Reviewerly/exHarmony huggingface dataset:
Combining microfluidic spleen-like filtering unit with machine learning algorithms to characterize rare hereditary hemolytic anemia

Abstract Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic spleen-like filtering unit and video data analysis are combined for the characterization of RBCs in RHHA. A filtering unit is designed to measure deformability by maintaining fixed the RBC orientation to study its capacity of restoring the original shape after crossing microconstrictions. Two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.
Research on bank fraud transaction detection based on LSTM-Focalloss

Bank fraud transaction has brought huge losses to consumers and banks, and the original rule-based fraud detection method is not suitable for various new fraud way. According to the fact that bank fraud transaction is a typical unbalanced data classification problem, two neural network models of DNN and LSTM are established, with a new type loss function, Focalloss is used to train and test on the Kaggle's TESTIMON Dataset. As the test results, LSTM-Focalloss's network was able to detect fraud transactions significantly higher than other methods, indicating that this network model is very effective in detecting bank fraud transactions.
End-to-end Learning of Body Weight Prediction from Point Clouds with Basis Point Sets

The body weight of a patient is an important parameter in many clinical settings, e.g. when it comes to drug dosing or anesthesia. However, assessing the weight through direct interaction with the patient (anamnesis, weighing) is often infeasible. Therefore, there is a need for the weight to be estimated in a contactless way from visual inputs. This work addresses weight prediction of patients lying in bed from 3D point cloud data by means of deep learning techniques. Contrary to prior work in this field, we propose to learn the task in an end-to-end fashion without relying on hand-crafted features. For this purpose, we adopt the concept of basis point sets to encode the input point cloud into a low-dimensional feature vector. This vector is passed to a neural network, which is trained for weight regression. As the originally proposed construction of the basis point set is not ideal for our problem, we develop a novel sampling scheme, which exploits prior knowledge about the distribution of input points. We evaluate our approach on a lying pose dataset (SLP) and achieve weight estimates with a mean absolute error of 4.2 kg and a mean relative error of 6.4% compared to 4.8 kg and 7.0% obtained with a basic PointNet.
Kesiapan Guru Dalam Menggunakan eknologi Informasi Untuk Pembelajaran Kurikulum 2013 Pada SMP Negeri Kota Palembang

The 2013 curriculum is a new curriculum issued by the Ministry of Education and Culture of the Republic of Indonesia. The 2013 curriculum itself is a curriculum that prioritizes understanding, skills, and character education, where students are required to understand the material, be active in the discussion and presentation process and have high manners and discipline. This curriculum officially replaces the Education Unit Level Curriculum which has been implemented since 2006. However, there are still many teachers who find it difficult to apply this approach in teaching and using learning media, one of which is information technology. The results of the research explanation can be revealed that the readiness of teachers to use information technology in the 2013 curriculum is around 79% ready and categorized as high, in this case technology and information are not only used in the computer field but can also be used as learning media in the 2013 curriculum-scientific approach , without eliminating the essence of the basic concepts of the 2013 curriculum.
A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance

Assessing the proper difficulty levels of reading materials or texts in general is the first step towards effective comprehension and learning. In this study, we improve the conventional methodology of automatic readability assessment by incorporating the Word Mover's Distance (WMD) of ranked texts as an additional post-processing technique to further ground the difficulty level given by a model. Results of our experiments on three multilingual datasets in Filipino, German, and English show that the post-processing technique outperforms previous vanilla and ranking-based models using SVM.
A Review on the Detection of Offensive Content in Social Media Platforms

Offensive content refers to messages which are socially unacceptable including vulgar or derogatory messages. As the use of social media increases worldwide, social media administrators are faced with the challenges of tackling the inclusion of offensive content, to ensure clean and non-abusive or offensive conversations on the platforms they provide. This work organizes and describes techniques used for the automated detection of offensive languages in social media content in recent times, providing a structured overview of previous approaches, including algorithms, methods and main features used. Selection was from peer-reviewed articles on Google scholar. Search terms include: Profane words, natural language processing, multilingual context, hybrid methods for detecting profane words and deep learning approach for detecting profane words. Exclusions were made based on some criteria. Initial search returned 203 of which only 40 studies met the inclusion criteria; 6 were on natural language processing, 6 studies were on Deep learning approaches, 5 reports analysed hybrid approaches, multi-level classification/multi-lingual classification appear in 13 reports while 10 reports were on other related methods.The limitations of previous efforts to tackle the challenges with regards to the detection of offensive contents are highlighted to aid future research in this area. Keywords— algorithm, offensive content, profane words, social media, texts
Construction of Domain-Specific DistilBERT Model by Using Fine-Tuning

