Reviewerly/exHarmony huggingface dataset:
Multi-Document Abstractive Text Summarization through Semantic Similarity Matrix for Telugu Language

Telugu is one of the popular south Indian languages which is currently spoken by 84 million population in Telangana and Andhra Pradesh. Text summarization is an area of research with a goal to provide short text from huge text documents. Extractive text summarization methods have been extensively studied by many researchers. There are various type of multi document ranging from different formats to domains and topic specific. With the application of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. This approach has been attempted for Telugu language in this article. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well.  The work implements semantic based filtering using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang Similarity and making use of a Recurrent Neural Network (RNN) with an attention mechanism to generate summary. ROUGE score is used for measuring the performance of the applied method on Telugu Language.
Non-Participating Set Based Eclat Algorithm For Rule Generation On Vertically Partitioned Cloud Data

Frequent and uncommon itemset mining is a significant area in information mining procedures. Creating repeated items can promote the detailing of affiliation rule to infer connections or examples in an information base exchange to anticipate the succeeding projected things in the heterogeneity of requests. The association rule mining algorithms have built upon horizontal or vertical data formats for database structure. This work emphasizes the vertical data format. Several vertical mining calculations have presented as of late for affiliation rule mining specifically demonstrated to be compelling and typically beat flat methodologies. Equivalence Class Transformation is a distinct procedure for vertically divided information due to its ‘fast intersection’ property. Even though Eclat calculation is a proficient calculation for mining affiliation rules, there are a few inconveniences that restrict the competence of Eclat. In this paper a novel Non-Participating Set Based Eclat algorithm was proposed to overcome the limitations of the Eclat procedure with the set union operation. With this calculation, NP set Eclat radically cut down the memory size needed to stock halfway outcomes. The projected technique when merged with past vertical mining strategies, improve the recital suggestively.
Laboratory Management System Based on Internet of Things

In order to improve the efficiency and management performance of the laboratory, this paper proposes a laboratory management system based on Internet of things. The overall scheme of the new system is designed and demonstrated. Six subsystems of laboratory equipment management system, access control system, environmental monitoring system, wireless control system, LAN detection system and LAN communication system are designed in detail. The experimental results verify the effectiveness of the new scheme.
UT-ATD: Universal Transformer for Anomalous Trajectory Detection by Embedding Trajectory Information

Natural Language Interface or NLI has the potential to add syllogistic reasoning over the already existing facts and develop a new kind of knowledge dataset in itself.In this paper, we have demonstrated a Recognizing Textual Entailment wherein the task is to recognize whether a given hypothesis is true (Entailment), false (Contradiction) or unrelated(neutral) with respect to the sentence called premise.The task is performed by training MNLI corpus along with the manually collected dataset from Amazon Product Reviews each having hypothesis and premise pairs with corresponding labels.With this use case, we propose to bring sustainable development in the classification methods used by major E-commerce companies.
Sentiment Analysis of Amazon Product Reviews using Machine Learning

The web could be a giant virtual house whereby to precise and share individual evaluations, influencing something of life, with implications for advertising and voice communication alike. Social media area unit influencing clients’ potentialities by the approach of shaping their attitudes and behaviors. Chase the social media activities could be a great way to live customers’ loyalty, retentive a tune on their sentiment towards brands or merchandise. Social media area unit the subsequent logical advertising space. Currently, Facebook dominates the virtual advertising and promoting space, followed fastidiously by twitter.  this paper describes a sentiment analysis examine finished on over one thousand FB posts concerning newscasts, examination the sentiment for rai - the Italian public broadcasting service - toward the rising and a lot of dynamic private organization la7. this incorporates a investigate maps investigate results with observations created by approach of the Observatories di Pavia, that is associate degree Italian institute of analysis specialized in media analysis at the theoretical and empirical level, engaged within the analysis of political voice communication within the mass media. this examine takes to boot under consideration the statistics provided through audited regarding news audience, correlating the analysis of social media, of Facebook particularly, with measurable facts, accessible to the general public domain. We tend to used supervised gaining information of technique on an oversized scale amazon dataset to polarize it and obtain fine accuracy.
Pseudonym Management through Blockchain: Cost-efficient Privacy Preservation on Smart Transportation

This chapter aims to privacy-preserving pseudonym management that is more cost-effective across the system lifecycle than existing approaches. Firstly, a pseudonym Management scheme by using blockchain technology is proposed as the first contribution. CYBER-PHYSICAL System could be considered as one of the most promising techniques to improve the life quality of people. The state-of-the-art schemes introduce pseudonym and pseudonym certificate to protect Vehicular Communication System (VCS) security and privacy. Vehicles carry a set of pseudonyms which are used under different time periods in VCS communication. In the vehicular networks, the privacy tends to be specific for mding vehicular identity, position, and to prevent tracking by malicious entities. Vehicles equip a set of pseudonym certificates which are used under different time periods in VCS communications. Proof of Work is the only consensus mechanism that has been tested successfully and in a sustained manner in a highly adversarial environment, and is the only known cryptographic puzzle that meets these testing requirements.
基于BERT的意图分类与槽填充联合方法(Joint Method of Intention Classification and Slot Filling Based on BERT)

