Reviewerly/exHarmony huggingface dataset:
Class Interference Regularization.

Contrastive losses yield state-of-the-art performance for person re-identification, face verification and few shot learning. They have recently outperformed the cross-entropy loss on classification at the ImageNet scale and outperformed all self-supervision prior results by a large margin (SimCLR). Simple and effective regularization techniques such as label smoothing and self-distillation do not apply anymore, because they act on multinomial label distributions, adopted in cross-entropy losses, and not on tuple comparative terms, which characterize the contrastive losses. Here we propose a novel, simple and effective regularization technique, the Class Interference Regularization (CIR), which applies to cross-entropy losses but is especially effective on contrastive losses. CIR perturbs the output features by randomly moving them towards the average embeddings of the negative classes. To the best of our knowledge, CIR is the first regularization technique to act on the output features. In experimental evaluation, the combination of CIR and a plain Siamese-net with triplet loss yields best few-shot learning performance on the challenging tieredImageNet. CIR also improves the state-of-the-art technique in person re-identification on the Market-1501 dataset, based on triplet loss, and the state-of-the-art technique in person search on the CUHK-SYSU dataset, based on a cross-entropy loss. Finally, on the task of classification CIR performs on par with the popular label smoothing, as demonstrated for CIFAR-10 and -100.
Report of the Authority Control Interest Group Meeting. American Library Association Midwinter Meeting, Seattle, January 2019

"Report of the Authority Control Interest Group Meeting. American Library Association Midwinter Meeting, Seattle, January 2019." Technical Services Quarterly, 37(1), pp. 58–59
Evaluation of Electronic Management in Administrative Units at the International University of Africa

This was study aimed at evaluating and activating the role of electronic management in the International University of Africa and knowing the fields in which electronic management was applied, and to reveal the difficulties that hindered the application of the electronic management system. A sample of the administrative staff and the statistical packages program (SPSS) were used to analyze the results, and the researcher followed the scientific management theory that aimed to reach an optimal work performance. Considering the results, the study showed that the role of electronic management in the university needed to be activated more because of the shortcomings in its activation in the management of personnel affairs, the university administration and student affairs in varying proportions that did not achieve the required level represented in the absence of coordination between its units and the lack of knowledge of workers in this technology and it was applied in all areas of the university, and the existence of difficulties impeding the mechanism of its application. The expectations of this study lie in that it will practically contribute to the development of administrative units in Sudanese universities.
Machine Learning Applications and Examples for Natural Resources

Summary Machine learning continues to advance technologies across a broad network of industries. For natural resources specifically, we have developed a general machine learning algorithm (VNet) and successfully applied it to a wide variety of problems. These include mineral and water prospectivity mapping in 2D, 3D, or 4D, airborne induced-polarization detection, airborne EM regression and segmentation, geologic mapping, structural geology prediction and interpretation, borehole and grade classification, and seismic segmentation and horizon picking. These new developments use deep convolutional neural networks to rapidly detect subtle and complex patterns across many geoscience data layers and spatial scales, in order to provide deeper insights to the geoscientist.
Analysis and Countermeasure on Information Security in Universities

Universities shoulder the important mission of cultivating and providing talents for the society, carrying out scientific research and experiments and transforming achievements. The security and stability of universities are important factors concerning the stable development of society. Working on the information security in universities is the strategic deployment that implements the overall national security concept persisted by the CPC Central Committee and paves out a path of national security with Chinese characteristics. This paper summarizes the connotation and main work of information security in universities, analyzes the current situation of information security protection in universities from four aspects: resistance capacity of information security, overall environment of security protection system, working mode of information construction and security awareness of all personnel in universities, and concludes the countermeasures of constructing security protection system in universities.
STATUS OF RESEARCH AND HISTORY OF ORIGIN OF THE CONCEPT “COMPETENCE APPROACH”

In recent years, a frequently discussed problem is the guideline of training on the educational result and the formation of students' competence. These factors form the prerequisite for improving the quality of education. Higher educational institutions of the republic pay special attention to the question of the formation of students' competence. In order to build students' competence, the educational process should be based on a competency- based approach. The article explores the issue of the formation and development of the problem of competence in education. The development of the problem is divided into three stages. The role and significance of the competency-based approach in training is revealed, the point of view of scientists on competence is analyzed.
DESIGN OF ACCEPTANCE INFORMATION SYSTEM OF NEW STUDENTS OF NATIONAL FLIGHT VOCATIONAL HIGH SCHOOL

