm in the CCL 2018 competition and won the first place in the Chinese humor category recognition.
The Syndrome-Trellis Sampler for Generative Steganography

We adapt the Syndrome-Trellis Code algorithm to generative steganography, giving a method for sampling from a specified distribution subject to linear constraints. This allows the use of syndrome codes, popular in cover-modification methods, for cover-generation steganography. The SyndromeTrellis Sampler works directly on independent and Markov-chain distributions, and can be plugged into an existing STC-based method to extend it to Gibbs fields that can be decomposed into conditionally-independent sublattices. We give some experiments to show that the method is correct, and to quantify how the payload condition forces the sampled distribution away from the target. The results show that the secrecy of the parity-check matrix of the syndrome code is important. We also show how to exploit sparsity in the conditional cover distribution, in a simple example from linguistic steganography.
Image Classification Based on IndCRNN Module

As a core of computer vision, image classification has become a hot topic in recent years. RNN framework can not only model sequence data, predict the trend of sequence data, but also show good results in image classification. In this paper, an improved model IndCRNN is proposed for the over-fitting phenomenon of Independent Recurrent Neural Network (IndRNN) in the training process. IndCRNN is the Independent Convolutional Recurrent Neural Network, which applies the convolutional idea of Convolutional Neural Network (CNN) to IndRNN. On the one hand, the dimension reduction can be realized. In the process of training iteration, the training time will be reduced due to the reduction of dimension. On the other hand, if the high-dimensional feature is taken as the input of IndRNN, the basis of the final output classification becomes the feature of two dimensions of space-time. Another advantage of IndCRNN is that it selects Leaky ReLU as the activation function, replaces the traditional ReLU activation function, maximizes the historical information of the training data, and reduces the vulnerability in the process of updating iterative training. Finally, we did an experiment with TensorFlow environment, experiment selects the MNIST dataset and permuted MNIST dataset. The IndCRNN model is compared with IRNN, LSTM, RNN and IndRNN on two datasets respectively. The experimental results show the feasibility and superiority of the proposed model.
A Peer Learning Method for Building Robust Text Classification Models

Classification is an essential task in many practical problems. A machine learning based classification model is built to minimize the error between actual labels and predicted labels generated by the model. When the model depends on only actual labels during the training, it can generate monotonous distributional predictions. In order to make a robust model, it needs to use other sources of information in addition to the original labels. To address this issue, we propose a peer learning method that enables the target model to reference multiple peer models and that can control the impact of peers on the target model during the training phase. The experiment results indicate that the proposed method is promising.
An Automatic Phoneme Recognizer for Children’s Filipino Read Speech

In this paper, we developed a phoneme-level speech recognizer using hidden Markov models (HMMs) for children's Filipino read speech. Forced alignment was done resulting to 60% of phoneme locations were within 20ms threshold agreement with human transcriptions. Experiments showed that the highest accuracy of 57.47% was attained using a 5-active state HMM model. Increasing the number of Gaussian mixtures from 1 to 6 gave an increased accuracy of 10%. There was also an increase of 2% accuracy when vocal track length normalization was implemented. Further research may involve improving the accuracy of detection by increasing the number HMM's active states, varying the number of Gaussian mixtures for the training data set, integrating a language model, and implementing hybrid Deep Neutral Network (DNN)-HMM models. Moving from phoneme- to word-level children's Filipino speech recognizer provides a more comprehensive way to design an automatic children's oral reading fluency assessment system.
Typology of schools operating in the Moscow Electronic School system based on the analysis of network indicators

