Reviewerly/exHarmony huggingface dataset:
Development of a Bangla Sense Annotated Corpus for Word Sense Disambiguation

Sense annotated corpus can be treated as an essential resource for lexicon development, morphological processing and also for evaluating the performance of a word sense disambiguation (WSD) system. In this paper, a Bangla sense annotated corpus is generated from a raw collection of Bangla text, where only the sentences which contain at least one Bangla ambiguous word are retrieved from the raw corpus. All individual word forms of the sentences stored in our Bangla sense annotated corpus are tagged with their corresponding root word forms and POS types and the detected ambiguous words in the sentences are also tagged with their actual senses. The developed Bangla sense annotated corpus initially contains 5028 Bangla sentences with proper annotation and the overall performance of our Bangla sense annotated corpus creation system is 86.95%.
Multilabel Aspect-Based Sentiment Classification for Abilify Drug User Review

Multilabel text classification plays an important role in text mining applications such as sentiment analysis and health informatics. In this paper, we propose a multilabel aspect-based sentiment classification model for Abilify drug user reviews. First, we employ preprocessing techniques to obtain the quality of data. Second, the term frequency-inverse document frequency (TF-IDF) features are extracted with Bag of words (BoWs). Third, a joint feature selection (JFS) method with Information Gain (IG) is applied to select label specific features and label sharing features. Moreover, multilabel classification task can be solved using the problem transformation approaches, adapted algorithm approaches, and ensemble approaches. Finally, we study the problem transformation approaches, binary relevance (BR), classifier chains (CC), and label Powerset (LP) to classify Abilify user reviews into a set of aspect term sentiment (ATS). The baseline classifiers Naïve Bayes (NB), decision tree (DT), and support vector machine (SVM) is employed on both feature sets. The proposed method evaluated on multilabel metrics such as accuracy, Hamming Loss, F1-micro averaged, and accuracy per Label. The empirical results show that the support vector machine outperforms.
A brief overview of User Authentication in Internet of Things architecture

The recent years have seen a huge growth in the adoption of internet-enabled devices in both industrial as well as consumer domains. This interconnected network of `things', collectively referred to as internet of things, provides a great opportunity for economic gains as well as societal advancement but at the cost of transmitting a staggering amount of potentially sensitive information through untrusted networks. Researchers have been tirelessly analyzing the security limitations of this emergent technology and have presented several approaches in mitigating, or at the least minimizing, the security and privacy issues associated with Internet of Things architecture. In this paper, we categorize and review the proposed user authentication schemes in this field. Finally, we conclude with our analysis of the research trends and future scopes.
Research on Intellectual Property Protection of Network Information in China

This paper aims to deal with that the growth of network information resources challenges the protection of intellectual property. Based on the deficiency of conception of traditional intellectual property in hearing and punishing the network information infringement cases, this paper gives some suggestions on the protection of network information intellectual property right. This paper deals with the problems of network information intellectual property by literature review and analysis of legal provisions. Then, the paper does further analysis to solve the existing problems and tries to put forward feasible recommendations. This paper explores the intellectual property problems of network information. It elucidates the insufficiency of traditional intellectual property laws in dealing with network information issues and the difficulties in trying and punishing the network information infringement cases. It gives some suggestions for addressing these problems. The suggestion points out that we should raise the public and online media’s awareness of protecting Internet information intellectual property and emphasize on the mechanism innovation and legal system construction of the network intellectual property protection. This paper gives practical measures to raise the public and online media’s awareness of protecting Internet information intellectual property, for example, adding the intellectual property law to the content of the law popularization work. This paper suggests that we should construct innovative mechanism of intellectual property protection of networked information that makes the information diffuse reasonably in a network environment.
Comparative Analysis of Watermark Reconstruction Using Discrete Wavelet Transform and Slantlet Transform for User Identification in Social Media