Acceptance of New Students of Padang National Space Flight Vocational High School conveys information about the Padang National Space Flight Vocational High School to the public through the internet. Besides, prospective students who will enroll in the Padang National Space Flight Vocational High School can make it easier for them to register online. The purpose of this research is to develop a new student registration information system at the Padang National Space Flight Vocational High School quickly and efficiently in the management and selection of prospective students. The data collection methods used in this Final Task preparation are Observation, Library Studies, and Interviews. The program using PHP and MySQL.
Formation of Research Competence of Students on the Basis of Information Technologies

In modern society, the skills of working with information technology are becoming one of the most important factors in training future specialists in any field of activity. The use of a competence-based approach in the educational process contributes to the achievement of the main goal of higher education. One of the main goals of using information technologies in education is determined by the fact that they are most effectively implemented such didactic principles as science, accessibility, visibility, consciousness and activity of students. Information technologies open up wide opportunities for activating the formation of students research competence. The article deals with the place and effectiveness of using information technology for the formation of research competence of students.
Real-Time Panoptic Segmentation with Prototype Masks for Automated Driving.

In this paper we propose a fast fully convolutional neural network for panoptic segmentation that can provide an accurate semantic and instance-level representation of the environment in the 2D space. We tackle panoptic segmentation as a dense classification problem and generate masks for stuff classes as well as for each instance of things classes. Our network employs a shared backbone and Feature Pyramid Network for multi-scale feature extraction which we extend with dual-decoders that learn background and foreground specific masks. Guided by object proposals, the panoptic head assembles location-sensitive prototype masks using a learned weighting scheme. Our solution runs in real-time, in 82 ms on high resolution images, making it suitable for robotic applications and automated driving. Extensive experiments on the Cityscapes dataset demonstrate that our panoptic segmentation network is robust and accurate, with 57.3% PQ and 76.9% mIoU.
Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels

Label noise is a natural event of data collection and annotation and has been shown to have significant impact on the performance of deep learning models regarding accuracy reduction and sample complexity increase. This paper aims to develop a novel theoretically sound Bayesian deep metric learning that is robust against noisy labels. Our proposed approach is inspired by a linear Bayesian large margin nearest neighbor classification, and is a combination of Bayesian learning, triplet loss-based deep metric learning and variational inference frameworks. We theoretically show the robustness under label noise of our proposed method. The experimental results on benchmark data sets that contain both synthetic and realistic label noise show a considerable improvement in the classification accuracy of our method compared to the linear Bayesian metric learning and the point estimate deep metric learning.
Study on an Extended and Granular Classification of Cloud’s Taxonomy and Services

In the recent time cloud computing has come forwarded as one of the most admired computing model in knowledge domain that concerns about the distributed information systems to support the whole world as a cloud community. Distributed, virtualization and service-oriented nature have given ascendancy to cloud computing to distinguish from its core descendants like grid computing, geographical information systems, and distributed system. Although cloud computing dominants the e-society, but it is still in under research, progress. The architecture of cloud’s taxonomy and its services are very significant issues for cloudifications because every day some new advancements and developments are adjoined under its umbrella. The proposed taxonomy captures in a forthright way the concepts of cloud computing, and is therefore very appropriate for understanding cloud computing for cloudification. Having a clear taxonomy enables us to clearly identify the category our problem falls in. In this paper we proposed an extended and granular classification of taxonomy for cloud computing and specified services that is a detailed ontology of cloud, which will be helpful for researchers and stakeholders in better understanding, developing, and implementing cloud technology and services to their lives.
Indonesian Parsing using Probabilistic Context-Free Grammar (PCFG) and Viterbi-Cocke Younger Kasami (Viterbi-CYK)

Parsing is a tool for understanding natural grammar patterns. The problem of structural ambiguity in identifying sentence patterns often occurs in parsing. Syntactic parsing is one approach to solving structural ambiguity problems using the Probabilistic Context-Free Grammar (PCFG) and Viterbi-Cocke Younger Kasami (Viterbi-CYK) methods. Meanwhile, a large number of Indonesian language resources are needed as machine knowledge to parse. This research build a parsing of Indonesian sentence patterns with Indonesian Tagged corpus resource then solve the ambiguity problem of Indonesian sentence pattern parsing using PCFG and Viterbi-CYK algorithms. The corpus data is processed to obtain grammar rules using the PCFG algorithm. Then, the sentence on the corpus is processed by the PCFG rule that generated and uses the Viterbi-CYK algorithm to get the parse tree taken based on the highest probability value. The results of the research produced an average value of similarity production rules which the highest values is 92.95%. This shows that the Indonesian parsing successfully parses Indonesian sentence and can solve the problem of structural ambiguity in the parsing of Indonesian sentence patterns.
Diagnosis and Suggestions for the History of Liberal Arts Education Research

