Reviewerly/exHarmony huggingface dataset:
Low Cost Player Tracking in Field Hockey

In the paper, we describe the technical details of a multi-player tracker system using tracking data obtained from a single low-cost stationary camera on field hockey games. Analyzing the tracking data of the players only from the transmitted video opens a multitude of applications that allows the cost of technology to be reduced. This method does not depend on the cooperation of the players (by using sensors) or their teams (by sharing data with a third party). The approach taken in this paper uses a variety of computer vision and tracking techniques. Making player tracking data more accessible lowers the barrier to entry for sports research and increases the period during which advanced analysis methods can be applied. The proposed system runs the full pipeline at 3 fps on a computer with a simple graphics card.
Construction and Evaluation of Chinese Word Segmentation Datasets in Malay Archipelago

In recent years, there has been numerous mature research on Chinese word segmentation (CWS). However, the existing research mainly focuses on mainland Mandarin word segmentation, and the research on CWS of other countries/regions is still far from satisfactory. Although Chinese in the Malay Archipelago countries and Mandarin in mainland China are homologous, there exist some differences between them during their respective development processes. Therefore, common CWS tools cannot accurately and effectively segment Chinese texts of different countries. This paper conducts research on the Chinese texts of five countries in Malay Archipelago (Indonesia, Malaysia, Brunei, Singapore, and the Philippines), builds five CWS datasets respectively for each country, and explores the performance of some advanced word segmentation tools and sequence labeling models on the constructed datasets. The experimental results show the effectiveness of BERT (Bidirectional Encoder Representations from Transformers) model in CWS task, providing a baseline for CWS in five Malay Archipelago countries. Furthermore, we explore the enhancement of two training strategies on CWS task, and the experimental results show that these two strategies cannot significantly improve the CWS performance of Malay Archipelago. Besides, in view of the different performances on CWS of different countries, we deeply analyze their objective and historical reasons. The reasons behind it mainly fall into the corpus size, the Chinese language policy and the language education norms on different Malay Archipelago countries.
Blockchain and IoT Enhanced Clinical Workflow

Continuous health monitoring for complex healthcare conditions such as chronic comorbid diseases and post-operative care after complex surgical procedures can be implemented using Internet of Medical Things (IoMT) devices that collect sensor data from various devices for efficient health diagnosis and monitoring and treatment. However, given the complex data schemes, disparate devices, and multiple stakeholders involved, collecting and utilizing such IoMT data streams in clinical practice is not a straightforward process. We have developed a blockchain and IoMT integrated system called BlockIoT that simplifies various aspects of such complex healthcare management processes by providing a trustworthy and reliable method for aggregating IoMT device data and smart contracts that automatically provide relevant alerts to the healthcare providers. The use of blockchain technology to transfer previously inaccessible data from IoMT devices to healthcare providers enables these healthcare providers with relevant insights into their patients’ situations, which provides better outcomes for patients in general. We evaluated the utility of BlockIoT using a participatory design study that provided further insights as to how such a system can simplify healthcare providers’ complex workflows.
Rectified Neighborhood Construction for Robust Feature Matching With Heavy Outliers

This letter is concerned with constructing reliable neighborhoods for the local consistency-based feature matching methods. To alleviate the impact of outliers on neighborhood construction, we propose a rectified neighborhood construction strategy (RNC), which can effectively enlarge the distribution between inliers and outliers. Besides, we also integrate an adaptive parameter estimation into the aforementioned rectified strategy, and it can contribute to determining a reasonable parameter for the rectified strategy. Finally, the experimental results on two representative remote sensing image data sets show that the proposed method can achieve satisfactory feature matching results compared with some state-of-the-arts.
Interpretive Psychotherapy of Text Mining Approaches

The current scenario data collection grows rapidly, and most of it is organized in an unstructured format, from which it is difficult to extract useful information. Mining of text methodologies includes summary, characterization, grouping, information retrieval, and apparition. Mining of text can be defined as a process for fetching fascinating facts or acquaintance from content documents. It is the method of finding relevant and heterogeneous matches or acquaintance from content articles are also includes. Mining of text data or acquaintance detection is recognized from textual databases. Mining of text is regarded as further large thing in knowledge discovery. This paper proposes the improvement in several text mining approaches.
Variety of languages on the status of Facebook users

