Reviewerly/exHarmony huggingface dataset:
The Similarity of Essay Examination Results using Preprocessing Text Mining with Cosine Similarity and Nazief-Adriani Algorithms

Exams are one way to measure the level of students' ability to participate in learning. One type of exam given to students is the essay type. This study focuses on making automatic assessments for essay-type exams using cosine similarity. This method has several stages such as folding Case, tokenizing, filtering, stemming, analyzing, weighing of words in documents with cosine similarity. The stemming process uses the Nazief &amp; Adriani algorithm. The results of this study are to conclude that the choice of words that are considered as keywords in the answer key greatly affects the results of the system's assessment. This is evidenced by testing applying the cosine law of 89.5%. However, there are several types of questions that are significantly different because there are unique characters in the database and answer keys that do not contain keywords that match the correct answer.
Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons

Modelling a word's polarity in different contexts is a key task in sentiment analysis.Previous works mainly focus on domain dependencies, and assume words' sentiments are invariant within a specific domain.In this paper, we relax this assumption by binding a word's sentiment to its collocation words instead of domain labels.This finer view of sentiment contexts is particularly useful for identifying commonsense sentiments expressed in neutral words such as "big" and "long".Given a target (e.g., an aspect), we propose an effective "perturb-and-see" method to extract sentiment words modifying it from large-scale datasets.The reliability of the obtained targetaware sentiment lexicons is extensively evaluated both manually and automatically.We also show that a simple application of the lexicon is able to achieve highly competitive performances on the unsupervised opinion relation extraction task.
GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases

Abstract Background Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions. Results An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time. Conclusion The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures.
TMR: Evaluating NER Recall on Tough Mentions

We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of "tough" mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data.We demonstrate the usefulness of these metrics by evaluating corpora of English, Spanish, and Dutch using five recent neural architectures.We identify subtle differences between the performance of BERT and Flair on two English NER corpora and identify a weak spot in the performance of current models in Spanish.We conclude that the TMR metrics enable differentiation between otherwise similar-scoring systems and identification of patterns in performance that would go unnoticed from overall precision, recall, and F1.
Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks

Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the context of graph neural networks. In this paper, we evaluate multiple sampling methods (i.e., ascending and descending) that sample based off different definitions of centrality (i.e., Voterank, Pagerank, degree) to observe its relation with network topology. We find that no sampling method is superior across all network topologies. Additionally, we find situations where ascending sampling provides better classification scores, showing the strength of weak ties. Two strategies are then created to predict the best sampling method, one that observes the homogeneous connectivity of the nodes, and one that observes the network topology. In both methods, we are able to evaluate the best sampling direction consistently.
The Students' Perceptions of Using Zoom Application on Mobile Phone in Improving Speaking Skills During Online Learning at Ban Loeiwangsai School, Loei Province, Thailand.

This study focused on the impact of using the Zoom application on mobile phones by examining students' perceptions of speaking skills and their attitude during online learning. The students have 4 months of experience using the Zoom application. This research method used a survey design and collects data by giving 12 questionnaire items through Google form to students. The participants are elementary school students with 3 categories, namely grade 4, grade 5 and grade 6 at Ban Loeiwangsai school, Thailand. The findings of this study are that the strategic design of online learning on the use of the Zoom application has an impact on students performance in independent learning and managing time. This also has an impact on the ability and knowledge of students learning English especially in the speaking skill. Their academics are particularly affected by the use of the Zoom application during online learning.
Classification of handwritten Javanese script using random forest algorithm

The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.
Interactive E-book for Physics Learning: Analysis of Students’ Characters and Conceptual Understanding

The national education curriculum is concerned with character building. The development of media with character needs to be done to strengthen character and to help students understand the momentum and impulses concept. This study aims to (1) develop an interactive e-book and (2) describe the character and understanding of students’ concepts after using the e-book. The development model used Lee and Owens with the stages: analysis, design, development and implementation, and evaluation. An interactive e-book was prepared by combining character values in the content, discussion sheets, and online evaluation. The e-book product contains images, videos, text input functionality and can be accessed via a smartphone. Students can study matter, momentum, and impulses without the basis of space and time. The results of the study state: (1) interactive e-books are feasible and easy to use by students (2) e-books that provide character reinforcement and are effective in increasing understanding of concepts in 10% low categories to 60% high categories
Meta-Learned Specific Scenario Interest Network for User Preference Prediction

User preference prediction is a task of learning user interests through user-item interactions. Most existing studies capture user interests based on historical behaviors without considering specific scenario information. However, the users may have special interests in these specific scenarios and sometimes user historical behaviors are limited. In this paper, we propose a Meta-Learned Specific Scenario Interest Network (Meta-SSIN) to predict user preference of target item by capturing specific scenario interests. Meta-SSIN uses multiple independent meta-learning modules to model historical behaviors in each scenario. The independent module can capture special interests based on limited behaviors. Experimental results on three datasets show that Meta-SSIN outperforms compared state-of-the-art methods.
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning

We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies.First, we present an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments.This oracle actions are designed to capture enough information from the partial input before writing the output.Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning.Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality while keeping the translation delay low.
European Language Grid: A Joint Platform for the European Language Technology Community

Europe is a multilingual society, in which dozens of languages are spoken.The only op tion to enable and to benefit from multilingual ism is through Language Technologies (LT), i. e., Natural Language Processing and Speech Technologies.We describe the European Lan guage Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella plat form for the European LT landscape, includ ing research and industry, enabling all stake holders to upload, share and distribute their ser vices, products and resources.At the end of our EU project, which will establish a legal en tity in 2022, the ELG will provide access to ap prox.1300 services for all European languages as well as thousands of data sets.
A Method to Detect Breast Cancer Based on Morphological Operation

Breast cancer is one of the most common cancer in women worldwide.Early detection of breast cancer can lead to better treatment and decrease in mortality.Mammogram image in medical technology, made it easier to analyze breast cancer.Mammography exam is a specialized imaging technique in medical to scan breast which results in mammogram image.Detecting breast cancer earlier, a patient can have several treatment options and also can save live.Early detection of breast cancer can leads to survive 93 percent or greater in the initial five years.This paper proposes a brseast cancer detection method from mammogram image sample by applying morphological operation on gray image rather than binary.Firstly, image is sent for gamma correction.Then it is converted to gray and applied morphological dilation.Again morphological opening operation is formed on the dilated image.Output of dilated and opening operation is then binarized.An AND operation is performed between both binary images.Some post processing likesmall area filtering and hole filling task is took place.Then common unwanted object is removed.Finally rest of the region is the desired cancer infected region.Achieved performance is acceptable and satisfactory through the proposed method.
A Novel Approach to Automated Behavioral Diagram Assessment using Label Similarity and Subgraph Edit Distance

Unified Modelling Language (UML) is one of the standard languages used in modelling software. Therefore, UML is widely taught in many universities. Generally, teachers assign students to build UML diagram designs based on a predetermined project. However, the assessment of such assignments can be challenging and teachers may be inconsistent in assessing students’ answers. Thus, automated UML diagram assessment becomes essential to maintaining assessment consistency. This study uses a behavioral diagram as the object of research since it is a commonly taught UML diagram. The behavioral diagram can show a dynamic view of the software. This study proposes a new approach to automatically assessing the similarity of behaviour diagrams as reliably as experts. We divide the assessment into two portions: semantic assessment and structural assessment. Label similarity is used to calculate semantic assessment, while subgraph edit distance is used to calculate structural assessment. The results suggest that the proposed approach is as reliable as an expert in assessing the similarity between two behaviour diagrams. The observed agreement value suggests strong agreement between the use of experts and the proposed approach.
Object Motion Direction Detection and Tracking for Automatic Video Surveillance

In today's world having a smart reliable surveillance system is very much in need.In fact in many public places like banks, jewellery stores, malls, schools and colleges it is basic necessary to have a surveillance system (CCTV).Most of today's implementations are not smart and they record videos during night even when there is no motion.This will lead to unnecessary storage usage and difficult to get the important part of the footage.And also, most of the today's implementations are stationary, they can't track the moving object.This report will outline a naive approach to implement a smart video surveillance system using object motion detection and tracking.Here we are using conventional Background subtraction model to detect motion and we estimate the direction of motion of object by comparing the centroid of the moving object in subsequent frames and track the moving object by rotating the camera using servo.Video recording takes place only when there is movement in the frame which helps in storage efficiency.We are also improving the speed of email alert delivery by using multithreading.
One-class Text Classification with Multi-modal Deep Support Vector Data Description

This work presents multi-modal deep SVDD (mSVDD) for one-class text classification.By extending the uni-modal SVDD to a multiple modal one, we build mSVDD with multiple hyperspheres, that enable us to build a much better description for target one-class data.Additionally, the end-to-end architecture of mSVDD can jointly handle neural feature learning and one-class text learning.We also introduce a mechanism for incorporating negative supervision in the absence of real negative data, which can be beneficial to the mSVDD model.We conduct experiments on Reuters and 20 Newsgroup datasets, and the experimental results demonstrate that mSVDD outperforms uni-modal SVDD and mSVDD can get further improvements when negative supervision is incorporated.
Interception and detection of drones using RF-based dataset of drones

The usage of Unmanned Aerial Vehicles (UAVs) is accessible for different applications to a wide range of users. However, this wide range of users raises a great concern about the threat (passive or active threats) of malicious actors who can use UAVs for criminal activities. The detection of UAVs is considered to be the first step in the process of UAVs countering (c-UAV). Nowadays, the c-UAV applications offer systems that include different sensors such as electro-optical, thermal, acoustic, radar and radio frequency sensors. Information gathered by these sensors can be fused in order to increase the reliability of threat's detection, classification and identification. It is necessary to have datasets from the different sensors in order to develop methods and algorithms for detection and classification of UAVs. This paper presents a dataset of communication signals between the drone and the control station that is used in the process of detection and classification.
Knowledge Base Question Answering through Recursive Hypergraphs

