Reviewerly/exHarmony huggingface dataset:
Towards transferring lean software startup practices in software engineering education

In the modern economy, software drives innovation and economic growth. Studies show how software increasingly influences all industry sectors. During the last five decades, software engineering has also changed significantly to advance the development of various types and scales of software products. In this context, Software Engineering Education plays an essential role in keeping students updated with software technologies, processes, and practices popular in industries. In this Ph.D. work, I want to answer the following research questions: (1) To what extent are SE Trends presented in SEE research? (2) What do we know about the Lean Startups paradigm? (3) What is the impact of Lean Startup practices to software engineering students and curriculum? I utilize (1) literature review and (2) Mixed-methods approaches (data and methods triangulation) in gathering empirical evidence. In the first phase of the research, I pinpoint the relevance of Lean Startup within the software engineering education throughout an extensive literature review. I gather empirical evidence on Lean Startup practices and their potential transfer in software engineering education during the second research phase. I demonstrate that Lean Startup is part of the emerging software engineering trends within software engineering education research. I identify the gap of growth phase Lean Startup research in present software paradigms. I demonstrate that students can acquire soft, hard, and project management skills in a more realistic context while introducing growth phase Lean Startup practices throughout external course activities. I expect that the present software engineering curricula can benefit from a model and framework, which I intend to propose, facilitating Lean Startup practice transfer within the software engineering curriculum.
SYSTEM ARCHITECTURE FOR SUPPORTING MULTIPLE LIVE ACTORS IN WEB3D VIRTUAL CONFERENCE

For live actors to perform as 3D models with seamless graphics computing, we need to overcome the huge challenges of developing a methodology to construct a cleaned and smooth model in real time and a system architecture design that supports the streaming of multi-model live actors. By applying the technique of semantic image segmentation to generate the entire human body, a 3D model needs to be built up with a segmented image as a texture. Moreover, to enable a live actor to virtually interact with others, multi-actor models must be produced for telecommuting systems. Therefore, by combining the concepts above, a system with multiple live actors can be created to conduct live group conferences in virtual reality that provide an immersive experience.
Solving Software Project Scheduling Problem using Whale Optimization Algorithm

Abstract In the software projects, one of the essential and difficult problems faced by the managers in the competitive software industry is the Software Project Scheduling Problem (SPSP). With the increase in employees and tasks’ numbers the problem is becoming an NP-hard problem. The goal of this proposal is to resolve the problem of the software project scheduling with Whale optimization algorithm (WOA) and utilized it on various instances from three datasets. In order to prove the soundness and viability of Whale optimization algorithm (WOA), we illustrated some experimental results. This algorithm gave good outcomes for datasets that have a few tasks but failed to find feasible solutions when increasing the number of tasks.
Verification method to improve the efficiency of traffic survey

Road traffic volume survey is a survey to determine the number and type of vehicles passing at a specific point for a certain period of time. Previously, a method of classifying the number of vehicles and vehicle types has been used while a person sees an image photographed using a camera with the naked eye, but this has a disadvantage in that a lot of manpower and cost are incurred. Recently, a method of applying an automated algorithm has been widely attempted, but has a disadvantage in that the accuracy is inferior to the existing method performed by manpower. To address these problems, we propose a method to automate road traffic volume surveys and a new method to verify the results. The proposed method extracts the number of vehicles and vehicle types from an image using deep learning, analyzes the results, and automatically informs the user of candidates with a high probability of error, so that highly reliable traffic volume survey information can be efficiently generated. The performance of the proposed method is tested using a data set collected by an actual road traffic survey company. The experiment proved that it is possible to verify the vehicle classification and route simply and quickly using the proposed method. The proposed method can not only reduce the investigation process and cost, but also increase the reliability due to more accurate results.
1-to-N Large Margin Classifier

Cross entropy with softmax is the standard loss function for classification in neural networks. However, this function can suffer from limitations on discriminative power, lack of generalization, and propensity to overfitting. In order to address these limitations, several approaches propose to enforce a margin on the top of the neural network specifically at the softmax function. In this work, we present a novel formulation that aims to produce generalization and noise label robustness not only by imposing a margin at the top of the neural network, but also by using the entire structure of the mini-batch data. Based on the distance used for SVM to obtain maximal margin, we propose a broader distance definition called 1-to-N distance and an approximated probability function as the basis for our proposed loss function. We perform empirical experimentation on MNIST, CIFAR-10, and ImageNet32 datasets to demonstrate that our loss function has better generalization and noise label robustness properties than the traditional cross entropy method, showing improvements in the following tasks: generalization robustness, robustness in noise label data, and robustness against adversarial examples attacks.
LSTM Based End-to-End Text-Independent Speaker Verification Using Raw Waveform

