Reviewerly/exHarmony huggingface dataset:
Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File

Concomitant with the virtual ubiquity of dashcams in vehicles, the instantaneous speed of vehicles during accidents can now be measured using dashcam videos to understand the causes of such accidents.In this study, we estimated vehicle speeds using dashcam videos recorded in various road environments and analyzed them by comparing them to ground truth.We also considered various scenarios yielding retaken and re-encoded videos from additional recording devices, depending on the circumstances, wherein the original video can be manipulated in terms of playback speed and/or frames per second.The experimental results revealed that the actual and measured speeds were similar for most user-specific intervals.Moreover, the results suggest that our estimated speed is adequately valid as evidence, regardless of retake and re-encoding states.Furthermore, we adopted a statistical approach (i.e., Pearson's correlation coefficient analysis) to verify the proposed vehicle speed estimation method.The results showed that when playback speed was manipulated in re-encoded or retaken video, the proposed method consistently measured speeds at 99.99 % accuracy.This verified method will play an important role in deriving reliable results from various evidential video sources.
Open Source Hardware

Open source hardware (OSH) refers to physical components generated by a decentralized de sign and development model encouraging open collaboration.OSH is an analogy to open source software.OSH usually intends that information about the hardware is readily identifiable so that others can make it.OSH refers to hardware designs whose specifications, con struction instructions, and documentation can be publicly accessed, modified, and used by others.Hardware generally can refer to everything that can be built.Hence, it also applies to the doityourself culture and its maker subculture in the broadest sense.The maker scene is technol ogy oriented with a major interest in but not restricted to electronics, mechanics, and robotics.Open source software (OSS) is free, that is, everybody has the freedom to download or copy, use, study, and distribute it, with or without modifications for free.So, one requires only a computer and a respective runtime system to execute the software.However, additional tools such as editors and compilers are required for inspection, modification, and translation.Yet most of these software tools are also available as open source.Sometimes OSH is also referred to as free and open source hardware (FOSH).However, hardware is physical and cannot just be downloaded or replicated.Producing hardware requires physical resources and also costs money.Manufacturing a product requires a bill of materials (that is, a list of required raw materials, components, and tools), which obviously has a specific price.Thus, here freedom refers to hardware made from a free design, for example, mechanical drawings, schematics, a layout data of printed
The Relation between Reading Performance and Eye Movement Parameters of High-Skilled and Low-Skilled Readers

In this study, we investigated the relations between reading performance and eye movements obtained during reading in fourth-grade high-skilled and low-skilled readers. We examined the relations among students' reading speed and reading comprehension scores along with their eye movement parameters, as indicated by total reading time, gaze duration, first fixation duration, number of fixations, and number of regressions into areas of interest (AOI) obtained from two text types: Narrative and expository. Correlation analyses were conducted to determine the strength of the relationships among eye movement parameters and reading speed and reading comprehension scores of high-skilled and low-skilled readers. The participants in this study were 38 high-skilled readers (24 girls and 14 boys; mean age = 9.4 years) and 26 low-skilled readers (11 girls and 15 boys; mean age = 9.4 years) who were fourth-grade students. The data obtained from two text types were used to examine the correlations between students' reading speeds and eye movement parameters and findings showed that as the reading speed of real words increased, fixation durations and fixation numbers decreased, yet as the reading speed of real words decreased, fixation durations increased. These results indicated significant correlations among the reading speed of real words, total reading time, and gaze duration in both text types. We also found similar correlations between the reading speed of high-skilled readers in pseudowords and their total reading time, gaze duration, and the number of fixations in both text types. On the other hand, a correlation between the reading speed of pseudowords and the gaze duration only in narrative text was found in low-skilled readers.
Constructing Assessment Indicators Weight System of Digital Business Chinese Materials

