Reviewerly/exHarmony huggingface dataset:
Building a skeleton-based 3D body model with angle sensor data

Human body activity recognition is beneficial for health monitoring, and wearable angle sensors are one of the most significant techniques to provide accurate body motion information, like joint angles and bone lengths. However, angle sensor data are partial and separated, which is inconvenient to describe a whole body motion. In the paper, we built a skeleton-based 3D body model to recover whole body motions with angle sensor data. We took advantage of two sub models: a multi-output regression model to expand body information and a skeleton recovery model to produce joint positions. The multi-output regression model included Gradient Boosting and Random Forest learning regressions. The skeleton recovery model made use of the law of cosines and binary quadratic equations. In addition, we developed an Android application prototype to show the practical scenarios of the 3D body model. In the evaluation, we measured the performance of the multi-output regression model by calculating statistic metrics, including mean absolute error (MAE), mean square error (MSE) and R2 score. Moreover, we computed the absolute errors and error percentages of the 3D body model for different joints and different motions. In addition, we evaluated the impact of bone lengths to make the evaluation complete. In total, the accuracy of this 3D body model achieves about eighty percent, while most joints’ bias are limited into 10 cm in real world datasets.
FSPVDsse: A Forward Secure Publicly Verifiable Dynamic SSE Scheme

A symmetric searchable encryption (SSE) scheme allows a client (data owner) to search on encrypted data outsourced to an untrusted cloud server. The search may either be a single keyword search or a complex query search like conjunctive or Boolean keyword search. Information leakage is quite high for dynamic SSE, where data might be updated. It has been proven that to avoid this information leakage an SSE scheme with dynamic data must be forward private. A dynamic SSE scheme is said to be forward private, if adding a keyword-document pair does not reveal any information about the previous search result with that keyword. In SSE setting, the data owner has very low computation and storage power. In this setting, though some schemes achieve forward privacy with honest-but-curious cloud, it becomes difficult to achieve forward privacy when the server is malicious, meaning that it can alter the data. Verifiable dynamic SSE requires the server to give a proof of the result of the search query. The data owner can verify this proof efficiently. In this paper, we have proposed a generic publicly verifiable dynamic SSE (DSSE) scheme that makes any forward private DSSE scheme verifiable without losing forward privacy. The proposed scheme does not require any extra storage at owner-side and requires minimal computational cost as well for the owner. Moreover, we have compared our scheme with the existing results and show that our scheme is practical.
AdaCompress: Adaptive Compression for Online Computer Vision Services

With the growth of computer vision based applications and services, an explosive amount of images have been uploaded to cloud servers which host such computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the \em de facto compression and encapsulation method before one uploads the images, due to its wide adaptation. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model, e.g., the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models including InceptionNet, ResNet, etc. Knowing this, designing a better JPEG configuration for online computer vision services is still extremely challenging: 1) Cloud-based computer vision models are usually a black box to end-users; thus it is difficult to design JPEG configuration without knowing their model structures. 2) JPEG configuration has to change when different users use it. In this paper, we propose a reinforcement learning based JPEG configuration framework. In particular, we design an agent that adaptively chooses the compression level according to the input image's features and backend deep learning models. Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the \em interactive training environment and feeding a reward with comprehensive consideration of accuracy and data size. In our real-world evaluation on Amazon Rekognition, Face++ and Baidu Vision, our approach can reduce the size of images by 1/2 -- 1/3 while the overall classification accuracy only decreases slightly.
Expectations and Standards for Accounting Professions and Implications in the Learning Pattern in Industrial 4.0

