Reviewerly/exHarmony huggingface dataset:
The comparison between Conditional Generative Adversarial Nets and Deep Convolutional Generative Adversarial Network, and its GUI-related application

In recent years, Generative Adversarial Nets (GAN), Conditional Generative Adversarial Nets (CGAN), and Deep convolutional generative adversarial networks (DCGAN) have generally been well-received in Artificial Intelligence (AI) industry. This paper first briefly introduces the fundamentals of GAN, CGAN, and DCGAN. Next, we focus on comparing two improved GAN variants– CGAN and DCGAN. To be specific, we train them with certain architectural constraints on two datasets – MNIST and Animation images. We show convincing evidence that DCGAN outperforms CGAN in terms of processing image datasets to a large extent. Additionally, we make a Graphical User Interface (GUI), enabling users to choose face photos with different tags generated by DCGAN.
Comparing of educational programs in terms of achievement of competencies and learning outcomes of compulsory educational components

The most important elements of educational programs are the educational components (disciplines), as well as the competencies and learning outcomes they provide. The article presents a comparative analysis of some compulsory disciplines of the educational and professional program "Informatics" of the first (bachelor's) level of higher education in the field of knowledge 12 "Information Technology", specialty 122 "Computer Science" with disciplines of educational and professional programs of the same level and specialties of other institutions of higher education. The work analyzes the educational and professional program "Computer Science" of the first (bachelor's) level of higher education in the specialty 122 "Computer Science". It is implemented by the Faculty of Computer Science and Cybernetics of Taras Shevchenko National University of Kyiv in terms of comparing compulsory educational components educational-professional program "Informatics" and provided (achievable) standard learning outcomes and competencies with compulsory educational components of other educational programs.
Color Image Segmentation by Utilizing Coarse-to-Fine Strategy

Image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method; (2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions; (3) a one-step Chan–Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.
Exploring resources for teaching entrepreneurship during COVID-19 pandemic

ABSTRACTABSTRACTThe COVID-19 pandemic brought changes to entrepreneurship education. This new reality brings a big challenge for entrepreneurship professors. This study explores the use of resources by professors during the COVID-19 pandemic to determine changes, support, and success factors to teach entrepreneurship. Also, we explore the resources that professors perceived that they would need postpandemic. We performed 13 qualitative interviews with entrepreneurship professors in Mexico. We identified institutional and emotional support and a network of support as essential elements for professors. Flexibility is a recurrent theme. Results from this research may be helpful for those in charge of educational institutions who need to adapt workplace policies and make sure to fulfill the needs of professors in times of crisis.KEYWORDS: Entrepreneurship educationOnline learningCOVID-19CoronavirusTeaching resources
Ontology Engineering at the Assertion Level Based on Semantic Annotation of Tabular Data

Web tables are an important source of knowledge and can be used for ontology engineering. In this paper, we propose the approach for the semi-automated formation of domain ontologies at the assertion level (ABox) based on web tables. The proposed approach consists of 6 steps and is based on the use of the semantic annotation of separate elements of web tables. With the use of the semantic annotation procedure, we extract specific entities (ontology instances) from table cells. Our approach is implemented in the form of a web-based tool. We present a description of the architecture of this tool, as well as its experimental evaluation in the case of domain ontology engineering for the TALISMAN framework. We have formed a special dataset for our experiment. The results obtained show the prospects of using our approach and tool to support ontology engineering based on the semantic annotation of web tables.
Super Resolution with Sparse Gradient-Guided Attention for Suppressing Structural Distortion

Generative adversarial network (GAN)-based methods recover perceptually pleasant details in super resolution (SR), but they pertain to structural distortions. Recent study alleviates such structural distortions by attaching a gradient branch to the generator. However, this method compromises the perceptual details. In this paper, we propose a sparse gradient-guided attention generative adversarial network (SGAGAN), which incorporates a modified residual-in-residual sparse block (MRRSB) in the gradient branch and gradient-guided self-attention (GSA) to suppress structural distortions. Compared to the most frequently used block in GAN-based SR methods, i.e., residual-in-residual dense block (RRDB), MRRSB reduces computational cost and avoids gradient redundancy. In addition, GSA emphasizes the highly correlated features in the generator by guiding sparse gradient. It captures the semantic information by connecting the global interdependencies of the sparse gradient features in the gradient branch and the features in the SR branch. Experimental results show that SGAGAN relieves the structural distortions and generates more realistic images compared to state-of-the-art SR methods. Qualitative and quantitative evaluations in the ablation study show that combining GSA and MRRSB together has a better perceptual quality than combining self-attention alone.
Analysis on Quality Evaluation of Animation Majors' Works from Perspective of DANP model

