Reviewerly/exHarmony huggingface dataset:
DYNAMIC PROBABILITY SELECTION FOR FLOWER POLLINATION ALGORITHM BASED ON METROPOLISHASTINGS CRITERIA

Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e. the switch probability, pa) to choose from the global search (i.e. exploration) and local search (i.e. exploitation) is the main strength of FPA as compared to other meta-heuristic algorithms. However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimization function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. Adopting the problem of t-way test suite generation as the case study and with the comparative evaluation with the original FPA, FPA-MH gave promising results owing to its dynamic and adaptive selection of search operators based on the need of the current search.
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation

Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A^2-FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A^2-FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A^2-FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A^2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
중국조선어 대중가요 어휘에 대한 계량언어학적 연구

The purpose of this study is to clarify the characteristics of lexical usage of Korean language in China (a variation of the Korean language) through the analysis of vocabulary in popular songs of Chinese Koreans. To do this, in Chapter 2 of this paper introduces the concrete compliment process of the corpus of popular songs created by Chinese Koreans from the 1950s to the 2000s - design, collecting and keying texts, especially POS tagging. This is followed by quantitative and statistical analysis: calculate the mean length of the per song, extract a list of word frequencies, analyze Type/Token Ratio, high-frequency words and their range. Furthermore, the results are compared and analyzed with the results of previous studies on popular songs in Korea. It is hoped that this study will contribute to clarifying the lexical usage characteristics of the Korean language in China as a Korean variation.
Advantages of Portfolio Approach in Development of Teacher and Teacher Competencies

This article discusses the issues of improving the quality of educational and extracurricular activities in the pre-school institutions.
Game-Theory based Approach for Virtual Machine Placement in Cloud Data Centers

The resource on demand facility of cloud computing attracts various kinds of users to access the resources based on their Quality of Service (QoS) require- ments. The QoS differ from user to user where some users prefer time over money and vice versa. This work considers two types of users i.e. (i) deadline users (time over money) and (ii) non-deadline users (money over time) allocated to respective Virtual Machine (VM). However, managing the placements (mapping) of such different VMs into hosts (physical machines) is always an essential role in cloud data centers with objective to reduce energy usage and residual resource. This paper proposes a Game-theory approach to place two types of users’ VMs optimally in a cloud data center environment to reduce energy usage, resource wastage, and Service Level Agreement (SLA) violations. The VM migration plan considers fair migration among user VM type, SLAs, and resource utilization as well. The game is modeled with hosts as a player, VMs as a strategy, and the player’s playoff is represented by energy consumption and resource utilization. The focus of the proposed algorithm is to maximize the playoff of each player in the game to achieve the objectives. The data center’s energy consumption is minimized by the proposed algorithm, thus satisfying the Quality-of-Service (QoS) criteria of users to comply with the SLAs. The as- assessment of the proposed approach is performed using the CloudSim toolkit and compared with other participant VM placement approaches. The experimental findings indicate that the proposed approach reduces energy consumption, SLA violations while making efficient use of resources.
Application of Autonomous Learning Strategy in Computer Teaching of Vocational and Technical Schools

In order to cultivate practical and innovative talents, many computer majors in vocational and technical schools have increased their efforts in teaching innovation reform in recent years, and have also increased their investment in professional practice places and laboratories. But in fact, the students' learning results and employment path are not very ideal, the main reason is that the school only pays attention to the cultivation of students' professionalism, but neglects the cultivation of students' interest and autonomy. Therefore, from the perspective of autonomous learning, this paper discusses the teaching strategies suitable for computer majors in vocational and technical schools.
Comparison of statistical methods and application of Grabbs's criterion for determining and eliminating a contradiction