In this paper, we point out the problem that BERT is domain dependent, and propose to construct the domain specific pre-training DistilBERT model by using fine-tuning. In particular, parameters of a DistilBERT model are initialized using a trained BERT model, and then these parameters are tuned from the specific domain corpus. Consequently, we can construct efficiently the domain-specific DistilBERT model. In the experiment, we experiment the test set for each domain; the estimation of a masked word in a sentence. Through this experiment, we show the superiority of our proposed model (domain-specific DistilBERT model) by comparing it with the general BERT model.
USING LEARNING BY DOING METHODOLOGY FOR TEACHING MULTI-AGENT SYSTEMS

In recent years the teaching of subjects related to Artificial Intelligence has grown notably in higher education degrees.This is the case of the discipline of multi-agent systems, which usually is part of the majority of master's degrees in Artificial Intelligence.Multi-agent systems (MAS) offer solutions for distributed decision making, where a set of autonomous intelligent agents must reach an agreement to solve a problem.These types of problems are usually complex and distributed, difficult to abstract and simplify for classroom teaching.The main problem that teachers of this subject have to face, is to be able to integrate the whole set of related techniques and algorithms in a practical example that is easy to understand and address within the framework of the planning of a course.This paper deals with the use of the "learning by doing" methodology in a subject of multi-agent systems in the Master's Degree in Artificial Intelligence at the Universitat Politècnica de València.This methodology is applied by avoiding master classes to focus on practice.The classes become a scientific-technological experience.The students and the teacher are a team working with a common purpose, seeking to achieve a goal.To do this, the whole course has been reformulated, proposing the students to solve different typical problems of the MAS area on the same domain, in this case the improvement of urban mobility and the efficient use of energy in the cities.It is considered to be a sufficiently current topic that can motivate the student to participate and propose solutions.To achieve this objective, a multi-agent system tool has been developed that allows students to simulate the different situations proposed and develop solutions.The tool provides them with an urban simulation environment where they can easily introduce their own strategies to be carried out by each simulation agent.In this way, students are proposed different challenges where they can develop negotiation strategies to simulate the operation of urban taxi fleets, and cooperation strategies, where different agents help each other to achieve a common goal.This tool, called SimFleet, has been developed in an open way and published as open source, so that it can be used by any teaching team that wishes to do so, and even receive external contributions and improvements thanks to its open character.This learning by doing methodology supported by the SimFleet simulation tool has been applied in two consecutive academic years obtaining better results in student assessment and learning than in previous courses.Furthermore, the results of the student satisfaction surveys have shown a notable increase when using these technologies, which reinforces the idea that this type of learning is more useful and more satisfactory for students.
On the formation of soft skills in foreign research

The article analyzes foreign psychological and pedagogical studies on the problem of the formation of flexible skills in college students. Undoubtedly, today's students are the future employers and employees of the world's most profitable enterprises. The difficult situation in the modern economy shows that it is no longer enough for new graduates to have only knowledge in academic subjects, they are increasingly required to increase the level of additional skills that cannot be obtained in the learning process: creativity, responsibility, communication skills, and many others. For students to stand out as promising assets for multinational organizations, they must invest in the development of what are designated as soft skills. They will increase his prospect of successful employment. Therefore, employers especially value people who have well-developed skills. Soft skills are useful in all areas and are related to emotional intelligence. We could say that soft skills refer to personality traits, social grace, fluency of language, personal habits, friendliness, and optimism. Flexible skills complement the rigid skills that are the technical requirements of the profession. It can also be an important part of an organization, especially if the organization deals with people face-to-face. This study provides an overview of foreign literature devoted to the study of the formation of soft skills. According to most Western scientists, flexible skills are the most important competencies necessary for the successful realization of potential; the professional education system is not ready for the formation of soft skills, as a result, a radical change in educational programs and curricula is required; the formation of soft skills involves the use of modern innovative technologies and the involvement of leading specialists in various fields. Soft skills super-professional skills that help you solve life problems and work with other people. Soft skills can not be learned in a training or course, they are laid down in childhood and develop throughout life.
Latent Skill Planning for Exploration and Transfer

