Reviewerly/exHarmony huggingface dataset:
A New Spoken Language Teaching Tech: Combining Multi-attention and AdaIN for One-shot Cross Language Voice Conversion

Computer aided pronunciation training(CAPT) plays an important role in oral language teaching. The main methods of traditional computer-assisted oral teaching include mispronunciation detection and pronunciation scoring and assessment.However, these two techniques only give negative feedback information such as scores or error categories. In this case,it is difficult for learners to refine their pronunciation through these two indicators without the guidance of correct speech.To tackle this problem, we proposed a cross language voice conversion(VC) framework that can generate speech with template speech content and learners’ own timbre,which can guide the learner’s pronunciation.To improve VC effect,we apply AdaIN in the fore-end and after the Value matrix in multi-head attention once respectively,called attention-AdaIN,which can improve the style transfer and sequence generation ability.We used attention-AdaIN to construct VC framework based on VAE.Experiments conducted on the AISHELL-3 and VCTK corpus showed that this new aprroach improved the baseline VAE-VC.
IoT-Based Photography Practice Learning Design for Basic Photography Subjects at Indonesian Vocational High Schools

In the Industry 4.0 era, the Indonesian government implemented the “Merdeka Belajar” or Independent Learning curriculum to improve the competence and skills of vocational school graduates. This curriculum gives schools flexibility and autonomy to design learning tailored to students’ ability levels, relevant to the industry, and contextual. One technology pillar of Industry 4.0 widely implemented in various fields is the Internet of Things (IoT). This research proposes an architecture design for implementing IoT-based technology for basic photography learning in broadcast and film majors in Indonesian Vocational High Schools (VHS). The challenges in learning basic photography are limited tools, practice time, fast and accurate feedback to students, and data on all student activities. The proposed system is implemented in a photography laboratory. The proposed system can store student practice activities, gamification-based game leveling, system automation according to levels and tasks, and provide fast and accurate feedback. This research’s output is system design, consisting of system architecture design and Unified Modeling Language (UML) design. UML diagrams built in this study include use cases, activities, and entity-relationships diagrams.
BRL: Learning behavior representations of Reversi players

Player behavior modeling is of the utmost importance in game development and player matching. This problem is challenging because behavior is multi-semantic and hard to represent. Existing work often suffers from low generalization ability and high demand for supervisory information. In this paper, we present a behavior representation learning method (BRL) for Reversi players. It learns entirely from unlabeled game records. First, we develop an asymmetric encoder-decoder architecture to learn the mapping between states and actions. The encoder maps game records into a latent subspace for behavior representation. Second, we mask random states to force the encoder to capture the high-level features of the policy. The decoder predicts the corresponding actions according to the latent representation. Coupling these two designs, the semantic relevance of behaviors can be more effectively measured. Experiments were conducted in Reversi, using 14,000 game records of different players to learn behavior representations. Transfer performance in downstream tasks outperforms the supervised method and shows promising scaling ability. This work opens a new way for analyzing and modeling player behavior.
Visualization of Human Posture Based on Radar Time-Frequency Spectrogram

Visualization of human postures is of great significance for research in areas such as autonomous driving, smart home monitoring and AR human interaction. However, RGB cameras and two-dimensional radar arrays commonly exploited for visualization of human postures have problems such as susceptibility to environmental interference and high cost. Based on this, the paper proposes a method to visualize human postures using radar time-frequency time-frequency spectrograms. A stepped-frequency continuous wave (SFCW) radar is needed to obtain radar time-frequency spectrograms in one direction, which realizes the visualization of human postures from the initial state of human and radar time-frequency time-frequency spectrograms. Experimental results demonstrate that this algorithm effectively solves the problem of using multiple radar information to visualize human postures.
Summary On The ISCSLP 2022 Chinese-English Code-Switching ASR Challenge

Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomenon between multilingual language and the frequent occurrence of code-switching phenomenon in daily life. The ISCSLP 2022 Chinese-English Code-Switching Automatic Speech Recognition (CSASR) Challenge aims to promote the development of code-switching automatic speech recognition. The ISCSLP 2022 CSASR challenge provided two training sets, TAL_CSASR corpus and MagicData-RAMC corpus, a development and a test set for participants, which are used for CSASR model training and evaluation. Along with the challenge, we also provide the baseline system performance for reference. As a result, more than 40 teams participated in this challenge, and the winner team achieved 16.70% Mixture Error Rate (MER) performance on the test set and has achieved 9.8% MER absolute improvement compared with the baseline system. In this paper, we will describe the datasets, the associated baselines system and the requirements, and summarize the CSASR challenge results and major techniques and tricks used in the submitted systems.
Towards Language-universal Mandarin-English Speech Recognition with Unsupervised Label Synchronous Adaptation

