Reviewerly/exHarmony huggingface dataset:
Improving Students’ Listening Skill and Vocabulary Masteryn through Contextual Teaching and Learning (CTL) By Using Online Learning for Senior High School

The aims of this research is to describe the process of improving the students’ listening skill and vocabulary mastery through contextual teaching and learning (CTL) by using online learning at the eleventh grade of SMAN 1 Sajira. This study also attempts to find out the improvement of the students’ skill in listening and vocabulary mastery through contextual teaching and learning (CTL) by using online learning. The method used in this research was qualitative research method by implementing classroom action research (CAR). This research was conducted in three cycles actions in cycle I, cycle 2 and cycle 3. The result of the research is the use of contextual teaching and learning (CTL) by using online learning can improve the students’ listening skill and vocabulary mastery. The finding of the research were: (1) the process of teaching and learning using CTL through online learning was conducted successfully in terms of the feeling of happiness and enthusiasm of the students, (2) there was an improvement of the students’ listening skill from the increased mean of score from 57,85 in pre-test became 81,11 in post-test. This study suggests that the English teacher may improve her teaching strategy by optimizing the use of materials at school; the teacher should be creative in selecting, developing and giving materials at class.
Weakly-supervised Learning Using Pretraining for Classification in HER2 Immunohistochemistry Image of Breast Cancer

Recently supervised deep learning method has achieved good performance in image classification tasks. However, it is very difficult to annotate pathological images accurately for supervised learning tasks. So, the limited amount of labeled data brings great challenges to the supervised learning model. In this paper we propose a weakly-supervised learning method which combines the pretraining technology of transfer learning with deep learning in the HER2 immunohistochemistry (IHC) pathological image classification task of breast cancer. It is worth mentioning that on the network architecture of VGG16 model, we train the model with three different images from pathological images, apply the pretraining model to the HER2 IHC image classification task of breast cancer. The experimental results show that the weakly-supervised learning implemented by the pretraining technology of transfer learning can significantly improve the performance of HER2 IHC pathological image classification task.
Accurate Graphic Symbol Detection in Ancient Document Digital Reproductions

Abstract Digital reproductions of historical documents from Late Antiquity to early medieval Europe contain annotations in handwritten graphic symbols or signs. The study of such symbols may potentially reveal essential insights into the social and historical context. However, finding such symbols in handwritten documents is not an easy task, requiring the knowledge and skills of expert users, i.e., paleographers. An AI-based system can be designed, highlighting potential symbols to be validated and enriched by the experts, whose decisions are used to improve the detection performance. This paper shows how this task can benefit from feature auto-encoding, showing how detection performance improves with respect to trivial template matching.
Self-supervised Feature Detection and Binary Description in Hamming Space for Mobile Platforms

Local features are essential for fundamental visual tasks, e.g., image matching, visual localization, etc. In recent researches, learning-based feature detection and description methods demonstrate remarkable performance. The extracted features are represented as float data and matched in Euclidean space, leading to calculation and memory burden in applications on resource-limited mobile platforms. This paper proposes a single-input multi-output model for feature extraction and a descriptor quantization approach to embedding the features into Hamming space. A self-supervised learning strategy is designed to train the model for high repeatability and matching robustness first. Then we implement the descriptor quantization as an iterative hashing procedure and quantize the extracted features offline to reduce the memory and calculation demands. We also provide a dimensionality reduction process to make a trade-off between performance and resource limitation. The proposed method is suitable for feature-related tasks on hardware-limited platforms and achieved convincing results in the image matching and visual localization benchmarks.
On the behaviour of coalgebras with side effects and algebras with effectful iteration

Abstract For every finitary monad $T$ on sets and every endofunctor $F$ on the category of $T$-algebras, we introduce the concept of an ffg-Elgot algebra for $F$, i.e. an algebra admitting coherent solutions for finite systems of recursive equations with effects represented by the monad $T$. The goal is to study the existence and construction of free ffg-Elgot algebras. To this end, we investigate the locally ffg fixed point $\varphi F$, i.e. the colimit of all $F$-coalgebras with free finitely generated carrier, which is shown to be the initial ffg-Elgot algebra. This is the technical foundation for our main result: the category of ffg-Elgot algebras is monadic over the category of $T$-algebras.
Resnet-based quality evaluation of image mosaic and training data balance

