Reviewerly/exHarmony huggingface dataset:
Multi-class Short Text Classification Using Ensemble of Deep Learning Classifier

With the substantial outgrowth of e-commerce, social media and online news portals have witnessed a great wave in expressing views through short text. Most textual contents are unstructured and messy forms, which are impractical and cumbersome to organize or manipulate by human experts. Therefore, developing an automatic short text classification model concerning low-resource languages, including Bengali, is critical. Moreover, the crucial barrier to classifying short text in Bengali is the unavailability of text corpora, scarcity of linguistics tools, a limited number of words in the text, and a lack of dependencies between the words. This paper presents a short text classification model using the ensemble of four base deep learning classifiers (Neural Network (NN), Convolutional Neural Network (CNN), Bidirectional Long Short Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU)). Additionally, a corpus of around 0.13 million Bengali texts is developed for short text classification into six categories (e.g., international, national, sports, amusement, technology, and politics). The evaluation results on the developed corpus demonstrated that the proposed method outperformed all the baselines machine learning and deep learning models by obtaining the highest weighted f1-score of $$84.4\%$$ .
Controlling Home Appliances Adopting Chatbot Using Machine Learning Approach

In the last decades, home automation becomes popular and rapidly increased artificial intelligence-based controlling systems. So, many researchers have been interested in the Internet of things so that every appliance should be autonomous. Smart home technology is one of them. It involves certain electrical and electronic systems in a building with some degree of computerized or automated control. It can control elements of our home environments (e.g. light, fans, electrical devices, and safety systems). We propose an approach that fully controlled the home appliances by chatbot technology. In our research, the system can extract the device name such as light, fan, etc. using synonyms. In the device name extraction part, we use Jaro-Winkler string matching algorithms. We have also used the Naive Bayes algorithm to take command for action. Finally, a Firebase-based system connects the users and controls hardware. Our model can control the home appliances from a long distance because we used the wireless fidelity system.
Content Based Email Spam Classifier as a Web Application Using Naïve Bayes Classifier

Spam has emerged as a significant issue that is endangering the reliability of existing email networks. The email has become an essential means of sharing information worldwide for personal or commercial purposes. For this reason, creating an effective spam filter is one of our biggest challenges. Considering this demand, we build a dynamic spam filter that can filter the standard message and spam messages more efficiently using the most common Naive Bayesian algorithm. Our proposed model works mainly by considering the content of messages. We used a supervised machine learning model, which contains primarily two phases: Training and Testing. We build a model based on the Bayesian concept in the training phase. In the testing phase, we test our messages by dividing them into words and sentences and calculating their probability for both spam and non-spam categories. Finally, the highest probability value is our desired result and deployed as a web application. Our suggested spam filtering model achieved 98% accuracy. It worked well on both online and offline systems.
UIT-ADF: A System for Android Device Forensics

Today, Android mobile phones have shown their popularity with more than two billion users worldwide. Through the use of the application, the user’s personal data will be stored on the Android device. These data are especially important in digital investigation. The logical extraction method is one of the popular methods for data extraction on mobile devices. This method does not require expensive specialized hardware devices and can analyze the data easily. There have been many tools built on top of this approach, both free and paid. While free tools collect little information and limit many features, commercial tools require users to pay a sizable fee. In this paper, an information extraction tool on Android devices called UIT-ADF will be proposed. It is an open-source web-based data collection system. The results show that UIT-ADF allows easy use while providing features that current free tools lack.
Is RDBMS or NoSQL Better Suited for MIS?: A Comparative Analysis

This research compares and analyzes NoSQL and relational database management system (RDBMS) and identifies and recommends the best databases for management information systems (MIS). With the advent of database systems, we now have more alternatives for creating any system and deciding between SQL and NoSQL for an MIS. Every second, a rising amount of data is produced at MIS. Exponential data generation is forcing us to reconsider data management strategies. Almost every business or corporation requires an MIS to keep track of their data. Many corporations and commercial organizations use a variety of database systems. Traditional relational databases are built around atomicity, consistency, isolation, and durability (ACID) properties, whereas NoSQL is built around basically available, soft state, and eventually consistent (BASE) features. Most management information system applications use RDBMS but not NoSQL. Through performance studies, we find out why RDBMS, rather than NoSQL, is the most often utilized database in MIS.
A Hybrid Hotel Recommendation Using Collaborative, Content Based and Knowledge Based Approach