Research on the variety of formal languages in social networking sites facebook and is a study that provides new knowledge about the development of Indonesian used.In accordance with the nature of language, language is dynamic and can develop.The purpose of this research is to describe the variety of languages in terms of formality in facebook user status and describe the variety of languages in terms of usage in facebook user status.This form of research uses qualitative approaches to methods in this research and the methods used are descriptive methods of data collected in the form of words, images and not numbers.Based on the results of research conducted it can be concluded that there are, variations in language in terms of usage there are five examples of words that show the existence of characteristics or forms in the field of education, three examples in the field of literature, two fields of medicine, one in the field of religion.In addition, there is also a variety of languages in terms of formality consisting of a variety of businesses, relaxed varieties, and familiar varieties.In addition to getting informal words, the results of this study also revealed the use of informal language variations.The unaradishness of the word used, errors resulting from inappropriate placement of words, diction errors, words that experience errors due to their formation.
How to Use Python Data Science Packages More Productively

Python is, without any doubt, the most used and fastest growing programming language of choice for data scientists (and other related professionals such as machine learning engineers or artificial intelligence researchers) all over the world. There are many reasons for this explosive growth of Python as the lingua franca of data science. Probably the most important reason for its growth is the amazing open-source community activity and the resulting ecosystem of powerful and rich libraries and frameworks focused on data science work. However, just because these libraries provide easy APIs and smooth learning curves does not mean that everybody uses them in a highly productive and efficient manner. One must explore these libraries and understand both their powers and limitations to exploit them fully for productive data science work. This is the goal of this chapter: to show how and why these libraries should be used in various typical data science tasks for achieving high efficiency.
An Overview of Word Embedding Models Evaluation for Arabic Sentiment Analysis

Sentiment Analysis (SA) or opinion mining has become one of the essential research fields whose application is clearly visible in a large variety of domains. Due to Arabic structure complexities at the level of morphology, orthography, and dialects, manual feature extraction is a time-consuming and challenging mission, Besides, Arabic Sentiment analysis (ASA) is very difficult and is considered a more difficult task compared to other languages. In this paper, we discuss the issue of word embedding models that is very important in the field of Arabic Sentiment Analysis also provide a comparative analysis of the most famous word embedding models namely: Word2Vec, Glove, FastText, Elmo, and Bert. We found through this deep evaluation, that FaxtText, Elmo, and Bert embedding models outperform other Models.
MLLP-VRAIN UPV systems for the IWSLT 2022 Simultaneous Speech Translation and Speech-to-Speech Translation tasks

This work describes the participation of the MLLP-VRAIN research group in the two shared tasks of the IWSLT 2022 conference: Simultaneous Speech Translation and Speech-to-Speech Translation.We present our streaming-ready ASR, MT and TTS systems for Speech Translation and Synthesis from English into German.Our submission combines these systems by means of a cascade approach paying special attention to data preparation and decoding for streaming inference.
Statistical Prediction of Extreme Events from Small Datasets

We propose Echo State Networks (ESNs) to predict the statistics of extreme events in a turbulent flow. We train the ESNs on small datasets that lack information about the extreme events. We asses whether the networks are able to extrapolate from the small imperfect datasets and predict the heavy-tail statistics that describe the events. We find that the networks correctly predict the events and improve the statistics of the system with respect to the training data in almost all cases analysed. This opens up new possibilities for the statistical prediction of extreme events in turbulence.
Mapping Competencies to Cybersecurity Work Roles

The increase of dependence on digital services as a result of the COVID-19 pandemic reinforced the general need to effectively address the long-term perceived cybersecurity expertise gap. One of the instruments that allow better coordination and optimisation of efforts towards expedient and meaningful cybersecurity training and innovation is an alignment of perspectives on the competency requirements associated with the sought-after cybersecurity experts. To this goal, our research team is developing a cybersecurity qualifications framework modelled on the US-focused NICE framework. In this contribution, we present our progress in mapping the expected competencies to the defined work roles.
Development of Slims Framework Modification and Migration for LibraryInformation System in Electrical Engineering Department, Diponegoro University of Indonesia