Many image Mosaic algorithms have been pro-posed in recent decades, we also need the algorithm to evaluate the quality of image stitching. This paper verifies the possibility of image mosaic quality evaluation based on neural network. Experiment results show it is effective and has the strong scalability on different data sets. Furthermore, the influence of training data balance and the network deep of ResNet are analyzed. Ten times amount data will reduce the mean error from 2 to 1. Balance data set can reduce the mean error significantly. The deeper ResNet34 is better than ResNet18 which can train faster but needs more memory space.
Design and Implementation of Hotel Reservation Management Platform Based on SOA Framework

To solve the problems of hotel information island, complex network reservation process and inconvenient management, this paper analyzes and studies the hotel business management, and designs an extreme reservation management platform of face-to-face service combined with SOA framework and Webservice system. The system adopts unified channel entrance to form centralized management and distributed operation. Base on the B/S architecture, we adopt the development framework SSH and front-end development technology (jQuery, bootstrap, etc.), combined with the lightweight database Mysql, to achieve seamless heterogeneous platform. At the same time, the compatibility of mobile platform is also considered, and the specific module design and implementation method are provided. Finally, the test results of the system operation show that such scheme can process and analyze the customer booking data in time, provide data basis for the management decision analysis, and improve the satisfaction of customers and merchants.
Formation of Future Teachers’ Professional Reflection during Pedagogical Practice

The research objectives are as follows: to reveal meaningful aspects of future teachers’ professional reflection, to identify conditions of professional reflection formation during pedagogical practice. The article describes features of professional pedagogical reflection formed as a result of interaction of cooperative, communicative, personal and intellectual reflection; it is shown that professional reflection determines specificity of pedagogical consciousness and pedagogical activity, being a key element of professional readiness. The authors present results of studying students’ professional reflection level; identify gaps in their professional training. The paper justifies the thesis that future teachers’ professional reflection is formed through algorithmic analysis of pedagogical phenomena and through reflection on pedagogical situations arising during pedagogical practice. A model to form students’ professional reflection is proposed. Scientific originality of the study involves developing a model to form students’ professional reflection during pedagogical practice. The proposed model contains purpose-oriented, organizational, controlling analytical and correctional blocks. The research findings are as follows: for each stage of training, the authors specify goals, content, forms and methods of work on forming future teachers’ professional reflection, identify areas of diagnostics and correctional work associated with this process.
Labelled EPIE: A Dataset for Idiom Sense Disambiguation

Natural Language Understanding has made recent advancements where context-aware token representation and word disambiguation have become possible to a large extent. In this scenario, comprehension of phrasal semantics particularly in the context of multi word expressions (MWE) and idioms, is the subsequent task to be addressed. Word level metaphor detection is unable to handle phrases or MWE(s) which occur in both literal and idiomatic context. State of the art transformer architectures can be useful in this context, but the absence of a large comprehensive dataset is a bottleneck. In this paper, we present a labelled EPIE dataset containing 3136 occurrences for 358 formal idioms. To prove the efficacy of our dataset, we also train a sequence classification model effectively and perform cross-dataset evaluation on three independent datasets. Our method achieves good results on all datasets with F1 score of 96% on our test data, and 82%, 74% and 76% F1 score on SemEval All Words, SemEval Lex Sample, and PIE Corpus datasets respectively.
A Research and Analysis on the Demand of Blended Learner's Learning Support Service

In order to analyze the demand of blended learner's learning support services, we conducted a questionnaire survey in a university. Empirical analysis results show that: blended learning satisfaction is positively and significantly related to learning support service; blended learners have a higher degree of demand for learning support services; female students have significantly higher demand than male students; non-history and art majors have significantly higher demand than history and art majors; the number of blended courses significantly affects the demand; but there is no significant difference in demand across grades. Therefore, according to the needs of learners, colleges and universities should enhanced awareness of learning support services, ensure investment, provide targeted support services, and construct evaluation systems to assess the quality of services.
Disentanglement Learning of Emotions and Identities from Facial Image

