Reviewerly/exHarmony huggingface dataset:
An Ensemble Based Stacking Architecture For Improved Bangla Optical Character Recognition

Bangla handwritten character recognition holds an important position since it is the world's seventh most widely spoken language. However, the task is challenging as there exists wide range of individual writing styles and structural similarities across characters. Deep learning models like CNN have been employed to categorize Bangla characters although the accuracy hasn't improved much. In this research, the comparison of accuracy between the use of single machine learning model to stacking of the models has been shown by ROC curves. Stacking of Random Forest, Extra Trees, and XGBoost models based on ensemble and feature extraction has been used in this research that outperformed all current models in the Bangla OCR with 99.98% accuracy on the CMATERdb 3.1.3.3 dataset.
Predicting response probability by embedding questions in online question recommendation

Currently, there are many Q&A sites, including Yahoo! Answers, Quora, and StackOverflow. Although the number of questions posted on these sites is enormous, many remain unanswered. This is detrimental to the user experience, so service operators are motivated to obtain more answers to posted questions. It is also diffcult for users to find specific questions they can answer among the vast number that are asked. Therefore, a system that recommends questions that can be answered by the user is needed. In this study, we first propose a method for predicting response probability. Specifically, we propose a method for learning embedding vectors that takes into account cases in which the required answers are similar, even if the question texts are different, based on a contrastive learning method. We also implemented a recommendation method that increases respondent satisfaction by optimizing and analyzing the theoretical properties of our method. Finally, we conduct an experimental validation of these two methods to demonstrate their effectiveness using data from a Q&A service for child care.
A Survey of Explainable E-Commerce Recommender Systems

Since the growing information overload has become more and more serious on the Web, especially in the field of e-commerce. The explainable recommender system plays a crucial role in providing users with explanations of recommendations to enhance customer satisfaction and loyalty. In recent years, various explainable recommender approaches have been proposed and applied in e-commerce. This survey will review the development of explainable recommender systems, existing methods to generate explainable recommendations, applications in the e-commerce field, and further discuss future directions that can be incorporated and implemented to improve the quality of explainable recommender systems.
enVolve: Are You Listening? Inertial Sensing to Monitor the Involvement of Silent Listeners during an Online Interaction

Understanding the level of participation for a remote attendee in an online meeting setup could significantly improve the quality of experience for virtual interaction. However, gauging audience involvement over an online meeting becomes particularly challenging when the attendees prefer to turn off the cameras. IMU data have shown promising results in the past to pervasively monitor users’ body language, including the determination of various bodily gestures, postures, and facial expressions. This paper demonstrates how earables could help address the stated problem. We provide a motivational study to assess earables for detecting body language corresponding to involved listeners. We further compare it with other sensing modalities like smartwatches and smartphones and accordingly develop a platform called enVolve. A lab-scale study with 17 participants demonstrates the efficacy of the proposed system with an average F1 score of more than .
Multiple Linear Combination Approaches for Information Search in Ranking

Since the well-known BM25 [1] was proposed, BM25 and its enhanced version [2] – [4] have dominated the document/passage ranking task for a long time. However, with the advent of deep learning models like BERT [5] , these pre-trained models have achieved noticeable progress in various information retrieval (IR) tasks. But, as BM25 is a "bag of words" retrieval method by matching keywords, it remains a better option for passage ranking in some exceptional cases, like identifying names [6] . Therefore, fusing BM25 with deep learning models is a natural idea to benefit the ranking results. This paper discusses various linear methods of combing BM25 with BERT to see how they affect the final results of the models. We conduct experiments on the MS MARCO V2 dataset, which show convincing results.
Telecommunications Regulation, Mobile Money Innovations and Financial Inclusion

This study assesses how corporate telecommunication (telecom) policies follow telecom sector regulation in mobile money innovation for financial inclusion in developing countries. Telecom policies are understood in terms of mobile subscriptions, mobile connectivity coverage and mobile connectivity performance while mobile money innovations represent mobile money accounts, the mobile used to send money and the mobile used to receive money. The empirical evidence is based on Tobit regressions. Telecom sector regulation positively influences mobile money innovations. From net influences, mobile subscriptions and connectivity policies moderate telecom sector regulation to positively influence mobile money innovations; exclusively within the remit of mobile money accounts because the corresponding net influences on the mobile used to send money and the mobile used to receive money are negative. The interactive influences are consistently negative and hence, thresholds for complementary policies are provided in order to maintain the positive influence of telecom sector regulation on mobile money innovations. This study has complemented the extant literature by assessing how corporate telecommunication policies follow telecommunication sector regulation in mobile money innovations for financial inclusion.
A Design and Implementation of a Control Circuit Using a Sensing Network for a Heat Source