Speaker can be discriminated either at voice source level or vocal tract system level. Conventionally Mel-Frequency Cesptral Coefficients (MFCCs) or Mel filterbank energies are employed as input acoustic feature in neural network based speaker verification systems. In this paper, we investigate the LSTM based speaker verification using raw waveform as input feature. The basic LSTM based SV model and the model with attention layer are trained and optimized on two datasets using raw waveform feature and Fbank feature respectively. And experimental results show that compared with the model trained using Fbank feature, the model trained using raw waveform can achieve promising performance, raw waveform is a competitive acoustic feature for LSTM based speaker verification.
STEGRT1: A dataset for evaluating steganalysis systems in real-world scenarios

The existence of practical reference datasets for evaluating the performance of image steganalysis algorithms and comparing their capabilities in the real-world is essential. Most of the recent steganalysis systems evaluate their results only under laboratory conditions. However, a reliable steganalysis system should be able to detect the existence of a secret message in real situations where there is no knowledge about the employed steganographic method, cover image properties, and other parameters. In this paper, we introduce Steganalysis Real Test version 1 (STEGRT1) dataset for both BITMAP and JPEG images to enable researchers to evaluate their steganalysis schemes in real-world scenarios. The proposed dataset contains 8000 cover and stego images of different sizes and properties, considering several steganographic methods, payloads, and quality factors.
The role of cloud technologies in digital transformation of education (on the example of secondary vocational education)

The article describes the capabilities of cloud technologies (using the example of Google services) in implementing the target model of the digital educational environment and developing the first stage of digital transformation of education using the example of the general education course “Informatics” in secondary vocational education. The article substantiates the relevance of updating the content of educational programs of secondary vocational education in accordance with the requirements of the digital economy in the context of the implementation of Federal State Educational Standards of Secondary Vocational Education for 50 most demanded in the labor market, new and promising professions and specialties (FSES of Secondary Vocational Education for TOP-50). The novelty of the work in the context of digital transformation of secondary vocational education is shown. The relationships between the didactic capabilities of cloud technologies and the types of educational activities of students and the requirements of digital transformation of education are analyzed. A comparative analysis of the functional support of the components of the target model of the digital educational environment by means of cloud technologies is carried out. The experience of organizing the training of students of the Perm Commerce and Technology College in the groups of the profession “Cook, Pastry Chef” in the framework of the general educational discipline “Informatics” based on the use of cloud technologies as a model of a digital educational environment in the formation of general and subject competences is presented. In the presented experience, cloud technologies (Google services) are considered as the main and as an auxiliary didactic tool in the context of general functionality of Google services, types of didactic tasks for open source software, approximate thematic planning in the subject of “Informatics” for secondary vocational education. The results of the work can be useful to scientists, teachers and methodologists of higher and secondary vocational education, and can also be used directly by teachers of the course “Informatics” in secondary vocational education.
Self-supervised Learning of Orc-Bert Augmentor for Recognizing Few-Shot Oracle Characters

This paper studies the recognition of oracle character, the earliest known hieroglyphs in China. Essentially, oracle character recognition suffers from the problem of data limitation and imbalance. Recognizing the oracle characters of extremely limited samples, naturally, should be taken as the few-shot learning task. Different from the standard few-shot learning setting, our model has only access to large-scale unlabeled source Chinese characters and few labeled oracle characters. In such a setting, meta-based or metric-based few-shot methods are failed to be efficiently trained on source unlabeled data; and thus the only possible methodologies are self-supervised learning and data augmentation. Unfortunately, the conventional geometric augmentation always performs the same global transformations to all samples in pixel format, without considering the diversity of each part within a sample. Moreover, to the best of our knowledge, there is no effective self-supervised learning method for few-shot learning. To this end, this paper integrates the idea of self-supervised learning in data augmentation. And we propose a novel data augmentation approach, named Orc-Bert Augmentor pre-trained by self-supervised learning, for few-shot oracle character recognition. Specifically, Orc-Bert Augmentor leverages a self-supervised BERT model pre-trained on large unlabeled Chinese characters datasets to generate sample-wise augmented samples. Given a masked input in vector format, Orc-Bert Augmentor can recover it and then output a pixel format image as augmented data. Different mask proportion brings diverse reconstructed output. Concatenated with Gaussian noise, the model further performs point-wise displacement to improve diversity. Experimentally, we collect two large-scale datasets of oracle characters and other Chinese ancient characters for few-shot oracle character recognition and Orc-Bert Augmentor pre-training. Extensive experiments on few-shot learning demonstrate the effectiveness of our Orc-Bert Augmentor on improving the performance of various networks in the few-shot oracle character recognition.
Element Fill-in-Blank Problems in Python Programming Learning Assistant System

