Reviewerly/exHarmony huggingface dataset:
Segmentation-grounded Scene Graph Generation

Scene graph generation has emerged as an important problem in computer vision. While scene graphs provide a grounded representation of objects, their locations and relations in an image, they do so only at the granularity of proposal bounding boxes. In this work, we propose the first, to our knowledge, framework for pixel-level segmentation-grounded scene graph generation. Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e.g., Visual Genome) through transfer and multi-task learning from, and with, an auxiliary dataset (e.g., MS COCO). Specifically, each target object being detected is endowed with a segmentation mask, which is expressed as a lingual-similarity weighted linear combination over categories that have annotations present in an auxiliary dataset. These inferred masks, along with a novel Gaussian attention mechanism which grounds the relations at a pixel-level within the image, allow for improved relation prediction. The entire framework is end-to-end trainable and is learned in a multi-task manner with both target and auxiliary datasets.
Relative stability toward diffeomorphisms in deep nets indicates performance.

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this question by defining a maximum-entropy distribution on diffeomorphisms, that allows to study typical diffeomorphisms of a given norm. We confirm that stability toward diffeomorphisms does not strongly correlate to performance on four benchmark data sets of images. By contrast, we find that the stability toward diffeomorphisms relative to that of generic transformations $R_f$ correlates remarkably with the test error $\epsilon_t$. It is of order unity at initialization but decreases by several decades during training for state-of-the-art architectures. For CIFAR10 and 15 known architectures, we find $\epsilon_t\approx 0.2\sqrt{R_f}$, suggesting that obtaining a small $R_f$ is important to achieve good performance. We study how $R_f$ depends on the size of the training set and compare it to a simple model of invariant learning.
Analisa Sifat Mekanik Bahan Komposit Polimer Diperkuat Lembaran Serat Buah Pinang Akibat Beban Tarik

The high availability of areca nut fibers makes it potential to be developed as an alternative material for polymer composites. This paper investigates the mechanical performance of unsaturated polyester resin composite materials reinforced with betel nut fibers. Areca fiber obtained from farmers is dried under the sun. The fibers made in finer fiber sheets which are glued together with an adhesive agent made from thermoplastic acrylic. Based on the results of the tensile test, this composite material has brittle material behaviours which is indicated by the low ability of the material to elongate. The maximum tensile strain of the areca fiber reinforced composite material is less than 2%. The stress-strain curve does not show a clear yield point and plastic zone, along with the fracture shape of the specimen that is perpendicular to the loading direction without any necking, which confirms that this composite material is not an elastic material. Based on the comparison of the number of fiber sheets, the specimen with two sheets of fiber showed a more optimal mechanical performance with a maximum tensile strength of 10.4023MPa and a Modulus of Elasticity of 709.0269MPa.
Health Information and Health Communication Technology: Impacts and Implications

The study was undertaken to ascertain the impact and implications of health communication and health information technology (HIT) in Nigeria. Four research questions were formulated for the study. Literature review included the concept of health communication, HIT, the impact and implications of health information technology (HIT), theoretical framework using the theory of diffusion of innovation and empirical review of related literature. A descriptive design was used to collect data on 28 health workers in District Hospital Agbani and Agbani Health Centre who were the population of the study. The whole population formed the sample due to its size. Data were collected using a structured questionnaire, and the collected data were analysed using frequency, which were converted to percentages. Findings revealed that 53.6% of the respondents had knowledge of e-health records and 42.9% on e-health. 53.6% of the respondents indicated that e-health records are in use in their health facilities while none indicated that tele-nursing and e-compliance are in use in their health facilities; 57.1% of the respondents indicated that HIT could reduce the time spent in delivery and sourcing health care, while 50% also indicated that HIT increases access to information, making it easier to track health and wellness goals, resulting in increased access to customized information and support for shared decision making. Based on the above findings, the study made the following recommendations: the health workers should be trained on information technology to enhance their competence in the application of HIT; extend Internet services to rural areas to enhance health care delivery through technology; and create public awareness on the already-deployed HIT facilities.
An Optimization of Feature Selection for Classification using Bat Algorithm

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Bat algorithm on UCI datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset (UCI) has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly.
26th Workshop on Logic, Language, Information and Computation – WoLLIC 2019

Fusion grammars are a novel approach to the generation of hypergraph languages. Context-dependent fusion grammars extend them in such a way that the application of fusion rules can be restricted by positive and negative context conditions. In this paper, we show that several variants of Petri nets can be transformed into these grammars such that the reachable markings are in one-to-one correspondence to the members of the generated language. As this includes place/transition systems with inhibitor arcs, we get as a corollary that context-dependent fusion grammars can generate all recursively enumerable languages.
Star DGT: a robust Gabor transform for speech denoising