DANP combined with DEMATEL and ANP methods is proposed to analyze the key factors in the evaluation system and evaluate four students' works by using the optimized grey correlation analysis method. The quality evaluation of animation students' works is taken as the research object. The Optimized Grey correlation analysis method is applied to evaluate four students' works. The results show that A2 has the greatest impact on the whole system and other factors, and A1, B2, and A3 have a high contribution to the evaluation system. C2 cause degree is −0.0403, which reflects that the index is little affected by other factors. The heart degree value is 3.3738, which also reflects that the index plays an important role in the whole evaluation system. Thus, it is identified as a key influencing factor. Judging from the two gray values, the final evaluation index is A1 (1.5191), followed by A4 (1.4087), indicating that A1 has the best performance among the four students' works. The result provides reference for the quality evaluation of students' works in Chinese universities.
Basic TensorFlow

TensorFlow is a scientific computing library of deep learning algorithms. All operations are performed based on tensor objects. Complex neural network algorithms are essentially a combination of basic operations such as multiplication and addition of tensors. Therefore, it is important to get familiar with the basic tensor operation in TensorFlow. Only by mastering these operations can we realize various complex and novel network models at will and understand the essence of various models and algorithms.
Asymmetric Multi-Task Learning with Local Transference

In this article, we present the Group Asymmetric Multi-Task Learning (GAMTL) algorithm that automatically learns from data how tasks transfer information among themselves at the level of a subset of features. In practice, for each group of features GAMTL extracts an asymmetric relationship supported by the tasks, instead of assuming a single structure for all features. The additional flexibility promoted by local transference in GAMTL allows any two tasks to have multiple asymmetric relationships. The proposed method leverages the information present in these multiple structures to bias the training of individual tasks towards more generalizable models. The solution to the GAMTL’s associated optimization problem is an alternating minimization procedure involving tasks parameters and multiple asymmetric relationships, thus guiding to convex smaller sub-problems. GAMTL was evaluated on both synthetic and real datasets. To evidence GAMTL versatility, we generated a synthetic scenario characterized by diverse profiles of structural relationships among tasks. GAMTL was also applied to the problem of Alzheimer’s Disease (AD) progression prediction. Our experiments indicated that the proposed approach not only increased prediction performance, but also estimated scientifically grounded relationships among multiple cognitive scores, taken here as multiple regression tasks, and regions of interest in the brain, directly associated here with groups of features. We also employed stability selection analysis to investigate GAMTL’s robustness to data sampling rate and hyper-parameter configuration. GAMTL source code is available on GitHub: https://github.com/shgo/gamtl .
A Dynamic Cooperative Multi-Agent Online Coverage Path Planning Algorithm

This paper proposes a novel approach for finding an optimized solution for the online coverage path planning in unknown environments problem employing cooperative multi robotic agents. The suggested approach lessens the time required for solving the coverage problem by using cooperative multiple robotic agents working together to achieve enhanced complete coverage while sustaining minimum time, and the minimum number of repeated cells and respecting the service time. The distributed multi-agent planning with coordination process is optimized using dynamic programming with a rolling horizon limited look-ahead policy for online capability. In addition to expanding the usual single-agent action tree to hold a multi-agent scheme for possible collision-free sub-trajectories generation con-cerning quality, repetitions count, length per single agent to plan and execute cooperatively for covering a given unknown sur-roundings. Regarding coverage optimization, we utilize a multi-objective optimization genetic based algorithm with uniform cost search for the single-agent coverage sub-trajectory planning. In multi-agent-based systems, communication introduces overhead and have a direct influence on the system's robustness. Hence, we proposed a voting and coalition formation based on the K-means clustering algorithm for minimizing the communication henceforth improving the system's robustness. Simulation results highlight an enhancement regarding speeding up the complete coverage process compared to optimum single-agent approaches while advancing near-optimal utilization and efficiency minimizing repetitions in different unknown environments.
Modified Nominal Group Technique (NGT) for Evaluating HyTEE Model (Hybrid Software Change Management Tool with Test Effort Estimation)

