Reviewerly/exHarmony huggingface dataset:
CompactIE: Compact Facts in Open Information Extraction

A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents.This severely limits the usefulness of OpenIE extractions in many downstream tasks.The utility of extractions can be improved if extractions are compact and share constituents.To this end, we study the problem of identifying compact extractions with neural-based methods.We propose COMPACTIE, an OpenIE system that uses a novel pipelined approach to produce compact extractions with overlapping constituents.It first detects constituents of the extractions and then links them to build extractions.We train our system on compact extractions obtained by processing existing benchmarks.Our experiments on CaRB and Wire57 datasets indicate that COMPACTIE finds 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.
Capturing document context inside sentence-level neural machine translation models with self-training

Neural machine translation (NMT) has arguably achieved human level parity when trained and evaluated at the sentence-level.Document-level neural machine translation has received less attention and lags behind its sentence-level counterpart.The majority of the proposed document-level approaches investigate ways of conditioning the model on several source or target sentences to capture document context.These approaches require training a specialized NMT model from scratch on parallel document-level corpora.We propose an approach that doesn't require training a specialized model on parallel document-level corpora and is applied to a trained sentence-level NMT model at decoding time.We process the document from left to right multiple times and self-train the sentence-level model on pairs of source sentences and generated translations.Our approach reinforces the choices made by the model, thus making it more likely that the same choices will be made in other sentences in the document.We evaluate our approach on three document-level datasets: NIST Chinese-English, WMT19 Chinese-English and Open-Subtitles English-Russian.We demonstrate that our approach has higher BLEU score and higher human preference than the baseline.Qualitative analysis of our approach shows that choices made by model are consistent across the document.
Project iPad: Investigating Tablet Integration in Learning and Libraries at Ryerson University

The year 2010 saw a major revolution in tablet technology with the introduction of the Apple iPad. Curious about the potential of this new technology for libraries, a group of librarians at Ryerson University in Toronto seized an opportunity to investigate the emerging role of the tablet in the daily academic lives of students.
Physical Match

We present an approach to solving the problem of “physical match,” i.e., reconnecting back together broken or ripped pieces of material. Our method involves correlating the jagged edges of the pieces, using a modified version of the longest common subsequence algorithm.
A<scp>naconda</scp>: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing

We investigate distribution testing with access to non-adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a distribution: it submits a query set S to an oracle, which returns a sample from the distribution conditioned on being from S. In the non-adaptive setting, all query sets must be specified in advance of viewing the outcomes.Our main result is the first polylogarithmic-query algorithm for equivalence testing, deciding whether two unknown distributions are equal to or far from each other. This is an exponential improvement over the previous best upper bound, and demonstrates that the complexity of the problem in this model is intermediate to the the complexity of the problem in the standard sampling model and the adaptive conditional sampling model. We also significantly improve the sample complexity for the easier problems of uniformity and identity testing. For the former, our algorithm requires only Õ(log n) queries, matching the information-theoretic lower bound up to a O(log log n)-factor.Our algorithm works by reducing the problem from ℓ1-testing to ℓ∞-testing, which enjoys a much cheaper sample complexity. Necessitated by the limited power of the non-adaptive model, our algorithm is very simple to state. However, there are significant challenges in the analysis, due to the complex structure of how two arbitrary distributions may differ.
Implications of Blockchain Technology in Supply Chain Management

Due to rapid developments in technology, the supply chain becomes a bright area of interest among up-and-coming careers in various industries.People with careers in this field oversee such activities as product development, production, information systems, transportation, and day-to-day logistics.But, the rapidly evolving environments of an easy and comfortable life, the demand for product visibility and end-to-end traceability have grown.The existing supply chain is inefficient, unadaptable, intractable and costly as compared to innovative and advanced technology.Blockchain is the emerging and revolutionary technology that impacts the supply chain networks significantly.Therefore, in this paper, the implication of blockchain technology in the supply chain is presented.
Multimodal KDK Classifier for Automatic Classification of Movie Trailers

