Reviewerly/exHarmony huggingface dataset:
Toward Data-Driven Collaborative Dialogue Systems: The JILDA Dataset

Today's goal-oriented dialogue systems are designed to operate in restricted domains and with the implicit assumption that the user goals fit the domain ontology of the system.Under these assumptions dialogues exhibit only limited collaborative phenomena.However, this is not necessarily true in more complex scenarios, where user and system need to collaborate to align their knowledge of the domain in order to improve the conversation and achieve their goals.To foster research on data-driven collaborative dialogues, in this paper we present JILDA, a fully annotated dataset of chat-based, mixed-initiative Italian dialogues related to the job-offer domain.As far as we know, JILDA is the first dialogic corpus completely annotated in this domain.The analysis realised on top of the semantic annotations clearly shows the naturalness and greater complexity of JILDA's dialogues.In fact, the new dataset offers a large number of examples of pragmatic phenomena, such as proactivity (i.e., providing information not explicitly requested) and grounding, which are rarely investigated in AI conversational agents based on neural architectures.In conclusion, the annotated JILDA corpus, given its innovative characteristics, represents a new challenge for conversational agents and an important resource for tackling more complex scenarios, thus advancing the state of the art in this field.
Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset's values.In most research studies, the existence of missing values (MVs) is a vital problem.In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high.In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure.The proposed algorithm imputes MVs in cumulative order.The candidate feature to be manipulated is selected using similarity classifier with Parkash's fuzzy entropy measure.The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique.Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature.The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms.The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 score) were used to measure the performance.The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type.Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.
A Quantum Key distribution using Optical Frequency Comb Source

The use of quantum cryptography aims to perform a secure key exchange, so that agents outside the system are not able to obtain information flowing through the channel. This work proposes the use of a Quantum Key Distribution (QKD) technique, specifically the BB84 protocol, with the objective of randomly generating a key for the encryption of information and distribute it securely afterwards. As a result, the same key is successfully generated at each end of the communication channel, and is never transmitted over it, for which it proposes the use of an Optical Frequency Comb Source (OFCS), seeking to increase the capacity of the optical medium to support more transmitter-receiver pairs (Client-Server)
PENGENALAN HURUF LATIN PADA ANAK USIA DINI DENGAN PENERAPAN METODE BACKPROPAGATION

Early childhood has difficulty remembering Latin letters or Roman characters than adults. Some of the factors that cause it are cognitive development, motivation, interest in learning, emotions and environmental factors. To overcome this, an innovative media is needed so that children can easily remember Latin letters. One of the innovative media applies digital image processing techniques and artificial intelligence. The fonts used are 10 types of letter models with image processing techniques such as preprocessing, binaryization, pixel mapping and creating vector as feature extraction. While the artificial intelligence used is the backpropagation method. The total data is 208 letter images with 625 input features with 500 epochs, the best learning rate used by the system is 0.025 so that the best training accuracy is 93.96% and testing accuracy is 92.31%.
The Factors of Professional Training of a Primary School Teacher in the Context of the Second Higher Education

The article highlights the problem of effective education of adults receiving the second higher education. The importance of this problem is related to the specifics of the formation of the contingent of part-time students majoring in 013 Primary education, which is heterogeneous in age, basic education, and professional activity. The data collection methods and methods of quantitative mathematical analysis are used in our pedagogical research. It was found that in the process of professional training of primary school teachers in the second higher education should take into account the following factors: age of students, their needs, social status, the availability of main education, and work due to the specialty. This determines the selection of methods and techniques of teaching, technologies that provide quality practical training, taking into account their professional and life experience, intrinsic motivation and a strong desire for self-realization. The study identified the most effective teaching methods’ to be relevant in acquiring the necessary knowledge and skills required for future teachers– the practical, interactive teaching methods, case studies, simulation, problem solving, critical thinking etc.
Modern Trends in Education in the Era of Digitalization: Task and Prospects of Development

