Reviewerly/exHarmony huggingface dataset:
To Balance or Not to Balance: A Simple-yet-Effective Approach for  Learning with Long-Tailed Distributions

Real-world visual data often exhibits a long-tailed distribution, where some ''head'' classes have a large number of samples, yet only a few samples are available for ''tail'' classes. Such imbalanced distribution causes a great challenge for learning a deep neural network, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose a simple-yet-effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefit of using self-supervised learning as the auxiliary task. Without using any bells and whistles, our model achieves superior performance over the state-of-the-art solutions.
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking  and Vehicle Re-ID in Multi-Camera Networks

As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and vehicle re-identification (re-id), where we implement a zero-shot learning (ZSL) system that performs automated tracking of all vehicles all the time. Our evaluations on VeRi-776 and Cars196 show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.
Cantonese Automatic Speech Recognition Using Transfer Learning from  Mandarin

We propose a system to develop a basic automatic speech recognizer(ASR) for Cantonese, a low-resource language, through transfer learning of Mandarin, a high-resource language. We take a time-delayed neural network trained on Mandarin, and perform weight transfer of several layers to a newly initialized model for Cantonese. We experiment with the number of layers transferred, their learning rates, and pretraining i-vectors. Key findings are that this approach allows for quicker training time with less data. We find that for every epoch, log-probability is smaller for transfer learning models compared to a Cantonese-only model. The transfer learning models show slight improvement in CER.
DIGITAL COMPETENCIES OF A MODERN MANAGER: ANALYSIS OF NEEDS AND DIRECTIONS OF DEVELOPMENT

To date, digitalization is an inevitable companion of social development, which causes the emergence of various tools for its implementation. The process of diffusion of this phenomenon into all spheres of life, including management, is dynamic. Due to the increasing need of management in the use of digital tools, the question of the formation of modern managers' competencies corresponding to changing conditions is extremely acute. The process of digitalization of the economy is a challenge for education, and universities, according to the authors of the article, should respond to it by forming new competencies not only for information and communication technology specialists, but also for future managers. This process should be carried out in cooperation between universities – represented by teachers, scientists and students, industry and markets - represented by experts from among managers. Students of management specialties need to master both information retrieval and system-algorithmic thinking, and even imagine the possibilities of specific programming languages. The hypothesis of the study, which consists in the fact that managers of the future are quite definitely aware of the need to master competencies in the field of information technology, is confirmed by the results obtained, which are detailed and visualized. Moreover, the study showed that some respondents already have specific knowledge in this area and their own experience in using complex information and communication tools. The "digital" knowledge, skills and abilities identified in the course of working with experts and students, which are necessary for managers of the future in addition to typical managerial ones, are summarized in the form of competencies.
Group Activity Recognition Based on Interaction Contextual Information in Videos Using Machine Learning

This paper is about recognizing multiple person actions occurring in videos, including individual actions, interactions,and group activities. In an environment, multiple people perform group actions such as walking in groupsand talking by facing each other. The model develops by retrieving individual person action from video sequencesby representing interactive contextual features among multiple people. The novelty of the proposed frameworkis the development of interactive action context descriptors (IAC) and classifying group activities using MachineLearning. Each individual person and other nearby people’s relative action score are encoded by IAC in thevideo frame. Individual person action descriptors are important clues for recognition of multiple person activityby developing interaction context. An action retrieval technique was formulated based on KNN for individualaction classification scores. This model also introduces Fully Connected Conditional Random Field (FCCRF) tolearn interaction context information among multiple people. FCCRF regularizes activity categorization by thespatial-temporal model. This paper also presents threshold processing to improve the performance of contextdescriptors. The experimental results compared to state-of-the-art approaches and demonstrated improvement inperformance for group activity recognition.
Managing Streaming Video License Renewals

Managing license renewals is a challenging process: Do we acquire another short-term license? A longer term or perpetual license? Should we automatically renew a license or complete a review before proceeding? What criteria should we use to review a streaming video? To ensure continued access to content while also managing a limited budget, Eastern Michigan University’s Halle Library created a review process that includes internal criteria and outreach to faculty. This process has reduced the number of unnecessary renewals and helped to ensure required content is available, while also improving communication between the library and faculty and increasing awareness of the cost of streaming video access. This lighting talk will briefly describe our process for involving faculty in the review process, detail the criteria we use to recommend renewal or cancelation, and share the outcome of our efforts in 2021.
Multi-qubit noise deconvolution and characterization

