Reviewerly/exHarmony huggingface dataset:
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective

Training generative adversarial networks (GANs) with limited real image data generally results in deteriorated performance and collapsed models. To conquer this challenge, we are inspired by the latest observation, that one can discover independently trainable and highly sparse subnetworks (a.k.a., lottery tickets) from GANs. Treating this as an inductive prior, we suggest a brand-new angle towards data-efficient GAN training: by first identifying the lottery ticket from the original GAN using the small training set of real images; and then focusing on training that sparse subnetwork by re-using the same set. We find our coordinated framework to offer orthogonal gains to existing real image data augmentation methods, and we additionally present a new feature-level augmentation that can be applied together with them. Comprehensive experiments endorse the effectiveness of our proposed framework, across various GAN architectures (SNGAN, BigGAN, and StyleGAN-V2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, ImageNet, and multiple few-shot generation datasets). Codes are available at: https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training.
SSE Tech Support: Python Basics: Home

Learn to program in Python, one of the world’s most popular high-level programming languages. In this series of short sessions, you will learn to download Python, set up an appropriate development environment, and get familiar with the fundamentals of Pyt
대졸 신입사원의 초기 경력적응과 경력전환 의미에 대한 이해

The purpose of this study was to understand the experience of career adaptation in the first workplace and the meaning of the first workplace career transition among new college graduates. In particular, we noted the voluntary career transition of research participants and applied Savickas's Career Adaptation 4C concept to understand experiences among the college graduates’career adaptation. The research questions were first to learn about the career adaptation experiences of new college graduates and second to understand what the meaning of their first work life was. The authors conducted a qualitative study using semi-structured interview with nine participants who have less than two year experiences working life. The study results have shown that first, new college graduates were interested in a better job in their career(concern), contemplating in ‘holding up’and ‘leaving out’(control), finding opportunities for a new start(curiosity). It was found that they experience the first step toward reemployment(confidence). Second, their first work life had the meaning of first-time enlightenment and opportunity for growth through learning. Also, their first work experience had a new meaning through reflection, and was positively changing their lives through personal growth. These findings provided implications for effective employment support services for higher education institutions and business sectors.
Quantum walk-based vehicle routing optimisation

This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm(QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developedfor the indexing and unindexing of the solution space and for implementing the required alternatingphase-walk unitaries, which are the core components of QWOA. Results of numerical simulationdemonstrate that the QWOA is capable of producing convergence to near-optimal solutions for arandomly generated 8 location CVRP. Preparation of the amplified quantum state in this exampleproblem is demonstrated to produce high-quality solutions, which are more optimal than expectedfrom classical random sampling of equivalent computational effort.
Feature Selection by a Mechanism Design

In constructing an econometric or statistical model, we pick relevant features or variables from many candidates. A coalitional game is set up to study the selection problem where the players are the candidates and the payoff function is a performance measurement in all possible modeling scenarios. Thus, in theory, an irrelevant feature is equivalent to a dummy player in the game, which contributes nothing to all modeling situations. The hypothesis test of zero mean contribution is the rule to decide a feature is irrelevant or not. In our mechanism design, the end goal perfectly matches the expected model performance with the expected sum of individual marginal effects. Within a class of noninformative likelihood among all modeling opportunities, the matching equation results in a specific valuation for each feature. After estimating the valuation and its standard deviation, we drop any candidate feature if its valuation is not significantly different from zero. In the simulation studies, our new approach significantly outperforms several popular methods used in practice, and its accuracy is robust to the choice of the payoff function.
Optimization of Expert System for Agriculture Development of Zalacca Tree in Srumbung: A Literature Study

The people of Srumbung need knowledge in determining the suitability of land for planting salak plants.This is due to the lack of information on determining the suitability of land for planting salak plants and the limited knowledge of the Srumbung community.So, we need a system that can help the community in determining the land for the development of zalacca plants.In this research, an expert system will be built that can help determine the suitability of land for zalacca plants.The method used is literature study, namely by analyzing the literature that has been selected from various sources through an online database.Based on the results of a review of several literature, it was found that an expert system of land adjustment for zalacca plants.The development of this expert system is expected to be a solution to community problems in determining the land to be used for planting zalacca.
Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya

Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.
Comparison of Performance of Phishing Web Sites with Different DeepLearning4J Models