Everybody plans vacations, and the first step in that process is to book a hotel. With the hospitality sector being so competitive, it’s critical to maintain best practices and stay on top of client demands and wants. They want individualized experiences, one-of-a-kind amenities, and a general sense of well-being on all levels. A consumer of a hotel recommendation system frequently encounters challenges in obtaining and fulfilling his or her wishes. Content-based filtering and collaborative filtering are two well-known strategies for creating a recommender system. Content-based filtering does not use human opinions to produce predictions, whereas collaborative filtering does, resulting in more accurate predictions. Collaborative filtering, on the other hand, cannot forecast objects that have never been rated by anyone. Both approaches can be merged with a hybrid methodology to cover the disadvantages of each approach while gaining the benefits of the other. This research employed Item-Item collaborative filtering (CF) and content-based filtering (CB) to calculate hotel similarity in our suggested method. It uses cosine similarity to calculate user similarity. For content-based filtering, natural language processing (NLP) is also employed. Our model employs a knowledge-based approach for Cold-User scenarios. Precision, recall and f1 used to evaluate the recommendation system.
Adaptive Metaheuristics for Evaluation-Number Constrained Optimization Problems

This paper is related to an optimization problem constrained by the evaluation number of the objective function. This class of the problem, called the “Evaluation-number Constrained Optimization (ECO)” problem, has pragmatic value when the objective function evaluation is expensive in terms of money or time because such costs are usually limited in any real-world project. This paper first shows a solution framework for this problem, proposed previously by the author, that adapts metaheuristic algorithms to any evaluation-number constraint. Second, the proposed framework is applied to a standard Differential Evolution (DE) algorithm. The expected value of the population state of the DE algorithm is calculated to obtain the adaptive parameter adjusting method in the framework. Finally, a numerical experiment on the proposed adaptive DE algorithm is conducted to confirm its effectiveness for three test functions under different evaluation-number constraints.
Fitwave: A Posture Correction System Based on Machine Learning

Staying fit requires dedicated and regular exercise. However, without supervision from professionals, this could lead to long term injuries. Pandemic restrictions and insufficiency of time may prevent some from going to local gyms where they may seek guidance. Our study revolves around a fitness application that allows users to exercise from anywhere. Pose estimation models used in the app return spatial key points from each frame of the video (camera). We propose to use these points to monitor and correct the posture of users while performing exercises. To complete the application, multiple other fitness utilities such as calorie trackers and workout planners have been integrated. Throughout the manuscript, we discuss the methodologies used while developing the application and ultimately discuss the insights gained from the study.
An Inter-modal Attention Framework for Multimodal Offense Detection

In recent years, posting offensive content on social media platforms has increased, and memes play a vital role in continuing this trend. Memes usually come in multimodal forms and can often promulgate offensive views by implicitly expressing a sense of humour. A meme’s visual and textual information is often disparate and can easily evade surveillance systems. Therefore, an automatic offensive meme detection system is required to combat malign content and maintain social media harmony. Past studies explored various multimodal fusion techniques to combine the visual and textual information of the memes. However, they fail to learn the multimodal content’s significant relationships and perform poorly in offensive meme classification. This paper proposes an inter-modal attention framework that synergistically fuses the visual and textual information by focusing on the modality-specific features. The state-of-the-art pre-trained VGG19 and BERT models are employed to extract the visual and textual features, respectively. The proposed method is compared against several baseline models on a publicly available multimodal offensive dataset. The results demonstrated that the proposed approach outperformed all the models by achieving the highest weighted f1-score of 0.635.
An Encoding and Decoding Technique to Compress Huffman Tree Size in an Efficient Manner

Now a days transfer of gigantic amount of information in an accurate format is a big problem. Storage is needed to store all this information is limited to be calculated before sending data over the internet. The intercommunication path which is also determinate with limited and restricted communication lines. This restriction driven to necessity of data compression. To compress data effectively a most widely popular used compression technique is Huffman. By minimizing the memory space for Huffman tree it will be more feasible. Our paper focused on minimizing the size of Huffman tree for encoding and decoding and also proposed an efficient method for storing Huffman tree. For this purpose we also design an encoding algorithm as well as a decoding algorithm.This method works more efficiently among all present techniques in where total memory needs to represent the tree structure for Huffman tree are 10.75n−3 bits for worst case and in best case it is 9n bits.
Innovation is the Way Forward: The Impact of Organisational Culture on Innovation in the United Arab Emirates (UAE)