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how these two approaches can be integrated into a single reinforcement learning agent. Specifically, we leverage the idea of partial amortization for fast adaptation at test time. For this, actions are produced by a policy that is learned over time while the skills it conditions on are chosen using online planning. We demonstrate the benefits of our design decisions across a suite of challenging locomotion tasks and demonstrate improved sample efficiency in single tasks as well as in transfer from one task to another, as compared to competitive baselines. Videos are available at: this https URL
IMPLEMENTATION OF SMART GLASSES INTO THE EDUCATIONAL PROCESS AT UNIVERSITIES: A CASE STUDY

J. Husár , L. Knapčíková , S. Hrehová, M. TrojanováTechnical University of Košice, Faculty of Manufacturing Technologies with a seat in Prešov (SLOVAKIA)
MEDIATION IN ENGLISH FOR SPECIFIC ACADEMIC PURPOSES EDUCATION

The CEFR Companion Volume elaborated on the concept of mediation, presenting illustrative descriptor scales that refer to a number of aspects such as communication and learning as well as social and cultural mediation. Although the Common European Framework of Reference for Languages (CEFR) developed the concept of mediation, it seemed to be reasonable to calibrate a number of descriptors that describe the abilities and skills of language learners at different levels of proficiency due to the fact that mediation combines reception, production and interaction. English for Specific Academic Purposes contrasts with academic courses provided to university students whose major is English or general courses in the content and aims of the course. The difference is based on the students’ specific needs that should be identified by means of needs analysis. The concept related to specific vocabulary mapped on to general grammar has been broadened by the development of four communicative modes typical for ESAP. Apart from reading and writing academic texts related to specific scientific disciplines, it is important to focus on the way these texts are used in the target community. Therefore it is necessary to focus on developing language learners’ sociolinguistic and sociocultural competences.Our research is based on the questionnaires distributed to university students attending ESAP courses in their doctoral studies with the aim to obtain detailed information about their previous language education. To design a course, it was necessary to be aware of their way of learning, influenced either by traditional or modern teaching. Their attitude to reading a text word by word, translating sentence by sentence, focusing on isolated words and grammatical rules rather than the message that is conveyed put forward the idea that it is necessary to change students’ approaches to language learning and the use of English for real-life purposes. The article will present the data achieved from the questionnaires. After an analysis of the data, a number of activities will be piloted and evaluated with the aim to draw up a set of tips for tackling the discovered problems.
Meaning and Intonation of Final ending ‘geodeun’ for Korean Education

‘Geodeun’ is divided into a function as a connecting ending and a function as a final ending. The meaning of the connecting ending ‘geodeun’ is 「condition」, and there is no disagreement. The meaning of the final ending ‘geodeun’ has been mainly discussed as 「reason」 and 「background」 in previous studies, and this is a function mainly used in the discourse context. In this paper, the basic characteristic of the final ending ‘geodeun’ is identified as 「what only the speaker knows」. When used as a connecting ending and final ending, the meaning is not the same. Sometimes Korean textbooks tend to treat both as one grammar item, but these have different meanings, so they should be treated as separate grammar items. In spoken language, the intonation plays a big role in conveying the speaker’s intentions, so it is very important to know how the intonation is realized according to the function of ‘geodeun’ used in Korean language education. This paper explains what to suggest to Korean learners about the meaning and intonation of ‘geodeun’. (Catholic University)
Blockchain Technology Adoption via Contractual Coordination Mechanisms

This paper collects, organizes, and synthesizes existing literature in recent academic journals and case studies related to Blockchain Technology (BT) adoption through Supply Chain (SC) contractual coordination. We shall review the background of SC coordination segments and explain the development of SC coordination challenges with BT introduction. Based on the concepts stated, we discuss the reasons for contractual coordination via cost and revenue sharing contract in the adoption of BT. We close the paper with recommendations for future research in the adoption of BT through contractual coordination mechanism on the global spread SC that is futuristic and practically oriented.
Learning Versatile Neural Architectures by Propagating Network Codes