Movie trailer classification is a field of automation of analyzing the movie trailers and classify them into one of the various genres. In this paper, we proposed a classifier to identify the genre of a movie trailer by analyzing it's audio and visual features simultaneously. Our Approach decomposes a trailer video into frames and audio file and then analyze them based on certain specific features to categorize them into four genres. Our aim was to minimize the number of parameters involved in analyzing the trailer since other papers use many arguments which are impractical. The proposed classifier was trained on 4 audio, 2 broad visual features extracted from over 900 movie trailers distributed across 4 different genres, namely Drama, Horror, Romance, and Action. The Classifier Model has been trained using Neural Networks and Convolutional Neural Networks. Our Classifier Model can be used in Recommendation Systems and various websites like IMDB for automation of the genre classification process. As the common humanly approach is to generalize the results obtained from many inputs, the same way we use multiple models to obtain different outputs from multiple ANN models and then combine all the obtained results to get a final output. Also a Dataset containing 1000 movie trailers was introduced in this paper with trailers spanning to almost all Hollywood movies from 2010-2019 .After training and conducting Experiments on around 1000 movie trailers, the classifier model showed a maximum accuracy of 81 percent in determining top 1 genre and 91 percent in determining top 2 genres of a movie trailers in the test set.
Detect Frauds in Credit Card using Data Mining Techniques

In today era credit card are extensively used for day to day business as well as other transactions. Ascent within the variety of transactions through master card has junction rectifier to rise in the dishonest activities. In trendy day's fraud is one in every of the most important concern within the monetary loses not solely to the merchants however additionally to the individual purchasers. Data processing had competed a commanding role within the detection of credit card in on-line group action. Our aim is to first of all establish the categories of the fraud secondly, the techniques like K-nearest neighbor, Hidden Markov model, SVM, logistic regression, decision tree and neural network. So fraud detection systems became essential for the banks to attenuate their loses. In this paper we have research about the various detecting techniques to identify and detect the fraud through varied techniques of data mining.
Implementation of a high-presence immersive traditional crafting system with remote collaborative work support

A high presence immersive traditional crafting system was developed to provide users, who interact with the system through head-mounted displays, with a highly realistic traditional crafting presentation experience that allows moving functions, such as free walk-through and teleportation. Users can also interactively operate traditional craft objects in space. In addition, the system supports collaborative work in a virtual space shared by remote users. To evaluate the effectiveness of this system, a questionnaire survey was administered to 124 subjects, who provided overwhelmingly positive responses regarding all functions. However, there is still room for improvement in the operability and relevancy of the system.
Learning and Evaluating a Differentially Private Pre-trained Language Model

Contextual language models have led to significantly better results, especially when pretrained on the same data as the downstream task.While this additional pre-training usually improves performance, it can lead to information leakage and therefore risks the privacy of individuals mentioned in the training data.One method to guarantee the privacy of such individuals is to train a differentially-private language model, but this usually comes at the expense of model performance.Also, in the absence of a differentially private vocabulary training, it is not possible to modify the vocabulary to fit the new data, which might further degrade results.In this work we bridge these gaps, and provide guidance to future researchers and practitioners on how to improve privacy while maintaining good model performance.We introduce a novel differentially private word-piece algorithm, which allows training a tailored domain-specific vocabulary while maintaining privacy.We then experiment with entity extraction tasks from clinical notes, and demonstrate how to train a differentially private pre-trained language model (i.e., BERT) with a privacy guarantee of = 1.1 and with only a small degradation in performance.Finally, as it is hard to tell given a privacy parameter what was the effect on the trained representation, we present experiments showing that the trained model does not memorize private information.
Research on facial expression recognition of video stream based on OpenCV

In order to overcome the poor performance of expression similarity measurement in traditional video stream facial expression recognition methods, an OpenCV based facial expression recognition method is proposed. In this method, the video stream face detection image is obtained by the window detection of various features in each position for the video stream image through the cascade classifier, and the image preprocessing is implemented. Based on OpenCV, the most important eyes and mouth in the facial expression are modeled, the eye feature model and mouth feature model are constructed, and the facial expression recognition of the video stream is realised through the constructed model. The experimental results show that the performance of expression similarity measurement is better, and the recognition rate of different expressions is more than 90%.
DETECTION OF FAKE REVIEWS ON SOCIAL MEDIA USING MACHINE LEARNING ALGORITHMS