Nowadays, most of the student are learning the python programming for their research area as data science, image processing, robot control and web technology etc. So, python programming learning assistant system (PYPLAS) is developed. It will improve the student programming skill and reduce the teacher workload in university. It will include many types of exercise problems from beginner to advanced levels. In this paper, fill-in-blank problem for python code is proposed so that student can study the grammar and basic python programming. To generate the fill-in-blank problem of python code, blank element selection algorithm is improved according to python grammar. For evaluations, 100 fill-in-blank problems are generated to verify the correctness of blank elements and blank number rate for all program codes. We generated 27 problems and asked 5 learners from two universities to solve them in offline. The results show that the improvement the user programming skill in python programming education.
Company Security Assesment with Agent Based Simulation

A network security simulation system is useful as a network security application in network security training exercises. Also, the simulation system enables the system administrator to observe how changing different parameters and security restrictions can affect the security of a network. This paper presents a simulation system for security risk assessment for companies. An agent-based modeling paradigm is used for the implementation of the proposed simulation system. An agent-based system can estimate network security risks and provide a guide for effective policies in order to protect the network from attacks. The proposed system allows security professionals to simulate varying network environments, attacker and defender scenarios in accordance with different situations. Therefore, the proposed agent-based system captures companies' network system dynamics that consist of interactions of multiple agents, including attackers and defenders that the system can use. The proposed model is evaluated through a case study in order to assess security risks for various security policies in a representative company's network.
Voronoier

We describe an interactive application for transforming an image into a Voronoi diagram. We combine a variety of methods for generating a spatial point cloud from an input image. In addition, several methods for pruning the spatial point cloud are introduced. These pruning methods can significantly reduce the computation time needed for the transformation. A Voronoi diagram can be constructed from the pruned spatial point cloud using either a naive approach or a Delaunay triangulation. Moreover, an order-k Voronoi diagram can be constructed using this naive approach. We introduce many configuration parameters and we integrate interactivity in the Voronoi diagram by giving users the ability to manually add and remove centroids. To make the application accessible to everyone, we provide a web-based solution by using the Vue.js framework based on the JavaScript programming language. This solution supports the transformation from an image to a Voronoi diagram in a browser, and hence, has the advantage of not being restricted to a certain kind of environment. We illustrate the usefulness of our application by applying it to several images.
Text File Protection Using Least Significant Bit (LSB) Steganography and Rijndael Algorithm

Nowadays, thousands of kilobytes personal data are transmitted every day through insecure communication media (such as the Internet, computer networks, communication systems, etc.). This makes data vulnerable to information theft, especially for fraud, illegal trade, and so on. So, there is a need for protecting the information in its storage and transmission. To improve data and information security, in this study, we propose a Least Significant Bit (LSB) steganography to insert message information in a 24-bit jpg image and Rijndael cryptography that is used to encrypt jpg images so that message information can be secured from unauthorized parties.
Improved Pigeon-Inspired Optimization Algorithm and Its Application to Minimum-Fuel Ascent Trajectory Optimization

An improved pigeon-inspired optimization (PIO) algorithm for global numerical optimization is proposed to perceive deceptiveness and preserve diversity. In the proposed algorithm, quantum evolution is introduced into the PIO algorithm to solve the problem that the PIO algorithm is prone to fall into local optimal. The improved PIO algorithm is applied to hypersonic vehicle trajectory optimization under multiple constraints. The simulation results show that the performance of the optimal ascent trajectory obtained by the improved PIO algorithm is improved, which also proves the effectiveness of the proposed trajectory optimization algorithm.
A Contribution-based Resource Allocation Scheme for Multi-population Methods in Dynamic Environments

