Reviewerly/exHarmony huggingface dataset:
Local Site Effects Influence on Earthquake Early-Warning Parameters

Earthquake early-warning (EEW) system is a valuable device to detect an impending earthquake in real time and helps in the mitigation of seismic risk. The technology of EEW is used for calculating the size of an earthquake based on the first three seconds of recorded P-wave ground motion at the given site. The issues of precision are still challenging in this system. In this paper, an endeavor is made to show the influence of local site effects on earthquake early-warning parameters such as average ground motion period (τc) and the peak displacement amplitude (Pd) in the initial three seconds of the motion records. The analysis is carried out using the ground motion records of the Japanese dataset. The vertical component of the acceleration waveforms is used to determine the earthquake early-warning parameters. The comparison is made between the variations of τc and Pd parameters with the magnitude τc, epicentral distance, and average shear wave velocity for the surface motions. The results indicated that the local site effects significantly influence the earthquake early-warning parameters.
A Survey on Bengali Image Captioning: Architectures, Challenges, and Directions

The image captioning process generates human-like captions for an image. In recent years of an appeared research area, automatic image captioning is acquiring more attention. Despite being a challenging problem, several approaches have been proposed where deep learning methods can handle these problems for image captioning and proved to be state-of-the-art. Most of the methods use the convolutional neural network (CNN) and recurrent neural network (RNN) variants. In this survey, we examine various approaches to Bangla image captioning. We investigate image captioning datasets, classify all image captioning systems, review, and represent all of the existing models’ methodologies. We give an overview of the evaluation metrics and provide a comparison of the present methods. Moreover, we discuss their strengths and limitations. Finally, for automatic image captioning, we investigate additional possibilities and future directions.
Multi-label Text Classification and Text Adversarial Attack

Multi-label classification is an extension of multi-class classification. For multi-label problem, each instance may not be restricted to have only one label. In this paper, the methods to solve multi-label classification are divided into four aspects which are binary relevance method, label combination method, classifier chain and ensemble classifier chain. In order to enhance the performance of the text classifier, text adversarial attack should be used to enrich the training dataset. Thus, the related works with text adversarial attack are also introduced. In the end, we also explore some potential future issues in multi-label text classification and text adversarial attack.
Self-Supervised Spoken Question Understanding and Speaking with Automatic Vocabulary Learning

Spoken language acquisition involves automatically developing symbolic word concepts grounding their meaning to the world, recognizing the words in spoken utterances, and pronouncing them. Previous research only partly covered these aspects. One of the most comprehensive agent systems supported the word concept acquisition from pairs of raw speech and image and utterance pronunciation. However, the agent listened to nothing when interacting with the world, only pronouncing a food name to choose a favorite one among two shown images. In this work, we add a function to the agent to recognize a verbal question. Namely, we design a task where the agent must recognize a question in a sound utterance and understand the logical “not” concept. Experimental results show that the agent successfully learns the task. It appropriately behaves even for unseen combinations of images, correctly answering the food names it wants or the opposite one according to the question.
Character Customization With Cosmetic Microtransactions in Games: Subjective Experience and Objective Performance

Free games that are monetized by selling virtual items, such as cosmetic microtransactions for one's avatar, seem to offer a better gaming experience to paying players. To experimentally explore this phenomenon, the effects of character customization with cosmetic microtransactions on objective and self-estimated player performance, subjective identification with the avatar, fun and the players' perceived competence were examined in the game League of Legends. This study introduces a new laboratory-based, experimental task to objectively measure within-game player performance. Each participant performed this game-based task in two different conditions: With a character that was customized using a provided set of cosmetic microtransactions and with a default character. Results showed that customization increased subjective identification with the player character. However, objective performance measures were unaffected by this manipulation although the novel experimental approach provided reliable performance results. Additionally, identification was positively related to perceived competence, fun, and self-estimated performance. Implications for the design of cosmetic microtransactions and their influence on competitive gaming are discussed.
Autonomous Parking System Perception and Control Simulations on ROS-Gazebo

Autonomous systems are being developed in the automotive industry as an aid for the driver in terms of safety and comfort. Automated parking technology is one such key aspect in the domain. It provides the driver with quick and safe parking without any manual inputs. This paper discusses the method of parking scene recognition, perception, path tracking, and control of the vehicle for autonomous parking systems. Simulations based on control of the car while parking mode are also demonstrated.
Automatic Detection of Certain Unwanted Driver Behavior