Many libraries have been built for the library management system in a more active era of development, one of which is Slims. Slims is an open-source library management system that aims to simplify book search management while expanding existing capabilities. The Department of Electrical Engineering library initially used a PHP 4website; however, in the present development, the library is innovating by adopting Slims based on PHP 7.4. The rationale for the change is that the former website had flaws, such as insufficient search tools, a lack of available book data, a less appealing website design, and a manual borrowing process. As a result, inventions and modifications are made to address these flaws. It's vital to map the previous data to make transferring data more accessible. Matching fields from several datasets into a schema or database is data mapping. Thus, innovation and modification are carried out to make it easier for librarians to maintain the library system.
SQL Injection Attacks, Detection Techniques on Web Application Databases

Most of web-application Databases are vulnerable to SQLi Query Injection Attacks, which lead to the entry of confidential data right through the client-side. They run by entering malicious SQL Injection Query codes through the web API on the client-side and return all the sensitive and private data from the database. SQL Injection is a technique where the web attackers post the malicious SQL injection Query with a specific end and goal there is to change the structure, behavior of the Query proposed by the computer programmer and occupying up the full admin login access of the web database, for malicious Input data modifications or deletion of the existing user User's Information. The SQLi attacking method is a critical web application attack; using this technique, attackers execute or run evil SQLi queries or an arbitrary code on the database servers using the web based application to gain fully take over the important and Private Data on the database bypassing authentication level. According to the owaps (Open Source Foundation for Application Security), the SQLi (“SQL Injection”) attacks is mentioned as a significant Database Security Issue. The SQL injection query attack (SQLi) is the web-based vulnerability that allows the web attackers to spoof the identity, destroy the data presented on the database server, Insert new entries in the database and change the records submitted on the database. The significant issues of SQL Injection (SQLi) include loos of users’ confidentiality and authentication problems, Authorization Issues for users, and loss of Data Integrity as any unauthorized use may change any sensitive information. In (SQLi) attack, there are three main types of SQL injection attack, i.e. In-band (classic (SQLi) Attack), inferential (blind(SQLi) attack), and Out-band SQLi Attack. In the Classic-SQLi Attack, the attackers use the same channel of communication that authorized user uses to launch attacks & extract the results. The Attack can generate errors on the database or use UNION operation to integrate multilevel Select statements to get only one single database as HTTP Response in web application. There are other procedures methods that researchers have proposed to block or find SQL injection attack. That paper will include comparative study of advanced technique and tools to find SQL injection query attack (SQLi).
Collecting Geospatial Data Under Local Differential Privacy With Improving Frequency Estimation

Geospatial data provides a lot of benefits for personalized services. However, since the geospatial data contains sensitive information about personal activities, collecting the raw data has a potential risk of leaking private information from the collectors. Recently, local differential privacy (LDP), which protects the privacy of users without trusting the collector, has been adopted to preserve privacy in many real applications. In this paper, we investigate the problem of collecting the locations of individual users under LDP, and propose a perturbation mechanism designed carefully to minimize the expected error of perturbed locations according to the privacy budget and the data domain. The frequency distribution of perturbed locations inevitably has a large error. To tackle the problem, we also propose a postprocessing algorithm to estimate the original frequency distribution of collected data by using convex optimization. By experiments with various real datasets, we show the effectiveness of the proposed algorithms.
Region of Interest Using Viola-Jones Algorithm for Video Steganography

In this paper, Viola-Jones’s face detection method is used to find a human face in videos that act as a Region of Interest. Region of Interest can be used for concealing secret messages to increase the robustness of the output stage file. Viola-Jones’s method can work efficiently in unlike situations such as different skin color, aging, different facial expressions, scars on the face, makeup on face, different hairs color, and complex background details. The Viola-Jones detect faces with minimum calculation time while maintaining high detection precision. Integrating Viola-Jones algorithm with steganography technique challenge of robustness can be avoided by the user.
Learning Reading: Formats and Practices

The reading research projects accomplished by RF libraries are discussed. The current state and focuses of research of adult and children’s reading in Russia are characterized. The contribution of the National Library of Russia and Russian State Children’s Library is demonstrated, in particular, acquisition of research database “Reading and library users: Library studies and sociological research”, all-Russia contest “”To study reading”, etc. Based on diverse sources of information on library research activities, several typical problems are specified, namely insufficient number of integrated research, in particular, in the regions; narrow subject scope; limited methodology; lower quality of research projects. Subjective problems of library research are related to library professional concept of reading as a process calling for no further study; too wide understanding of “research” and “research project” terms; librarians’ “transcendental” knowledge of readers and their preferences; shift of liability to respondents; closed mind with the fixation on the library and library profession. Meanwhile, research activities are important for library identity formation and efficient organization of library services.
A Multi-channel Deep Learning Architecture for Understanding the Urban Scene Semantics