Due to the new type of coronavirus (COVID-19) disease, which was first seen in Wuhan, China in 2019, a pandemic was declared by the World Health Organization (WHO) on March 11, 2020.The pandemic, which is still in effect throughout the world, has changed the daily life activities and habits of the whole world community in a short time and shifted them towards digital media applications.Accordingly, increasing cyber-attack attacks and data breaches have created great risk for the pandemic society.In this context, the security of digital media applications has become a much more important issue with the COVID-19 outbreak.The issue has been observed especially on phishing websites.Web phishing is the practice of stealing personal information such as name, last name, password, and credit card numbers of individuals by imitating a reputable business.It will result in the exposure of information and the financial damage.The focus of the study is based on several DeepLearning4j (DL4j) models used to identify phishing websites.However, the main purpose of the study is to efficiently monitor the effectiveness of DeepLearning4J (DL4J) models with the aim of improving the performance of evaluation metrics.
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering

Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data. One of the most popular approaches to this goal is machine reading comprehension(MRC). In recent years, many novel datasets and evaluation metrics based on classical MRC tasks have been proposed for broader textual QA tasks. In this paper, we survey 47 recent textual QA benchmark datasets and propose a new taxonomy from an application point of view. In addition, We summarize 8 evaluation metrics of textual QA tasks. Finally, we discuss current trends in constructing textual QA benchmarks and suggest directions for future work.
IMPACT OF CYBER SECURITY SYSTEM ON INFORMATION KEEPING: A STUDY OF SELECTED TERTIARY INSTITUTIONS IN THE NORTH WEST OF NIGERIA

This study examined the impact of cybersecurity policies on information protection in some of the tertiary institutions in the North West of Nigeria. The research was conducted based on two research questions and the use of descriptive survey design. The data used was primary data gathered by a structured questionnaire. The population of the study consists of 3257 and 356 were sampled using Taro Yamen formula. The data gathered was analyzed by employing Z-test with the help of a statistical package for social sciences (SPSS). The findings revealed that the cybersecurity system has a notable consequence on staff information protection in the chosen tertiary institutions in North West of Nigeria and that the cybersecurity system has a notable impact on students' information (database) in the chosen tertiary institutions. From the analysis, the findings recommend that tertiary institutions should have effective and efficient cybersecurity to secure vital information in tertiary institutions.
Toward Personalizing Alzheimer’s Disease Therapy Using an Intelligent Cognitive Control System

Subjective cognitive decline is an early state of Alzheimer’s Disease which affects almost 10 million people every year. It results from negative emotions such as frustration which are more present than healthy adults. For this reason, our work focuses on relaxing subjective cognitive decline patients using virtual reality environments to improve their memory performance. We proposed in our previous work a neurofeedback approach which adapts the virtual environment to each patient according to their emotions using a Neural Agent. We found that the Neural Agent can adapt the environment to each participant but have limitations. This work is a continuation of our approach in which we propose a Limbic Agent able to monitor the interactions between the Neural Agent and patients’ emotional reactions, learn from these interactions, and modify the Neural Agent in order to enhance the adaptation to each patient with an Intelligent Cognitive Control System. Our goal is to create a system able to support the Limbic System which is the main area in charge of controlling emotions and creating memory in the human brain. We used data collected form our previous work to train the Limbic Agent and results showed that the agent is capable of modifying the weight of existing rules, generating new intervention rules, and predicting if they will work or not.
Evaluating the Impact of Counseling Services in Senior High Schools in Ghana: The Case of Okere District

Lately, it is common to observe violence and other deviant behaviors among senior high schools. This many have attributed it to poor implementation of guidance and counseling services rendered in most SHS in the country and the Okere District. The primary research purpose was to assess the importance of guidance and counseling units in some selected Senior High Schools in the Okere District. The nature of the research was descriptive, with the questionnaire as the primary data collection instrument. The research employed simple random sampling and purposive sampling methods to select students and teachers, respectively. Statistical tools employed were mean, charts, percentages, crosstabulation, and ANOVA. The findings revealed that most Senior High Schools in the District render orientation service. The findings also revealed that lack of training physical facilities, inadequate funds, missing pertinent reference materials, few guidance and counseling sessions, and no involvement of peer counselors impede the implementation of guidance and counseling units in the District. Finally, the findings also revealed guidance and counseling services rendered in the schools in the District affect students' academic performance. Therefore, it is recommended that schools in the District be assisted in improving their guidance and counseling units since it will affect students' academic performance when well carried out.
CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