The use of social media is common in day to day life. People transfer digital data in the form of images, texts or videos to convey the news/information to others. Sometimes, people unintentionally forward the wrong/sensitive information over social media, which can cause negative effects in society. Most of the times this information message is only forwarded, which is being shared by others but the source of that information is the specific user. It is important to find such users, who start spreading this type of digital information over the internet. Such users should be identified and stopped them from doing such activities. A comparative analysis is proposed using two different transforms namely Slantlet transform (SLT) and discrete wavelet transform (DWT) in watermark reconstruction for user identification over the internet. In the proposed robust image watermarking, a 10-digit number is considered and each digit is assigned to a 32 bit randomized Walsh code and 8 additional digits are reserved as padding to obtain the binary watermark. The watermark insertion and extraction process are performed using two different transforms to compare their performance in terms of different watermarking parameters. Although both transforms provide significant results against many signal processing attacks, the experimental evaluation proved that the Slantlet transform gives better results as compared to the DWT.
A Relation Learning Hierarchical Framework for Multi-label Charge Prediction

In legal field, multi-label charge prediction is a popular and foundational task to predict charges (labels) by a case description (a fact). From perspectives of content analysis and label decision, there are two major difficulties. One is content confusion that the case descriptions of some charges are almost identical. The other is dynamic label number that the numbers of labels (label number) of different cases may be different. In this paper, we propose a relation learning hierarchical framework for multi-label charge prediction with two models, i.e., dynamic merging attention (DMA) and number learning network (NLN). Specially, DMA can improve the charge prediction performance by dynamically learning the similarity relation between a fact and external knowledge (provisions) and the difference relation between different provisions, which alleviates the phenomenon of content confusion. NLN mitigates the dynamic label number by learning the co-occurring relation between labels. Moreover, we put the two models into a unified framework to enhance their effects. Conducted on a public large real-world law dataset, experimental results demonstrate that our framework with DMA and NLN outperforms well-known baselines by more than 3%–23%.
Genetic Programming of Pipelined Datapaths for FPGA

A new method of the pipelined datapath synthesis which is based on the genetic programming approach is proposed. The method is based on the representation of the algorithm by the spaced synchronous data flow (SDF), transferring it to a chromosome, and using the conventional genetic optimization algorithm. The respective creation, duplication, mutation and recombination functions are proposed as well. The method effectiveness is shown on the example of the DCT transform datapath synthesis.
Weather Monitoring Architecture for Smart Home Using Alexa, Raspberry Pi, and DarkSky API

In recent years, the development of domotics has been increasing thanks to the use of the Raspberry Pi Internet of Things (IoT) capabilities. This allows to automate tasks at home or monitor specific parameters. This article proposes an improvement to the interaction between the smart home system and the end user using an intelligent voice assistant instead of mobile applications or a local web platform. Besides, other sources of information for weather forecasts are also explored, such as the application programming interfaces (APIs) that provide this service.
Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and Validation

Background Big data technology provides unlimited potential for efficient storage, processing, querying, and analysis of medical data. Technologies such as deep learning and machine learning simulate human thinking, assist physicians in diagnosis and treatment, provide personalized health care services, and promote the use of intelligent processes in health care applications. Objective The aim of this paper was to analyze health care data and develop an intelligent application to predict the number of hospital outpatient visits for mass health impact and analyze the characteristics of health care big data. Designing a corresponding data feature learning model will help patients receive more effective treatment and will enable rational use of medical resources. Methods A cascaded depth model was successfully implemented by constructing a cascaded depth learning framework and by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. To develop a medical data feature learning model based on probabilistic and deep learning mining, we mined information from medical big data and developed an intelligent application that studies the differences in medical data for disease risk assessment and enables feature learning of the related multimodal data. Thus, we propose a cascaded data feature learning model. Results The depth model created in this paper is more suitable for forecasting daily outpatient volumes than weekly or monthly volumes. We believe that there are two reasons for this: on the one hand, the training data set in the daily outpatient volume forecast model is larger, so the training parameters of the model more closely fit the actual data relationship. On the other hand, the weekly and monthly outpatient volume is the cumulative daily outpatient volume; therefore, errors caused by the prediction will gradually accumulate, and the greater the interval, the lower the prediction accuracy. Conclusions Several data feature learning models are proposed to extract the relationships between outpatient volume data and obtain the precise predictive value of the outpatient volume, which is very helpful for the rational allocation of medical resources and the promotion of intelligent medical treatment.
Ontology-based modelling for content delivery systems