End-to-end multilingual and code-switching speech recognition are two challenging tasks that are studied separately in many previous works. In this work, we jointly study multilingual and code-switching problems and present a novel unsupervised label synchronous adaptation algorithm for Mandarin-English speech recognition. Specifically, we use two parallel encoders to decompose the Mel-spectrum of speech into semantic information and other acoustic attributes, such as speaker identity, accents, pronunciation characteristics of different languages, etc. During the autoregressive decoding process of the speech recognition system, an adaptive decoder is used in parallel with the speech recognition decoder to generate an adaptive embedding for each character, so that the speech recognition model can be adaptive for Mandarin, English, and code-switching cases. Our experiments show that our proposed algorithm obtains 13.5% relative error reduction over a strong baseline in the code-switching case, and outperforms both the state-of-the-art Mandarin and English monolingual models.
Maneuvering Target Tracking Performance Online Monitoring Based on Fréchet Distance

In the application of maneuvering target tracking, online performance monitoring helps to optimize the way of radar resource allocation and improve tracking accuracy. To solve the problem of very high computation cost in the existing online monitoring methods based on information geometry, we propose an online monitoring method for tracking performance based on the Frèchet distance in this paper. The Frèchet distance under the multivariate Gaussian distribution takes into account the mean and covariance in the filtering, which can get a more reasonable error measurement. The advantage of the method is high computational efficiency, which is conducive to online evaluation. Simulation results show that the performance monitoring results of this method for maneuvering targets are equivalent to the shooting algorithm based on information geometry, but the computation cost is significantly lower than that of the shooting algorithm.
Modified chi-square test of goodness of fit (MCSTGF) based on least square method: An application to water transportation

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Friday Zinzendoff Okwonu, Nor Aishah Ahad, Festus Irimisose Arunaye, Malina Zulkifli, Zahayu Md. Yusof; Modified chi-square test of goodness of fit (MCSTGF) based on least square method: An application to water transportation. AIP Conference Proceedings 8 February 2023; 2500 (1): 020011. https://doi.org/10.1063/5.0110548 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Unknown-Unknown Risk Mitigation Through AI: Case of Covid-19

The purpose of this study is to investigate how artificial intelligence can mitigate unknown-unknown risks, taking COVID-19 as a case. The utilization of this technology has helped with the risk management and mitigations process by providing efficient solutions. Today, the artificial intelligence of applications plays a critical role in containing the fast spread of COVID-19 virus and reducing the number of infected individuals. This research will explore how countries around the world have benefited from contact tracing applications. The research discussed the UAE Alhosn application compared to the applications developed by China, Australia, Thailand, France, South Korea to identify the best features and better utilization of AI technology in the applications. In addition, a mixed-method approach used in this research, and a combination of a qualitative and a quantitative approach to make a clear understanding of the topic and find out accurate results. For qualitative research, interviews were conducted with five people from different companies in the UAE. For quantitative research, ten questions survey was sent to a sample of 50 random respondents who are the users of Alhosn application. Domain analysis was used to organize the ideas and to generate other ideas. The research will also gather secondary data based on reviews of literature related to the research topic. Based on the gathered information and research results recommendations were given.
An adaptive strong tracking Cubature Kalman filter based on noise estimation

This paper proposes to combine the noise estimation algorithm and the strong tracking cubature Kalman filter (STCKF)algorithm to establish an adaptive strong tracking cubature Kalman filter algorithm (ASTCKF). Using the idea of covariance matching, the adaptive adjustment of noise covariance in the state estimation process is realized. The simulation comparison with STCKF algorithm shows that the filtering algorithm proposed in this paper has better accuracy.
Covid-19 Epidemic Spread Countermeasures: Malaysian Authorities Experience