This work is devoted to the automatic detection of unwanted driver behavior such as smoking, using a mobile phone, and eating. The various existing datasets are practically unsuitable for this task. We did not find suitable training data with RGB video sequences shot from the position of the inner mirror. So we investigated the possibility of training the algorithms for this task on an out-of-domain set of people faces images. We also filmed our own test video sequence in a car to test the algorithms. We investigated different existing algorithms working both with one frame and with video sequences and conducted an experimental comparison of them. The availability of temporal information improved quality. Another important aspect is metrics for assessing the quality of the resulting system. We showed that experimental evaluation in this task should be performed on the entire video sequences. We proposed an algorithm for detecting undesirable driver actions and showed its effectiveness.
Communication efficient distributed learning of neural networks in Big Data environments using Spark

Distributed (or federated) training of neural networks is an important approach to reduce the training time significantly. Previous experiments on communication efficient distributed learning have shown that model averaging, even if provably correct only in case of convex loss functions, is also working for the training of neural networks in some cases, however restricted to simple examples with relatively small standard data sets. In this paper, we investigate to what extent distributed communication efficient learning scales to huge data sets and complex, deep neural networks. We show how to integrate communication efficient distributed learning into the big data environment Spark and apply it to a complex real-world scenario, namely image segmentation on a large automotive data set (A2D2). We present evidence based results that the distributed approach scales successfully with increasing number of computing nodes in the case of fully convolutional networks.
Query Processing over Multiple Knowledge Bases and Text Documents

Recently, knowledge bases, which represent general knowledge as graphs, have attracted much attention and are used in various fields. As a consequence, the number of knowledge bases have been growing and more than 600,000 datasets exist with different targets in wide spectrum of domains. Thus, it is desirable to make it possible to process queries over different knowledge bases in order for the users to get more integrated information. However, it is still difficult because one needs to cope with different vocabularies. Besides, there is another problem in knowledge bases that new facts tend to be missing, while we can get latest information from text documents, e.g., new sites. In this paper, we propose a method for query processing over multiple knowledge bases and text documents. We apply open information extraction (OIE) over text documents to extract knowledge graphs. To deal with the heterogeneity of vocabulary and schema, we perform distributed queries to multiple knowledge bases using mediator/wrapper approach. Besides, we introduce different strategies to join triples retrieved from different knowledge graphs of different RDF vocabulary. The evaluations show that our approach can improve the coverage of query results by using information from multiple knowledge bases and information from texts.
A Denoising Radio Classifier with Residual Learning for Modulation Recognition

Deep learning has great potential in modulation recognition. However, when noise causes signals distortion, the performance of deep learning algorithms degrades dramatically, and the performance deterioration is especially noticeable at low SNR. In this paper, a deep learning-based classification architecture is proposed to enhance modulated signals. The architecture consists of a denoiser and a classifier. The denoiser based on generative adversarial networks (GAN) with residual learning, which implicitly removes the potentially clean signals from the original modulated signals to learn the distribution of noise, and finally enhances the original modulated signals. The classifier is the radio classify convolutional neural network (CNN) based on the CNN. Compared with some renowned machine learning methods and deep learning methods used in modulation recognition, this method can obtain higher classification accuracy, especially when the SNR is low.
Perspectives Of Using Of OWC In Digital Storytelling Content Delivering In Museums

Content management in education, advertisement, business, and even science today it's the main part of visitor attraction. Using the intellectual integrated information technologies in providing visitor-oriented content helps to erase the borders between nations, languages, and mentality. The key to explaining something to a person is in words and symbols, that is how our ancestors shared their knowledge. Using speech humans controlling their minds, interact, and sensing the mood of each other. This paper introduces perspectives and possibilities of using parts of Optical Wireless Communication (OWC) technology for museums. The proposed scheme of network and software architecture is described in the text.
Personalized Assistive Learning System for Primary Education

Due to the COVID 19 pandemic, almost all educational institutions, including schools, remain closed. This caused a dramatic change in the educational systems. The sudden shift away from the classroom made the profound transformation of the teacher-centered education system prevail so far; consequently, the education of primary level students has been collapsed. Therefore, the primary students from grades 1 to 3 cannot acquire the primary education given by the school. This research proposes a personalized assistive learning system for primary education from grade 1 to 3 students, aiming to improve their learning skills. The proposed system aims to increase the automation and self-learning of students. A novel method is proposed to acquire personalized course materials for students of grades 1 to 3 according to their knowledge level. The system is founded on a solid theoretical foundation and enables children to grow cognitive and psycho-therapeutic skills such as drawing, writing, recognizing numbers, enabling self-learning, and focusing on measuring the progress of the students and reporting it to parents. CNN is the primary classifier used in image recognition and classification tasks in computer vision. The components' median accuracy is 94.74%.
Leveraging Knowledge Graph and Self-Attention with Residual Block for Paper Recommendation