When changes are made to a software system during development and maintenance, they need to be tested again, i.e., regression tests to ensure that changes behave as intended and have not impacted the software quality. This research will produce an automated tool that can help the software manager or a maintainer to search for the coverage artifact before and after a change request. Hence, the purpose of this research is to improve software changes with the traceability model and test effort estimation during regression testing. To achieve this goal, this study used one case study that matches the HYTEE(Hybrid Software Change Management Tool with Test Effort Estimation) model as well. This paper reports the use of the modified Nominal Group Technique (NGT) as a method to evaluate the Evaluation of the HYTEE Model. From the studies, it is hoped that an efficient and improve the solution to regression testing can be realized, thus, gives the benefits to software testers and project manager manage the software changes task since it is a critical part in software maintenance.
Determination of the Features of the Author’s Style of A.S. Pushkin’s Poems by Machine Learning Methods

This paper presents the study of the author’s style of A.S. Pushkin based on the comparison of his poetic texts with the texts of contemporary poets. The purpose of this study is to determine the features of the author’s style of A.S. Pushkin using machine learning methods. This paper describes the construction of several classifications based on different groups of features, as well as the classification based on a combined set of features from different groups. The quality of all constructed classifications is also analyzed; special attention is paid to the interpretation of the neural network solution and the identification of features of the author’s style.
Multi-Label Classification for Scientific Conference Activities Information Text Using Extreme Gradient Boost (XGBoost) Method

One of the government’s aims is to improve the quality of education that requires lecturers to conduct scientific research. This scientific research must be disseminated later, for example through scientific conferences. One important aspect of submitting scientific articles is field suitability. A large number of scientific conferences is accompanied by a large number of fields. This of course will make it difficult for researchers to find suitable scientific conferences. Therefore, to assist in the selection, an automatic grouping of information on scientific conference activities was carried out. This study aims to classify the information data on scientific conferences in Indonesia with a total of 1005 data. The data that has been collected will be classified into three labels, namely Economics, Science and Engineering, and Social Studies. This classification is multi-label, meaning that each data can have more than one label. This study uses the Extreme Gradient Boost (XGBoost) method. Testing of the hyperparameters is carried out to get the optimal model. This test produces a Word2Vec hyperparameter with a dimension of 100 and a window size of 15. For the XGBoost hyperparameter, optimal conditions are obtained when estimators 100, learning rate 0,1, maximum depth 6, minimum child weight 10, and gamma 3,6. This model was then evaluatedusing the K-fold Cross-Validation which resulted in an average hamming score of 79.52% and an f1 score of 85.88%.
Analysis of Conversational Speech with Application to Voice Adaptation

Conversational speech has always been challenging in the context of text-to-speech synthesis (TTS). Most speech synthesis systems are trained on read speech data recorded in a studio environment. But, the intelligibility of TTS systems degrades drastically when using conversational speech. The proposed work attempts to perform extensive analysis on the issues in dealing with conversational speech compared to read speech. As an application, we try to dub the lectures available in English into an Indian language (Hindi) in the original speaker's voice. The task is difficult as classroom lectures are extempore, with variations in speaking rate, and contain speaker mannerisms that lead to disfluencies. We analyze the capability of end-to-end TTS systems in modeling lecture-based data. Based on the analysis, an attempt is made to adapt “read speech TTS system” using conversational speech data to produce lectures in the original speaker's voice.
Personalized Recommendation Algorithm for Curriculum- and Politics-Oriented Hybrid Teaching Resources

To meet the needs of personalized learning of social talent and students and enhance the effect of hybrid teaching resource recommendations, this paper proposes a personalized recommendation algorithm for hybrid teaching resources oriented to curriculum ideology. Based on the idea of curriculum thinking, the author chooses and acquires mixed teaching resources, judges the difficulty of teaching resources and calculates the similarity of learning resources. The mixed teaching resources are represented by classification, and the knowledge level of students is modeled, analyzed and diagnosed by combining the answer situation matrix and the knowledge point matrix. According to the learning preference of students and the sequence of knowledge points and the difficulty of learning resources, the personalized recommendation of hybrid teaching resources can be realized. Experimental results show that the proposed algorithm has a higher recommendation accuracy, recall andF1 than the minimum value, which fully proves that the proposed algorithm is better.
Video Anomaly Detection Using Dual Discriminator Based Generative Adversarial Network