“口语理解是自然语言处理的一个重要内容,意图分类和槽填充是口语理解的两个基本子任务。最近的研究表明,共同学习这两项任务可以起到相互促进的作用。本文提出了一个基于BERT的意图分类联合模型,通过一个关联网络使得两个任务建立直接联系,共享信息,以此来提升任务效果。模型引入BERT来增强词向量的语义表示,有效解决了目前联合模型由于训练数据规模较小导致的泛化能力较差的问题。实验结果表明,该模型能有效提升意图分类和槽填充的性能。”
Sistem Pemesanan dan Pembayaran Menggunakan Teknologi Quick Response Code (QR Code) Berbasis Web pada Kedai Cangkir Gubug

The results of this study are in the form of a product, namely an android application, an ordering and payment system using Android-based QR Code technology. According to the research design, the research results go through two testing or validation processes, namely design validation by experts in this case expert lecturers in the computer field, and product validation by users.Validation is carried out using an assessment instrument in the form of a questionnaire or validation sheet. The questionnaire contains 10 questions that must be filled in with a value between 1 and 4. The total score will get a score of not good, enough, good, or very good. The final conclusion is a statement that the product must be replaced, can be used with many revisions, can be used with few revisions, or can be used without revision.The results of the design validation test by experts gave a total score of 37, included in the score indicator 31 n 40, namely Very Good. The conclusion drawn by the expert for system design is Can be Used Without Revision.The results of product validation testing by users give an average value of 35 so that it is in the indicator category and gets the Very Good predicate. The final conclusion of user validation is a product or prototype Can Be Used Without Revision.This research was conducted in a restaurant, Restaurant isn’t just a place to eat something but also  a place to relaxing, meet someone or hang out with friends or colagues. Service of the restaurant is the main factor to make customer feel comfortable and relax. Many restaurant make their own order  and payment system   for  better service but they  forgot about  the speed of their services.Using research methods data collection, observation, and  interviewin Kedai Cangkir Gubug,  face to face service like greeting, take order, and serving order  all of it   is waiter’s  job.   All of that tasks need time at most that makes if the restaurant    have many customers, every single customer have to wait for  a longer time waiting for waiter that serving another customer. The payment method is quite troublesome for customer because they have to save order paper for payment in cashier later.The solution that writer’s made is to decrease the task of waiter so that services can be done equally with QRcode technology for ordering menu with the scanner is android smartphone. Payment only need name of customer or table number and customer can do it in cashier or they can request the bill at their table. Therefore waiter no need to take order one after another and customer can do payment easier.
NUTRITION EDUCATION IN TARAKANITA ELEMENTARY SCHOOLIN MAGELANG AND ITS EFFECTS ON THE CHANGES IN KNOWLEDGE, BEHAVIOR, AND ATTITUDE

Wrong eating behavior in school-age children may end up in serious nutrition problems. During the periods of growth of elementary-school-age children, attention should be paid to their nutrition fulfilment. Inadequate knowledge of nutrition education may lead them to unhealthy food consumption. Healthy food itself is one which contains balanced nutritions. The introduction of fast food into the food health sector in Indonesia can also influence public diets. In order to evade future nutrition problems, nutrition education is thus necessary for elementary-school-age children, especially for the purpose of enhancing knowledge, attitude, and selection practice healthy foods and snacks. The menus for school-age children must have nutrition contents which are balanced and needed by the body. This research determines the effect of nutrition education on SD Tarakanita Magelang fifth graders’ knowledge of healthy and nutritious food consumption. The 64 respondents enrolled in this research were divided into two classes: education and non-education classes. The respondents consisted of 10–13-year-old fifth graders. This research used a questionnaire for data collection. Pre-test was first conducted to both education and non-education classes. Then, to the education class four nutrition education materials were delivered. Two months afterward the final data were derived through post-test. The research results showed that the provision of nutrition education for the fifth graders of SD Tarakanita Magelang affected their food consumption knowledge, attitudes, and behaviors.Keywords: elementary school, nutrition education, behavior, attitude
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification

Text classification is a fundamental problem in the field of natural language processing. Text classification mainly focuses on giving more importance to all the relevant features that help classify the textual data. Apart from these, the text can have redundant or highly correlated features. These features increase the complexity of the classification algorithm. Thus, many dimensionality reduction methods were proposed with the traditional machine learning classifiers. The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and fastText classifier. We then present three ways of implementing a feature selection and neural network pipeline. We observed a reduction in training time when feature selection methods are used along with neural networks. We also observed a slight increase in accuracy on some datasets.
Dr John Cleary and Dr Jack Ryan examine shock waves

Research School of Earth Sciences - Dr. Ian MacDougall, Wang Sung Shan, Dr. Bill Compston, Chung Fu Tao, Dr. S. W. J. Clements, W. Sinclair, A. E. Ringwood, Prof. Lew Gustafson, Leigh Schmidt, Dr. John Cleary, Dr. Jack Ryan, Dr. I. D. Ryabchikov, Dr. David Green, Mr. Bill Hibberson, Dr. Mike McElhinny, Mr. Charles Barton, Mr. Kurt Kloska, Mr. Dieter Burmann, Prof. Hales & others
Sistem Pakar Diagnosa Penyakit dan Hama Tanaman Pepaya Menggunakan Metode Forward Chaining dan Naïve Bayes

Papaya is an agricultural product that can be grown anywhere. This causes papayas to be susceptible to pests and diseases, which can cause delays in the harvest period. So we need an expert system application that can help diagnose pests and diseases in papaya so that it can make it easier for papaya plant cultivators. The research has a goal to create an expert system application that can provide information on papaya plant diseases and can make it easier to diagnose diseases that exist in papaya and can be accessed easily anywhere by the public. This system is designed using Forward Chaining and Naivei Bayes Methods. This expert system application is expected to make it easier for users to diagnose diseases and pests on papaya plants without having to require experts directly, based on the discussion and results in this research, the accuracy value of this expert system application has an accuracy value of 95% in diagnosing diseases and pests on papaya plants.
Quantum Query-to-Communication Simulation Needs a Logarithmic Overhead.

Buhrman, Cleve and Wigderson (STOC'98) observed that for every Boolean function f : { -1, 1}n → {-1, 1} and • : {-1, 1}2 → {-1, 1} the two-party bounded-error quantum communication complexity of (f ο •) is O(Q(f) log n), where Q(f) is the bounded-error quantum query complexity of f. Note that the bounded-error randomized communication complexity of (f ο •) is bounded by O(R(f)), where R(f) denotes the bounded-error randomized query complexity of f. Thus, the BCW simulation has an extra O(log n) factor appearing that is absent in classical simulation. A natural question is if this factor can be avoided. Razborov (IZV MATH'03) showed that the bounded-error quantum communication complexity of Set-Disjointness is Ω([MATH HERE]). The BCW simulation yields an upper bound of O([MATH HERE] log n). Hoyer and de Wolf (STACS'02) showed that this can be reduced to clog*n for some constant c, and subsequently Aaronson and Ambainis (FOCS'03) showed that this factor can be made a constant. That is, the quantum communication complexity of the Set-Disjointness function (which is NORn ο ∧) is O(Q(NORn)).Perhaps somewhat surprisingly, we show that when • = ⊕, then the extra log n factor in the BCW simulation is unavoidable. In other words, we exhibit a total function F : {-1, 1}n → {-1, 1} such that Qcc(F ο ⊕) = Θ(Q(F) log n).To the best of our knowledge, it was not even known prior to this work whether there existed a total function F and 2-bit function •, such that Qcc(F ο •) = ω(Q(F)).
Knowledge Economy Classification in African Countries: A Model-Based Clustering Approach

Knowledge economy (KE) has been a central issue in the political economic literature of advanced economies, but little research has focused on the transition towards a KE in Africa. Using a latent profile analysis, six clusters of the KE were found in the region. The clusters range from very prepared with good performance in all KE dimensions (institutional, education, and innovation output) to very unprepared with low performance inf each KE dimension. Lastly, we offer policy recommendations that shed some light on the national and international economic policies towards a more knowledge-oriented environment. One such recommendation is that effective policies should consider both the similarities and dissimilarities of African knowledge economies. How precise that can be done is one direction future research can take.
Failure Analysis of Hadoop Schedulers using an Integration of Model Checking and Simulation