We present a noise deconvolution technique for obtaining noiseless expectation values of noisy observables at the output of n-qubits quantum channels. Our protocol applies to any noise model and for any number of qubits, also in presence of correlations. For a generic observable affected by Pauli noise it provides a quadratic speed up, always producing a rescaling of its Pauli basis components. It is possible to achieve the deconvolution while experimentally estimating the noise parameters, whenever these are unknown (bypassing resource-heavy techniques like process tomography). We provide several examples and a simulation.
A DAG-free Method to Event Correlation for Revealing Abnormal Connections of Cloud Instances

Abstract We proposes an event correlation method with rule evaluation(ECRE) to reveal abnormal connections from cloud Instances. We combine some characteristics of status-pair to categorize all abnormal status-connections as far as possible. Our method can classify abnormal connections of instances but doesn’t require the DAG of tasks. Those labels that unmatched the supervision rules are removed to remain more and better samples for training. The results that our method achieved show high accuracy, and a significant correlation between the accuracy and the usage count of supervision-rules.
Preface

The 2022 2nd International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2022) is held on May 13-15, 2022. Due to the COVID-19 pandemic around the world, and with the strict travelling rules in China, it is still difficult to take international travel for our attendees abroad. Therefore, AIITA 2022 is held both in physical (Dali, China) and online (Zoom). AIITA primary goal of the conference is to promote research and developmental activities in Artificial Intelligence and Industrial Technology Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. AIITA 2022 invited over 150 well-known scholars in the field of Artificial Intelligence and Industrial Technology Applications at home and abroad. The conference model is divided into three sessions, including oral presentations, keynote speeches, and online Q&amp;A discussion. In the first part, some scholars, whose submissions are selected as the excellent papers, are given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers are each allocated 30-45 minutes to hold their speeches. In this conference, we are greatly honor to invite Prof. Zhiwu Li, Xidian University, China to serve as our Conference Chairman. List of Committee member are available in this pdf.
Bias in Hate Speech and Toxicity Detection

Many Artificial Intelligence (AI) systems rely on finding patterns in large datasets, which are prone to bias and exacerbate existing segregation and inequalities of marginalised communities. Due to their socio-technical impact, bias in AI has become a pressing issue. In this work, we investigate discrimination prevention methods on the assumption that disparities of specific populations in the training samples are reproduced or even amplified in the AI system outcomes. We aim to identify the information from vulnerable groups in the training data, uncover potential inequalities in how data capture these groups and provide additional information about them to alleviate inequalities, e.g., stereotypical and generalised views that lead to learning discriminatory associations. We develop data preprocessing techniques in automated moderation (AI systems to flag or filter online abuse) due to its substantial social implications and existing challenges common to many AI applications.
Contrasting Logical Bayesianism and Frequentism

This chapter contrasts Bayesian foundations and practices with frequentist statistical foundations and practices.
Research in Pesantren-based Higher Education: Exploring The Factors Improving Lecture's Research Performace

Integrating two different systems between higher education and pesantren (Islamic boarding schools), Pesantren-based Higher Education (PTBP) is alleged as an ideal Islamic higher education model considering its critical role in the process of integration-interconnection of science and Islamic teaching. Like other Islamic higher educations in Indonesia, PTBP represents poor organizational performance, particularly in research productivity and quality. Since research performance has the most considerable portion in the higher education performance evaluation both nationally and internationally, this study attempted to explore the factors that possibly improve lecturers' research performance at PTBP, categorized into the level of the individual, institutional and resource factors. This study involved 84 lecturers at 7 PTBP in Bogor, Indonesia, selected based on the stratified random sampling and voluntary sampling method. The collected data was analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS). The results showed that only research competence positively affected research performance at the individual level, but not for work motivation and time management. Research culture and academic leadership insignificantly affected research performance. However, only high-performance work practice (HPWP) at the institutional level positively affected research performance. Meanwhile, collaboration networks at the resource level positively affected research performance, but not for university research funding. The managerial implications are further discussed in this study.
Employee engagement: a study of select service sector organisations using qualitative and quantitative approach