With the development of the Internet, technology and e-commerce, online-purchasing is easier and more convenient these days.Online reviews become the main source of information that customers usually refer to for their purchasing decision.However, many of reviews given by the online users are not considered truthful.Because of commercial benefits, fake reviews were generated to mislead customers.Therefore, it is necessary to detect fake reviews effectively.This paper aims to improve the performance of fake review classifiers by integrating different techniques into classifying models.More specifically, we analyzed similarity between reviews and utilized the EM (Expectation Maximization) clustering algorithm to recognize the review patterns.We also applied the sentiment analysis to analyze the reviews.Using the results from clustering models, sentiment analysis, and non-textual features of reviews and reviewers, we built machine learning models to classify fake reviews.We compare three supervised machine learning algorithms: Support Vector Machine, Artificial Neural Network and Random Forest.The empirical results from our experiments showed that the Random Forest algorithm outperforms against other algorithms.It also proved our assumption about text clustering and non-textual features in fake review detections.
Enhanced Weighted Round Robin Load Balancing Algorithm in Cloud Computing

The cloud/utility computing model requires a dynamic task assignment to cloud sites with the goal that the performance and demand handling is done as effectively as would be prudent. Efficient load balancing and proper allocation of resources are vital systems to improve the execution of different services and make legitimate usage of existing assets in the cloud computing atmosphere. Consequently, the cloud-based infrastructure has numerous kinds of load concerns such as CPU load, server load, memory drain, network load, etc. Thus, an appropriate load balancing system helps in realizing failures, reducing backlog problems, adaptability, proper resource distribution, expanding dependability and client fulfillment and so forth in distributed environment. This thesis reviewed various popular load balancing algorithms. Modified round robin algorithms are popularly employed by various giant companies for scheduling issues and load balancing. An enhanced weighted round robin algorithm is discussed in this paper concentrating on efficient load balancing and effective task scheduling and resource management.
Multilabel Classification for Emotion Analysis of Multilingual Tweets

Emotion Analysis of text targets to detect and recognize types of feelings expressed in text. Emotion analysis is successor of Sentiment analysis. The latter does coarse-level analysis and classify the text into positive and negative categories while former does fine-grain analysis and classify text in specific emotion categories like happy, surprise, angry. Analysis of text at fine-level provides deeper insight compared to coarse-level analysis. In this paper, tweets are classified in discrete eight basic emotions namely joy, trust, fear, surprise, sadness, anticipation, anger, disgust specified in Plutchik’s wheel of emotions [1]. Tweets for three languages collected out of which one is English language and rest two are Indian languages namely Gujarati and Hindi. The collected tweets are related to Indian politics and are annotated manually. Supervised Learning and Hybrid approach are used for classification of tweets. Supervised learning uses tf-idf as features while hybrid approach uses primary and secondary features. Primary features are generated using tf-idf weighting and two different algorithms of feature generation are proposed which generate secondary features using SenticNet resource. Multilabel classification is performed to classify tweets in emotion categories. Results of experiments show effectiveness of hybrid approach.
Electrocoagulation Treatment for Landfill Leachate using Stainless Steel Electrode

Electrocoagulation (EC) process uses direct electric current source between metal electrode submerged in the effluent that results in electrode dissolution, with a suitable pH, metal ion can form a wide range of metal hydroxide and coagulated species that destabilized and dissolved contaminants absorbed. Electrocoagulation (EC) has been working for the percentage removal of BOD (Biochemical oxygen demand)/ chemical oxygen demand (COD) ratio, Color and COD on leachate in a batch Electrocoagulation reactor using stainless steel (SS) electrode. EC technology depends on so many factors such as electrode material, initial pH, applied voltage, inter-electrode distance, and electrolysis time. From the experimental work, results reveal that the maximum percentage of removal achieved were COD and Color 73.5% and 65.0% respectively and increasing BOD/COD ratio 0.11 to 0.62. The optimum inter-electrode distance 1cm with electrode surface area 35 cm2 and optimum electrolysis time of 120 min at optimum applied voltage 12V, stirring speed 250 rpm and pH 9.8. These results proved that the EC process is an appropriate and proficient approach for treating the landfill leachate.
Decision Support System of Lecturer Selection Recommendation with Collaborative Filtering Method