Instead of managing metadata in a content management system (CMS) for content delivery, this research uses an ontology to make use of augmented intelligence. Hence, the retrieval of specific information in a content delivery system (CDS) is provided by a concise metadata concept. Ontology-based metadata concepts can have advantages in comparison to taxonomy-based metadata concepts because of the possibilities they offer to display linked information. The creation of an ontology depends on the vendor’s system, the vendor’s approach and most importantly the particular use case. Due to the ontology modelling, a data transformation for an external CDS is needed. When working with different vendors, requirements and considerations need to be discussed because the outcome can add value to user experience. The goal of this research is to identify the advantages of an ontology system over metadata management in a CMS. These advantages can be extracted by opportunities offered by the ontology modelling and by the transformation process.
TMMI Integration with Agile and Test Process

Test Maturity Model Integration (TMMI) has a staged architecture and is a detailed model for process improvement. This model focus on test process improvements and contains five maturity levels. Applying this approach to the project has a positive impact on product quality and test effort. Besides positive impact, Test Maturity Model Integration contains heavy documentation. Applying scrum practice with this model can reduce the documentation and improve test process quality. In this case, Test Maturity Model Integration (TMMI) Level - 2 Managed step is mapped with agile and test practices. Test Policy and Strategy, Test Planning, Test Monitoring and Control, Test Execution and Test Environment areas are discussed and they matched with not only scrum practice but also some test practices (Risk Based Testing, Use Case Testing). In the end, TMMI coverage percentage will be given and improvements areas will be defined. TMMI Level 4 metrics will be used for a development project and results will be showed. This model also can be adapted different areas, which is using agile and scrum practice.
Design of Quantum Cost and Delay Optimized Code Converter Using New Reversible Quantum Circuit Block (QCB)

Background: In this article, we have proposed a new reversible quantum circuit block along with the quantum cost (QC), constant input (CI), garbage output (GO) and delay optimized code converterusing quantum circuit block. Method: Initially, new quantum circuit block has been designed and later reversible code converter circuits have been implemented using it. The proposed new quantum blockused to design 2’s complement code converter (2SCCC), cost efficient BCD to Excess-3 code converter (BECC) and can also be used to implement different logic functions. The QC of proposed quantum circuit block is 8. The QC and delay of the proposed 2SCCC is 8 and 1 respectively. Similarly, the QC and delay of the proposed BECC is 11 and 2 respectively. The proposed cost efficient BECC is designed using two NOT gate, one Feynman gate and one new quantum circuit block with QC is 11. Results: The improvement of QC for 2SCCC and BECC are 27.27 % and 21.43% respectively. The improvement of delay for 2SCCC and BECC are 66.67% and 50% respectively compared with respect to the latest reported results. Conclusion: So the improvement of QC and delay are very high using QCB.
Proposed Infrastructure for Census Enumeration and Internet Voting Application in Digital India with Multichain Blockchain

Census and election are important aspects of Indian government. Census helps India to keep track of demographic, social, cultural, and economic information of India’s growing population, whereas election helps in electing representatives of people. Digitization of census enumeration procedure and building online voting system in India will answer to many problems associated with existing system. Security associated with blockchain technology makes it suitable for building online census and voting system rather than using other models like client–server model, cloud-based technology, or involvement of mobile-based services. Existing infrastructure based on Internet is not suitable to accommodate limitation of blockchain technology. Keeping security and privacy in mind, building a required infrastructure for blockchain-based census and voting system is necessary and challenging. This paper proposed infrastructure required for efficient implementation of online voting system and online census process using private blockchain in India, considering current infrastructure problem. With help of online census enumeration, it is possible to consolidate people’s data by mapping all census data and voting information to UIDAI Aadhaar card. This paper provides requirements to build Web application for proposed infrastructure with multichain blockchain, considering security features and storage problem of such huge data of entire country. The proposed application for voting system will ensure voting from any nearest constituency, tamper proofing of vote, anonymity of voter, resistance to multiple voting, proposed application will provide method or way to verify integrity of vote cast, if authority want to cross check (on demand). This paper provides test analysis result of proposed system with proposed application over small blockchain node to find out real-time possibility and requirement for large-scale implementation.
Synectics as a Modern Method of Solving Creative Problems

An important need in the conditions of modern education is the development and application of innovative technologies in the teaching of students. Nowadays it has become very important to be a creative person. The formation and development of creative personality requires the coverage of the entire period of human life. One of the directions of the educational organization should be the development of creative thinking of students. Since creative thinking is important to society, it is necessary to develop strategies that are conducive to its development. Various strategies such as creative problem solving, lateral thinking, and synectics have been devised to facilitate growth in thinking and creativity. This study investigated the effect of one of those techniques, synectics, and its effect on schoolchildren.
A Survey on Multimodal Information Retrieval Approach