This paper aims to investigate the impact of organization culture on innovation using the United Arab Emirates (UAE). A quantitative approach was used for the study collecting data from 557 individuals working across the seven states of UAE in different organisations. The data was analysed using SmartPLS where partial least squares – structural equation modeling were used to test the relationship between organisation culture and innovation. The results show that, the unique working environment in UAE where there is a vast mix of cultures from all over the world making it difficult to establish a steady working culture, organisation culture still has a positive impact on innovation. The finding suggests that organisations and the government should take advantage of this situation and drive innovation using the correct approach.
Evaluation of Recommendation Models for Matchmaking

Matchmaking to build interpersonal relationships has been greatly affected during the Covid-19 pandemic. Social distancing interventions for Covid-19 have changed the way people communicate. People used to meet at cafes, streets, and other places, and these socialising occasions seemed luxurious due to social separation. Thus, the demand for online matchmaking has increased inevitably. Matchmaking apps in the market commonly use collaborative filtering (CF) that recommends matches based on user ratings or preferences. As a result, users are overloaded with match choices. In addition, CF has a cold-start issue. Thus, we aim to find a performing hybrid recommendation model that can overcome CF’s weakness. In this study, we built two recommendation models for matchmaking: (i) content-based filtering + distance similarity + collaborative filtering (CBF+DS+CF), (ii) content-based filtering + demographic-based filtering + collaborative filtering (CBF+DF+CF). We then compared them with (iii) collaborative filtering (CF). These three models perform matching calculations and recommendations based on user’s profile data and latent preferences, i.e., likes, dislikes, and profile searching. We implemented these three models in a mobile application and conducted a usability test. CBF+DF+CF is found to be the best recommendation model for matchmaking.
Remote Work in Technical Services: What Have we Learned? Where Are we Going?

This conference report covers a presentation from the 2022 North Carolina Serials Conference (NCSC) on the topic of remote work in library technical services units. The presentation discussed findings from a literature review conducted for a 2020 Serials Review column, and also investigated scholarship on remote work in technical services as published since that time. The online audience offered questions and comments from their experiences, which are also summarized in this report.
Balancing Efficiency and Inclusivity

Journalism studies is facing a collaborative turn. The growing number of researchers collaborating not only increases the methodological complexity but also raises ethical challenges related to inclusivity. Not every collaborator is equally equipped and automatically benefits likewise from cooperation. This commentary argues that open science principles and practices help balancing efficiency and inclusivity in large-scale collaborative projects. First, the commentary draws on the Worlds of Journalism Study as a source of experience to provide hands-on-solutions for researchers. It then identifies ways forward focusing on funding applications, pragmatic options for sharing methodological experiences, the h-index, and empirical insights into open science practices.
Customer-Category Interest Model: A Graph-Based Collaborative Filtering Model with Applications in Finance

The financial domain naturally contains multiple different types of entities such as stocks, product categories, investment participants, intermediaries, and customers, and interactions between these entities. This paper introduces a Graph-based Collaborative Filtering Category Recommendation (GCFCR) system as a first step in modelling the financial domain as an inter-connected, heterogeneous, dynamic system of nodes and edges. The goal of this paper is to identify customer interest based on the neighborhood of each node and make personalized suggestions or identify relevant content for each customer. Matching relevant products and services to customers is a key foundation of building and maintaining strong customer relationships, facilitating more personalized marketing which can ultimately result in increased customer activity, trust, and revenue. In this paper, we run a set of experiments to compare different recommendation techniques, concluding that the proposed GCFCR approach outperforms in this real-life application.
The study of computer graphic based on appearance modeling and subsurface scattering