Learning disentangled representations of emotions and identities from facial images is important for understanding human faces. This is because the human face consists of emotions and identities, and the emotions affect the face as expressions. However, because these factors interact with each other, it is difficult to learn to decompose them from facial images. Such entangled representations lead to failure in facial recognition and facial image synthesis tasks. Therefore, we aim to disentangle emotions and identities from the facial image. To achieve this goal, we propose a novel method for learning disentangled representations of emotions and identities. In the experiment, we confirmed that our method achieves to learn disentangled representation of emotions and identities, and allows us to perform an emotion-controllable image synthesis by swapping two facial images.
Geochemical anomaly recognition based on Gaussian mixture model

In order to find the optimal method for identifying geochemical anomalies in geochemical exploration, the Gaussian mixture model and the deep autoencoder network were compared in this paper. Based on the determination of the optimal L value of the Gaussian mixture model and the structure of the deep autoencoder network, geochemical anomaly detection was carried out in the Yawan-Daqiao area, Gansu Province, China. The experimental results show that the geochemical data modeling results of the Gaussian mixture model and the deep autoencoder network are highly consistent with the mineralization characteristics of the study area, and the separated geochemical anomalies have a highly consistent spatial relationship with the known deposit positions in the study area.
Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi

Abstract In unmanned aerial vehicle (UAV) image‐processing applications, one needs to implement different parallel image‐enhancement algorithms on several high‐performance computing platforms utilizing various programming models. To speed up the parallelization procedure and improve its efficiency, the automatic parallel software package, Par4All, is applied in this work. We find that the performance of the original automatic parallelization algorithm produced with Par4All is inefficient. To resolve this problem, we propose different optimization approaches for Par4All based on Intel®'s Xeon Phi high‐performance computing platform that are based on the structural features of the image‐enhancement algorithms, which can further optimize the original parallel algorithm. These approaches mainly include: (1) Par4All automatic parallel search module optimization, (2) dynamic thread setting optimization, and (3) the collaborative parallelization of both CPU and many integrated core (MIC) processors. According to the results of the comparison experiments involving different algorithms, it is shown that the proposed optimization approaches for these kinds of algorithms can significantly improve the performance of automatic parallel algorithms. The acceleration ratio increases approximately by 30%, 70%, and 80% for the multiscale Retinex, Gaussian‐filtering and median‐filtering algorithms, respectively. As continuation and deepening of our previous research work, this research has the potential to be beneficial for other researchers in image‐processing applications with image‐enhancement algorithms.
PCA compression of image deblurring neural networks

In this work, we describe the compression of an image restoration neural network using principal component analysis (PCA). We compress the SRN-Deblur network that was developed by Tao et al.1 and we evaluate the deblurring performance at various levels of compression quantitatively and qualitatively. A baseline network is obtained by training the network using the GOPRO training dataset9. The performance of the compressed network is then evaluated when deblurring images from the Kohler8, Kernel Fusion13 and GOPRO datasets, as well as from a customized evaluation dataset. We note that after a short retraining step, the compressed network behaves as expected, i.e. deblurring performance slowly decreases as the level of compression increases. We show that the SRN-Deblur network can be compressed by up to 40% without significant reduction in deblurring capabilities and without significant reduction of quality in the recovered image.
Development of Knowledge Base Using Human Experience Semantic Network for Instructive Texts