Smart city analysis becomes an emerging field in autonomous urban problems. Image semantics is a complex problem where the image classification, the semantic segmentation and the object detection subroutines are staged in a cascade framework through spatio-temporal datasets. Urban scene analysis has been coupled in several applications such as security, autonomous vehicles, and mass transport. The initial problem of an urban scene is characterized as the pursuit of discrete 2D-3D movements on the street. Therefore, an accurate segmentation of the scene is required to minimize the spatial gap in the scene. The complete framework provides critical decision-making tools to protect the human and the moving objects. New generation autonomous systems turn on their real time sensors to monitor all possible movements in a street. However, the sensing stage must be interpreted within semantics to generate the city insights. The task of semantic segmentation is to label every pixel including the background into a semantic class. The object detection locates the presence of objects and types or classes of the located objects in an image. The instance segmentation contains these two tasks. This paper is composed of the instance segmentation of base objects through Cityscapes dataset. YOLACT deep learning architecture has been applied on high resolution images. The method has been found fast as it requires one stage segmentation. We conclude that YOLACT architecture generates feasible labels in an accurate dataset where spatial gaps are lower. The smart city analysis would be processed better with new hierarchical labels.
Towards Urdu Name Entity Recognition Using Bi-LSTM-CRF with Self-attention

Named Entity Recognition (NER) is essential in many natural language processing activities, including machine translation and automated question-answering systems. Focusing on the importance of NER, several NER approaches for Western and Asian languages have already been developed. Despite the fact, there are over 490 million Urdu speakers globally, NER resources for Urdu are either non-existent or insufficient. However, very less work is done in Urdu NER. In this work, we developed a deep learning-based method for Urdu NER using Bi LSTM with self-attention and conditional random field (CRF). We have performed tenfold cross-validation using the proposed method. The result shows that our method outperforms the state-of-the-art methods.
Covering Possible Reasoning Errors for Intelligent Tutoring Systems: Order of Expression Evaluation Case

One of the problems in computer-supported education is defining meaningful errors that students can make during a complex automated exercise. A precise set, describing what can go wrong in the student’s reasoning, allows better measurement of students’ knowledge, asking pointed follow-up questions to stimulate the student’s thinking, and providing precise explanatory feedback. We describe a method for building a set of possible low-level errors in reasoning during solving a complex task. We demonstrate an example of using this method for the task of determining the order of evaluation of a programming language expression and discuss prospects of applying the described method.
MultiEmo: Language-Agnostic Sentiment Analysis

We developed and validated a language-agnostic method for sentiment analysis. Cross-language experiments carried out on the new MultiEmo dataset with texts in 11 languages proved that LaBSE embeddings with an additional attention layer implemented in the BiLSTM architecture outperformed other methods in most cases.
Sentimental Analysis on Online Education Using Machine Learning Models

Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students’ mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students’ learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils’ feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students’ feelings. Students’ favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy.
Comparative Analysis of Various Optimizers on Residual Network Architecture for Facial Expression Identification

In recent years, the interaction between humans and computers has become a popular global challenge. The emergence of social robot technology, also known as Humanoid robot technology, is one manifestation of this interaction. How to create communication with humans is one of the most essential components of social robots. Nonverbal communication, such as reading body language and recognizing facial expressions, is one approach for robots to communicate. This study will design a facial expression recognition system using CNN method, which has been proven to be superior in the field of digital image-based classification. In the future, facial expression recognition will become increasingly common in human–computer interface devices. Residual Network 34 architecture was used to classify the Cohn Kanade (CK+) data set consisting of 981 images, divided into seven classes of facial expressions; angry, disgust, fear, happy, sadness, surprise, and contempt. The investigation process is carried out by comparing three optimization algorithms: Adaptive moment (Adam), Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp). Based on the results of system testing, it shows that the ResNet-34 architecture has a fairly optimal performance in the classification of facial expressions, with an average accuracy of 99.3%, precision of 99.6%, recall of 99.3% and F1-Score of 99.4%.
Text Manipulation

Automation of Unix-like systems often involves text manipulation. Many programs are configured with textual configuration files. Text is the output format and the input format of many systems.