This paper talks about the impact from the commercial transformation 4.0 on accounting scientific research and accounting education and learning patterns. The commercial transformation can be turbulent and make development throughout accounting scientific research and designs from accounting knowing or education and learning in the future. To ensure that the adjustment needs from accounting professionals and trainees that are taking accounting research studies are required to have the ability to instantly adapt to the instructions from modifications in market 4.0 that are currently operating. It‘s anticipated that accounting will modification to "huge information" based upon accounting innovation, consisting of automation from journaling and monetary coverage. Besides, using Synthetic Knowledge will be huge in analysis monetary declarations and examining the behavior from companies or business. Consequently, this impacts the knowing patterns from representing trainees that will place much more focus on grasping info systems regarding the real truth from business globe
A Novel Adaboost Regression Classifier for Video Retrieval in Video Sequence

This paper presents a new method for video retrieval, based on machine learning with regression. The proposed classification technique integrates Adaboost and regression classifier for significant retrieval of video frame. The proposed method consists of three stages such as key frames segmentation and gradient of pixels. In this technique, Adaboost classifier is involved in removal of noisy or blurred pixel of the segmented frame. Regression technique converts the video frame pixel either 0’s or 1’s which eliminates the noises in the frame. For the query video, the adopted classifier evaluates the machine learning system for retrieval of similar frames in the databases using proposed Adaboost Regression (ABR) classifier. Experimental analysis is conducted for video datasets to evaluate the proposed ABR classifier performance evaluation. Results stated that through proposed ABR approach incorporated in machine learning system effectively retrieve video frame for query frame. The proposed ABR classifier technique significantly improves the retrieval rate in terms of accuracy, precision.
Research on College English Teaching Based on POA in the Context of Big Data and Oriented at Cultivating Critical Thinking Ability

Critical thinking ability is an important goal put forward for training students in the new era of big data.For this goal, different education experts and scholars have given different corresponding theories and coping strategies.College English teaching often emphasizes on the explanation of English grammar and knowledge points, as well as language output and input, lays particular stress on English knowledge and skills, but neglects the cultivation of students' critical thinking ability.Combined with the advantages of information technology in the era of big data and based on the Production-Oriented Approach and the hierarchical model of critical thinking ability as a reference, this paper expounds the critical thinking elements in the Production-Oriented Approach, citing the actual classroom teaching of "New Horizon College English", discussing the aspects of teaching content, teaching objectives, teaching platform, teaching process, etc.Based on POA theory, this paper sets up the construction of college English classroom oriented to cultivate critical thinking ability, provides some enlightenment for college English reform.
State distinguishability under weak measurement and postselection: A unified system and device perspective

We quantify the disturbance of a quantum state undergoing a sequence of observations and particularly focus on a weak measurement followed by postselection and compare these results to the projective counterpart. Taking into account the distinguishability of both the system and the device, we obtain the exact trade-off between the system state disturbance and the change in the device pointer state. We show that for particular postselection procedures the coupling strength between the system and the device can be significantly reduced without losing measurement sensitivity, which is directly transferred to a reduced state disturbance of the system. We observe that a weak measurement alone does not provide this advantage, and only in combination with postselection is a significant improvement in terms of increased measurement sensitivity and reduced state disturbance found. We further show that under realistic experimental conditions this state disturbance is small, whereas the exact postselection probability is considerably larger than the approximate value given by the overlap of the initial and final states when neglecting the system state disturbance.
QUANTILE-BASED APPROACH TO ESTIMATING COGNITIVE TEXT COMPLEXITY

This paper introduces an approach to measuring the cognitive complexity of texts on various language levels. While standard readability indices are based on the linear combination of primary statistics, our general approach allows us to estimate complexity on morphological, lexical, syntactic, and discursive levels. Each model is defined by the tokens for the specific language level and the complexity function of a single token. We then use the reference collection of moderately complex texts and the quantile-based approach to spot the abnormally rare tokens. The proposed supervised ensemble, based on the ElasticNet model, incorporates models from all language levels. Having collected a labeled dataset through crowdsourcing, consisting of pairs of articles from the Russian Wikipedia, we consider several models and ensembles and compare them to common baselines. Suggested models are flexible due to the freedom in choosing the reference collection. The described experiments confirm the competitiveness of the proposed approach, as the ensembles demonstrate the best target metric value.
The problem of the reliability of the teachers’ assessment judgments in formative assessment