A heat energy source with automatic control of supply can make it safer and reliable for users. In this study, networks of sensors are proposed that can be utilized for detecting drastic temperature change to design a controlled and safe heat source. As a feasibility study, experiments are performed to explore the effectivity of proposed network with circuit parameters. An array of sensors-for the detection of irregular or accidental temperature deviation-is proposed for ensuring a safe heat source through real time monitoring. The results indicate the potential of using the network with given constraints for use in the control circuitry design.
Mixed-Methods Self-Tracking: Qualitative Styles of Quantifying the Self

Self-tracking technologies face inconsistent reactions from users that range from excitement and enthusiasm to frustration and discomfort. Many of those on the latter end of this spectrum deal with complex feelings of disempowerment and objectification; they also face adverse effects from the excessive quantification of their lived experiences. Rather than serve as active agents in their process of monitoring and improving themselves, users feel reduced to docile spectators who are coerced into changing their behavior. This position paper explores the tensions in power redistribution and depersonalization present in existing self-tracking methods and contemplates on qualitative alternatives that may improve the design of future technologies, particularly in the field of learning.
InertiaVibe: Low-fidelity Simulation of Inertia using Head-mounted Vibrotactile Feedback to Reduce Cybersickness and Enhance VR Experience

Visually induced motion sickness in VR, or cybersickness, is a major barrier to VR adoption. We present InertiaVibe, a low-fidelity approach to simulate the experience of lateral inertial forces using headset-integrated vibration motors. Specifically, we use 4 vibration motors positioned at both sides of the forehead and the occipital bone, and vibrate the pair of motors in the direction of the inertia force during lateral acceleration, deceleration, and turning. To first evaluate how users think about the vibrotactile feedback in different scenarios, we conducted a pilot study with 12 participants. Results show that their opinions on the vibration were positive. Most users reported improvement of both realism and immersion, and they generally felt less discomfort.
A novel blockchain tumbling scalability framework in a bitcoin network

Summary Bitcoin is a decentralized digital currency system that uses a distributed data structure called blockchain, a log that lists all transaction details accomplished with the currency. The bitcoin network delivers a promising infrastructure for one who makes use of e‐currency in e‐commerce. However, the transaction of bitcoin is highly suffered with some scalability issues that could not be overwhelmed with the prior methodologies. Hence, a novel scalability blockchain tumbling framework is proposed to tackle the scalability issues in which a rake interior blockchain etiquette is designed to increase the number of bitcoin transactions in which peer faction algorithm groups blocks that reduces propagation delay and ravenous algorithm ensure higher credibility of transaction with tamper resistant. However, transaction size of the record is high hence, to manage transaction record a novel batch payment into one transaction approach is utilized to limit the size of the transaction records by initiating a precise block using block summing‐up and chagrin algorithms that collects all trust information about blocks. Thus the proposed framework attains 550 bitcoin transaction within 2 min and latency of proposed model is reduced below 60 s.
Is Blockchain the New Normal in Financial Sector? A Comprehensive Review

Purpose: The global financial services business has been transformed by Blockchain technology, making it safer and more efficient. Keeping this fact in mind, the authors will study how Blockchain technology improves financial services, including the banking and insurance sectors. The risks and roadblocks in the path of Blockchain adoption in financial services will also be discussed.Need of the Study: Blockchain operates without any central authority. Instead, it could be understood as a transaction-containing ledger shared among many users. The adoption of Blockchain is gaining traction in every field, but still, a sense of doubt about its reliability can be observed among ordinary people. Thus, an investigation of the operational intricacies and technicalities could assist in clarifying the confusion associated with this technology.Methodology: To achieve the aims mentioned above, an exploratory research design involving a review of the secondary data linked with the implementation and impact of Blockchain technology in the domain of finance is conducted.Findings: The mode of operation of Blockchain technology is thoroughly explained, along with the influence it has exercised in the financial domain in recent years.Practical Implications: The findings of this study can mainly assist global investors and users worldwide by clarifying the concept and operations of Blockchain technology. Also, it could guide future studies assessing the role of Blockchain in the financial domain.
Entrepreneurship Mentorship for HCT Education Alumni to Transition as Lead Businesswomen in the UAE Education Sector