The use of conflict detection methods is not limited to the search and correction of errors in the data, they can also be used to find distorted data. Text analysis algorithms are used to detect inconsistencies and identify artificially created copies of original texts. This article discusses the effectiveness of statistical methods used to identify and eliminate inconsistencies and anomalies in large data. Compliance with the law of normal distribution of data was determined. The study is based on the comparison of statistical criteria with the Grubbs criterion, which gave high results in identifying conflicting data. A block diagram of the general algorithm of the Grubbs criterion was created and the results of iterations obtained in the Python programming environment were presented.
Financial Innovations In Modern Financial System – Analytical Basics And Practical Aspects

Last century was full of innovations: new technologies, new products, new services and a plethora of new industries emerged. Yet the call for innovation in business, especially in financial services, has never been more intense. For all its alleged importance as a driver of the financial crisis, we know remarkably little about financial innovation, its importance, and its systematic implications. Therefore, the primary goal of this paper is a theoretical and empirical review of financial innovations and their role and importance in the modern financial system. Innovation in the financial sector is promoted by factors such as technological dynamism and globalization, amongst others. The recent wave of financial innovation, particularly innovation related to the application of information and communication technologies, poses a serious challenge to the financial industry’s business model in both its banking and non-banking components. The paper applies a compilation method to review existing theoretical and empirical research on the characteristics of financial innovation. The descriptive method, as a process of simply describing or reading facts, will be linked to explanations of the more important features of the facts described, their laws, and causal relationships. The main finding is that financial innovation is broadly beneficial and is needed to address many of society’s challenges; on the other hand, adverse outcomes associated with financial innovation are too serious to ignore. The importance of theoretical-empirical analysis is reflected in the fact that there are still no historical metrics in the world that would finally determine the impact of financial innovations at the global level, as well as an attempt to shed light on a wide range of challenges that must be addressed for society to continue to benefit from financial innovations in the sense of social and economic development. The analysis also highlights the need to build a more resilient financial system that is less prone to innovation with unintended negative consequences, as well as to encourage dialogue among stakeholders – financial sector, business, regulators, industry, consumers, and non-financial institutions.
UO_4to@TAG-it 2020: Ensemble of Machine Learning Methods

This paper describes the proposal presented in the TAG-it author profiling task from EVALITA 2020 for sub-task 1. The main objective is to predict gender and age of some blog users by their posts, as well as topic they wrote about. Our proposal uses an ensemble of machine learning algorithms with three of the most used classifiers and language model of the n-grams of characters represented in a Bag of Word. To face this task we presented two different strategies aimed at finding the best possible results.
Sarcasm Detection From Twitter Database Using Text Mining Algorithms

Sarcasm is well-defined as a cutting, frequently sarcastic remark intended to fast ridicule or dislike. Irony detection is the assignment of fittingly labeling the text as’ Sarcasm’ or ’non- Sarcasm.’ There is a challenging task owing to the deficiency of facial expressions and intonation in the text. Social media and micro-blogging websites are extensively explored for getting the information to extract the opinion of the target because a huge of text data existence is put out into the open field into social media like Twitter. Such large, openly available text data could be utilized for a variety of researches. Here we applied text data set for classifying Sarcasm and experiments have been made from the textual data extracted from the Twitter data set. Text data set downloaded from Kaggle, including 1984 tweets that collected from Twitter. These data already have labels here. In this paper, we apply these data to train our model Classifiers for different algorithms to see the ability of model machine learning to recognize sarcasm and non-sarcasm through a set of the process start by text pre-processing feature extraction (TF-IDF) and apply different classification algorithms, such as Decision Tree classifier, Multinomial Naïve Bayes Classifier, Support vector machines, and Logistic Regression classifier. Then tuning a model fitting the best results, we get in (TF-IDF) we achieve 0.94% in Multinomial NB, Decision Tree Classifier we achieve 0.93%, Logistic Regression we achieve 0.97%, and Support vector machines (SVM) we achieve 0.42%. All these result models were improved, except the SVM model has the lowest accuracy. The results were extracted, and the evaluation of the results has been proved above to be good in accuracy for identifying sarcastic impressions of people.
SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization

Artistic style transfer aims to transfer the style characteristics of one image onto another image while retaining its content. Existing approaches commonly leverage various normalization techniques, although these face limitations in adequately transferring diverse textures to different spatial locations. Self-Attention-based approaches have tackled this issue with partial success but suffer from unwanted artifacts. Motivated by these observations, this paper aims to combine the best of both worlds: self-attention and normalization. That yields a new plug-and-play module that we name Self-Attentive Factorized Instance Normalization (SAFIN). SAFIN is essentially a spatially adaptive normalization module whose parameters are inferred through attention on the content and style image. We demonstrate that plugging SAFIN into the base network of another state-of-the-art method results in enhanced stylization. We also develop a novel base network composed of Wavelet Transform for multi-scale style transfer, which when combined with SAFIN, produces visually appealing results with lesser unwanted textures.
A Study of Data De-duplication Methods

Data is the most imperative part of any organization for their productive need or to make more profit. Rapid growth of data with variations is solemn issue to handle or process. Data is generating at higher rate that has to be stored in the databases with uniqueness. Deduplication is an approach to abolish the duplicated data from the databases and provides the backup of the data. In data deduplication numerous algorithm are feasible that basically detect and eliminate the superfluous data and store unique copy of data contents. In our paper, we first survey the background and key features of de-duplication of data, and then classify the research in data de-duplication according to the key strategy of the data de-duplication process. The summary and locution of the state of the art on de-duplication helps identify and understand the most important design considerations for data de-duplication systems. Finally, we draft the open problems and future research directions covering de-duplication-based storage systems.
Advance Chatbot for Real Estate Company

With evolution of the AI infrastructure at a rapid rate,it is really helping people improve the quality of their lives with minimal and quality interaction. However the gist is to deal efficiently with user queries that don’t necessarily need manual input.It would have been unimaginable to document each and every reply of leads that have ever been dealt with.With distribution of these chatbots in various sectors like health,marketing and especially real estate industry ,it is evident from the statistics the kind of impact and also in the improvement of the quality and efficiency of conversation. Chatbots are good and really good to be useful in the real world. Whether real estate agent or customer, Real Estate negotiations are useful for both of them when it comes to saving time, money and additional resources. When a user arrives on your website, they can quickly have their questions answered in chat conversations. They do not have to wait for someone to come to their aid for answers. More inclusive than any robot saves enough time. Chatbots are no different. As a realtor, you will not waste your time on fruitless questions. Your sales team and you will require a well designed system that serves and operates efficiently .Chatbots operates at a low level, working with each leader who can be powerful in a personal way and keep the information collected in a database.As an agent of real estate there is definitely much more than just replying to user text and getting user leads.We need to know about the whereabouts of the user ,the nature of the requirement,checking the availability in areas by querying through the database and providing them with apt response .
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, and malicious attackers. Thus the safety risk of deploying NNs is now drawing much attention. In this paper, after the analysis of the possible faults in various types of NN accelerators, we formalize and implement various fault models from the algorithmic perspective. We propose Fault-Tolerant Neural Architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays devices. Then we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which is referred to as FTT-NAS. Experiments on CIFAR-10 show that the discovered architectures outperform other manually designed baseline architectures significantly, with comparable or fewer floating-point operations (FLOPs) and parameters. Specifically, with the same fault settings, F-FTT-Net discovered under the feature fault model achieves an accuracy of 86.2% (VS. 68.1% achieved by MobileNet-V2), and W-FTT-Net discovered under the weight fault model achieves an accuracy of 69.6% (VS. 60.8% achieved by ResNet-20). By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.
UCSF Guides: Getting Started at UCSF Library: Book Collections

This guide will show you the things you need to know to make the most of UCSF Library, whether you are a student, resident, fellow, faculty or staff
Algorithm-Agnostic Explainability for Unsupervised Clustering