Learning business Chinese through digital materials has become an inevitable tendency with the growth of the internet and impact of the COVID-19 pandemic. Despite the increasing research-based publications and practices relating to Chinese language textbooks, there is a dearth of assessment indicators weight systems constructed by alternative research tools and statistical methods to evaluate the quality of the growing digital business Chinese materials. To the best of our knowledge, no literature review has been published on using the analytic hierarchy process (AHP) method to construct the assessment indicators of digital business Chinese materials. Therefore, this study aims to fill this gap by reviewing the existing, but limited, literature and providing recommendations for developing a checklist—based on the assessment indicators weight system—to evaluate digital business Chinese materials. The preliminary PC2U (Production, Content, Use, and Usefulness) assessment indicators are constructed based on the current research on business Chinese, CLTML, and relevant assessment indicators. In addition, the analysis hierarchy process (AHP) is used to construct the weight system. The results show that it is crucial to prioritize the access of digital business Chinese content. Of the three criteria in the content dimension, learning needs and credibility are equally important, and both are more important than data update. The assessment indicators are not only beneficial to fill the knowledge gap on business Chinese research, but also helpful for publishers and teachers to compile and select high quality digital business Chinese materials.
A Safe Collaborative Chatbot for Smart Home Assistants

Smart home assistants, which enable users to control home appliances and can be used for holding entertaining conversations, have become an inseparable part of many people's homes. Recently, there have been many attempts to allow end-users to teach a home assistant new commands, responses, and rules, which can then be shared with a larger community. However, allowing end-users to teach an agent new responses, which are shared with a large community, opens the gate to malicious users, who can teach the agent inappropriate responses in order to promote their own business, products, or political views. In this paper, we present a platform that enables users to collaboratively teach a smart home assistant (or chatbot) responses using natural language. We present a method of collectively detecting malicious users and using the commands taught by the malicious users to further mitigate activity of future malicious users. We ran an experiment with 192 subjects and show the effectiveness of our platform.
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing

Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently.The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling.The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem.In this work, we propose an end-to-end neural model to tackle the task jointly.Concretely, we exploit a graphbased method, regarding frame semantic parsing as a graph construction problem.All predicates and roles are treated as graph nodes, and their relations are taken as graph edges.Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
Evaluating Syntactic Annotation of Ancient Languages

Abstract In this paper we introduce an extended version of the Vedic Treebank ( vtb , Hellwig et al. 2020) which comes along with revisited and extended annotation guidelines. In order to assess the quality of our annotations as well as the usability and limits of the guidelines we performed an inter-annotator agreement test. The results show that agreement between annotators is hampered by various factors, most prominently by insufficient understanding of the content because of the cultural and temporal gap and incomplete knowledge of Vedic grammar. An in-depth discussion of disagreeing annotations demonstrates that the setup of the workflow, too, has a major influence on inter-annotator agreement. We suggest some measures that can help increase the transparency and annotation consistency according to current knowledge of the language when annotating Vedic Sanskrit, or ancient language varieties in general.
Exploring cyber violence against women and girls in the Philippines through Mining Online News

Violence against women and girls (VAWG) is not a recent phenomenon. What is new is the additional increasing threats that millions of women and girls face because of the rapid spread of ICTs and the expansion of social media. Cases of VAWG wherein ICT and social media are used as platforms by cybercriminals can be seen in the news media coverage. This study aims to understand and determine the trend and the state of cyber VAWG to raise awareness through mining online news websites. News articles were scraped from popular news websites between 2015 to 2020. The preprocessed articles (N=3,506) were analyzed by year using the Topic Keyword Model (TKM). It was observed that the cyber VAWG articles topic trends are increasing with most of the articles focusing on the topics “Online sexual exploitation and sexual abuse of children” and “ICT-related violations of privacy”. Text mining methods may address the limitations of traditional qualitative approaches. Understanding the cyber VAWG issues by mining news articles is a novel approach that could help create programs and policies to address this societal concern. Additional studies should be conducted related to sentiment analysis of news data to verify and measure the influence of cyber VAWG-related topics. La violencia contra las mujeres y las niñas (VCMN) no es un fenómeno nuevo. Lo nuevo son los crecientes peligros a que se enfrentan los millones de mujeres y niñas debido a la difusión de las TIC y redes sociales. Los casos de la VCMN donde se utilizan como plataformas las TIC y las redes sociales se encuentran fácilmente por medio de la cobertura mediática. Este estudio tiene como objetivo la comprensión y la definición de la tendencia y el estado de la ciber-VCMN para crear conciencia por medio del análisis de los sitios web de noticias online. Entre 2015 y 2020, se recopilaron artículos de los principales sitios de noticias. Se utilizó el Modelo de palabras claves temáticas para evaluar los artículos preprocesados (N=3.506) por año. Se señaló que la mayoría de los artículos sobre ciber-VCMN se centraban en temas de «Explotación sexual y abuso sexual de niños en línea» y «Violaciones de la privacidad relacionadas con las TIC». El análisis de los textos ayuda a trascender las limitaciones de las metodologías cualitativas tradicionales. Comprender las preocupaciones de la ciber-VCMN mediante la extracción de artículos de noticias podría ayudar a crear iniciativas y políticas para solucionar este problema. Proponemos que se lleve a cabo una investigación utilizando análisis de sentimiento de los datos de noticias para verificar y cuantificar el impacto de los problemas relacionados con la ciber-VCMN.
Video Abnormal Event Detection Based on One-Class Neural Network

