Reviewerly/exHarmony huggingface dataset:
Construction of Virtual Piano Performance System Based on Visual Gesture Recognition

In order for computers to understand people’s gestures through cameras and react accordingly, through in-depth research, gesture recognition technology in natural human-computer interaction is proposed. Combining natural human-computer interaction technology with music performance, using computer vision-based gestures, music is played in a virtual environment. Experiments show that the virtual piano has 14 piano keys. For the realization of piano performance, it is each piano key; once the piano key is greater than the set value, just call m_Wave.Load() to make a sound. According to the CWave class in the object-oriented MFC class library under VC++, create an object m_Wave of class CWave. Then, according to the m_Wave.Load() function of the CWave class, the connection of the sound is realized. It successfully solves the troubles of music lovers, enriches people’s spiritual life, and has certain practicability and scalability.
Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis

Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the best accuracy from the naive Bayes variation by setting the minimum and maximum document frequency parameters on the Indonesian General Analysis Dataset for sentiment analysis. The naive Bayes classifier variations used include Bernoulli naive Bayes, gaussian naive Bayes, complement naive Bayes and multinomial naive Bayes. The research stage begins with downloading the dataset. Preprocessing becomes the next stage which consists of tokenizing, stemming, converting abbreviations and eliminating conjunctions. In the preprocessed data, feature extraction is carried out by converting the dataset into vectors and applying the TF-IDF method before entering the sentiment analysis classification stage. Tests in this study were carried out by applying the minimum document frequency (min-df) and maximum document frequency (max-df) for each variation of naive Bayes to obtain the appropriate parameters. The test uses k-fold cross validation of the dataset to divide the training data and sentiment analysis test data. The next confusion matrix is ​​made to evaluate the level of accuracy.&#x0D;
Study on image data cleaning method of early esophageal cancer based on VGG_NIN neural network

In order to clean the mislabeled images in the esophageal endoscopy image data set, we designed a new neural network VGG_NIN. Based on the new neural network structure, we developed a method to clean the mislabeled images in the esophageal endoscopy image data set. To verify the effectiveness of the proposed method, we designed two experiments using 3835 esophageal endoscopy images provided by West China Hospital of Sichuan University. The experimental results showed that the proposed method could clean about 93% of the mislabeled images in the data set, which was the first time in the cleaning of esophageal endoscopy image data set. Finally, in order to verify the generalization ability of this method, we cleaned the Kaggle open cat and dog data set, and cleaned out about 167 mislabeled images. Therefore, the proposed method can effectively screen the mislabeled images in the esophageal endoscopy image data set and has good generalization ability, which can provide great help for the development of high-performance gastrointestinal endoscopy image analysis model.
From feature models to feature toggles in practice

Feature Toggles (often also referred to as Feature Flags) are a powerful technique, providing an alternative to maintaining multiple feature branches in source code. A condition within the code enables or disables a feature at runtime, hence providing a kind of runtime variability resolution. Several works have already identified the proximity of this concept with the notion of Feature found in Software Product Lines. In this paper, we propose to go one step further in unifying these concepts to provide a seamless transition between design time and runtime variability resolutions. We propose to model all the variability using a feature model. Then this feature model can be partially resolved at design time (yielding an incomplete product derivation), the unresolved variability being used to generate feature toggles that can be enabled/disabled at runtime. We first demonstrate these ideas on the toy example of the Expression Product Line, and then show how it can scale to build a configurable authentication system, where a partially resolved feature model can interface with popular feature toggle frameworks such as Togglz.
Influence of Podcast In Improving Students’ Listening Skill of Senior High School

This research was conducted with the aim of finding the influence of podcast in improving students’ listening skill, especially for senior high school students. The method in this research is qualitative research method, where researchers collected data through questionnaires filled by the students and short interviews with students. The subjects in this research were the students of class XI Mia 1 in SMA Swasta Assisi Siantar. Based on research that the researchers did, researchers found that podcast is a media that has effect on students’ learning processes, especially when studying listening skill. Podcast is one of the learning media that makes students interested and motivated to learn listening in English.
Forgotten African AI Narratives and the future of AI in Africa