Engagement of employees has become a key construct for both practitioners as well as researchers. This study aims to identify various antecedents of employee engagement and relevance of demographic traits of employees working in service sector organisations on the level of employee's engagement. Job characteristics such as feedback, skill variety and autonomy were found to be significantly positively impacting employee engagement. While organisational politics, resource inadequacies, physical demands and work conditions etc were found to be impacting employee engagement negatively. Considering the data gathered from 121 service sector employees. ANOVA results show no significant impact of category of employee and sectors presently working with on employee engagement. But type of family individual has significant impact on employee engagement. Also unmarried employees have higher level of engagement than there married counterparts.
DWT based Image Super Resolution using Segmentation and Edge Detection

An image super-resolution is the development of creating a high-resolution image from low-resolution images for better image visualization for several recent applications. In this research paper, we propose DWT-based Image Super Resolution using Segmentation and Edge Detection for better picturing of images. The different kinds of colore images are considered and converted into greyscale images and resized to a uniform size of 166x304. The Discrete Wavelet Transform (DWT) is used to get one low-frequency and three high-frequency bands with a size of 83x152. The four bands are fused and dividing each coefficient by two to get Low Resolution (LR) images. Each coefficient of the LR image is inflated into 2x2 coefficients to get High Resolution (HR) image. The HR matrix is segmented into a 3x3 overlapped matrix and an average value is calculated and assigned to the whole HR matrix. The Canny edge detection is used on the original image to get the edge detected image. The pixel values of HR and canny edge detected images are compared and high-value coefficients are considered. The guided filter is applied to the highvalue coefficients matrix to improve the quality of the image by refining the image. The LL band of the DWT matrix is padded with zeros to convert matrix size to 166x304 and Inverse Discrete Wavelet Transform (IDWT) is used on it. The refined and IDWT images are merged by computing the average values to acquire Super Resolution (SR) image. It is noted that the anticipated system results are enhanced compared to current methods. Keywords: DWT, Fusion, Image Processing, Segmentation, Super Resolution
Spatiotemporal Variance-Guided Filtering for Motion Blur

Adding motion blur to a scene can help to convey the feeling of speed even at low frame rates. Monte Carlo ray tracing can compute accurate motion blur, but requires a large number of samples per pixel to converge. In comparison, rasterization, in combination with a post-processing filter, can generate fast, but not accurate motion blur from a single sample per pixel. We build upon a recent path tracing denoiser and propose its variant to simulate ray-traced motion blur, enabling fast and high-quality motion blur from a single sample per pixel. Our approach creates temporally coherent renderings by estimating the motion direction and variance locally, and using these estimates to guide wavelet filters at different scales. We compare image quality against brute force Monte Carlo methods and current post-processing motion blur. Our approach achieves real-time frame rates, requiring less than 4ms for full-screen motion blur at a resolution of 1920 x 1080 on recent graphics cards.
Detection of Breast Tumor in Mammograms Using Single Shot Detector Algorithm

The influence of AI over Healthcare is increasing day by day. Lot of efforts are being made to increase the efficiency of Cancer diagnosis at the early stages, where medical images play a vital role. Among the women, Breast cancer is on a steep rise with an alarming statistics of, Indian woman being diagnosed with Breast cancer, for every four minutes. Though Breast cancer is a curable disease, identifying the problem early is an important metric for the successful outcome. Earlier days, Mammograms were the only way of detecting Breast cancer. However, Mammogram is not effective for women belonging to every age, and hence yields to many false positive and false negative cases. This leads to disastrous results. To overcome this limitation, this experimental study tells, about using one of the Deep Learning algorithm called Single Shot Detector algorithm to detect the cancerous portion in the Mammograms. This experiment was done in the Real Time Data set of mammograms collected from a 1250 bed hospital, after the scientific and Ethical committee approval. With a total of 80 mammography images belonging to either category of being, either Malignant or Benign. The Single Shot Detector algorithm gave an accuracy of 74%, which was better compared with CNN (64%) and VGG (60%) deep learning algorithms.
AUGMENTED REALITY INTEGRATION PLATFORM FOR SHIP NAVIGATION