The COVID-19 outbreak has resulted in a significant loss of human life around the world, and it poses an extraordinary threat to public health. Almost every government in the world is fighting to stop the disease from spreading. Governments have used harsh measures such as quarantines, lockdowns, social isolation, and movement restrictions. Malaysia reached 22nd place in the Global COVID-19 recovery Index globally. However, the studies that present Malaysia's experiences in countering COVID 19 still very minimum. This study aims to review Malaysia's experience in courting Covid 19 epidemic Spread, the results showed that Malaysian authorities have imposed several measures to encounter covid-19 pandemic such as travel restrictions, movement control, contact tracing, website, and social media, regular press releases, manual contact tracing, contact tracing apps. In fact, the digital contact tracing apps application technology indeed helped Malaysians to slow down the outbreak. It supported policymakers in the suppression of the pandemic and helped in the prevention of any possible wave of the coronavirus outbreak.
SAR Image Change Detection Based on Geometric Mean Operator and Extreme Learning Machine

Synthetic Aperture Radar (SAR) image is independent of atmospheric and sunlight conditions and can be acquired under all weather and all day. As a part of SAR image application, change detection has high practical value, such as urban sprawl detection, hazard assessment of earthquake areas. In this paper, we put forward a new difference image (DI) generated by geometric mean operator which combined with log-ratio, neighborhood ratio and normal difference operator, and retained the advantages of each operator. Then, a change detection method of SAR image based on extreme learning machine (ELM) is used to classify the pixels in two original SAR images to form the final change image. Experimental results on two real SAR image datasets show that the proposed difference image generation method is robust to speckle noise, and the change detection method based on the new DI operator and ELM can effectively detect variation information in multi-phase SAR images.
The Role of Remote Work in Mitigating Unknown-Unknown Risk During Covid-19

From management perspective, understanding the strength of leadership, process, system, operation, and risks is essential to lead the organization to success.
DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices

Security threats in the Smart City Systems are becoming a challenge. These Smart City Systems, generating Big Data, are a revolutionizing application of the Internet of Things(IoT). Data Stream Mining, which is an efficient way of handling Big Data, is now of great concern. The acquired information is computationally expensive to process in terms of efficiency and runtime. Detection of suspicious activities on decentralized servers, generating and computing massive data streams requires time. Moreover, several stakeholders should be engaged to train the heterogenous malware data streams in the level of service application. Small experiments can be performed on the functionality of Batch ML on IoT datasets with available heap size resources. Among these candidate datasets, a little contribution has been already represented on the Mirai Attack. This research aims at the study of Data Stream Mining algorithms. Owing to the accuracy and interferences of the measurement, these algorithms are able to handle the non-hierarchical and unbalanced datasets similar to the Mirai Attacks. No single method can solely improve these critical standpoints. Thus, an Ensemble technique should be implemented. According to our study, a pool of meta or selective classifiers that interact based on the temporal Data Mining swiftly can outperform others. The maintainability and security concerns of such applications can be best fulfilled in meta-heuristics with the one-time scanning network approach for the recognition of the most frequent attacking pattern with the on-the-fly scheme. These are implemented in Create, Read, Update and Delete (CRUD) operations of the Big Data Systems.
IT Governance and Control: Mitigation and Disaster Preparedness of Organizations in the UAE

The success of any business continuity today depends on its levels of preparedness to address any disasters that could emerge from the risks it faces in its operations. Most of the businesses in the UAE have adopted information technology and systems to aid them in their operations. Unfortunately, the adoption of such systems enhances the risks that businesses face, hence the need to ensure they adopt reliable disaster management strategies to address any potential challenges they could face. The aim of this research is to review and measure the levels of information system disaster preparedness in both the private and public sector organizations operating in the UAE. A study was conducted to measure the use of some of the popular disaster risk management models such as STRIDE, DREAD, and BCM. The study indicated that many of the firms in the UAE are not fully prepared to tackle disasters emanating from their information management systems but have taken some steps and have invested part of their resources in improving the state of their preparedness. The findings in this paper can benefit researchers in the field of IT, private and public organizations in the region, government entities, and other stakeholders investing in the gulf country.
Development of Market Analysis Study in Aqaba