Collaborative Filtering paper recommendation aims to help users find papers they are interested in. And it is essential to alleviate the problems of information overload and solve data sparseness. As we known, there are various knowledge entities in academic papers. So, recent research attempts to use Knowledge Graph (KG) as auxiliary information to simulate users’ preferences for papers. However, most knowledge enhancement methods are incompetent in using neighborhood node information and seldom measure the user’s preferences in different papers. In order to solve these problems, we propose a paper recommendation method based on collaborative knowledge graph, which uses self-attention with residual block for weight calculation (CKGSR). CKGSR uses TransH to map the nodes of users and paper into low-dimensional dense vector representations. Then, it uses a BiLSTM method to obtain paper vectors. In addition, it introduces a novel neighborhood node weight calculation mechanism for text and structural information to handle user’s reading preferences. Experiments on two real-world datasets show that our model outperforms the latest benchmarks in paper recommendation. Moreover, the contribution of this paper is mining the potential semantic gaps between papers, and improving overall quality of paper recommendation.
Model Pembelajaran Kooperatif Tipe Take and Give Meningkatkan Prestasi Belajar Siswa pada Mata Pelajaran Ilmu Pengetahuan Sosial

The low learning achievement of students in social science subjects (IPS) is caused because students are less interested in the material. After all, the method does not support so that learning is not effective. The purpose of this study is to analyze the increase in learning achievement in social studies subjects for class IV during online learning by applying the take and give a type of cooperative learning model. This type of research is qualitative research. The research population is class IV which consists of 27 students. The data in this study were obtained through documentation, observation, and interviews. The results showed that students became more active in commenting in the Whatsapp group when they felt curious, asked things they did not understand, and asked whether the answer was correct or not. Students feel more interested because the presentation of images is presented in the form of cards which is certainly different from the images that are usually sent by teachers from Google. Student achievement also increases because students learn through the process of observing pictures, analyzing information, gathering information, communicating to their partner's friends, and automatically conducting discussions to match whether the cards are the correct pairs. Based on the results of the study, it can be concluded that the application of the take and give learning model improves the learning achievement of fourth-grade students in social science subjects (IPS) during online learning.
Distributed Reinforcement Learning with Self-Play in Parameterized Action Space

Self-play has been shown to be effective to provide a proper training curriculum for a reinforcement learning agent in competitive multi-agent environments without direct supervision. However, its performance is still unstable for problems with sparse rewards, e.g., the scoring task with goalkeeper for robots in RoboCup soccer. It is challenging to solve these tasks in reinforcement learning, especially for those that require combining high-level actions with flexible control. To address these challenges, we introduce a distributed self-play training framework for an extended proximal policy optimization (PPO) algorithm that learns to act in parameterized action space and plays against a group of opponents, i.e., a league. Experiments on the domain of simulated RoboCup soccer show that, the approach is effective and learns more robust policies against various opponents compared to existing reinforcement learning methods. A demonstration video is available online at https://youtu.be/BuLli1vND4.
Traditional implementing usage of core programming languages in the object oriented programming

In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-level object-oriented language, while C++ (the language that powers most operating systems) is a lower-level object-oriented language. Python and C++ are compared in this article. This book covers an introduction to various languages, memory management techniques, and reasons for their program execution speed. Furthermore, this study looked at the execution time and memory requirements of a variety of algorithms in both languages, comparing the best, average, and worst situations in each language. Furthermore, they are compared in terms of their benefits and drawbacks. C++ is faster than Python in terms of execution speed, according to the findings, but Python is a better option for beginners due to its simplicity. Aside from that, the language should be selected depending on the project type in order to get the best results. Programmers play a crucial role in computing. C/C++, Python, and Java are the most common languages. Beginning programming classes often utilize C++ and Java. Python has grown in popularity as an introductory programming language due to a variety of reasons. As an entry-level programming language, C, C++, Java, and Python were compared. More students selected Python over C++ because of its better pseudo-code syntax, simpler learning environment, and greated abstraction.
An Attention-Based Interactive Learning-to-Rank Model for Document Retrieval

