Reviewerly/exHarmony huggingface dataset:
Face mask detection using convolution neural network

In the recent times, the Coronaviruses that are a big family of different viruses have become very common, contagious and dangerous to the whole human kind. It spreads human to human by exhaling the infection breath, which leaves droplets of the virus on different surface which is then inhaled by other person and catches the infection too. So it has become very important to protect ourselves and the people around us from this situation. We can take precautions such as social distancing, washing hands every two hours, using sanitizer, maintaining social distance and the most important wearing a mask. Public use of wearing a masks has become very common everywhere in the whole world now. From that the most affected and devastating condition is of India due to its extreme population in small area. This paper proposes a method to detect the face mask is put on or not for offices, or any other work place with a lot of people coming to work. We have used convolutional neural network for the same. The model is trained on a real world dataset and tested with live video streaming with a good accuracy. Further the accuracy of the model with different hyper parameters and multiple people at different distance and location of the frame is done.
Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification

Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being vulnerable to the hard positive and negative samples in the target unlabelled dataset. Our HSR contains two parts, an inter-camera mining method that helps recognize a person under different views (hard positive) and a part-based homogeneity technique that makes the model discriminate different persons but with similar appearance (hard negative). By rectifying those two hard cases, the re-ID model can learn effectively and achieve promising results on two large-scale benchmarks.
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.
Causality in Neural Networks - An Extended Abstract

Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability, causal disentanglement are some important aspects of any machine learning model. Causal explanations are required to believe in a model's decision and causal disentanglement learning is important for transfer learning applications. We exploit the ideas of causality to be used in deep learning models to achieve better and causally explainable models that are useful in fairness, disentangled representation, etc.
Load Balancing In Cloud Computing Environment Using Quasi Oppositional Dragonfly Algorithm

In cloud computing (CC) environment, load balancing of tasks remains as an important problem of distributing resources from a data center to make sure that every virtual machine (VM) have balanced load to attain optimum utilization of its abilities.  Load balancing in CC environment is considered as a non-polynomial (NP) problem and metaheursitic algorithms can be applied to resolve it. This paper presents a new quasi oppositional dragonfly algorithm for load balancing (QODA-LB) in CC environment to achieve optimal resource scheduling. The proposed QODA-LB algorithm derives an objective function using three variables namely execution time, execution cost, and load. Based on the derived objective function, the QODA-LB algorithm allocates tasks to VM with respect to its capacity. Besides, the QODA-LB algorithm incorporates quasi oppositional based learning (QOBL) concept to improve the convergence rate of classical dragonfly algorithm (DA). Detailed set of experimentations were carried out to ensure the effective performance of the QODA-LB algorithm and the results are examined under several aspects. The simulation outcome has depicted optimal load balancing performance and demonstrated better results compared to state of art methods.
Chatbot using Zomato API

Chatbots are program that interact with humans using natural language like English. Computer based chatbots are getting to be distinctly famous as an intuitive and successful open framework between human and machines. They provide conversational output in response, and if commanded, can sometimes also execute tasks. One such bot is being designed in this project, where data about restaurants on the basis of some inputs are collected. Using this data, chatbot is trained to answer the queries. Our project describes the design and development of a conversational agent called Intelligent-Bot. Any questions regarding the restaurant details, are all answered by restaurant search bot.
A SURVEY ON BLOCKCHAIN APPLICATIONS

In this era of Intelligence, more devices become self-aware by connecting themselves to the internet and moved one step ahead.  This will result in a huge amount of data which is needed to be maintained with more care where security comes into the big picture. As the amount of data increases, the platform which transfers data gets more vulnerable. In this meantime, the applications will get expanded and step into the circle of cyber attacks, especially in an online transaction. To overcome these serious issues, blockchain technology can be ideally used and secure the data from being tampered. This paper gives an overview of blockchain technology and its influence in different sectors like health care, business, military, etc.
Local Algorithms for Finding Densely Connected Clusters

Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most (local) graph clustering algorithms is to find a vertex set of low conductance, there has been a sequence of recent studies that highlight the importance of the inter-connection between clusters when analysing real-world datasets. Following this line of research, in this work we study local algorithms for finding a pair of vertex sets defined with respect to their inter-connection and their relationship with the rest of the graph. The key to our analysis is a new reduction technique that relates the structure of multiple sets to a single vertex set in the reduced graph. Among many potential applications, we show that our algorithms successfully recover densely connected clusters in the Interstate Disputes Dataset and the US Migration Dataset.
LibGuides: Citing Sources Using NoodleTools: Print or in-hand

Tutorials for using NoodleTools to cite sources. Videos on how to cite a book or visual art.
ANALYSIS OF ABILITY TO WRITE THE ARGUMENTATION OF OBSERVATION RESULTS OF X GRADE STUDENTS OF GRAFIKA GADINGREJO VOCATIONAL SCHOOL

In writing an argument, a strong understanding is needed to be able to understand the language or the meaning contained in it. Therefore, readers who are going to write arguments must read them carefully in order to present evidence or determine the possibility of expressing an attitude or opinion about something. Seeing the importance of the ability to write arguments, the Indonesian language and literature subjects at SMA / SMK were given writing argumentation sub-topics. The purpose of this study was to determine the students' ability to write arguments. This study only consisted of one variable, namely the ability to write arguments with a population of 51 students. Data analysis was carried out by finding the average percentage per indicator of students' argument writing ability by dividing the acquisition score of each indicator by the maximum score of each indicator. Based on the results of the research data analysis, it can be concluded that the average ability of class X students of SMK Grafika Gadingrejo in writing arguments from the observation results is quite adequate with a value of 65.25%. With the average ability per indicator as follows, the list of opinion topics is developed into arguments with a value of 82%, the ability to formulate an argumentation writing framework with a value of 80%, compile arguments coherently with a value of 74%, using conjunctions. between the sentence value 53%, using words according to Enhanced Spelling (EYD) with a value of 47%, and using words according to word choice (diction) with a value of 52%.
Secure Data Transmission and Deletion between Two Clouds without Overhead

Due to rapid development of cloud storage, an increasing number of data owners prefer to outsource their data to the cloud server, which can greatly reduce the local storage overhead. Because different cloud service providers offer distinct quality of data storage service, e.g., security, reliability, access speed and prices, cloud data transfer has become a fundamental requirement of the data owner to change the cloud service providers. Hence, how to securely migrate the data from one cloud to another and permanently delete the transferred data from the original cloud becomes a primary concern of data owners.
Analysis of Significant Learning Applied d-Learning in the Teaching of Physics in Secondary Education

The unprecedented context of uncertainty caused by the COVID-19 disease stresses the educational system of secondary education. Isolation and social distancing forced the change from face to face education to remote emergency training to guarantee pedagogical continuity. Faced with this change in modality, learning analytics as a research method of techno-pedagogical d-learning models allow evaluating its effects on the development of meaningful learning. The present investigation was carried out in a group of 69 students with an average age of 16.06 years. The techno-pedagogical model was based on the use of the applications of the collaboration tools of Google Suit for Education. The contents were developed for the teaching of Newton's Laws of Motion from an interdisciplinary approach (Physics and Physical Education) based on the Science, Technology and Society perspective. The data collection of the learning process was carried out over a period of three months. For its analysis, four categories of analytics were applied: the instructional context, the motivation, the content and the discourse. The results identify the development of metacognitive skills: meta-learning and meta-knowledge. From the discussion, the idea of a meaningful learning analytics that allows transforming information and communication technologies into learning technologies and metacognitive knowledge is arrived at.
Analyzing Deep Learning Performance for Seismic Waveform Discrimination at Global Distances

&amp;lt;p&amp;gt;Deep Learning has shown a lot of promise in analyzing seismic waveforms for a number of tasks. However, most studies have focused on detections made at nearby stations that are roughly within 100km. These methods are not readily applicable for global-scale networks such as those maintained by the International Monitoring System (IMS). We look at the task of discriminating between earthquakes and explosions and attempt to apply a number of recent approaches for this task. In particular, we focus on events with magnitude between 3-4 mb that have been unambiguously classified by the International Seismological Center (ISC). We analyze the performance of methods that have been developed using a mix of Convolutional Neural Nets (CNNs) and Recursive Neural Nets (RNNs) as well as methods that use the so called GAN (Generative Adversarial Net)-&amp;amp;#8220;critic&amp;amp;#8221; approach of building features on seismic waveforms. We provide the guidance for the applicability of these methods for treaty monitoring purposes as well as building earthquake hazard maps using the IMS data.&amp;lt;/p&amp;gt;
Protagonismo da biblioteca universitária: tendências de gestão e avaliação com foco em atuação estratégica