The diversity of retinal imaging devices poses a significant challenge: domain shift, which leads to performance degradation when applying the deep learning models trained on one domain to new testing domains. In this paper, we propose a multi-scale input along with multiple domain adaptors applied hierarchically in both feature and output spaces. The proposed training strategy and novel unsupervised domain adaptation framework, called Collaborative Adversarial Domain Adaptation (CADA), can effectively overcome the challenge. Multi-scale inputs can reduce the information loss due to the pooling layers used in the network for feature extraction, while our proposed CADA is an interactive paradigm that presents an exquisite collaborative adaptation through both adversarial learning and ensembling weights at different network layers. In particular, to produce a better prediction for the unlabeled target domain data, we simultaneously achieve domain invariance and model generalizability via adversarial learning at multi-scale outputs from different levels of network layers and maintaining an exponential moving average (EMA) of the historical weights during training. Without annotating any sample from the target domain, multiple adversarial losses in encoder and decoder layers guide the extraction of domain-invariant features to confuse the domain classifier. Meanwhile, the ensembling of weights via EMA reduces the uncertainty of adapting multiple discriminator learning. Comprehensive experimental results demonstrate that our CADA model incorporating multi-scale input training can overcome performance degradation and outperform state-of-the-art domain adaptation methods in segmenting retinal optic disc and cup from fundus images stemming from the REFUGE, Drishti-GS, and Rim-One-r3 datasets.
ID-Based Hybrid Network Architecture for the AIoT

Recently, the frequent occurrence of security issues in the Internet of Things (IoT) systems has become serious concerns. In this article, we propose an identifier-based IoT security architecture IIAS that is well suited for resource-constrained IoT nodes. The architecture involves five layers: the sensing layer, the network layer, the storage layer, the computing layer, the service layer. Firstly, at the sensing layer, we propose automatic IoT device discovery and identification. Secondly, at the network layer, we propose an interconnection and interoperability security system. Thirdly, at the storage layer and computing layer, we provide secure data storage and access. Finally, at the data service layer, we propose secure smart service and cross domain service. Our proposed IoT security architecture with excellent compatibility and scalability is green and aware of the heterogeneous resource constraints of the devices, smart and able to adapt to diverse IoT application demands, as well as open and able to support a wide range of applications.
Modern Information Technology in Higher Education: A Challenge to COVID-19

This research has conducted a survey for the modern information technology software and online learning which was carried out in Iraq and the variety of platforms used. The survey was conducted to obtain information about the use of models and platforms that were effectively used in online learning during the social distancing period to prevent the infection of COVID-19 . In addition, the information obtained from the survey also includes obstacles in the implementation of learning, how to deliver the material and the number of online meetings that are held every week. The study reached a set of conclusions, the most important of which are that the most used and influential educational platforms and software in education are Google Classroom, Zoom, Moodle and Telegram.
Blockchain and Cybersecurity

A blockchain is a distributed database; its integrity is ensured by Cryptographic Hash functions; it can be public or private. Cryptographic Hash Functions are one-way functions that transform various inputs into a fixed length output. They are used for checking the integrity of files, protecting passwords, and for proof of work. A blockchain is stored on many nodes; once the data reaches a determined size, it is hashed, and its hash forms the first line of the next block. The advantages of the blockchains are they are immutable, data is not corrupted by the host computer, and they can provide good anonymity. The disadvantages are they consume many times more energy for storing the same quantity of data, and they are vulnerable to 51% Attacks. Blockchains can enhance cybersecurity to very high levels. They are and can be used in cryptocurrencies, smart contracts, transferring financial instruments, Distributed DNS, Public Key Infrastructure, Certifying Authorities, anonymizing data in health care industry, as repositories of public registers, IoT, traceability and supply chain management, public register of antiquities, etc. Blockchain is a paradigm shift in the way data is guarded and shared.
THE IMPORTANCE OF AESTHETIC EDUCATION IN THE FORMATION OF PERFORMANCE AND CREATIVE SKILLS OF STUDENTS IN MUSIC LESSONS IN SECONDARY SCHOOLS