Holograms are the subject of sci-fi but, like many ideas once deemed impossible, fiction and reality are converging. As new technology reduces in cost, opportunities arise in the maritime sector. This research strives to make objects that may be hazardous for navigation more easily seen in poor visibility. The wider study aims to allow the crew to see holograms of the navigation-lights of other vessels and chart-objects when they would be otherwise obscured by fog. This needs the bringing together of a variety of real-time data from bridge equipment. Each device has a different protocol/dated format, that in some cases may present conflicting information. The research presented here addresses the problem of reviewing data sources, categorising data types/strings, and converting them into a common language; to define/locate the holograms. The collected information is processed using bespoke algorithms. The outcomes present the selected methodologies and derived solutions for the holographic navigational enhancements.
Mata Kuliah Pendidikan Mitigasi Bencana Berbasis Riset Pencegahan Longsor Lahan dengan Cara Vegetatif

This study aims to determine the impact of research-based learning on disaster mitigation education courses for students of the Geography Education study program at the Muhammadiyah University of Purwokerto. The method used is a survey of students both before learning and after learning. While the data analysis technique used qualitative analysis. The results obtained from this study are students can understand knowledge and attitudes in dealing with disasters, can understand disaster policies and guidelines, can understand disaster emergency response plans, and can understand disaster early warning systems.
PERANCANGAN MOTION GRAPHIC PROMOSI WISATA ALAM KOTA PADANG

West Sumatera have a great potential in the field of tourism. The potential is very diverse and is not inferior to other regions. One area that has great tourism potential in West Sumatera is Padang City. The city of Padang has a variety of natural beauty such as mountains, rivers, beaches, and hills. Because there are a lot of nature tourism in Padang city and the magnitude of the community for recreation, as well as the wishes of the Departement of Tourism and Culture of Padang, The media needs to be made with the tittle “Designing motion graphic for the promotion of nature tourism in Padang”.The design of motion graphic regarding the promotion of natural tourism in Padang using glass boxes and black boxes method. The Result of motion graphic in the form of H264 or MP4 format video with HD 720P resolution 25fps and promoted on social media. In addition to the main media, this design also uses supporting media in the form of posters, x-banners, t-shirts, mugs, stickers, totebags, dan key chains. The Result of this motion graphic design, in order to promote natural of tourism in the city of Padang. The motion graphic result are also utilized by the departement of tourism and culture of Padang to support the promotion of nature tourism that is more creative and innovative.Keywords : Tourism, Motion Graphic, City of Padang
Challenges of the Bitcoin in the Arabic Countries

Recently, the world has seen the emergence of new phenomenal technology named crypto currency (Bitcoin) which is attracted to several international networkers across the globe.Yet, the Bitcoin was the online currency which is replaced the hard currency in a business online transaction and was accepted only by internet users so far.Though, this invisible currency was not allowed or even accepted in Arab world society because of religious circumstances or governmental consent.In this study, we tried to analyze the real challenges and necessitates that face the Bitcoin in the Arabian region.Therefore, the outcomes have been found that Islamic business rules somehow have restricted the using of the Bitcoin process in financial transactions.Furthermore, the lowest and highest service cost of internet represent the key consequences behind lessen of using Bitcoin.According to Google trends Data from 2013 to 2019, many Arabian-individuals did not know or even unfamiliar with the use of Bitcoin.
SCRATCH JUNIOR APP IN PRESCHOOL EDUCATION

In a world where new technologies appear all the time, offering a vast amount of information, entertainment and games, it is children who are one of the most attractive target groups.However, since they cannot distinguish between right and wrong, they are also the most vulnerable one.Therefore, it is vital that children start to learn about digital technology at the preschool age.And if teachers intend to use digital technology in preschool education, they need to educate themselves in this area.The first part of the paper focuses on the changes in Czech preschool education that digital technology will bring about.The second part of the paper centers on how future kindergarten teachers are being prepared for those changes at the Pedagogical Faculty of the University of Ostravawork with a touch device, work with the Scratch application and its implementation in the teaching of preschoolers.