Video anomaly detection is of great significance due to its wide applications in video surveillance. Recently, there is a trend of using a video prediction framework to tackle this problem. This kind of methods detects anomalies according to the difference between a predicted frame and its ground truth. However, existing prediction methods lack the consideration of multi-scale temporal constraints on generating future frames. Therefore, based on the prediction framework, this paper proposes a temporal enhanced anomaly detection approach, which designs a generative adversarial network with dual discriminator (frame discriminator and sequence discriminator) to predict future frames. In order to obtain more realistic predictions, other than commonly used spatial constraints and the adversarial penalty from the frame discriminator, we also consider both short-range and long-range motions to impose constraints. Specifically, for short-range motion modeling, we utilize the optical flow loss to ensure temporal continuity over two adjacent frames, while for long-range motion modeling, we design a sequence discriminator to identify fake contained sequences from real sequences, making frames more consistent with their previous consecutive frames. Experiments on three datasets, UCSD Ped1, UCSD Ped2 and Avenue, demonstrate the effectiveness of our method in terms of various evaluation criteria for video anomaly detection.
The influence of image enhancement algorithm on face recognition system

Aiming at the low efficiency of face recognition devices such as access control systems due to resolution and uneven illumination in dark environments, the Harr feature is used to detect the face, and the EigenFace method is used to extract the characteristic face information, and a face recognition system is established for simulation the actual situation. Using image enhancement methods such as histogram equalization, Retinex, and Median filtering to process portraits, experiments were conducted on the ORL library and self-collected face training set. The experimental results show that some image enhancement algorithms can significantly improve the face recognition rate, and Image enhancement algorithms are conducive to machine recognition in the field of portraits.
Visual Data Analysis

Data visualization is a research discipline involving computer science techniques in which numeric data is transferred into visual information. This is especially important when dealing with the exploratory analysis of data since humans are better at interpreting visual information rather than just numbers on their own. Thus, in this chapter, basic visualization techniques will be introduced, and their application in different stages of the data analysis process will be discussed. Alongside theoretical explanations, a practical example using Python code as well as the latest data visualization libraries and tourism data will be provided.
A Study on Accuracy Improvement of Small Object Detection Using Deep Neural Networks

The accuracy of visual object detection, which es-timates locations and classes of target objects in input images, has been drastically improved by the rapid advancements of deep convolutional neural networks (CNNs). The evaluation of existing methods based on CNNs is usually conducted using major datasets such as MS-COCO and PASCAL-VOC, and these datasets include several sizes of target objects. The accuracy of detecting of larger objects has become remarkable via recent methods; however, it remains difficult for recent CNNs to accurately detect small objects. To address this problem, this study investigates how to improve the accuracy of small object detection using CNNs. For the investigation, two types of datasets that solely comprise small target objects were created: the Bird and SAVMAP datasets that solely include flying objects in the sky and mammals in the savannah, respectively. Experimental results obtained with the datasets indicate that the input size, depth of CNN layers, and surrounding context of target objects were important factors for small object detection. Furthermore, these results demonstrate that EfficientDet-DO achieved accuracies of 0.6585 and 0.6501 for the Bird and SAVMAP datasets, respectively.
Speeding up Block Propagation in Bitcoin Network: A Cut-through Relaying Scheme

We design and validate a new block propagation protocol for the peer-to-peer (P2P) network of the Bitcoin blockchain. Despite its strong protection for security and privacy, the current Bitcoin blockchain can only support a low number of transactions per second (TPS). In this work, we redesign the current Bitcoin&#x0027;s networking protocol to increase TPS without changing vital components in its consensus-building protocol. In particular, we improve the compact-block relaying protocol to enable the propagation of blocks containing a massive number of transactions without inducing extra propagation latency. This improvement is achieved by replacing the existing store-and-forward compact-block relaying scheme with a cut-through compact-block relaying scheme. Since our protocol only needs to rework the current Bitcoin&#x0027;s networking protocol and does not modify the data structures and crypto-functional components, it can be seamlessly incorporated into the existing Bitcoin blockchain. To validate our design, we implement a Bitcoin network simulator on Network Simulator-3 (NS-3) to run different block propagation protocols. The experimental results confirm that our new block propagation protocol could increase the TPS of the Bitcoin blockchain by 100x without compromising security and consensus-building.