The core issue of learning-to-rank (LTR) for document retrieval lies in finding an optimal ranking policy to meet the search intent of the user. The majority of proposed LTR approaches treat the ranking as a static process, employing a fixed ranking policy to immediately assign scores to documents. By contrast, ranking is not a static but an interactive process where the user continues interacting with the document retrieval system through information exchange such as search intent (e.g., rating or clicking for the retrieved items). We model the interactive ranking process (IRP), and propose an Attention-Based Interactive LTR model (AIRank) to constitute an intent-aware flexible ranking policy to gratify the user’s need. To enhance the ranking quality, the inherent relations among documents are procured by the self-attention method to contribute to an enriched user intent representation. Furthermore, we mend the policy gradient learning method to train the AIRank in the IRP. Experiments demonstrate the effectiveness of AIRank compared to the state-of-the-art methods in terms of normalized discounted cumulative gain and expected reciprocal rank.
The Study of Blockchain Technology to Enhance the Organizational Performance: Theoretical Perception

Blockchain Technology (BT) enables decentralized, distributed, encrypted, and immutable logging of digital transactions. Blockchain features a decentralized database and public registry which helps transactions across a peer-to-peer network secured through cryptography. The aim of this paper is to how BT could be useful in enhancing the business performances of an organization based on research in the past decade. The study will be based on theoretical aspects of blockchain implementations, their application in various processes of an organization. This study focused on the detailed description of blockchain technology and its various business application, showing its impact on the different business models. The study highlights various barriers, motivators, and tools for BT and its impact on different parts of an organization.
Time-measurement constructions in English

Time-measurement expressions such as five-year plan, 10 years’ time and 25 years service occur frequently in English. All such expressions consist of a cardinal numeral, followed by a time-noun (N1) then a second noun (N2). The time-noun has one of three orthographic forms: the bare-form, the S-form with apostrophe or the S-form without apostrophe. Using a dataset of 17,591 time-measurement tokens from the British National Corpus and mixed-effects logistic regression modelling, this chapter tests the hypothesis that these three orthographic forms represent three different constructions. Our first model, using only expressions with S-form N1, shows that the presence or absence of an apostrophe is not correlated with any other formal or semantic property that would justify the recognition of two constructions. In contrast, our second model using the whole dataset, shows that bare-form N1 and S-form N1 (with or without apostrophe) are highly correlated with aspects of both form and meaning. In our dataset, 96% of tokens with bare-form N1 have a countable N2 and 87% also follow a determiner. Conversely, 94% of tokens with S-form N1 have an uncountable N2, and 91% also lack a determiner. We conclude that these clusters of properties represent distinct pairings of form and meaning, and are therefore characteristic of two different constructions, which we call the time-measurement compound construction and the time-measurement construction respectively. The time-measurement compound construction (five-year plan) has the distribution of a nominal; semantically, it denotes a kind of bounded entity (N2) with some relation to numeral-N1, usually duration. The time-measurement construction (10 years’ time, 25 years service) has the distribution of a noun phrase; semantically, it denotes a quantity (numeral-N1) of some unbounded entity (N2). The chapter ends with a qualitative exploration of the central and more peripheral representatives of the two constructions, including borderline cases.
A Study On Cipher-Text Attribute Based Encryption Using Secret Sharing Schemes

Encryption has an important role in the aspects of information security. A term attribute-based encryption uses the cipher-text which depends on the attributes. Decryption of such a text may be used when set of users’ attributes matches corresponding cipher-text. In such a cipher-text policy attribute-based encryption (CP-ABE); the cipher-text associates to the access policy while that of the secret key was associated with the attribute set. In this proposed construction, we discuss Linear Integer Secret Sharing scheme (LISS) in which access policy of the encryptor is distributed over a LISS matrix M involving the attributes of the system. The security makes much stronger using LISS matrix. We use AND and OR logic gates for multi-value attributes to make a secure stronger.
Detection of Offensive Language in Social Networks Using LSTM and BERT Model

The uses of offensive languages on social media's like Instagram, Facebook, Twitter, etc. are increased tremendously. Nowadays, huge growth in the number of social media users are seen. Peoples are knowingly or unknowingly flooded the social media platforms with offensive posts. This become very challenging task to detect offensive posts. Manually solving this problem is not feasible, therefore automatic detection of offensive language is needed. Our objective of the work is to detect offensive language with the highest accuracy. The Davidson dataset is used for experiments with annotated tweets and implemented based on LSTM and BERT methods. The proposed deep learning methods is compared with other known machine learning classifier. Overall, result analysis is show that proposed deep learning method is outperforming others.