The multi-population method is a common method for solving dynamic optimization problems. However, to design an efficient multi-population method, one of the challenging issues is how to allocate computational resources between populations given a limited computing buget in dynamic environments. This paper designs a contribution-based resource allocation mechanism. In this mechanism, a contribution degree of a population is defined according to the performance of the population, which determines the probability of the population to obtain the computing resource. This mechanism is implemented in an adaptive multi-population method. Experimental results on the moving peaks benchmark show that the algorithm equipped with the resource allocation mechanism outperforms the original algorithms.
A Research on Online Grammar Checker System Based on Neural Network Model

Abstract Grammar error correction is one of the most important research fields in natural language processing, while grammar checker is the tool to help people correct grammatical error. We focus our work on researching a better approach to an online grammar checker system to help people correct the grammatical error in their text more precisely, user-friendly, and efficiently. This new method of online grammar checker system is based on neural network model, Transformer, and is able to detect about 25 different types of grammatical errors in the text. We did the evaluation on our new online grammar checker system, and the final experiment results proved that we have found a good approach to the online grammar checker system.
MetaProfiles - A Mechanism to Express Metadata Schema, Privacy, Rights and Provenance for Data Interoperability

Documenting datasets in an actionable way is an essential approach to ensure data interoperability. Guidelines like FAIR (Findability, Accessibility, Interoperability, and Reusability) ensures better use-cases for the data. Proposals like metadata applications profiles provide mechanisms to express constraints and metadata schema of the datasets. In order to provide Ethical, Legal, and Social Aspects/Implications (ELSA/ELSI), datasets require more than the application profiles. Along with the schema, expressing privacy aspects of the data and constraints on rights and licenses also ensures proper ELSI. A good dataset profile needs validation rules provided in actionable formats and with human-readable documentation. A sample data will help the consumers to streamline the process of adapting the datasets. Different solutions exist to express these various components required to represent the datasets, such as DCAT to express the datasets, ShEx, and SHACL to provide validation for datasets, Datapackage for providing the schema for tabular data, DCAP for creating metadata application profiles, vocabularies like DPV to provide privacy constraints and ORDL to express rights of datasets. However, there is no simplified mechanism to interlink and distinguish these various elements in an actionable format. This research is intended to devise a mechanism to express a complete profile package for datasets, as ‘MetaProfile.’ MetaProfile is intended to cover a dataset’s profile with privacy, rights, and other essential components to ensure ELSI and interoperability of datasets. This research’s expected outcome is to provide a format and vocabulary to fill in the gaps of existing solutions for interlinking and notating different components of a profile.
Robust Risk Minimization for Statistical Learning From Corrupted Data

We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a risk function defined by a non-parametric distribution with unknown probability weights. We derive and analyse the optimal weights and show how they provide robustness against corrupted data. Furthermore, we give a computationally efficient coordinate descent algorithm to solve the risk minimization problem. We demonstrate the wide range applicability of the method, including regression, classification, unsupervised learning and classic parameter estimation, with state-of-the-art performance.
Convolution Forgetting Curve Model for Repeated Learning

Most mathematical forgetting curve models can fit the forgetting data well under the condition of one-time learning, rather than repeated learning. In the paper, a convolution model of the forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in the working memory model) and learning material (i.e. the input in the working memory model) is regarded as the system function and the input function, respectively. The status of forgetting (i.e. the output in the working memory model) is regarded as output function or the convolution result of the memory ability and learning material. The model is applied to simulate the forgetting curves in different situations. The results show that the model is able to simulate the forgetting curves not only in one-time learning conditions but also in multi-times conditions. The model is further verified in the experiments of Mandarin tone learning for Japanese learners. And the predicted curve fits well with the test points.
Classification of Technical Debts in Software Development Using Text Analytics

Technical debt refers to the technical trade-offs made by the software teams at the time of development of the software to fasten the delivery process. These trade-offs lead to a higher system maintenance cost, and it is difficult to often enhance the application. In some cases, enhancements can result in the entire modules being rewritten. So, in order to reduce the debt from the system, it is required to identify the debt and the project cycle where most of the debts occur. To automatically address this problem, this work uses the machine learning and text analytics model. Random forest and support vector machine (SVM) algorithms extract features from incident tracker documents and effectively classify the technical debts. The proposed technical debt classification model is made sustainable to handle the growing volumes of project documents by executing a distributed framework. The performance of both the models is examined, and it is found that SVM outperforms the other.