Network analysis methods are actively used to research the behavior of digital repository users who utilize and create digital objects. At the same time, the research into the collective behavior of a group of participants who are members of the same school is much less common. The library of the Moscow Electronic School is a rather complicated system with multiple roles offered to users. The actors of the repository are teachers, students, parents, and publishers – anyone performing any actions with the objects. In this study, the school is seen as an actor performing actions with objects – lesson scenarios within the Moscow Electronic School repository of digital objects. Within the study, the authors compare the sociograms of schools that unite teachers and the scenarios created by the teachers and divide schools into factions based on network indicators in sociograms. The main method of presenting and analyzing data is network analysis and sociogram creation. The authors identify two types of networks: the network of single participant’s relationships and the network of relationships of teachers from a single school. The authors not only describe the data structure in the Moscow Electronic School system that records the digital trace of every individual and collective user but also create a digital map that reflects the dynamics of actions in the Moscow Electronic School system and identify the indicators that characterize the common activity of key participants. Moreover, the authors identify graph factions for schools that characterize the degree of interaction between teachers: disconnected groups, sparse graphs, crystallization centers, dense graphs.
Depictions of Women in “Duga” and “Tena”: A Computational Analysis

By combining two contrasting fields, the humanities and digital technologies, a new discipline was born – that of digital humanities. After a long period of constantly using a single approach in literary science – interpretation – in recent years, a new approach has been introduced in this field of science – an empirical type of research, that of computational analysis. Not many efforts have been made in Croatia to implement or even introduce computational analysis as a possible approach in literary studies. This paper analyzes the depiction of women in two particular short stories: “Tena” by Josip Kozarac and “Duga” by Dinko Šimunović. These texts are considered appropriate and representative because their main characters are women whose portrayals are given in great detail. That is the reason why analyzing such texts can be fruitful for deriving data for the construction of the general model. Both texts are written in Croatian, which points to one of the main purposes and intentions of this paper – the use of canonical literary texts from Croatian literature to build a model for the quantitative analysis of female characters and hopefully apply it to other texts in the future. The methods which are used in this paper are computational analysis (a quantitative approach) and interpretation (a traditional, qualitative approach) of quantitative results based on literary science. This paper represents a pilot study of sorts, in which the authors will use the NooJ environment to take the initial steps toward building the aforementioned model.
Uzbek speech commands recognition and implementation based on HMM

In developed countries, many effective speech recognition systems have been developed using advanced technologies. But this speech recognition systems are not suitable for Uzbek language or the level of recognition of speech commands may be low. To solve this problem, the article discusses the stages of recognition of speech commands and intelligent processing algorithms. Based on the hidden Markov model, a recognition system for Uzbek speech commands was implemented in Python using CMU Sphinx technology that recognizes Uzbek speech commands on a computer and Raspberry Pi3 devices. The accuracy of the recognition system for Uzbek speech commands are showed 80-95%.
The BabelBox: An Embedded System for Score Distribution on Raspberry Pi with INScore, SmartVox and BabelScores

The slow but steady shift away from printed text into digital media has not yet modified the working habits of chamber music practitioners. If most instrumentalists still heavily rely on printed scores, audiences increasingly access notated music online, with printed scores synced to an audio recording on youtube for instance. This paper proposes to guide the listener and/or the performer with a cursor scrolling on the page with INScore, in order to examine the consequences of representing time in this way as opposed to traditional bars and beats notation. In addition to its score following interest for pedagogy and analysis, the networking possibilities of today’s ubiquitous technologies reveal interesting potentials for works in which the presence of a conductor is required for synchronization between performers and/or with fixed media (film or tape). A Raspberry Pi-embedded prototype for animated/distributed notation is presented here as a score player (such as the Decibel ScorePlayer, or SmartVox), in order to send and synchronize mp4 scores to any browser capable device connected to the same WIFI network. The corpus will concern pieces edited at BabelScores, an online library for contemporary classical music. The BabelScores pdf works, composed in standard engraving softwares, will be animated using INScore and video editors, in order to find strategies for animation or dynamic display of the unfolding of time, originally represented statically on the page.
Preparing teachers for the use of digital technologies in educational activities