This article diagnosed the research results accumulated in the field of liberal arts education for academic theorization of liberal arts education and exploring the direction of future liberal arts education research. The study classified the results of various liberal arts education research conducted from 1946, when university liberal arts education began, to 2020 into eight types, including academic theses, doctoral dissertations, books, and policy research, and then analyzed historical trends and progress. According to the result of the analysis, 75.3% of academic papers were published during 2010s and since then the study of liberal arts education has dramatically increased. Majority field of research was basic education such as writing and English. This study confirmed that there were only 49 dissertations for doctoral degree in liberal arts education and most papers were from the major in pedagogy.<BR> For the sustainable development of liberal arts education research, the study suggests that it is necessary to establish an archive compiling various research achievements, strengthen the theoretical systematization of liberal arts education, revitalize exchange and cooperation between academic journals, build an institutional foundation for fostering academic successors, and expand the publication of specialized academic journals in liberal arts education. Now is the time for liberal arts education to secure an academic identity as a research-based academic field.
세계금융위기와 우리나라 항만교역량 변동성 간 관계 분석

Purpose–This paper aims to investigate the contagion effects for four main container ports that are faced with competitive relations with...
HISTORICAL AND METHODOLOGICAL BASES OF TEACHER’S PROFESSIONAL DEVELOPMENT RESEARCH

The article reveals the historical and methodological bases of the teacher’s professional development. The paper analyzes national and foreign researchers’ ideas about the teacher’s professional development, as well as factors that ensure its effectiveness. The authors emphasize that the concept of teacher’s professional development is rendered by researchers in different ways, but allocation of professional and personal components in the teacher’s development is seen as common to all definitions. The research generalizes philosophical, pedagogical works devoted to the problem of personality development, the main statements of personality psychology and personal approach in education, system-activity and acmeological scientific approaches. It is revealed that many research works on the problem of research are based on the idea of L. S. Vygotsky on the correlation between learning and personal development, as well as on the leading role in this training. Despite the multifaceted analysis of the teacher’s professional development problem in educational institutions of different types, the matter of organizational and pedagogical conditions of teacher’s professional development organizations of secondary vocational education remains insufficiently investigated. Though it is the level of the education system, which allows solving the problem of shortage of skilled workers, that in recent years is affected by active reforms associated with the modernization of educational organizations.
Review of the Edited Volume by Enrico Bonadio and Nicola Lucchi, Non-Conventional Copyright

Aptly depicting street art by two of the most celebrated artists Stik and LA2 on its cover, this book is a timely addition to the bookshelves of academics, practitioners and policy makers with an interest in copyright law. Historically, copyright law has faced a constant demand to expand protection to newer types of subject matter, beyond the conventional categories of literary, artistic, musical and dramatic works. This expansionary trend can be attributed to socio-cultural transformations in post-war societies coupled with technological advancements. Before any commitment to legislative expansions, it is worth examining whether and to what extent the existing regimes can accommodate newer subject matter which challenges traditional boundaries. Enrico Banadio and Nicola Lucchi should be congratulated for curating this edited volume which is both topical and ambitious in its coverage. This volume includes a range of subject matter from graffiti, tattoos, scent, comedic material and magic productions to engineered gene sequences and creations generated by artificial intelligence.
LibGuides: Resources for Online Teaching and Learning: Business: Library Resources

This guide provides Educational Material for Business Online Teaching and Learning Online Teaching: Library Resources
8 Decentralized solutions for data collection and privacy in healthcare

Using artificial intelligence (AI) for data-driven medical diagnosis requires processing a wide variety and massive amounts of medical data collected from different resources. Resilience, controllability and privacy of the medical data are the most important concerns for enabling AI-based data-driven medical diagnosis. Health data storage and its security and privacy requirements emerge as one of the most important challenges. The privacy concerns and lack of trust in the system became the main barrier for collecting and storing personal medical data. The patient should be given the data ownership and the track of the collected data should be kept. The privacy and security requirements of this data can be covered by using decentralized solutions. Decentralized solutions give us the opportunity of removing the intermediaries and establish trust between peers. This chapter aims at summarizing the current studies and their possible impacts on the health industry and medical studies. Possible future uses of integrating blockchain with AI are given. AI can also be used to solve the challenges in blockchain and this chapter also addresses some solutions. Multiplatform interoperable scalable architecture model for healthcare data sharing is proposed.
Demonstrating the Evolution of GANs through t-SNE