An organized knowledge structure or knowledge base plays a vital role in retaining knowledge where data are processed and organized so that machines can understand. Instructive text (iText) consists of a set of instructions to accomplish a task or operation. Hence, iText includes a group of texts having a title or name of the task or operation and step-by-step instructions on how to accomplish the task. In the case of iText, storing only entities and their relationships with other entities does not always provide a solution for capturing knowledge from iTexts as it consists of parameters and attributes of different entities and their action based on different operations or procedures and the values differ for every individual operation or procedure for the same entity. There is a research gap in iTexts that created limitations to learn about different operations, capture human experience and dynamically update knowledge for every individual operation or instruction. This research presents a knowledge base for capturing and retaining knowledge from iTexts existing in operational documents. From each iTexts, small pieces of knowledge are extracted and represented as nodes linked to one another in the form of a knowledge network called the human experience semantic network (HESN). HESN is the crucial component of our proposed knowledge base. The knowledge base also consists of domain knowledge having different classified terms and key phrases of the specific domain.
Efficient Integration Model of MAS and Blockchain for emergence of Self-Organized Smart Grids

This paper introductory presents a multi agent system model that uses blockchain as the basic technology for successful integration of advanced management solutions into a smart grid environment, realized through the SEGIP project. The paper explains the need for innovative management solutions and provides an overview of energy exchange based on peer-to-peer trading. Additionally, the paper covers the role of a multi-agent system in effective integration and outlines the structure of the proposed project solution. The functioning of such a smart grid is explained through a description of the roles and interactions of different actors, and a smart contract proposal is described through an example of a transaction between different types of network nodes. The conclusion completes everything presented in the paper and confirms the validity of the presented approach.
Dealing with Ethical Challenges in HCI Fieldwork

We are witnessing an increase in fieldwork within the field of HCI, particularly involving marginalized or under-represented populations. This has posed ethical challenges for researchers during such field studies, with "ethical traps" not always identified during planning stages. This is often aggravated by the inconsistent policy guidelines, training, and application of ethical principles. We ground this in our collective experiences with ethically-difficult research, and frame it within common principles that are common across many disciplines and policy guidelines – representative of the instructors’ diverse and international backgrounds.
The Geranium Platform: A KG-Based System for Academic Publications

Knowledge Graphs (KGs) have emerged as a core technology for incorporating human knowledge because of their capability to capture the relational dimension of information and of its semantic properties. The nature of KGs meets one of the vocational pursuits of academic institutions, which is sharing their intellectual output, especially publications. In this paper, we describe and make available the Polito Knowledge Graph (PKG) –which semantically connects information on more than 23,000 publications and 34,000 authors– and Geranium, a semantic platform that leverages the properties of the PKG to offer advanced services for search and exploration. In particular, we describe the Geranium recommendation system, which exploits Graph Neural Networks (GNNs) to suggest collaboration opportunities between researchers of different disciplines. This work integrates the state of the art because we use data from a real application in the scholarly domain, while the current literature still explores the combination of KGs and GNNs in a prototypal context using synthetic data. The results shows that the fusion of these technologies represents a promising approach for recommendation and metadata inference in the scholarly domain.
Facial Feature Information Based Computer-aided Emoticon Design System

Computer-aided design has been studied for decades. Under the current technical background, computer technology can be used to express designers' ideas more efficiently and design suitable products for users. However, there are not many applications on emoticons. This paper proposes a social network emoticon design system framework which combines face recognition technology and feature extraction technology. Through case practice, the conclusion is that the emoticon design system based on facial feature information can be used in large quantities for different users to design personalized emoticons, software tools for imaging can make a large number of personalized design into reality. Based on this, intelligent image design software has great development space and application value.
A Study of Machine Learning Techniques in Cryptography for Cybersecurity

The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.
Key Information Recognition from Piping and Instrumentation Diagrams: Where We Are?

Nowadays, the increase of technical drawings in different industries such as construction, mechanical and the energy sector makes the task of information analysis and interpretation more complex and fastidious. In this context, the automatic digitization of these drawings is becoming important. Piping and instrumentation diagram (P&ID) is a type of engineering drawing where the flow and components are represented by lines, texts and symbols. In this paper, we propose an industrial research approach in order to detect symbols, texts and lines. We focus on the application of recent computer vision and natural language processing techniques to automatically detect and recognize the different components. First experimental results on real-world data show that the proposed pipeline can achieve competitive results.