This exploratory research aimed to identify trends in university library management and services, as well as the raising of standards for evaluation, based on literature review. It starts from the conception that university libraries need to play a leading role due to several changes, which impact universities and communication in science, demanding a review of their role and priorities. It found wide discussions around the world, which suggest the reconceptualization of its way of acting, as a result of changes in teaching, research, in the learning process, and the impact of information and communication technologies. It evidenced the reorientation with focus on the student; support for research and researchers; transformations in the library space; integration with other sectors of the university; increasing specialization of services; incorporation of technological innovations; strengthening of the library in its activities in open access and in the formation and development of informational and digital competence; alignment with the institutional mission and priorities; contribution to institutional reputation and production impact; participatory and ethical management. It showed opportunities for libraries to take leadership in strategic areas and concerns about the limits of their competence. It identified the use of evaluation standards applicable to the measurement of impact, performance and quality and the advantages of using libraries in the management process.
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering

Random dimensionality reduction is a versatile tool for speeding up algorithms for high-dimensional problems. We study its application to two clustering problems: the facility location problem, and the single-linkage hierarchical clustering problem, which is equivalent to computing the minimum spanning tree. We show that if we project the input pointset $X$ onto a random $d = O(d_X)$-dimensional subspace (where $d_X$ is the doubling dimension of $X$), then the optimum facility location cost in the projected space approximates the original cost up to a constant factor. We show an analogous statement for minimum spanning tree, but with the dimension $d$ having an extra $\log \log n$ term and the approximation factor being arbitrarily close to $1$. Furthermore, we extend these results to approximating solutions instead of just their costs. Lastly, we provide experimental results to validate the quality of solutions and the speedup due to the dimensionality reduction. Unlike several previous papers studying this approach in the context of $k$-means and $k$-medians, our dimension bound does not depend on the number of clusters but only on the intrinsic dimensionality of $X$.
Rancang Bangun Sistem Informasi Akademik Lembaga Kursus Berbasis Web Pada Ohayo Drawing School Batam

Technology and information are the most effective solutions to solve various problems, especially in organizations both small and large. The presence of technology is not to replace human work but can help and facilitate all work that occurs in an organization. The academic information system of the course institution that was built can help the course institution in better data management and delivery of information through the official website and simplify the registration process, attendance, report making and others. The data collection method used is by using observation, literature study, and interviews. the system design method used is the waterfall where the stages are planning, analysis, designing, and testing the system. Website design uses CodeIgniter 4, Bootstrap 4, and MySQL framework to shorten the time with satisfactory results. With the development of this information system is expected to be a solution to the problems faced by the Ohayo Drawing School course institution in conveying information, managing data, and other features.
Performance Evaluation of Nested Watermarked Scheme using Objective Image Quality Metrics

In this digital era, extensive usage of the internet has resulted in images being subjected to various distortions during processing and transmitting over open networks. For watermarking applications, watermarked image quality is evaluated in comparison to the original image. In the last decade literature, many performance metrics have been used, comprising of both subjective and objective quality metrics. The subjective image quality metrics are generally time-consuming and expensive in contrast to objective image quality metrics. This paper attempted an investigation for measuring the performance of nested watermarking schemes and objective image quality metrics which included Peak Signal to Noise Ratio (PNSR), Mean Square Error (MSE), Correlation Coefficient (CC), Normalized Correlation Coefficient (NCC), and Structural Similarity Index (SSIM). For the proposed nested watermarking method, these objective image quality metrics were evaluated using a combination of Discrete Wavelet Transforms (DWT) and Singular values (SVD) in the four frequency bands. Performance of objective metrics can be judged based on their sensitiveness to watermarking artifacts. It is clear from the results obtained, for most watermarking applications SSIM provides better insight into the performance of the algorithm used whereas PNSR fails for certain watermark artifacts. The findings drawn in this paper are based on the experimentation of a nested watermarking algorithm using a DICOM image database.
IMPLEMENTATION OF VERSION 1.0 SIMKIM TO SIMKIM VERSION 2.0 ON THE ISSUANCE OF CASE STUDY PASSPORT IMMIGRATION OFFICE OF NON-BORDER CONTROL SOUTH JAKARTA