Generative Adversarial Networks (GANs) are powerful generative models that achieved strong results, mainly in the image domain. However, the training of GANs is not trivial, presenting some challenges tackled by different strategies. Evolutionary algorithms, such as COEGAN, were recently proposed as a solution to improve the GAN training, overcoming common problems that affect the model, such as vanishing gradient and mode collapse. In this work, we propose an evaluation method based on t-distributed Stochastic Neighbour Embedding (t-SNE) to assess the progress of GANs and visualize the distribution learned by generators in training. We propose the use of the feature space extracted from trained discriminators to evaluate samples produced by generators and from the input dataset. A metric based on the resulting t-SNE maps and the Jaccard index is proposed to represent the model quality. Experiments were conducted to assess the progress of GANs when trained using COEGAN. The results show both by visual inspection and metrics that the Evolutionary Algorithm gradually improves discriminators and generators through generations, avoiding problems such as mode collapse.
Outcrop-Based Field Geologic Studies and Description of Measured Stratigraphic Successions

Geologic outcrops are visible exposures of bedrocks on the earth’s surface. These exposures help in understanding the rock and the processes that may have acted upon it, which produces geologic structures that may be visible on the outcrop. This chapter presents finds from some recently exposed sections through large-sacle and artisanal quarrying activities on the structurally distorted E-W and NE-SW parallel trending sandstone ridges. Thus, giving rise to renewed interest in detailed outcrop study within the two basins of interest in Afikpo area and other surrounding inland basins. Hence, satellite imagery and outcrop-based data from several outcrop locations with information obtained from measured/logged lithologic sections have been utilized to generate a detailed geologic map with cross-section of the outcropping rock facies in the area.
Self-Adaptive Bee Colony Optimisation Algorithm for the Flexible Job Shop Scheduling Problem

The bee colony optimisation (BCO) algorithm is a nature-inspired algorithm that models the natural behaviour of honey bees as they find nectar and share food sources information with other bees in the hive. This paper presents the BCO algorithm for the flexible job-shop scheduling problem (FJSP), furthermore, to improve the neighbourhood search in the BCO algorithm we introduce a self-adaptive mechanism to the BCO algorithm (self-adaptive-BCO algorithm) for adaptively selecting the neighbourhood structure to enhance the local intensification capability of the algorithm and to help the algorithm to escape from a local optimum. We perform computational experiments on three well-known benchmarks for FJSP. The BCO algorithm is compared with the self-adaptive-BCO algorithm to test the performance of the latter. The results demonstrate that the self-adaptive-BCO algorithm outperforms the BCO algorithm, the proposed approach also outperforms the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets.
Australia: A Poor Model for QR Data ‘Attendance Tracking’

In Australia, two main technologies have been used for COVID-19 contact tracing: a Bluetooth-based proximity tracking app; and QR Codes to assist tracking of those attending venues or events. They take radically different approaches to the protection of privacy, including that the first is voluntary, the second is (in effect) compulsory. Effective contact tracing is one of the highest priorities for countries like Australia that have had good success to date in limiting the number of COVID-19 infections and resulting deaths. Few jurisdictions appear to have utilised QR Codes for attendance tracking in a way similar to the compulsion used in Australia.This article is about the risks to privacy, to public trust, and to effective contact tracing, if QR Codes are implemented without sufficient privacy protections. It contrasts protections given to the app, and recommends that a similar approach be taken to QR Code data.The protections against misuse of the COVIDSafe app included in ‘COVIDSafe Act’, a new Part VIIIA of the federal Privacy Act 1988, are first discussed. The use of QR Codes in all eight States and Territories to facilitate this contact tracing by requiring venues/events to keep records of attendees, by use of QR Codes or other electronic means is then set out. QR Code data is dangerous in two different ways: ID data; and location/attendance data. These are explained, differentiating the dangers of provision by private sector providers and government providers. The need for legislative protections against misuse of QR Codes is then detailed, based largely on protections in the COVIDSafe Act.The article concludes that a new form of location surveillance, ‘attendance tracking’ has developed in Australia, with few parallels elsewhere. The current Australian systems pose many unnecessary and unacceptable dangers to data privacy. They can and should be remedied by legislation modelled on the strict data privacy protections in Australia’s COVIDSafe Act. Other jurisdictions considering introducing attendance tracking systems should take note of these dangers, and aim to avoid them occurring.