The article suggests an overall review of modern technological trends in education. The authors aimed to figure out the objectives, benefits and obstacles of their development. The article deals with innovations, which have already been and are being implemented in education system with Industry 5.0 shortly ahead. The main aim is to determine social and cultural impact of rapid digitalization in the sphere of education. Research methods were based on deductive categories. Elements of descriptive and comparative analysis were applied for identification of major patterns and tendencies. The review given by the authors proves that education is one of the main challenges for authorities, teachers and students in terms of digitalization. The results of the research indicate the importance of new paradigm and reconsidering of educational environment in general. It involves technological, social and communicative aspects. Some conventional standards have become obsolete and we need to acknowledge the fact. Modern society is facing not only technological challenges but also the ones concerning attitude and skills. We can conclude that effective agreement between social and technological process can be reached if legal framework and standardized approaches are involved.
Ultra-High-Throughput Multi-Core AES Encryption Hardware Architecture

Security issues in high-speed data transfer between devices are always a big challenge. On the other hand, new data transfer standards such as IEEE P802.3bs 2017 stipulate the maximum data rate up to 400 Gbps. So, security encryptions need high throughput to meet data transfer rates and low latency to ensure the quality of services. In this paper, we propose a multi-core AES encryption hardware architecture to achieve ultra-high-throughput encryption. To reduce area cost and power consumption, these cores share the same KeyExpansion blocks. Fully parallel, outer round pipeline technique is also applied to the proposed architecture to achieve low latency encryption. The design has been modelled at RTL (Register-Transfer-Level) in VHDL and then synthesized with a CMOS 45nm technology using Synopsys Design Compiler. With 10-cores fully parallel and outer round pipeline, the implementation results show that our architecture achieves a throughput of 1 Tbps at the maximum operating frequency of 800 MHz. These results meet the speed requirements of future communication standards. In addition, our design also achieves a high power-efficiency of 2377 Gbps/W and area-efficiency of 833 Gbps/mm2, that is 2.6x and 4.5x higher than those of the other highest throughput of single-core AES, respectively.
Efficient Lattice-Based Cryptosystems with Key Dependent Message Security

Key-dependent message (KDM) security is of great research significance, to better analyse and solve the potential security problems in complex application scenarios. Most of the current KDM security schemes are based on traditional hard mathematical problems, where the public key and ciphertext are not compact enough, and make the ciphertext size grow linearly with the degree of the challenge functions. To solve the above problems and the inefficient ciphertext operation, the authors propose a compact lattice-based cryptosystem with a variant of the RLWE problem, which applies an invertible technique to obtain the RLWE* problem. It remains hard after the modification from the RLWE problem. Compared with the ACPS scheme, our scheme further expands the set of challenge functions based on the affine function of the secret key, and the size of public key and ciphertext is O˜(n), which is independent of the challenge functions. In addition, this scheme enjoys a high level of efficiency, the cost of encryption and decryption is only ploylog(n) bit operations per message symbol, and we also prove that our scheme is KDM-CPA secure under the RLWE* assumption.
COMPARISON OF SOFTWARE COMPLEXITY OF SEARCH ALGORITHM USING CODE BASED COMPLEXITY METRICS

Measures of software complexity are essential part of software engineering. Complexity metrics can be used to forecast key information regarding the testability, reliability, and manageability of software systems from study of the source code. This paper presents the results of three distinct software complexity metrics that were applied to two searching algorithms (Linear and Binary search algorithm). The goal is to compare the complexity of linear and binary search algorithms implemented in (Python, Java, and C++ languages) and measure the sample algorithms using line of code, McCabe and Halstead metrics. The findings indicate that the program difficulty of Halstead metrics has minimal value for both linear and binary search when implemented in python. Analysis of Variance (ANOVA) was adopted to determine whether there is any statistically significant differences between the search algorithms when implemented in the three programming languages and it was revealed that the three (3) programming languages do not vary considerably for both linear and binary search techniques which implies that any of the (3) programming languages is suitable for coding linear and binary search algorithms.
Teachers’ personal and professional competence development of postgraduate education

Hrytsai Serhii. Teachers' personal and professional competence development of postgraduate education
Face Reconstruction with Variational Autoencoder and Face Masks

Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are made possible, including face reconstruction and face synthesis. In this work, we investigated how face masks can help the training of VAEs for face reconstruction, by restricting the learning to the pixels selected by the face mask. An evaluation of the proposal using the celebA dataset shows that the reconstructed images are enhanced with the face masks, especially when SSIM loss is used either with l1 or l2 loss functions. We noticed that the inclusion of a decoder for face mask prediction in the architecture affected the performance for l1 or l2 loss functions, while this was not the case for the SSIM loss. Besides, SSIM perceptual loss yielded the crispest samples between all hypotheses tested, although it shifts the original color of the image, making the usage of the l1 or l2 losses together with SSIM helpful to solve this issue.
Open Innovation Model Of Improving Critical Thinking Competence In University Students

The article explores the possibilities of introducing the Open Innovation concept into educational practices.The aim of the study is to shape the Open Innovation model which will enable the improvement of critical thinking competence in students in the learning environment.The research was carried out on the basis of the Open Innovation paradigm and problem-solving approach to the critical thinking competence.The critical thinking competence framework based on the logics of problem solving (identifying the problem, representing the problem, selecting an appropriate strategy, implementing the strategy and evaluating solutions) is viewed as a complex system of interconnected functional and structural components: critical thinking dispositions, metacognitive critical thinking skills, critical thinking operational skills and reflective critical thinking skills which function for cognitive, developmental, interactive and creative purposes with reference to problem solving.Meaningful learning, active thinking-based learning, cooperative thinking, competitive learning and critical thinking challenges are the main pedagogic principles underlying the Open Innovation model in education.The Open Innovation model is an open system of problem-solving activities realized at four sequencing stages: diagnostic stage, cognitive stage, operational stage and a stage of reflection, and it is aimed at the improvement of the critical thinking competence components.The novelty of the study is associated with the conceptualization of the Open Innovation model in the educational context.
Hierarchical Text Classification Based on the End-to-End MCHA-BERT

Hierarchical text classification (HTC) is a significant task, in which the text has multiple labels that constitute a hierarchical structure. Because of its significant application value, HTC has attracted the attention of researchers in the field of natural language processing. Existing methods tend to adopt the same strategy to tackle different levels' label prediction, which ignores the difference between various levels. Moreover, they don't utilize upper-level effective information and discards part of the hierarchical attributes. In this paper, we propose an end-to-end MCHA-BERT model, where a multi-granularity convolution module (MCM) and a hierarchical attention mechanism (HAM) are introduced based on multi-task structure. Firstly, every level's label prediction is regarded as one task, and different strategies are adopted for different tasks. In MCM, multi-size 1D convolutions are adopted to encode features of various granularities for different levels' label prediction. Secondly, HAM is proposed to fully integrate different levels' multi-granularity features for subsequent prediction. In general, experiments prove that our proposed MCHA-BERT outperforms other main methods on HTC. The ablation experiments also illustrate the effectiveness of MCM and HAM respectively.
Multistage Polymerization Network for Multiperson Pose Estimation

Multiperson pose estimation is an important and complex problem in computer vision. It is regarded as the problem of human skeleton joint detection and solved by the joint heat map regression network in recent years. The key of achieving accurate pose estimation is to learn robust and discriminative feature maps. Although the current methods have made significant progress through interlayer fusion and intralevel fusion of feature maps, few works pay attention to the combination of the two methods. In this paper, we propose a multistage polymerization network (MPN) for multiperson pose estimation. The MPN continuously learns rich underlying spatial information by fusing features within the layers. The MPN also adds hierarchical connections between feature maps at the same resolution for interlayer fusion, so as to reuse low-level spatial information and refine high-level semantic information to obtain accurate keypoint representation. In addition, we observe a lack of connection between the output low-level information and the high-level information. To solve this problem, an effective shuffled attention mechanism (SAM) is proposed. The shuffle aims to promote the cross-channel information exchange between pyramid feature maps, while attention makes a trade-off between the low-level and high-level representations of the output features. As a result, the relationship between the space and the channel of the feature map is further enhanced. Evaluation of the proposed method is carried out on public datasets, and experimental results show that our method has better performance than current methods.
Using Language Games to Enhance EFL Students’ Speaking Skill in Indonesia