Supervised machine learning explainability has developed rapidly in recent years. However, clustering explainability has lagged behind. Here, we demonstrate the first adaptation of model-agnostic explainability methods to explain unsupervised clustering. We present two novel "algorithm-agnostic" explainability methods - global permutation percent change (G2PC) and local perturbation percent change (L2PC) - that identify feature importance globally to a clustering algorithm and locally to the clustering of individual samples. The methods are (1) easy to implement and (2) broadly applicable across clustering algorithms, which could make them highly impactful. We demonstrate the utility of the methods for explaining five popular clustering methods on low-dimensional synthetic datasets and on high-dimensional functional network connectivity data extracted from a resting-state functional magnetic resonance imaging dataset of 151 individuals with schizophrenia and 160 controls. Our results are consistent with existing literature while also shedding new light on how changes in brain connectivity may lead to schizophrenia symptoms. We further compare the explanations from our methods to an interpretable classifier and find them to be highly similar. Our proposed methods robustly explain multiple clustering algorithms and could facilitate new insights into many applications. We hope this study will greatly accelerate the development of the field of clustering explainability.
Integrating 21st century skills and competencies: A Model for Effective leadership of the 21st Century Universities

This study presents a comprehensive new model of educational leadership for 21st century universities. It is based on the role, competencies, skills and style of effective leadership needed for 21stcenturyuniversities.  The study provides a practical approach for university leadership to develop healthy, happy, and Resilient institutes by substantially influencing and motivating their followers through their intellectual stimulation to achieve set institutional objectives. To get in-depth knowledge, systematic literature analysis was done. Based on the thematic analysis of review of related literature, a new model for 21st century university leadership was developed.  The assumption lies behind this model is that for healthy, happy and resilient academic institutes, both leaders and followers are significantly important. Leaders’ stimulus leads the followers to deliver their best effectively and efficiently. It has been observed that competition among universities because of new global economy, the emerging technologies, and globalization spurred by the Internet along with many other factors has changed the role of leadership. The new model development was necessary as the uncertainty faced by today’s universities is putting excessive pressures on their heads/ leaders for adopting practical approach to motivate their followers which ultimately results in their increased job performance, promote the visions and philosophies of their institutes so as to realize their set objectives.
Library Home: eBooks: Troubleshooting Tips

The d'Alzon Library has an extensive collection of eBooks, searchable from our library search bar. These eBooks cover a variety of disciplines including business, economics, technology, engineering, humanities, arts, and sciences. Tech-tips for using eBooks from various vendors.
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning

Unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, given labeled data in a source domain, whose data distributions differ from the target domain. However, existing works are inapplicable to face recognition under privacy constraints because they require sharing sensitive face images between two domains. To address this problem, we propose a novel unsupervised federated face recognition approach (FedFR). FedFR improves the performance in the target domain by iteratively aggregating knowledge from the source domain through federated learning. It protects data privacy by transferring models instead of raw data between domains. Besides, we propose a new domain constraint loss (DCL) to regularize source domain training. DCL suppresses the data volume dominance of the source domain. We also enhance a hierarchical clustering algorithm to predict pseudo labels for the unlabeled target domain accurately. To this end, FedFR forms an end-to-end training pipeline: (1) pre-train in the source domain; (2) predict pseudo labels by clustering in the target domain; (3) conduct domain-constrained federated learning across two domains. Extensive experiments and analysis on two newly constructed benchmarks demonstrate the effectiveness of FedFR. It outperforms the baseline and classic methods in the target domain by over 4% on the more realistic benchmark. We believe that FedFR will shed light on applying federated learning to more computer vision tasks under privacy constraints.
Development Technology of Excel Data Server Application with DELPHI ADO + RemObjectcs Combined (Part 2 the Client Side)

Abstract This paper is second part that Client side, the client program is retrieved by the query language SQL, Excel data file can perform various operations and customer interface directly to form a Web-based application systems, in Delphi7.0 support, using ADO and RemObjects SDK tools to develop an Excel data server. Once the server is started and the client can connect to it, the user can communicate with the excel server through the client’s application program to carry out data access and query of various conditions. Using our programming techniques and methods, server-side and client-side programming efficiency is high, and data processing is more convenient.