Video abnormal event detection is a challenging problem in pattern recognition field. Existing methods usually design the two steps of video feature extraction and anomaly detection model establishment independently, which leads to the failure to achieve the optimal result. As a remedy, a method based on one-class neural network (ONN) is designed for video anomaly detection. The proposed method combines the layer-by-layer data representation capabilities of the autoencoder and good classification capabilities of ONN. The features of the hidden layer are constructed for the specific task of anomaly detection, thereby obtaining a hyperplane to separate all normal samples from abnormal ones. Experimental results show that the proposed method achieves 94.9% frame-level AUC and 94.5% frame-level AUC on the PED1 subset and PED2 subset from the USCD dataset, respectively. In addition, it achieves 80 correct event detections on the Subway dataset. The results confirm the wide applicability and good performance of the proposed method in industrial and urban environments.
Development of Smart Home Gesture-based Control System

In recent years, smart speakers have been widely used to control related smart home appliances.For people with speech disabilities, smart speakers cannot be used to perform related voice control tasks.To help people with speech difficulties and effectively control related smart facilities, we use the Internet of Things to develop a smart home gesture-based control system.The prototype of the system is established by means of sensors and a control panel system.When a sensor detects a control gesture command, it can send related control signals to a controlled smart device.The controlled devices used in this study include smart home appliances such as doors and windows, air conditioners, curtains, dehumidifiers, and air purifiers.The system functions include the opening/closing of doors, windows, and curtains; the activation/ deactivation of heating and cooling systems, dehumidifiers, and air purifiers; temperature and humidity adjustment; and fan operation.With the development of this system, it is possible for people with language disabilities and elderly people to control the operation of various smart devices with gestures.It can also solve the inconvenience of needing to look for remote controls for various facilities.Moreover, it can save precious time and provide a safe, convenient, and smart home living environment for people with speech disabilities.
IMPROVING EDUCATION QUALITY OF SECONDARY SCHOOL IN INDONESIA: AN EMPIRICAL RESEARCH

This study aims to determine the planning, implementation, and evaluation processes in improving the quality of education in the Inclusive Elementary School in Bantul Regency. Data collection techniques are through interviews, observation, and study documentation. Subjects were principals, vice-principals, teachers, and the committee chairperson in Elementary Inclusion in the Regional District Bantul. The results showed that: (1) program planning was based on the school’s vision, mission, and goals. Each school component compiles work programs by revising last year and verified by the school principal. The substance leads to efforts to improve the quality of education. However, it does not cover detailed results targets, (2) the implementation of the program is managed by each school component, by preparing written implementation instructions such as 2013 curriculum document, organizational structure, division of teacher and education staff assignments, regulations academic, and school rules, (3) program evaluation is more focused on academic programs than on the effectiveness and efficiency of learning and teacher performance, carrying out the Self- Inclusion Elementary School Evaluation in the District of Bantul and school accreditation. The evaluation results of the program's implementation made a report consisting of technical and financial reports to local governments.
USER EVALUATION OF INTERACTIVE THEMATIC 3D CITY MODELS – APPLICATION OF ASYNCHRONOUS REMOTE TESTING METHOD