In this paper, we address the speech denoising problem, where Gaussian and coloured additive noises are to be removed from a given speech signal. Our approach is based on a redundant, analysis-sparse representation of the original speech signal. We pick an eigenvector of the Zauner unitary matrix and—under certain assumptions on the ambient dimension—we use it as window vector to generate a spark deficient Gabor frame. The analysis operator associated with such a frame, is a (highly) redundant Gabor transform, which we use as a sparsifying transform in the denoising procedure. We conduct computational experiments on real-world speech data, using as baseline three Gabor transforms generated by state-of-the-art window vectors in time-frequency analysis and compare their performance to the proposed Gabor transform. The results show that the proposed redundant Gabor transform outperforms previous ones consistently for all types of examined signals of noise.
Investigating Individual Differences in Concepts

Semantic memory tasks can focus on intensions (features and properties) or extensions (reference and categorization). There is evidence for stable individual differences in each of these tasks, but these differences do not correspond across tasks. Two further studies are reported showing that under certain conditions the correspondence can be demonstrated. Reasons for the lack of connection are discussed in terms of different systems for conceptual understanding involving similarity versus theory-based conceptualisation.
Online College English Teaching Practice and Reflection in the Context of Covid-19

In the context of the COVID-19 epidemic, universities have responded to the Ministry of Education's “stop College English courses are also taking an online approach to teaching under the call of “never stop classes” Type. This paper introduces the practical process of online college English teaching, including online teaching. The choice of learning platform, the determination of teaching objectives, teaching content and teaching methods, this paper discusses the advantages and disadvantages of online college English teaching and points out the future universities. The development direction of English blended teaching mode combining online and offline.
A System for Providing Insights on a Tourist Spot based on Text Mining

In this paper, we introduce a framework that simplifies the process of finding information about a city. This is helpful when people are looking for information on various cities before planning a trip. As visiting various online portals and collecting information is a time consuming and tedious task , we design a service that automates this task. We also provide reviews and analyze them using sentiment analysis. This will provide better insights to the user. The text database is generated dynamically using web scraping hence the quality of final results depends on the quality of content available online.
Fibonacci transform algorithm for encryption and decryption of covid images

Picture encryption is a powerful strategy to ensure pictures or recordings by moving them into unrecognizable formats for various security purposes. In this paper, Fibonacci Algorithm for Covid pictures encryption and Discrete wavelet change based Orthogonal Frequency Division Multiplexing for transmission and gathering is proposed. This paper presents execution of Fibonacci Algorithm for picture encryption and Discrete wavelet change based Orthogonal Frequency Division Multiplexing handset utilizing Discrete wavelet transform using Field Programmable Gate Array.
Analyses on the Perception’s Differences of Online Teaching Activity according to the Level of Self-Directed Learning in Higher Education Using the IPA Technique

The purpose of this study was to identify learners' perception of teaching activities in online classes that were forcibly switched due to COVID-19, and to compare and analyze differences in importance and performance of online teaching activities according to learners' self-directed learning levels. To address these goals, 31 university students who took courses operated as online classes at universities participated in this study, and the collected data were analyzed according to IPA procedures and methods. As a result, for online teaching activities, class attendance check and encouragement, clarity of class objectives, the ability to deliver the content of learning, and the ability of the instructor to deliver lectures were identified as factors to be constantly emphasized. Depending on the level of self-directed learning, there were significant differences between importance and performance in high level learners' interaction strategy with instructor, interaction strategy with learner, and timely provision of instructor's feedback. On the other hand, there was no significant difference between the importance and performance of online teaching activities for learners with a low level of self-directed learning. Based on these research results, several implications for the design of effective online teaching activities in future university education were proposed.
Performance Evaluation of Convolutional Neural Networks (Cnns) and Vgg on Real Time Face Recognition System

Face Recognition (FR) is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. Existing Machine Learning (ML) methods are failed to achieve optimal performance due to their inability to accurately extract the features from the face image, and enhancing system’s recognition accuracy system becomes very difficult task. Majority of designed FR systems has two major steps: extraction of feature and classifier. Accurate FR system is still a challenge, primarily due to the higher computational time and separate feature extraction. In general, for various applications using images, deep learning algorithms are mostly recommended for solving these problems because it performs combined feature extraction and classification task. Deep learning algorithm reduces the computation time and enhances the recognition accuracy because of automatic extraction of feature. The major novelty of the work is to design a new VGG-16 with Transfer Learning algorithm for face recognition by varying active layers with three levels (3, 4, and 7). It also designs the Convolutional Neural Network (CNN) for FR system. The proposed work introduced a new Real Time Face Recognition (RTFR) system. The process is broken into three major steps: (1) database collection, (2) FR to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92. Second step, efficient RTFR algorithm is then used to recognize faces with a known database. Here two different deep learning algorithms such as CNN and VGG16 with Transfer Learning are introduced for RTFR system. This proposed system is implemented using Keras. Thirdly the performance of these two classifiers is measured using of precision, recall, F1-score, accuracy and k-fold cross validation. From results it concludes that proposed algorithm produces higher accuracy results of 99.37%, whereas the other existing classifiers such as VGG3, VGG7, and CNN gives the accuracy results of 75.71%, 96.53%, and 69.09% values respectively
Legal protection of employees of educational program service in the context of training using distance technologies