This paper is about the study of computer graphic based on appearance modelling. and. subsurface scattering mainly discussing that the purpose of computer graphics is to make the picture more beautiful and nicer because of the application of these two technologies. It describes in detail the typical physical process of light, the tracking of object features, and the recognition and transformation of this feature by computer from the two directions of appearance modeling and subsurface scattering. It also describes the outstanding theoretical achievements of some predecessors on computer graphics and the successful application of these technologies in the film and animation industry. At present, the development of any science has its own outstanding advanced aspects, as well as its own limitations and problems. Computer graphics has experienced a long disciplinary bottleneck period. Now, the in-depth development of computer digitization, informatics, physical optics and other sciences has brought infinite vitality and broad prospects to this science, let it better reproduce nature and draw more infectious and realistic beautiful pictures to service the growing demand for this technology. The appearance modeling techniques are constantly improving and people are also learning more and more about the subsurface scattering knowledge to better serve the computer graphics.
Research on Chinese summary generation based on pointer key information

Sequence-to-sequence models provide a feasible new approach for generative text summarization, but these models are not able to accurately reproduce factual details and subject information. To address the problem of unconstrained and uncontrollable content generation of generative text summarization models, this paper proposes a generative summarization method KGIT that uses Transformer as a skeleton and incorporates both BERT pre-training model and keyword information. The model uses a comprehensive keyword extraction algorithm, uses two results extracted by LSTM and TextRank as vocabularies respectively, and uses pointers keywords are selected and the extracted keywords are used as the guiding information to generate the summary based on the guiding information. KGIT model can associate the source text and keywords to avoid generating a summary of irrelevant topics. The ROUGE value is used as the evaluation criterion for text summaries, and the summaries generated by the KGIT model can contain more key information and are more accurate and readable when compared with the mainstream summary generation models on the NLPCC2017 Chinese news summary dataset.
Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree

Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with heterophilic graphs. Although many novel methods have recently been proposed to deal with heterophily, the effect of homophily and heterophily on classifying node pairs is not clearly separated in existing approaches and inevitably influences each other. To deal with various types of graphs more accurately, in this work we propose a new GCN-based model that leverages the explicitly estimated homophily and heterophily degree between node pairs and adaptively guides the propagation and aggregation of signed messages. We also design a pre-training process to learn homophily and heterophily degree from both original node attributes that are graph-agnostic and the localized graph structure information by using Deepwalk that reflects graph topology. Extensive experiments on eight real-world benchmarks demonstrate that the new approach achieves state-of-the-art results on three homophilic graph datasets and outperforms baselines on five heterophilic graph datasets.
Enhancing the Capacities of Teachers and State Implementation Mechanisms for Learning Recovery: The Experience of the National Teachers’ Institute (NTI), Kaduna, Nigeria

The National Teachers’ Institute (NTI) with technical support from Commonwealth of Learning (COL) conceived a Learning Recovery programme (LRP) aimed at enhancing the capacities of basic education teachers and state implementation mechanisms to effectively address eventualities that might occur in the future due to the covid-19 pandemic. A total of 2000 teachers, 42 teacher educators and an estimated 242,000 students were targeted by the project in two northwestern states of Nigeria (Kebbi &amp; Sokoto). The main principles that guided the implementation of the project included, concentration on the development of podcasts and other multimedia resources, enhancing the capacities of teachers and state mechanisms on the use of the developed multimedia resources, developing teacher networks and support structures for mobile learning and institutionalizing the use of the blended learning approach by teachers in 50 selected schools. A small sample of 42 Master Trainers (MTs) comprising 20 teacher mentors (10 each from Kebbi and Sokoto states) and 22 teacher educator mentors from NTI, Kaduna) were trained on podcasts development and accelerated learning approaches. This crop of MTs was used to cascade the training to 2000 teachers in the two states. Each of the 10 State MTs was allocated and virtually joined with 100 teachers in a WhatsApp Group for effective mentoring and each of the 20 State MTs was assigned to and virtually joined with two teacher educator mentors from the NTI who monitored and coordinated the implementation of the Project at the state level. A set of validated pre and post training questionnaires (Podcast Learning Questionnaire [PLQ]) and Learner-Centred Lesson Questionnaire [LCLQ]) was used to elicit information from a sample of 250 respondents, which was subjected to descriptive statistical analysis of means and standard deviation. The findings among others, revealed that with little tangible and guided technical support teachers could acquire knowledge and skills in the actual processes of podcast development and use in their instructional practices. Among the key recommendations of the Project was the need to scale up the training to include more schools and teachers using the capacity already developed in the two states.