This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition. This goal is challenging because both network architecture search (NAS) spaces and methods in different tasks are inconsistent. We solve this challenge from both sides. We first introduce a unified design space for multiple tasks and build a multitask NAS benchmark (NAS-Bench-MR) on many widely used datasets, including ImageNet, Cityscapes, KITTI, and HMDB51. We further propose Network Coding Propagation (NCP), which back-propagates gradients of neural predictors to directly update architecture codes along the desired gradient directions to solve various tasks. In this way, optimal architecture configurations can be found by NCP in our large search space in seconds. Unlike prior arts of NAS that typically focus on a single task, NCP has several unique benefits. (1) NCP transforms architecture optimization from data-driven to architecture-driven, enabling joint search an architecture among multitasks with different data distributions. (2) NCP learns from network codes but not original data, enabling it to update the architecture efficiently across datasets. (3) In addition to our NAS-Bench-MR, NCP performs well on other NAS benchmarks, such as NAS-Bench-201. (4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i.e., multitask neural architectures and architecture transferring between different tasks. Code is available at https://github.com/dingmyu/NCP.
Classification Model of Wireless Signals Based on Higher-order Statistics

Automatic modulation classification technology is an indispensable step in cognitive radio, and its recognition accuracy is related to the orderly progress of subsequent communications. In this paper, we introduce the higher-order statistics into residual neural network for the precise classification of different modulation types. The classification technology can recognize 24 digital and analog modulation types under both synthetic simulated channel effects and over-the-air recordings. We also consider a rigorous baseline method using residual neural network and compare performance between two approaches under a wide range of signal-to-noise ratio. Experimental results show that our proposed method achieves an average accuracy of 96.4% and obtains better performance in correct classification probability than the baseline method, especially in lower signal-to-noise ratio.
Weibo Text Classification Based on Knowledge Graph

Abstract As a platform for sharing and communication, Weibo can produce a large amount of short text data every day. Because the Weibo text is short and the text features are sparse, it contains less information, serious colloquialization and weak anti-noise ability, so it is difficult to achieve the desired results by using the traditional text classification method to classify Weibo text. This paper proposes a Weibo text classification method based on knowledge graph, which uses knowledge graph to expand text semantics and enrich text features on the basis of the original Weibo text. Then the TextCNN model is used to classify Weibo texts with extended semantics. The experimental results show that the classification accuracy of this method is better than that of Weibo text classification directly.
Congolese Swahili Machine Translation for Humanitarian Response

In this paper we describe our efforts to make a bidirectional Congolese Swahili (SWC) to French (FRA) neural machine translation system with the motivation of improving humanitarian translation workflows. For training, we created a 25,302-sentence general domain parallel corpus and combined it with publicly available data. Experimenting with low-resource methodologies like cross-dialect transfer and semi-supervised learning, we recorded improvements of up to 2.4 and 3.5 BLEU points in the SWC-FRA and FRA-SWC directions, respectively. We performed human evaluations to assess the usability of our models in a COVID-domain chatbot that operates in the Democratic Republic of Congo (DRC). Direct assessment in the SWC-FRA direction demonstrated an average quality ranking of 6.3 out of 10 with 75% of the target strings conveying the main message of the source text. For the FRA-SWC direction, our preliminary tests on post-editing assessment showed its potential usefulness for machine-assisted translation. We make our models, datasets containing up to 1 million sentences, our development pipeline, and a translator web-app available for public use.
The development of maritime-based educational games for mathematics learning media

Abstract This research was intended to develop the Android-based learning media on the system of linear equations in three variables whose theme was maritime. The results of this learning media development were reviewed based on the validity, practicality, and effectiveness. The instruments used were the media validation sheets, material validation sheets, students’ responses questionnaires, and test scores on the students’ learning outcomes. The learning media testing was given to the students of grade X at SMK Negeri 4 Probolinggo. The results showed that it was classified as the “excellent” category. The average scores of total validity obtained were 4.43 out of 5 on the learning media experts; it was then into “very valid” category. This learning media was considered very practical since the average score obtained on the students’ responses questionnaires was 4.49 out of 5. According to the test on the learning outcomes, 93.33% of the total number of students in grade X KTPI 1 scored ≥ 70 as the minimum scores. Hence, it can be said that this learning media was effective. The conclusion drawn from the research results revealed that maritime-themed educational games for mathematics learning media were valid, practical and efficient.