Digital technologies have a strong impact on changes in the educational environment and activities. In the context of such changes, aggravated by the spread of coronavirus infection and the transition of schools to distance learning, teachers and students are facing the need to actively master digital tools and educational platforms. The authors aim at analyzing the professional gaps of teachers associated with digital literacy and the ability to effectively use digital technologies in educational activities. The conducted sociological research and the results obtained allow the authors to study the existing internal teachers’ gaps and show the main problems in the organization of educational activities in the context of the transition to distance learning and the existing gaps in the use of digital technologies by school teachers. Based on the results of the study, the authors have identified the key areas of the professional development of teachers. They have developed educational programs aimed at skill development and further training of teachers in the Russian Federation and foreign countries on such topics as the digital educational environment and the use of open educational resources, methods of using digital technologies in the organization of educational activities, methods and techniques of organization feedback, pedagogical communication in the digital environment, and information security and ethics in the digital environment. The authors have determined the conditions for the professional development of teachers in the process of skill development and further training, as well as self-education (including spontaneous training in the workplace).The authors believe that the increase in teachers’ digital literacy, based on filling their professional gaps, will help to improve the quality of general education and create conditions for the elimination of the digital divide of the population.
Suspicious Criminal Behavior Prediction Based on Roaming Trajectory Characteristics

Roaming around the crime scene is a characteristic of some criminals' behaviors before committing a crime. In order to achieve the criminal purpose, it is common for criminals to check the crime scene many times in advance. However, the definition of the roaming behavior is a vague concept. This study firstly describes the relationship between the predetermined crime scenes and the roaming behaviors of suspects. Then we propose an algorithm to identify suspicious trajectories with roaming behaviors. The first step of the algorithm is to identify suspicious trajectories by applying a clustering method to extract the stagnant areas from different trajectories in real time. The second step is to identify the trajectory with loops from the suspicious trajectories based on the suspect's roaming behaviors for casing the site. The third step is to calculate a corresponding suspicious value by using speed and timestamp of the trajectory and then combine with crime rate of different areas to design an appropriate weighted scheme. Experiments with prisoners' trajectory data has proved the feasibility of the proposed method. Furthermore, the proposed method can effectively predict the abnormal behaviors of community inmates.
Construction of a Learner Growth Model and Learner Classification Method Using exMCRNNs

In recent years, with the development of information technology, the use of Web-Based Training (WBT) and other individualized learning programs are increasingly used and are gaining attention. In individual online learning, efficient learning is possible by providing individualized learning materials based on the degree of understanding and growth characteristics of each learner. However, actually, appropriate teaching materials for each individual learner is not be provided, which reduces his/her motivation to learn and makes it difficult for him/her to learn. The reason is that it is difficut to establish a desirable relationship between an educator model on the learning support system and each learner. This paper proposes a learner classification method based on learners' growth curves using the neural networks for the purpose of providing appropriate learning support to each learner. Since a huge amount of data is required for training of the neural networks, this paper construct an “educator-learner” model based on a control engineering approach representing the interaction between learning support systems and each learner, and virtual learner data is generated. The usefulness of the proposed method is shown by numerical experiments.
An Efficient and Scalable Sparse Polynomial Multiplication Accelerator for LAC on FPGA

LAC, a Ring-LWE based scheme, has shortlisted for the second round evaluation of the National Institute of Standards and Technology Post-Quantum Cryptography (NIST-PQC) Standardization. FPGAs are widely used to design accelerators for cryptographic schemes, especially in resource-constrained scenarios, such as IoT. Sparse Polynomial Multiplication (SPM) is the most compute-intensive routine in LAC. Designing an accelerator for SPM on FPGA can significantly improve the performance of LAC. However, as far as we know, there are currently no works related to the hardware implementation of SPM for LAC. In this paper, the proposed efficient and scalable SPM accelerator fills this gap. More concretely, we firstly develop the Dual-For-Loop-Parallel (DFLP) technique to optimize the accelerator's parallel design. This technique can achieve 2x performance improvement compared with the previous works. Secondly, we design a hardware-friendly modular reduction algorithm for the modulus 251. Our method not only saves hardware resources but also improves performance. Then, we launch a detailed analysis and optimization of the pipeline design, achieving a frequency improvement of up to 34%. Finally, our design is scalable, and we can achieve various performance-area trade-offs through parameter p. Our results demonstrate that the proposed design can achieve a very considerable performance improvement with moderate hardware area costs. For example, our medium-scale architecture for LAC-128 takes only 783 LUTs, 432 FFs, 5BRAMs, and no DSP on an Artix-7 FPGA and can complete LAC's polynomial multiplication in 8512 cycles at a frequency of 202MHz.