Abstract. Asynchronous remote usability testing is a method based on a software platform used to automatically record test participants' activities when they interact with a given product in their natural environment, for example, at home. This method has been frequently used in previous decades in web design and mobile application development but has rarely been utilised in geovisualization. The importance of remote usability testing has rapidly increased in 2021 due to the COVID-19 pandemic. The 3DmoveR (3D Movement and Interaction Recorder) application was used for asynchronous remote testing presented in this paper. 3DmoveR is a research tool designed for user testing of interactive 3D visualizations in web browsers using open technologies such as PHP, JavaScript, and the Three.js library. This study focuses on an evaluation of interactive 3D city models presenting thematic information expressed by colour scale. An experiment was designed as a within-subject study consisting of two simple questionnaires, a training task and six experimental trials. Finding a building of a given category (depicted as building colour) within an interactive 3D city model was used as the experimental task. Speed and accuracy of user performances were recorded, as well as user strategy, subjective evaluations, and possible intervening variables. The results were recorded from 110 participants, where 76 of them were correct and analysed further. It can be concluded that the tested colour scale (based on the Energy Performance Certificate) was not entirely appropriate. We further analysed and discussed intervening variables that may affect remote usability testing of 3D visualizations.
The Impact of Blockchain on the Healthcare Environment

Bitcoin was the first electronic payment system to truly exploit the power of blockchain technology. There is currently the problem of health information inequality and health information leakage. Physicians should conduct essential routine work that wastes human and financial resources and delays treatment processes. Blockchain provides a trust-free and cost-reducing solution to manage and secure valuable health information. The aim of this study is to discuss research into blockchain healthcare applications. It addresses the management of medical data, as well as the sharing of medical information, the sharing of images, and the management of logs. We also discuss papers that overlap with other fields, such as the Internet of Things, information management, drug monitoring along their supply chain, and aspects of security and privacy. Finally, we analyze and compare the research papers in the medical area and also summarize the strategies used in healthcare with their pros and cons.
Evaluation of Neural Network Transformer Models for Named-Entity Recognition on Low-Resourced Languages

Neural Network (NN) models produce state-ofthe-art results for natural language processing tasks.Further, NN models are used for sequence tagging tasks on lowresourced languages with good results.However, the findings are not consistent for all low-resourced languages, and many of these languages have not been sufficiently evaluated.Therefore, in this paper, transformer NN models are used to evaluate named-entity recognition for ten low-resourced South African languages.Further, these transformer models are compared to other NN models and a Conditional Random Fields (CRF) Machine Learning (ML) model.The findings show that the transformer models have the highest F-scores with more than a 5% performance difference from the other models.However, the CRF ML model has the highest average F-score.The transformer model's greater parallelization allows lowresourced languages to be trained and tested with less effort and resource costs.This makes transformer models viable for low-resourced languages.Future research could improve upon these findings by implementing a linear-complexity recurrent transformer variant.
FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover's distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.
Situated Dialogue Learning through Procedural Environment Generation

We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums.Our agents operate in LIGHT (Urbanek et al., 2019)-a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language.Goals in this environment take the form of character-based quests, consisting of personas and motivations.We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals.In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution-an easier environment is one that is more likely to have been found in the unaugmented dataset.An ablation study shows that this method of learning from the tail of a distribution results in significantly higher generalization abilities as measured by zeroshot performance on never-before-seen quests.
A Framework for Sentiment Analysis of Telugu Tweets

Now a day Social Media like Facebook, twitter and Instagram is major Sources for people to share their emotions based on the current situations in society. By knowing the interesting patterns in it, a government/appropriate person for that situation can take good and useful decisions. Sentiment analysis is a method where people can extract the useful information from the text like the emotions (happy, sad, and neutral) of people. Much research work was been underdoing in the area of sentiment analysis. Among that work the Machine learning and Deep learning approaches plays a maximum role. Existing works on sentiment analysis is going in the English language. In this paper, proposed a novel framework that specifically designed to do sentiment analysis of the text data, that available in the telugu language. The proposed framework was integrated with the word embedding model Word2Vec, language translator and deep learning approaches like Recurrent Neural Network and Navie base algorithms to collect and analyse the sentiment in tweeter data that present in telugu language. The results shows effective in terms of accuracy, precision and specificity.