&#x0D; &#x0D; &#x0D; Ancient and contemporary imaginative thoughts, stories, literary works, and beliefs about intelligent machines or otherwise known as AI narratives influence the development, implementation and governance of AI. Responsible AI therefore requires the understanding of these narratives. However, in the global AI narratives discourse, narratives of AI from Africa are missing or are often forgotten. Potentially, this has implications for how AI is or will be designed, deployed and regulated in Africa. This paper presents insights into our understanding of the reasons why Africa’s AI narratives are often missing, the implications this has for the future of AI in Africa, how the situation can be improved and the path to take to achieve responsible AI in Africa. These insights emerged following a workshop organized at Mozilla Festival 2021 and demonstrates the growing need to explore uncovered AI narratives in Africa to ensure better AI outcomes.&#x0D; &#x0D; &#x0D;
The effect of the activity-based approach on grade 11 learners’ performance in solving two-dimensional trigonometric problems

The study explored the effect of the activity-based approach (ABA) on grade 11 learners’ performance in solving two-dimensional (2D) trigonometric problems. A sequential explanatory design was used, starting with the quantitative, followed by the qualitative method. The experimental group (EG) and the control group (CG) were facilitated using ABA, while CG was taught using traditional teaching approach. A 2D trigonometry test was used as a pre- and post-test to measure learners’ performance. EG’s experiences of using ABA were captured through focus group interviews. The study findings reveal significant differences between EG and CG post-tests [t(100)=.3. 95; p=.05] with Cohen d=.79. Also, ABA did not segregate between boys and girls. Despite ABA being time-consuming, learners in EG developed collaborative skills, trigonometry vocabulary, the ability to solve trigonometric problems, and learning autonomy, which improved their performance. The study recommends ABA to improve learners’ performance.
Know Thyself as a Virtual Reality

&#x0D; &#x0D; &#x0D; Research-creation has existed in an ethical gray-area since its introduction to the academy. In developing the Know Thyself as Virtual Reality project, we realized that the current standards for ethics review for university- based artists are not adequate for research-creation projects which tend to involve ethical concerns distinct from conventional research and art. This is particularly clear when a research creation project, like KTVR requires the use and manipulation of the personal data of others. Digital data can be useful to researchers and artists alike, but it also implies a wide variety of unique ethical concerns. While regulations and policies need to be updated for all researchers, the lack of ethical guidelines for artist-researchers compounds the risk that they face when working with personal data. In order to gain a better understanding of the implications of the growing proliferation of data, much of the focus of the KTVR project (and the content of the VR artworks) has turned to understanding emerging and evolving frameworks for the ethical use of human data in research- creation projects.&#x0D; &#x0D; &#x0D;
A generic face detection algorithm in electronic attendance system for educational institute

This paper aims to develop a generic face detection and recognition system that will automate the process of collecting school attendance by recognizing students' frontal faces from classroom photographs. The reliability of the data collected is the biggest problem with the traditional attendance management systems. Many automated methods, such as biometric attendance, are being used. However, technical difficulties with scanning devices always affect the efficiency of such techniques. This paper employs principal component analysis approaches for face detection and OpenCV for face recognition to improve data quality and information accessibility for legitimate parties. The Python programming language was used for the development of the proposed system, while SQL was used for the development of the database that houses the information of users in the system. The new system was tested and shown to be not only safe but also protects students' identities by offering an anonymous attendance environment.
Key-cipher policy attribute-based encryption mechanism for access control of multimedia data in cloud storages

Cloud technology is advancing at a rapid pace. Many applications and multimedia data are hosted in the cloud, Security, confidentiality, and efficiency are the key drawbacks of cloud computing. There are a variety of access control systems on the market to secure the data and applications on the cloud. But key generation time is a major flaw both in multi-authority and single authority systems. Cipher policy attribute-based encryption (CPABE) is one of many cryptographic algorithms available for ensuring user confidentiality which provides fine-grained access control. It also addreses a number of issues related attribute revocation, key generation time, and issues in handling a large number of attributes. We present a mechanism called key-cipher-policy-based ABE (KCP) in this article, which is a hybrid approach and combines CP-ABE and KP ABE approaches which result in handling a wide range of attributes, an efficient key generation process, and addresses challenges in attribute revocation.
Ctrl-VIO: Continuous-Time Visual-Inertial Odometry for Rolling Shutter Cameras