Knowledge Base Question Answering (KBQA) is the problem of predicting an answer for a factoid question over a given knowledge base (KB). Answering questions typically requires reasoning over multiple links in the given KB. Humans tend to answer questions by grouping different objects to perform reasoning over acquired knowledge. Hypergraphs provide a natural tool to model group relationships. In this work, inspired by typical human intelligence, we propose a new method for KBQA based on hypergraphs. Existing methods for KBQA, though effective, do not explicitly incorporate the recursive relational group structure in the given KB. Our method, which we name RecHyperNet (Recursive Hypergraph Network), exploits a new way of modelling KBs through recursive hypergraphs to organise such group relationships in KBs. Experiments on multiple KBQA benchmarks demonstrate the effectiveness of the proposed RecHyperNet. We have released the code.
The Implementation of Question Answer System Using Deep Learning

Question-answer systems are referred to as advanced systems that can be used to provide answers to the questions which are asked by the user. The typical problem in natural language processing is automatic question-answering. The question-answering is aiming at designing systems that can automatically answer a question, in the same way as a human can find answers to questions. Community question answering (CQA) services are becoming popular over the past few years. It allows the members of the community to post as well as answer the questions. It helps users to get information from a comprehensive set of questions that are well answered. In the proposed system, a deep learning-based model is used for the automatic answering of the user’s questions. First, the questions from the dataset are embedded. The deep neural network is trained to find the similarity between questions. The best answer for each question is found as the one with the highest similarity score. The purpose of the proposed system is to design a model that helps to get the answer of a question automatically. The proposed system uses a hierarchical clustering algorithm for clustering the questions.
Enhancing the Transparency of Student Merit System Using QR Code Technology: A Smart Campus Initiative

Technology has led to the utilization of information systems into human daily life activities. Computer systems have helped improve the modern human lifestyle by simplifying and facilitating activities from listening to music, grocery shopping to communicating with peoples across the world. Gearing up to smart campus initiatives, one of the issues to be tackled was the transparency in validating the student’s attendance system used in many campuses and university events. The employment of technologies such as QR Code and facial recognition to validate the student’s attendance certainly have helped in addressing this issue. In order to ensure the transparency of the existing manual student merit system used in the campus, this paper presents a solution known as CyberScan QR Code for Student Merit System that allows the students to obtain merits by scanning QR codes, as well as allowing student councils to provide merits for any events by generating QR codes. Students can keep track of their current total merit points and the events that they have previously attended so if there are any problems regarding their merits, they can always refer to their history of attended events as evidence. This paperless solution merit system also helps to reduce the usual time taken to calculate a huge amount of merits thus preventing from any miscalculation. Clearly, this initiative creates not just an efficient, transparent service but another greater experience for students to feel connected academically and socially.
A Novel Basis Function Approach to Finite Population Parameter Estimation

Modeling non-linear data is a common practice in data science and machine learning (ML). It is aberrant to get a natural process whose outcome varies linearly with the values of input variable(s). A robust and easy methodology is needed for accurately and quickly fitting a sampled data set with a set of covariates assuming that the sampled data could be a complicated non-linear function. A novel approach for estimation of finite population parameter τ , a linear combination of the population values is considered, in this article, under superpopulation setting with known basis functions regression (BFR) models. The problems of subsets selection with single predictor under an automatic matrix approach, and ill-conditioned regression models are discussed. Prediction error variance of the proposed estimator is estimated under widely used feature selection criteria in ML. Finally, the expected squared prediction error (ESPE) of the proposed estimator and the expectation of estimated error variance under bootstrapping as well as simulation study with different regularizers are obtained to observe the long-run behavior of the proposed estimator.
Survey of Triangle Security in Cloud

Cloud computing remains the world's most demanded development trend. It is one of the most important topic whose application is currently being explored. The distributed storage has been one of the apparent administrations in distributed computing. Instead of providing the engaged worker for conventionally organizated storage, the distributed storage sets various outsider individuals with the knowledge. The customer does not care about any details on various external employees and no one knows exactly where information is spared. The distributed storage supplier asserts that the information can be secured, but no one trusts in it. Security risk is information placed in the plain content configuration over cloud and across the organization. This document offers a method that allows customers to securely store and access data from the distributed storage. It also ensures that neither the distributed storage provider, apart from the verified customer, can get details. This technique ensures the safety and confidentiality of cloud-related information. Another favorite position is that if the cloud provider breaks down, the information of the client remains safe as all information is encoded. Customers must also not stress that cloud providers have illegal access to their data.