The Hadoop scheduler is a centerpiece of Hadoop, the leading processing framework for data-intensive applications in the cloud.Given the impact of failures on the performance of applications running on Hadoop, testing and verifying the performance of the Hadoop scheduler is critical.Existing approaches such as performance simulation and analytical modeling are inadequate because they are not able to ascertain a complete verification of a Hadoop scheduler.This is due to the wide range of constraints and aspects involved in Hadoop.In this paper, we propose a novel methodology that integrates and combines simulation and model checking techniques to perform a formal verification of Hadoop schedulers, focusing on the following properties: schedulability, fairness and resources-deadlock freeness.We use the CSP language to formally describe a Hadoop scheduler, and the PAT model checker to verify its properties.Next, we use the proposed formal model to analyze the scheduler of OpenCloud, a Hadoop-based cluster that simulates the Hadoop load, in order to illustrate the usability and benefits of our work.Results show that our proposed methodology can help identify several tasks failures (up to 78%) early on, i.e., before the tasks are executed on the cluster.
Centralized archiving and recordkeeping in a new institution: the European Monetary Institute and its Archives 1994–1998

In 1994, the European Monetary Institute (EMI) was established. Based first in Basel (Switzerland), and subsequently in Frankfurt am Main (Germany), the newly created institute was one of the most structurally significant outcomes of the 1992 Treaty of Maastricht and was intended to shepherd the creation of a new currency for the European Union - the Euro banknotes and coins - as well as to prepare for the establishment of the future European Central Bank (ECB). Unlike many other new institutions, recordkeeping was considered from the organization’s inception. The initial approach adopted was highly centralized and was deemed unsustainable in light of the experience of a fast-growing institution. While unsuccessful initially, the experience provided many insights and avenues of theoretical development when new recordkeeping functions were instituted upon the establishment of the ECB in 1998.
Machine Learning: Tasks, Modern Day Applications and Challenges

During the last decade, we have witnessed significant development in artificial intelligence (AI) capabilities and its application areas such as healthcare, self-driving cars, eLearning, military, smart cities, industry, etc. Machine learning algorithms learned from available data. Further, this learning laid the foundation to develop AI for the various systems around us. These machine learning algorithms are a collection of complex mathematical models and human intuitions. Over the last decade, we are able to develop algorithms which can produce better accuracies so better decision making can be achieved. Particularly in today’s scenarios, deep learning algorithms are breaking all records. These algorithms mimic the neural system of humans and successfully breaking several barriers in image classification, NLP, and robotics, etc. The contributions of this paper are in three folds. Firstly, we reviewed current state-of-the-art research and development work in the area of machine learning. Secondly, we identified a machine learning task and reviewed them. Thirdly, we related these developments with how they are affecting human societies with respect to their applications such as image classification, autonomous driving, and data fusion. This paper provides reader with the direction of what has been done and what can be done in machine learning to exploit open problems in this area.
Álvaro Vieira Pinto e a dialética da informação

This article aims to expose and examine the dialectical-materialist concept of information elaborated by the Brazilian philosopher Álvaro Vieira Pinto in relation to theories and authors who developed the theory of information and cybernetics in the 1950s and 1970s, when Vieira Pinto developed his thought. The basic source is the second volume of his posthumously published treatise, The concept of technology. The article aims, firstly, to draw attention to the extreme originality, even in the Marxist field, of Vieira Pinto's dialectical approach to information. Secondly, in the conclusions, he draws attention to the importance of his ideas being incorporated into the current theoretical and political agenda, considering the characteristics of contemporary information capital.
EconLedger: A Proof-of-ENF Consensus based Lightweight Distributed Ledger for IoVT Networks

The rapid advancement in artificial intelligence (AI) and wide deployment of Internet of Video Things (IoVT) enable situation awareness (SAW). The robustness and security of IoVT systems are essential for a sustainable urban environment. While blockchain technology has shown great potential in enabling trust-free and decentralized security mechanisms, directly embedding cryptocurrency oriented blockchain schemes into resource-constrained Internet of Video Things (IoVT) networks at the edge is not feasible. By leveraging Electrical Network Frequency (ENF) signals extracted from multimedia recordings as region-of-recording proofs, this paper proposes EconLedger, an ENF-based consensus mechanism that enables secure and lightweight distributed ledgers for small-scale IoVT edge networks. The proposed consensus mechanism relies on a novel Proof-of-ENF (PoENF) algorithm where a validator is qualified to generate a new block if and only if a proper ENF-containing multimedia signal proof is produced within the current round. The decentralized database (DDB) is adopted in order to guarantee efficiency and resilience of raw ENF proofs on the off-chain storage. A proof-of-concept prototype is developed and tested in a physical IoVT network environment. The experimental results validated the feasibility of the proposed EconLedger to provide a trust-free and partially decentralized security infrastructure for IoVT edge networks.
Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression

The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real-valued output as in the Regression Tsetlin Machine (RTM). The C-RTM thus unifies the two approaches. We evaluated the performance of C-RTM using 72 different artificial datasets, with and without noise in the training data. Our empirical results show the competitive performance of C-RTM compared to two standard CNNs. Additionally, the interpretability of the identified sub-patterns by C-RTM clauses is analyzed and discussed.