This article discusses the role of scientists in the development of performance and creative skills of students, as well as their approach to music. Information on the aesthetics of music is also covered in theoretical theory. Important factors in the active development of musical perception skills in musical aesthetic education have been identified.
Bibliotherapy Intervention for Combating Aliteracy in High Schools: School Libraries to the Rescue

Aliteracy, the lack of interest in reading by capable readers had been observed to be prevalent among high school students and this has been a source of concern for education stakeholders. This study examined the remedial influence of bibliotherapy in reducing aliteracy among high school students in Ilesa, southwest Nigeria. The objectives of the study were to discuss the prevalence of aliteracy and examine the importance of reading among the high school students as well as describe the remedial impact of bibliotherapy in combating aliteracy among the high school students. All the 18 participants who identified as aliterates were exposed to Focus Group Discussion (FGD) which involved bibliotherapy. Content analysis was employed to analyse the responses of the participants to self-designed questions after the bibliotherapy sessions. The participants affirmed that exposure to bibliotherapy sessions helped in reducing aliteracy irrespective of their gender and that they needed the support of their parents to sustain an appropriate attitude to reading. Therefore, school librarians with the knowledge of bibliotherapy could be of help in reducing aliteracy among the high school students.
Mixed membership recurrent neural networks for modeling customer purchases

Models of sequential data such as the recurrent neural network (RNN) often implicitly treat a sequence of data as having a fixed time interval between observations. They also do not account for group-level effects when multiple sequences are observed generated from separate sources. A simple example is user purchasing behavior, where each user generates a unique sequence of purchases, and the time between purchases is variable. We propose a model for such sequential data based on the RNN that accounts for varying time intervals between observations in a sequence. We do this by learning a group-level "base" parameter to which each data-generating object can revert as more time passes before the next observation. This requires modeling assumptions about the data that we argue are typically satisfied by consumer purchasing behavior. Our approach is motivated by the mixed membership framework, with Latent Dirichlet Allocation being the canonical example, which we adapt to our dynamic setting. We demonstrate our approach on two consumer shopping datasets: The Instacart set of 3.4 million online grocery orders made by 206K customers, and a UK retail set consisting of over 500K orders.
GraphFPN: Graph Feature Pyramid Network for Object Detection

Feature pyramids have been proven powerful in image understanding tasks that require multi-scale features. State-of-the-art methods for multi-scale feature learning focus on performing feature interactions across space and scales using neural networks with a fixed topology. In this paper, we propose graph feature pyramid networks that are capable of adapting their topological structures to varying intrinsic image structures and supporting simultaneous feature interactions across all scales. We first define an image-specific superpixel hierarchy for each input image to represent its intrinsic image structures. The graph feature pyramid network inherits its structure from this superpixel hierarchy. Contextual and hierarchical layers are designed to achieve feature interactions within the same scale and across different scales. To make these layers more powerful, we introduce two types of local channel attention for graph neural networks by generalizing global channel attention for convolutional neural networks. The proposed graph feature pyramid network can enhance the multiscale features from a convolutional feature pyramid network. We evaluate our graph feature pyramid network in the object detection task by integrating it into the Faster R-CNN algorithm. The modified algorithm outperforms not only previous state-of-the-art feature pyramid-based methods with a clear margin but also other popular detection methods on both MS-COCO 2017 validation and test datasets.
Wolfgram Subject Guides: Scholarly Communication: Repositories & Self-Archiving

Scholarly communication is the system through which research and other scholarly writings are created, evaluated for quality, disseminated to the scholarly community, and preserved for future use (ACRL, 1996-2021).
Comparing Performance of Different Linguistically-Backed Word Embeddings for Cyberbullying Detection

In most cases, word embeddings are learned only from raw tokens or in some cases, lemmas. This includes pre-trained language models like BERT. To investigate on the potential of capturing deeper relations between lexical items and structures and to filter out redundant information, we propose to preserve the morphological, syntactic and other types of linguistic information by combining them with the raw tokens or lemmas. This means, for example, including parts-of-speech or dependency information within the used lexical features. The word embeddings can then be trained on the combinations instead of just raw tokens. It is also possible to later apply this method to the pre-training of huge language models and possibly enhance their performance. This would aid in tackling problems which are more sophisticated from the point of view of linguistic representation, such as detection of cyberbullying.