There has been more content with various types needed by users. It comes from specific websites or databases. It causes a need to access the content through a retrieval system that can produce content that meets the demand. The existing searching applications still provide services aimed at only one particular modality, such as text, audio, or video. Therefore, research aimed at developing multimodal searching systems is still a significant opportunity in this field. In this paper, we review the multimodal information retrieval systems that have proposed in the previous research. And they discussed the components and mechanisms of the multimodal retrieval used by them. In this way, it will be clear on how they apply their approach to problem-solving on multimodality content searching. At the end of the discussion, there are some ideas or issues related to the problem.
The Safari of Update Structures: Visiting the Lens and Quantum Enclosures

We build upon our recently introduced concept of an update structure to show that it is a generalisation of very-well-behaved lenses, that is, there is a bijection between a strict subset of update structures and vwb lenses in cartesian categories.We show that update structures are also sufficiently general to capture quantum observables, pinpointing the additional assumptions required to make the two coincide.In doing so, we shift the focus from special commutative †-Frobenius algebras to interacting (co)magma (co)module pairs, showing that the algebraic properties of the (co)multiplication arise from the module-comodule interaction, rather than direct assumptions about the magma-comagma pair.We then begin to investigate the zoo of possible update structures, introducing the notions of classical security-flagged databases, and databases of quantum systems.This work is of foundational interest as update structures place previously distinct areas of research in a general class of operationally motivated structures, we expect the taming of this class to illuminate novel relationships between separately studied topics in computer science, physics and mathematics.The Safari of Update Structures define update structures.For the former, we demonstrate that update structures are more general than vwb lenses, capturing both demolition and non-demolition viewing (Get) processes.For the latter, we demonstrate both that the application of decoherence to any update structure will produce a new update structure on classical objects, and that to characterise quantum observables it is sufficient to look at how the weak module-comodule interact without underlying assumptions on the magma-comagma pair 1 .Thus the focus is shifted from demands on both how the module and comodule interact and the algebraic structure of the (co)magma, to demands placed purely on the interaction between module and comodule.This approach places quantum observables and vwb lenses within a zoo of update structures with many other enclosures left to explore, examples of which we give in the last section.
Supporting STEM and critical thinking on energy transformation topic: pre-study of digital reality book

Abstract Technology is growing rapidly. Many devices could be used to support the learning process in the classroom, particularly in integrative science learning. Nowadays, learning materials can be acquired through various digital forms. In this study, the tendency of the needs and interests of students toward learning materials, specifically in energy transformation topic, would be briefly explained. Based on the data obtained, students were more curious when they were provided the supporting learning materials such as pictures and videos while learning the energy transformation topic. The data illustrated that 64.3% of respondents stated that the combination of text, images, and videos enhanced their curiosity on the matter. Some questions must also be provided to improve their STEM knowledge and critical thinking. Therefore, Digital Reality Book with many features can be selected as an alternative to deliver learning material about Energy Transformation in supporting STEM and critical thinking.
Intrinsic Evaluation of Bangla Word Embeddings

Word embeddings are vector representations of word that allow machines to learn semantic and syntactic meanings by performing computations on them. Two wellknown embedding models are CBOW and Skipgram. Different methods proposed to evaluate the quality of embeddings are categorized into extrinsic and intrinsic evaluation methods. This paper focuses on intrinsic evaluation - the evaluation of the models on tasks, such as analogy prediction, semantic relatedness, synonym detection, antonym detection and concept categorization. We present intrinsic evaluations on Bangla word embedding created using CBOW and Skipgram models on a Bangla corpus that we built. These are trained on more than 700,000 articles consisting of more than 1.3 million unique words with different embedding dimension sizes, e.g., 300, 100, 64, and 32. We created the evaluation datasets for the abovementioned tasks and performed a comprehensive evaluation. We observe, word vectors of dimension 300, produced using Skipgram models, achieves accuracy of 51.33% for analogy prediction, a correlation of 0.62 for semantic relatedness, and accuracy of 53.85% and 9.56% for synonym and antonym detection 9.56%. Finally, for concept categorization the accuracy is 91.02%. The corpus and evaluation datasets are made publicly available for further research.