Digital video annotation tools, which allow users to add synchronized comments to video content, have gained significant attention in teacher education in recent years. However, there is no overview of the research on the use of annotations, their implementation in teacher training and their effect on the development of professional competencies as a result of using video annotations as a supporting tool for video-based learning. In order to fill this gap, this paper reports on the results of a systematic literature review which was carried out to determine 1) how video annotations were implemented in studies in educational settings, 2) which professional competencies were investigated to be further developed with the aid of video annotations in these studies, and 3) which learning outcomes were reported in the selected studies. A total of 18 eligible studies, published between 2014 and 2022, were identified via database search and cross-referencing. A qualitative content analysis of these studies showed that video annotations were generally used to perform one or more of three functions, these being feedback, communication, and documentation, while they also enabled a deeper content knowledge of teaching, reflective skills, and professional vision, and facilitated social integration and recognition. The convincing evidence of the positive effect of using video annotation as a supporting tool in video teacher training prove them to be a powerful tool supporting the development of professional vision and other teaching skills. The use of video annotation tools in educational settings points towards further research as well.
Application of Blockchain in the Insurance Sector

Purpose: The purpose of the study is to investigate how blockchain, a new emergent technology, has spread throughout the insurance industry by evaluating incidents reported in the literature, either academic or trade publications. This study specifically aims to develop a comprehensive understanding of the main effects of blockchain on the insurance sector’s core operations.Design/Methodology/Approach: Review based approach has been utilised by evaluating incidents reported in the literature, either academic or trade publications.Findings: Blockchain is a technology attracting more and more interest from academics and business professionals. It can be seen that the technological advancements and benefits of blockchain in the insurance industry can help it expand manifold. The wise use of technology can only provide the same. If applied without thought, it can be disastrous too. With the future integration of AI and other machine learning technologies, the authors can see that clerical work in the industry can completely shift to computers and humans can use more of their time in more developmental activities and results.Practical Implications: The sector is moving in the direction of digital transformation employing emerging technologies and is ready for disruptions and change shortly regarding processes, industry structure, rivalry, and organisational structure.Research Limitations: Future researchers could investigate the existing technique to examine the dissemination of upcoming technologies, such as cognitive computing, blockchain, and artificial intelligence, in certain industries or organisational functions.
Emerging Technologies and Their Game-Changing Potential: Lessons from Corporate World

Purpose: The purpose of the chapter is to highlight the game-changing potential of emerging technologies and how these will change the very face of companies cutting across industries. Our work also aims to highlight the converging point of these technologies and describes how these new-age inventions can be more effective if stakeholders adopt a collaborative approach rather than competing against each other.Design/Methodology/Approach: Using real-world case studies, the chapter identifies the most significant emerging technologies heralding a new era of efficiency across business categories. This identification has been made based on both secondary data and expert interviews. The opinions of the domain and industry experts have also been included to select six emerging technologies for the analysis.Findings: Based on secondary and primary data, the chapter has identified the five emerging technologies that are expected to change how businesses are run completely today. The technologies are: (1) Metaverse Platforms, (2) Blockchain Technology, (3) Non-fungible Tokens (NFTs), (4) Decentralised Finance, and (5) Cryptocurrency.Research Limitations/Implications: By identifying emerging technologies and their application domains, the chapter contributes significantly to both the world of academia and practice. The research has important implications for scholars and practitioners as these stakeholders must prepare themselves to realise the maximum potential of these new-age inventions. Policymakers also need to contribute constructively so that the adoption and implementation of these technologies can be done smoothly and seamlessly.Originality/Value: The research is unique in the sense that it identifies the most-significant emerging technologies and offers a constructive roadmap for stakeholders to integrate these new inventions into their functional procedures and operational mechanisms effectively and efficiently.
Efficient Water Segmentation with Transformer and Knowledge Distillation for USVs