The use of decision support systems for the selection of the best lecturers at Adisutjipto Institute of Technology (STTA) is not yet at the level of application. The average tertiary institution in selecting the best lecturers uses certain criteria, for example the criteria in teaching, research and community service. Of the three indicators of lecturer performance in Indonesia, we only use two indicators, namely research and community service. From these two indicators, we made five criteria, namely the number of presenters at the conference, the amount of community service, the number of unpublished research, the number of published research, and the number of citations of scientific articles. The selection was made by users, namely STTA Director, Vice Director for Academic Affairs, Vice Director for Financial Affairs and Community Service Research Center to 62 lecturers who were active in research, community service and publications indexed on Google Scholar. This user restriction is adjusted to the organizational structure at our university, where the five users have authority in assessing the performance of lecturers and giving awards to lecturers who are declared as the best lecturers. After Collaborative Filtering method is done to predict the final result using the rating given by the user. Based on the results of the system testing, it was concluded that the system that was built could be a solution to help select lecturers who were eligible to be given awards as the best of lecturer in the field of research and community service.
Machine Learning Based Malicious URL Detection

Today Internet technology has become an essential part of our life for education, entertainment, gaming, banking and communication. In this modern digital era, it is very easy to have any information by one click. But everything which has pros and cons, as we have any information at our tips but Internet is an attack platform also. When we use Internet to make our work easy same time many attacker try to steal information from our system. There are many means for attacking, malicious URL one of them. When a user visits a website, which is malicious then it triggers a malicious activity which is predesigned. Hence, there are various approaches to find dangerous URL on the Internet. In this paper, we are using machine learning approach to detect malicious URLs. We used ISCXURL2016 dataset and used J48, Random forest, Lazy algorithm and Bayes net classifiers. As performance metrics, we calculate accuracy, TPR, FPR, precision and recall.
Empirical Study of Research Performance Leading to Education 4.0 using the iLearning Method

Lecturers as one of the agents of change are currently required to be able to contribute more in the educational revolution which currently also enters Education 4.0, especially in the development of higher education chess dharma in Indonesia.However, the rapid advances in technology have not been matched by the interest and quality of research from the lecturers themselves.The purpose of this study was to analyze the iLearning method in improving the quality of lecturer research that is currently oriented or leading to Education 4.0.The iLearning method was chosen as a strategy to see the level of interest and motivation of lecturers in conducting research.The method of analysis in this study is mixed qualitative and quantitative analysis, webbased questionnaire surveys used to collect primary data, and Structural Equation Modeling (SEM) used to test the proposed research model, distributed to 217 respondents by random sampling.The quality and performance of lecturers research is measured through an integrated system in universities by comparing the number of lecturers with research achievements.
A Research on impact of Blockchain in Healthcare

Blockchain refers to a distributed ledger technology that represents an innovation in recording and sharing information without the need for a trusted third party. Blockchain technology offers new tools for security and privacy concerns. Marching towards digitization and analytics, this technology emerges as a promising solution for authentication and authorization issues. It sounds so amazing that this technology that originated with cryptocurrencies could not only be applied in digital contracts, financial and public records, and property ownership but also in medicine, education, science and so on. The use case of this technology springs up in every possible direction. This article first analyses the need for this breakthrough technology and explains how this technology works. This work presents a review on various types of blockchain, the consensus mechanisms used, their advantages and limitations. It provides an overview on the various use cases of this technology. This work mainly focuses on its application in Healthcare. The goal of this article is to analyze the usage of Blockchain technology in various fields of Healthcare such as Electronic Health Record, Health Insurance, Biomedical Research, Drug Supply, Medical Education, Remote Patient Monitoring, Interoperability, Location Sharing etc., It investigates the current research trends and finds the gaps and limitations of these approaches. Moreover, it proposes some enhancements to fill in the gaps in the present approach. This work also analyses the importance of Wearable Internet of Things (IoT) devices in HealthCare and the integration of these devices with Blockchain. Finally, this work concludes by comparing Blockchain 3.0 with previous versions.
Illicit Economies of the Internet: Click Farming in Indonesia and Beyond

Click farms are often considered either a form of fraudulent online marketing or as a type of exploitative digital labour based on an illegitimate business operation that produces 'click spam'.There has, however, been limited research on how these entities operate, who the actors are, and how the market is organised.In an ethnographic spirit, in this essay Johan Lindquist takes click farmers not as an aberration, but rather as a starting point for approaching the 'like economy'.