This study aimed to understand the current conditions of the tourism product as well as tourist perceptions in Aqaba City. A quantitative approach methodology was developed to meet the research aims and objectives. The quantitative technique was used based upon the opinion of tourists who visited Aqaba to understand the current condition of tourism products and tourist perceptions. The result indicated that most of the tourists complained about the transportation and traffic. Therefore, there is a need for local institutions to overcome this problem by facilitating access to important tourist attractions areas. Moreover, these institutions should give more attention to the cleanliness issue in Aqaba. Tourists also indicated that Aqaba lacks good restaurants to use or café to enjoy in the city. This indicates that existing places are all popular initiating the need to construct new places with suitable entertainment for tourists. Aqaba Special Economic Zone (ASEZA) should encourage the private sector to establish more cafés, restaurants, and other facilities in Aqaba. Moreover, public institutions should control prices, as tourists complained about the manipulation of prices, especially with tourists using their ignorance of prices. Another weak element was raised by tourists is the poor activities included in the tourist programs in Aqaba. Tourists who visited Aqaba felt boring in particular during night times due to the poor program. The tourists expected to see the city with a wide range of tourist activities distributed in different places. Authorities and policymakers in Aqaba should create a suitable and enjoyable activates program for tourists. The study also recommended that tourism in Aqaba should be marketing along with Petra and Wadi Ram as standalone destinations. Furthermore, tourism in Aqaba needs a special Beach Management Board, which should be responsible for all tourism activities on the beach of Aqaba.
YOUTUBE-SOURCED VIDEOS AS TEACHING MEDIA FOR LISTENING COMPREHENSION: AN OPTIMIZING OF AUTHENTIC-UPDATED LEARNING SOURCE

The development of Information and Communication Technology (ICT) is globally unavoidable since it becomes a booster for all life aspects, including but not limited to teaching and learning. Internet provides limitless resources teaching and learning media, one of those are videos in YouTube. The objective of this study is to know, whether or not YouTube media can improve students’ listening skill in English teaching. The subject of the study was eleventh grader (11) SMK Negeri 2 Majene. The total population is 65 students from two classes, as the sample is consisted of 30 students. In this study, researcher used quantitative method on pre-Experimental research, with one group pre-test and post-test. The pre-test result was compared to the post-test result to determine whether or not the trajectory is successful. This research uses quantitative research. Result from pre-test to post-test students can hear English well. Thus, using YouTube media in learning process is effective to improve students' listening ability. This is proved by the mean score of pre-tests which is 17 and post-test obtained score of 30.1. That means the ability to listen to students increase 13.1 points. T-test value is 6,89 and T-table is 1,699 with degree of freedom (df) 29 and significant level at 5% is 1,699. Based on the analysis data, alternative hypothesis (HI) in this research is accepted because the result of T-value is higher than T-table (6.89&gt; 1.699). It indicates that YouTube media can be effectively applied in learning English for the 11th students in SMK Negeri 2 Majene.
The Effect of Using SCAMPER Strategy on Developing Students’ Achievement

The study aimed to find out the effect of using SCAMPER strategy on developing students' some habits of mind.. The participants were chosen from the Fifth-grade students prep stage and they were divided into two groups, experimental and control group. The study tools prepared by the researcher (achievement test and habits of mind questionnaire) were pre- administrated. The experimental group learnt the presented material using SCAMPER strategy and the control group studied in the traditional method. The study tools were post applied. The results showed that there were statistically significant differences at the level (0.01) between the mean scores of experimental and control groups in the post application of the tools in favor of the experimental group.
A Cryptocurrency Analysis Tool based on Social Metrics

Recent years have witnessed the dramatic popularity of cryptocurrencies, in which millions invest to join the cryptocurrency community or make financial gains [1]. Investors employ many ways to analyze a cryptocurrency, from a purely technical approach to a more utility-centred approach [2]. However, few technologies exist to help investors find cryptocurrencies with bright prospects through social metrics, an equally if not more important viewpoint to consider due to the importance of communities in the space. This paper proposes an application to evaluate cryptocurrencies based on social metrics by establishing scores and models with machine learning and other tools [3]. We verified the need for our application through surveys, applied it to test investment strategies, andconducted a qualitative evaluation of the approach. The results show that our tool benefits investors by providing them with a different lens to view cryptocurrencies and helps them make more thorough decisions.
Satellite Attitude Estimation Based on ISAR Image Sequence Interpretation

This paper presents a novel approach for the estimation of satellite attitude based on ISAR image interpretation. The solar panel of the satellite is segmented from the initial ISAR image using Pix2pixGAN network, and morphological method is used for edge extraction according to the parallelogram characteristics of the solar panel. According to the projection process of ISAR imaging, the optimization function for the 3D absolute attitude estimation of the satellite solar panel is established, and the attitude of the satellite is solved by iteration. The experimental results show the effectiveness of the proposed method.