Water segmentation is a critical task for ensuring the safety of unmanned surface vehicles (USVs). Most existing image-based water segmentation methods may be inaccurate due to light reflection on the water. The fusion-based method combines the paired 2D camera images and 3D LiDAR point clouds as inputs, resulting in a high computational load and considerable time consumption, with limits in terms of practical applications. Thus, in this study, we propose a multimodal fusion water segmentation method that uses a transformer and knowledge distillation to leverage 3D LiDAR point clouds in order to assist in the generation of 2D images. A local and non-local cross-modality fusion module based on a transformer is first used to fuse 2D images and 3D point cloud information during the training phase. A multi-to-single-modality knowledge distillation module is then applied to distill the fused information into a pure 2D network for water segmentation. Extensive experiments were conducted with a dataset containing various scenes collected by USVs in the water. The results demonstrate that the proposed method achieves approximately 1.5% improvement both in accuracy and MaxF over classical image-based methods, and it is much faster than the fusion-based method, achieving speeds ranging from 15 fps to 110 fps.
Introducing Sham Journalism: A Case-Study Analysis of <i>Newsmax</i>’ Discourse on Special Counsel John Durham in 2022

ABSTRACTIn entering the emerging literature on partisan rightwing news, this study introduces the concept of “sham journalism” to characterize activity that appropriates the trappings of reporting but practices a form of weaponized political discourse. Sham journalism tactics include polarizing blue lies, truth-indifferent bullshit and the reputational vandalism of flak that are pursued with an unswerving degree of partisan one-sidedness at the expense of even attempting ethical reporting toward public enlightenment. In sham journalism as practiced in the U.S., these tactics serve ideological ends that delegitimize political opposition and are compatible with one-party rightwing authority. I analyze Newsmax’ website as a case-study, specifically its coverage of William Barr/Donald Trump-appointed special counsel John Durham’s failed prosecution of Michael Sussmann. Newsmax’ exhibits strikingly one-sided discourse, politicized flak, and ethical lapses while, by the numbers, it relies almost exclusively on Republican Party sources including Trump-allied felons. Following Sussmann’s acquittal, Newsmax continued to refuse to educate readers toward understanding and instead inflamed unreality that exacerbates partisan division.KEYWORDS: Newsmaxsham journalismblue liesbullshitflakJohn Durham2016 election Disclosure StatementNo potential conflict of interest was reported by the author(s).
SOAR: Simultaneous Or‐of‐And Rules for classification of positive and negative classes

Algorithmic decision making has proliferated and now impacts our daily lives in both mundane and consequential ways. Machine learning practitioners use a myriad of algorithms for predictive models in applications as diverse as movie recommendations, medical diagnoses, and parole recommendations without delving into the reasons driving specific predictive decisions. The algorithms in such applications are often chosen for their superior performance among a pool of competing algorithms; however, popular choices such as random forest and deep neural networks fail to provide an interpretable understanding of the model's predictions. In recent years, rule‐based algorithms have provided a valuable alternative to address this issue. Previous work established an or‐of‐and (disjunctive normal form) based classification technique that allows for classification rule mining of a single class in a binary classification. In this work, we extend this idea to provide classification rules for both classes simultaneously. That is, we provide a distinct set of rules for each of the positive and negative classes. We also present a novel and complete taxonomy of classifications that clearly capture and quantify the inherent ambiguity of noisy binary classifications in the real world. We show that this approach leads to a more granular formulation of the likelihood model and a simulated annealing‐based optimization achieves classification performance competitive with comparable techniques. We apply our method to synthetic and real‐world data sets for comparison with other related methods to demonstrate the utility of our contribution.
Energy-Saving Strategy Based on Image Super-Resolution for Wireless Image Sensor Networks Assisted by Cloud

Wireless image sensor networks (WISNs) collect surveillance images, resulting in copious quantities of data requiring processing and transmission within the network. To reduce and balance energy expenditure during in-network image data processing and transmission, this study introduces an energy-saving strategy based on image super-resolution for WISNs assisted by cloud. The strategy constructs an image data processing and transmission system for WISNs, leveraging cloud infrastructure. By utilizing image quality feedback, the adaptive adjustment of image node sensing resolution is facilitated, minimizing image data transmission volume while ensuring high-quality super-resolution reconstructed image. The establishment of low-overhead, multipath transmission routes enables image nodes to transmit data in blocks, contingent upon neighboring transmission node statuses, thereby equalizing energy consumption for image data transmission. Cloud servers are employed to reconstruct high-resolution images and provide users with surveillance images that satisfy quality requisites. Empirical findings demonstrate that the proposed approach extends the lifetime of WISNs, furnishes users with superior-quality surveillance images, and markedly enhances network monitoring efficacy.