In this paper, we propose a probabilistic continuous-time visual-inertial odometry (VIO) for rolling shutter cameras. The continuous-time trajectory formulation naturally facilitates the fusion of asynchronized high-frequency IMU data and motion-distorted rolling shutter images. To prevent intractable computation load, the proposed VIO is sliding-window and keyframe-based. We propose to probabilistically marginalize the control points to keep the constant number of keyframes in the sliding window. Furthermore, the line exposure time difference (line delay) of the rolling shutter camera can be online calibrated in our continuous-time VIO. To extensively examine the performance of our continuous-time VIO, experiments are conducted on publicly-available WHU-RSVI, TUM-RSVI, and SenseTime-RSVI rolling shutter datasets. The results demonstrate the proposed continuous-time VIO significantly outperforms the existing state-of-the-art VIO methods. The codebase of this paper will also be open-sourced at \url{https://github.com/APRIL-ZJU/Ctrl-VIO}.
HLS-based Hardware Acceleration on the Zynq SoC: A Real-Time Face Detection and Recognition System

Face detection and recognition system (FDRS) is widely used in modern biometric security systems. In this paper, we implement a new model of FDRS to identify human faces under different poses. Particularly, we propose the Viola-Jones detector to locate and detect human faces and the VanderLugt correlator (VLC) technique for identification. We propose two implementations based on Zync SOC. A set of experiments has been considered using the PHPID database where the faces ranging from -30° to +30°. For more details, see [4].
The Autodidactic Universe

We present an approach to cosmology in which the Universe learns its own physical laws. It does so by exploring a landscape of possible laws, which we express as a certain class of matrix models. We discover maps that put each of these matrix models in correspondence with both a gauge/gravity theory and a mathematical model of a learning machine, such as a deep recurrent, cyclic neural network. This establishes a correspondence between each solution of the physical theory and a run of a neural network. This correspondence is not an equivalence, partly because gauge theories emerge from $N \rightarrow \infty $ limits of the matrix models, whereas the same limits of the neural networks used here are not well-defined. We discuss in detail what it means to say that learning takes place in autodidactic systems, where there is no supervision. We propose that if the neural network model can be said to learn without supervision, the same can be said for the corresponding physical theory. We consider other protocols for autodidactic physical systems, such as optimization of graph variety, subset-replication using self-attention and look-ahead, geometrogenesis guided by reinforcement learning, structural learning using renormalization group techniques, and extensions. These protocols together provide a number of directions in which to explore the origin of physical laws based on putting machine learning architectures in correspondence with physical theories.
Establishment of a Fuzzy Comprehensive Evaluation Random Matrix Model for the Assessment of Foreign Language Translation Level in Universities

In this paper, the random matrix method of a fuzzy comprehensive evaluation is used to conduct in-depth research and analysis on the assessment of foreign language translation levels in universities, and the corresponding model is designed for the automatic assessment of translation levels in university education. The Hessian matrix of the second-order optimization method is used to analyze the geometric properties of the loss plane of the deep neural network, and it is proved that the Hessian matrix can be constructed as a sample covariance matrix, which is the classical Wishart matrix in the random matrix theory. Using the study on the asymptotic distribution properties of the Wishart matrix in random matrix theory, the Hessian matrix is analyzed and the limiting spectral distribution, the eigenvalue extreme value distribution, and the standard conditional number distribution of the matrix are given. And the scores are basically distributed above 240, but some indicators have relatively low scores, all below 120 points, so the indicators distributed above 240 points are selected as reserved indicators. The analysis considers that the evaluation of foreign language translation in learning in colleges and universities is itself very fuzzy and constructs an evaluation model based on the fuzzy comprehensive judgment method, which provides a research strategy for the future evaluation of foreign language translation in learning in colleges and universities. Finally, the evaluation system of foreign language translation in learning in colleges and universities is designed and implemented. It is feasible to apply the fuzzy comprehensive judgment to the comprehensive evaluation of college students, the combined judgment of multiple methods has higher reliability and stability, and its research conclusions have certain reference value in the specific education management practice. The value, objectivity, feasibility, and wide adaptability of the fuzzy comprehensive evaluation method are analyzed, and some references are expected to be provided for translation quality assessment research and translation practice.
Trusted Data Analysis and Consensus Mechanism of Product Traceability Based on Blockchain