Introduction. The organization of the educational process involves the implementation of ongoing assessment, which provides support for students’ educational activities and the impact on the educational process to improve its effectiveness. One of the elements of the teacher's assessment activity is an assessment judgment, which is formulated by the teacher in the current process of monitoring, diagnosing, analyzing and interpreting the results of students' learning activities and is aimed at informing students about the degree of the planned result achievement, students’ motivation, approval, remark, etc. The study of the teacher's assessment judgments in the context of formative assessment correlates with the problem of reliability, which ensures the consistency, objectivity, fairness and stability of the results of assessment activities. Materials and Methods. The material for the research includes 20 articles published from 2017 to 2020 and devoted to the problems of teachers’ assessment judgments during the process of formative assessment. They were collected by using keywords in the full-text databases of published scientific research which were ScienceDirect and Wiley Online Library. The selected articles were analyzed and summarized from the point of view of considering the factors that influence the teacher's assessment judgments and create conditions for reducing reliability. Results. As a result of the analysis of publications, we identified the following groups of factors that affect the reliability of the teacher's assessment judgments: factors caused by the components of the assessment situation, which include factors related to the subject and object of the assessment, assessment target, scope of the assessment, the basis of the assessment, as well as the use of the assessment tool; factors related to the assessment judgment; external factors. Discussion and Conclusions. The study of the factors influencing the reliability of the teacher's assessment judgments determines the possibilities of increasing the effectiveness of the assessment activities in the process of formative assessment. Understanding the role of each factor creates conditions for preparing recommendations for the teacher to improve the reliability of the current assessment. In addition, on the basis of the classification under consideration, it is possible to conduct empirical studies to clarify the specifics of activating certain factors depending on the goals, conditions and individual characteristics of all participants of the educational process in order to increase the effectiveness of monitoring the achievements of planned educational results and support the educational process.
Cozies, Capers, and Other Criminal Endeavors: Utilizing Taxonomies of Mystery Fiction to Improve Genre Access

Catherine Oliver (coliver@nmu.edu) is Metadata &amp; Cataloging Services Librarian and Assistant Professor, Library and Instructional Support, Northern Michigan University.
Light and Shadow in Hate-Speech Cases: A Forensic Linguistics

Digital democracy has produced a wave of political activism led by a number of Key Opinion Leaders (KOL).However, this activity began to face a serious challenges following the issuance of the Information and Electronic Transaction Law (ITE) which is considered a silencing weapon for freedom of expression in cyberspace.Therefore, this study aims to reconsider several cases of hate speech that convicted three KOL people who were selected through the Forensic Linguistics approach.The data is sourced from utterances published on their respective social media accounts.Semiotic analysis and pragmatic discourse methods are integrated into the data interpretation and description process.The results of the analysis concluded that the posts from three convicted KOL (ADP, JG, BY) have fulfilled the element of hate speech acts through illocutionary speech acts that contain elements of humiliation, defamation, and incitement of violence and provocation that have the potential to create conflicts between religions, ethnicities and groups.
Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi

On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github
THE USE OF PICTURE AND PICTURE TECHNIQUE IN IMPROVING STUDENT WRITING SKILL

This study is aimed to know whether picture and picture technique can improve students’ writing skill. The method used was quantitative research with one group pre-test and post-test research design. The population was the 11th grade students at SMK Tunas Bangsa. The sample was one class of mechanical engineering major and consisted of 35 students.The result showed that null hypothesis of this study was rejected. It is proved by the data from T-test that shows the Sig. 2 tailed is 0.000. This value is less than 0.05. Null hypothesis, therefore, was rejected. This means that picture and picture could improve students’ writing skill. Keywords: Picture And Picture, Writing, Descriptive Text
Improving Student Preparedness for Entering the Workforce: A Hands-On Experience in Project Management for a Graduate-Level Protein Engineering Class