The authors of the paper have developed and proposed the structure of the educational program service (hereinafter referred to as the “EPS”) during the transition of the educational process in 2019-2020 to a new level (training is currently taking place both offline and online). The use of this system implies an increase in labor protection for EPS employees, the quality of the educational process in educational institutions of higher education (hereinafter referred to as the “Educational organizations”), simplification of work for the teaching staff, which should be consolidated at the state level. The necessity of using this structure in educational institutions of higher education has been substantiated; the concepts and principles of EPS work have been proposed. The authors of the study propose to provide for this structure both at the federal and at the local levels, thereby carrying out labor and legal protection of EPS employees.
LibGuides: Swahili/Kiswahili Studies: Streaming Media

This guide is meant to help students and scholars orient themselves with University of Virginia holdings in and about the Kiswahili language and culture and to highlight some open resources across the web.
TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition

Table Structure Recognition is an essential part of end-to-end tabular data extraction in document images. The recent success of deep learning model architectures in computer vision remains to be non-reflective in table structure recognition, largely because extensive datasets for this domain are still unavailable while labeling new data is expensive and time-consuming. Traditionally, in computer vision, these challenges are addressed by standard augmentation techniques that are based on image transformations like color jittering and random cropping. As demonstrated by our experiments, these techniques are not effective for the task of table structure recognition. In this paper, we propose TabAug, a re-imagined Data Augmentation technique that produces structural changes in table images through replication and deletion of rows and columns. It also consists of a data-driven probabilistic model that allows control over the augmentation process. To demonstrate the efficacy of our approach, we perform experimentation on ICDAR 2013 dataset where our approach shows consistent improvements in all aspects of the evaluation metrics, with cell-level correct detections improving from 92.16% to 96.11% over the baseline.
Importance of Web Based Services During the Pandemic: A Critical Analysis of the Content of College Library Website

Library websites are instrumental in the promotion and disseminating of information to all users. Academic and research activities are web-based services that play an important role. The present study was carried out to examine the extended library services over the internet by the college libraries in Goa (India) to their user community during the covid-19 Pandemic. The web pages of all the college libraries were analysed to see if the contents of the library meets the standards. Forty college libraries were chosen for the study but only Twenty nine college library websites were found to have their existence felt on the college web page and the Twenty nine college library websites were accessed and examined during this study. The websites were accessed from 27th July 2020 to 26th November 2020. In the study, it was found that the contents were not well-organized and well-structured to meet the user needs during this pandemic period. The study suggests there is a need to develop a unique type of library website which will develop well-organized content that will provide better quality services to satisfy the information needs of the academic user. It was also found out that eleven college libraries do not have their existence on the college website or on the internet. The findings in the study will further help the researchers to study the design concepts and various sections to be included in the library website which will extend better services for the user in future over the internet.
THE SUITABILITY OF DESCRIPTIVE TEXT IN TEXTBOOK OF STUDENT GRADE SEVEN BASED ON CURRICULUM

The objectives of this study were to analyze the extent does the presentation of descriptive text in the textbook student grade seven and analyze factors caused the presentation the way it is. The study used qualitative descriptive method. The subjects of this study were two textbooks of student grade seven. The technique of data collecting was byanalyzed using several steps according to Miles’s theory, Huberman and Saldana (2014) analyze data with three steps: data condensation, data display, and conclusion drawing and verification. After analyzing the data, the researcher found the text structure in two textbook is mpt perfect yet. The text structure of descriptive text which is classification in textbook by Kemendikbud is unavailable while in Erlangga, the location in descriptive text is unavailable. But, the components of lexicogrammatical is presented well. The result of the study showed that the reason underlying the imperfectness is financial factor. Erlangga publisher is purpose to gaining a financial benefit, while Kemendikbud publisher is not motivated by the profit. Keywords :  descriptive text ,  textbook, curriculum 2013
The Importance of Self-learning Ability for College English Learning

Based on the different objectives of English learning, this paper analyzes the difference between the English learning method before the university and the English learning method during the university, and then studies and discusses the necessity of independent learning in college English learning.
LibGuides: Access to library resources – issues and tips: Known issues

Having trouble getting access to an article or database? Find out about known issues and learn some tips and tricks.
A Complete Study of Algorithm Selection in Data Mining

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.