The application of immigration information technology at the Immigration Checkpoint (TPI) is a very important matter to always be considered and developed in the very long term in the future, one of them is to focus on developing the autogate system. With some literacy regarding immigration information technology at TPI in several countries, it can result in several comparisons between the immigration information technology system in Indonesia with the immigration information technology system in several foreign countries. This can be used as study or learning material in seeing some of the shortcomings or gaps that the immigration technology information system in Indonesia has. The results of the study carried out with this qualitative research method have explained that the immigration information technology system in TPI Indonesia still has several gaps or shortcomings when compared to the existing systems in several other countries. So, it is very necessary to make several further studies or research related to the development of an immigration information technology system at TPI Indonesia that can adapt several existing systems in several countries.
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction

The rapidly evolving literature of COVID-19 related articles makes it challenging for NLP models to be effectively trained for information retrieval and extraction with the corresponding labeled data that follows the current distribution of the pandemic. On the other hand, due to the uncertainty of the situation, human experts' supervision would always be required to double check the decision making of these models highlighting the importance of interpretability. In the light of these challenges, this study proposes an interpretable self-supervised multi-task learning model to jointly and effectively tackle the tasks of information retrieval (IR) and extraction (IE) during the current emergency health crisis situation. Our results show that our model effectively leverage the multi-task and self-supervised learning to improve generalization, data efficiency and robustness to the ongoing dataset shift problem. Our model outperforms baselines in IE and IR tasks, respectively by micro-f score of 0.08 (LCA-F score of 0.05), and MAP of 0.05 on average. In IE the zero- and few-shot learning performances are on average 0.32 and 0.19 micro-f score higher than those of the baselines.
Federated Myopic Community Detection with One-shot Communication

In this paper, we study the problem of recovering the community structure of a network under federated myopic learning. Under this paradigm, we have several clients, each of them having a myopic view, i.e., observing a small subgraph of the network. Each client sends a censored evidence graph to a central server. We provide an efficient algorithm, which computes a consensus signed weighted graph from clients evidence, and recovers the underlying network structure in the central server. We analyze the topological structure conditions of the network, as well as the signal and noise levels of the clients that allow for recovery of the network structure. Our analysis shows that exact recovery is possible and can be achieved in polynomial time. We also provide information-theoretic limits for the central server to recover the network structure from any single client evidence. Finally, as a byproduct of our analysis, we provide a novel Cheeger-type inequality for general signed weighted graphs.
Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding

Background: Keyword extraction is a popular research topic in the field of natural language processing. Keywords are terms that describe the most relevant information in a document. The main problem that researchers are facing is how to efficiently and accurately extract the core keywords from a document. However, previous keyword extraction approaches have utilized the text and graph features, there is the lack of models that can properly learn and combine these features in a best way. Methods: In this paper, we develop a multimodal Key-phrase extraction approach, namely Phraseformer, using transformer and graph embedding techniques. In Phraseformer, each keyword candidate is presented by a vector which is the concatenation of the text and structure learning representations. Phraseformer takes the advantages of recent researches such as BERT and ExEm to preserve both representations. Also, the Phraseformer treats the key-phrase extraction task as a sequence labeling problem solved using classification task. Results: We analyze the performance of Phraseformer on three datasets including Inspec, SemEval2010 and SemEval 2017 by F1-score. Also, we investigate the performance of different classifiers on Phraseformer method over Inspec dataset. Experimental results demonstrate the effectiveness of Phraseformer method over the three datasets used. Additionally, the Random Forest classifier gain the highest F1-score among all classifiers. Conclusions: Due to the fact that the combination of BERT and ExEm is more meaningful and can better represent the semantic of words. Hence, Phraseformer significantly outperforms single-modality methods.