As a decentralized, distributed system between functional and benefit management functions, blockchain is effective for financial transaction data security, data tracking and antitampering, product tracking, and access control. In this context, we have conducted experimental research on the blockchain product traceability trusted data analysis consensus mechanism and reached the following conclusions. (1) There are decentralization, irreversible tampering, traceability, and openness through the blockchain's own information and other functions so that a series of processes from raw material production, transportation, and logistics sales are well documented. (2) Under the same network environment, if the number of matches in the system increases, the average matching time consumed by the original engine is greater than the average matching time on the optimized engine. For example, taking 10% of Byzantine nodes in the system, the number of consensus increases, the average ITPBFT consensus time is about 5.74 s, and the average consensus time of the PBFT consensus mechanism is about 6.13 s. As a decentralized distributed data management system through nodes, blockchain is widely used in financial transactions, copyright protection, and product areas such as tracking and access control. In this regard, we conducted an experimental study of the consensus mechanism to analyze reliable data on the traceability of blockchain products and came to the conclusion of the experiment.
PRINSIP PENTING PUBLIKASI ILMIAH DAN PENCEGAHAN FALSIFIKASI FABRIKASI

The purpose of this study is to identify the important principles of publishing scientific research results, benefits, and systematic review of the constraints and solutions of scientific publications. The discussion of this research focuses on scientific errors in the form of falsification and fabrication. The identification of violations of scientific data distortion includes the character, causes, and prevention perspectives through a holistic approach.The research method used is qualitative research with the type of library research using keywords, namely scientific publication, manuscript, falsification, and fabrication. This study involved a systematic literature search from the online publishing database sources Sciencedirect, Web of Science, Scopus, and Google Scholar to find relevant articles published from 2000 to 2020. The study was conducted in September 2020. An electronic search revealed that there were 896 scientific publications published. relevant, of which 42 manuscripts are included in the bibliography. The synthesis of published data that was drawn was sourced from 88% of primary journals from all references, each of which was identified regarding the validity of the data for writing papers. A prospective design was used to maximize the homogeneity of the information extracted from the selected studies, as part of the research instrument.The results of the study found that the obstacles to scientific publications included the quality and novelty of research results, limited English skills, and minimal writing skills. Repeated violations of scientific thought include falsification and fabrication. This scientific error leads to distortion of research data. Individual falsification and fabrication errors are caused by unethical behavior, violations of applicable scientific norms and ethics.
Exploring Data and Models in SAR Ship Image Captioning

In recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images; however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: first, to better describe SAR ship images, we propose certain principles for SAR image annotation based on the characteristics of SAR images. Second, to make better use of SAR ship images, a large-scale SAR ship image captioning dataset is carefully constructed. Finally, we explore encoder&#x2013;decoder models and the attention mechanism and apply these methods to the SAR ship image captioning task. We conduct detailed experiments on the proposed dataset and find that the encoder&#x2013;decoder model and attention mechanism can obtain good results in the SAR ship image captioning task. The experiments also reveal that the generated sentences can accurately describe SAR ship images. This dataset has already been published on https://github.com/5132210/SSIC.git.
Using the Yammer cloud service to organize project-based learning methods

The article reveals and interprets the key features of project-based learning based on cloud-based services: social activity; convenient communication in a team during the project implementation and at the resulting stage; open educational space; self-learning and self-improvement; use of interdisciplinary links to combine students of different years of study (1--4 degrees of the first (bachelor's) level and students of the second master's level) to joint research teams to study through research; purposeful motivation of cognitive and research activity of students within the discipline with the use of interdisciplinary connections; formation of digital literacy of students. The advantages and disadvantages of the Yammer cloud service are presented and a comparative analysis of this service with similar cloud services is performed. Examples of using Yammer in professional project activity are given. The stages of using project methods using the small group method are analyzed and detailed: initiation; planning; conducting/implementation; presentation; assessment/defense.