A hands-on polypeptide engineering experience that focuses on project management was developed and incorporated in a graduate-level course. The goal was to have doctoral students in chemical engineering learn about project planning tools, and experience what it might be like to plan and execute a project in industry or business. The motivation behind this goal was to help students best-utilize their technical skills in the private sector, where 42% of doctoral recipients in science and engineering work.
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue

Neural Natural Language Generation (NLG) systems are well known for their unreliability.To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability.While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task.The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.
Universality of short distance corrections to quantum optics

As quantum optical phenomena are based on Maxwell’s equations, and it is becoming important to understand quantum optical phenomena at short distances, so it is important to analyze quantum optics using short distance corrected Maxwell’s equation. Maxwell’s action can be obtained from quantum electrodynamics using the framework of effective field theory, and so the leading order short distance corrections to Maxwell’s action can also be obtained from the derivative expansion of the same effective field theory. Such short distance corrections will be universal for all quantum optical systems, and they will affect all short distance quantum optical phenomena. In this paper, we will analyze the form of such corrections, and demonstrate the standard formalism of quantum optics can still be used (with suitable modifications) to analyze quantum optical phenomena from this short distance corrected Maxwell’s actions.
Recommendations for Services in a FAIR Data Ecosystem

[This corrects the article DOI: 10.1016/j.patter.2020.100058.].
SISTEM INFORMASI GEOGRAFIS LOKASI DESA TANGGUH BENCANA (DESTANA) BERBASIS ANDROID PADA BADAN PENANGGULANGAN BENCANA DAERAH KABUPATEN GORONTALO

Tangguh Disaster Village is a village that has a self-reliant ability to adapt and face disaster threats and recover immediately from adverse catastrophic impacts. The problem in designing this system is that there is no geographical information system based on Android in the process of searching the data location of Tangguh disaster Village (DESTANA) in Gorontalo district. This research aims to design an Android-based geographic information system that can be used to locate the Tangguh Disaster village (DESTANA) data location in Gorontalo District to help local people and migrants with Use Android mobile devices that provide information about the Tangguh disaster Village (DESTANA) data location and know the location of an area presented with digital map facilities. The results of the study concluded that the test results to search for the location information of disaster Tangguh Village (DESTANA) and display the appropriate and valid view so that the application can be used effectively and efficiently.
Sunshine-based Global Solar Radiation Modelling: Case Study of Putrajaya, Malaysia

Electricity demands are on the rise and with it, carbon dioxide emissions from many conventional power plants are increasing. In the efforts to mitigate such phenomena, the Malaysian government seeks to implement Building Integrated Photovoltaic (BIPV) projects. Early stage studies on Global Solar Radiation (GSR) have been carried out in several states in Malaysia including Penang, Kuala Lumpur and Kota Bharu. Afterward, data from the Malaysia Meteorological Department and the Malaysia National University have been used to estimate the monthly average daily global radiation in various locations in Malaysia. Putrajaya, a location which is implementing Malaysian Building Integrated Photovoltaic (MBIPV) is among the locations where a GSR study is currently absent. Conventional methods exist for GSR estimation with the aid of pyranometer. However, this method of GSR estimation is time consuming and not cost-effective practice. The main objective of this study is to estimate the GSR in Putrajaya. This is achieved in this study by utilizing sunshine-based data with calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine to plot a linearly fitted graph. Coefficients in the Angstrom-Prescott (A-P) model was generated from the plotted graph and was used for GSR estimation where a = 0.5 and b = 0.11. The mean percentage error (MPE) of the GSR estimation was found to be 3.4. Therefore, the estimation of GSR in Putrajaya have been successful for the first-time using sunshine-based data from dual locations method. The GSR estimation of Putrajaya in this study could benefit stakeholders in civil development sectors, policy and energy authorities.