This research aims at enhancing EFL students’ speaking skill through the use of language games. The research was designed in the form of Classroom Action Research. It was conducted in two cycles and six meetings. Each cycle consisted of plan, implementation, observation, and Reflection. The researchers employed an observation checklist, field notes, questionnaire, and test to collect the data. The main criterion of success of this research was that the students’ scores on the speaking test should achieve a score of 70, and it must be achieved by 75% of the total number of students. The second criterion was that the lecturer’s classroom performance should meet the “Success” category. The results of the first speaking test showed that from 24 students in the classroom, 15 students got scores of 70 or higher. It was equal to 62.5 %. In cycle 2, the total number of students who got scores of 70 or higher was 21. It was equal to 87.5 %. The result of this research indicated that the use of language games can develop the students’ speaking skills
Research on key influencing factors of university students' digital competence in blended teaching

Understanding the current state of information and communication technology (ICT) use in teaching situations will help schools better develop students' 21st-century skills to meet the digital age's challenges and opportunities. The purpose of this paper is to provide reference for colleges and universities to formulate the framework and implementation strategies of digital literacy education, by analysing the key factors that influence the digital literacy education of college students. Firstly, based on literature research and activity theory, this paper integrates and refines the influencing factors of digital literacy education of college students from five dimensions, and analyses the key factors affecting digital literacy education. Then, experts were invited to fill in the questionnaire to obtain sample data, and the interaction between the factors was analysed by using the decision-making trial and evaluation laboratory method to identify the key influencing factors. The results show that university policy, cultural environment, ICT infrastructure, teaching management and evaluation system are the five key influencing factors. Keywords: ICT, digital literacy education, university students, activity theory, DEMATEL
Recent Theoretical Advances with the Discrete Spike-and-Slab Priors

The key to inference in settings where the dimensionality of the parameter space is larger than the number of samples is to assume a parsimonious structure underlying the data generating process. While Bayesian hierarchical models offer a unified and coherent framework for structured modeling and inference, the properties of the posterior distribution and the exact role of the hierarchy have begun to be understood in the last few years. In this chapter, we review some of the recent theoretical works aiming to provide theoretical guarantees of discrete spike-and-slab priors. As one moves away from simple parametric models, understanding properties of a posterior distribution poses a stiff technical challenge and the prior choice (e.g., spike-and-slab) assumes a more fundamental role. Besides, full Bayesian computation using these hierarchical models often poses a significant computation bottleneck, necessitating the development of approximate Bayes methods such as variational inference.
Pengembangan Media Articulate Storyline dalam Pembelajaran Menulis Puisi pada Kelas X SMA Negeri 2 Sukabumi

Learning media is one of the supporting facilities in teaching and learning activities. In this time of the COVID-19 pandemic, online-based learning media is very necessary. The lack of teacher ability in using interactive learning media makes online learning even more boring, especially in learning to write poetry. The articulate storyline is one software that can produce more interesting learning media. Using many features can produce learning media that are more varied, innovative, and easy to use. The research method used is RND, which has ten research steps that the researchers then simplified into four main steps, namely the research and data collection stage, the planning stage, the product development stage, and the validation and testing stage. Media development results are a product of a poem made by class X students at SMA Negeri 2 Sukabumi. The product was produced after learning poetry material using online interactive learning media. Three validators assessed the media feasibility level. The material validator is given a feasibility value of 82%, the medium validator is given a value of 80%, and the Indonesian language learning validator is given 84%. Meanwhile, 89% of students responded "Ya" in student responses, and 11% of students responded "Tidak". Based on the validator's assessment results, the media was considered very feasible to be used in schools and received a positive response from students.
Deep Learning for the Improvement of Object Detection in Augmented Reality

In this article, we give a comprehensive overview of recent methods in object detection using deep learning and their uses in augmented reality. The objective is to present a complete understanding of these algorithms and how augmented reality functions and services can be improved by integrating these methods. We discuss in detail the different characteristics of each approach and their influence on real-time detection performance. Experimental analyses are provided to compare the performance of each method and make meaningful conclusions for their use in augmented reality. Two-stage detectors generally provide better detection performance, while single-stage detectors are significantly more time efficient and more applicable to real-time object detection. Finally, we discuss several future directions to facilitate and stimulate future research on object detection in augmented reality. Keywords: object detection, deep learning, convolutional neural network, augmented reality.