Reviewerly/exHarmony huggingface dataset:
JurisNN: Judging traffic differentiations as network neutrality violations according to the regulation

Network Neutrality (NN) is a principle that is not taken for granted on the Internet. Instead, it must be enforced by regulation that defines which Traffic Differentiation (TD) practices are allowed or prohibited from being adopted by ISPs. Thus, the regulation is the proper source of NN definitions to design solutions for detecting NN violations. However, the regulation set by legislators is valid within a geographical area named jurisdiction. Therefore, as an end-to-end network path may traverse multiple jurisdictions, distinct NN definitions along with this path must be considered. Thus, to be effective, solutions based on the regulation must consider where in the end-to-end network path the TD was deployed to evaluate the proper NN definitions stated there. We propose a solution named JurisNN that implements the functionalities required to judge TDs as NN violations considering the regulation. To evaluate the JurisNN judgments, we use TD information collected by a state-of-the-art solution to assess whether this information is enough to judge TDs as NN violations by analyzing the conclusiveness of the results (the TD is an NN violation or not). The results show that to help the signaling of NN violations according to the regulation solutions need both to collect the network paths traversed by tests and to pinpoint where the TD was deployed with better accuracy.
Intelligent search and rescue robot design based on KANO model and TRIZ theory

This paper mainly focuses on the problems of low efficiency, complex scenarios and low intelligence faced in the process of disaster rescue, uses KANO model to predict the evolutionary direction and summarizes the design requirements, based on the final ideal solution, technical contradiction, physical contradiction, physical field analysis and more than forty invention principles in TRIZ theory innovation tools, the rescue mode, walking device and main control system of rescue robot, the power configuration, etc. The innovative design of an intelligent search and rescue robot for disaster rescue is proposed, the actual design evaluation survey and statistical analysis of the results are carried out, and the overall evaluation is carried out by the fuzzy comprehensive evaluation method to make a preliminary evaluation and propose improvements. The purpose is to achieve a rescue robot with high adaptability to rescue scenarios and efficient rescue, and to provide some reference value for future intelligent disaster rescue robot design.
Interval type-2 fuzzy logic systems optimization with swarm algorithms for data classification

Fuzzy systems based on the interval type-2 fuzzy set have many advantages in processing uncertain data compared with the fuzzy systems based on the type-1 fuzzy set. The design of optimal interval type-2 fuzzy systems is often difficult due to many parameters. The selection and construction of membership functions used to map the crisp inputs to fuzzifier data play an important role and greatly influence the accuracy of the fuzzy system. The paper proposes a hybrid optimization model using swarm optimization algorithms to find the parameters for the membership function of the interval type-2 fuzzy logic system (IT2FLS). For the experiment, the paper uses optimization techniques such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) to find the optimal parameter for IT2FLS applied to the classification problem. Experimental results on datasets from the UCI machine learning library and satellite image data show that hybrid optimization models between the optimization algorithm and IT2FLS can help IT2FLS achieve higher accuracy in data classification problems.
Analyzing the Effectiveness of an Extensible Virtual Moderator

The problems associated with open-ended group discussion are well-documented in sociology research. We seek to alleviate these issues using technology that autonomously serves as a discussion moderator. Building on top of an extensible framework called Diplomat, we develop a "conversational agent", ArbiterBot to promote efficiency, fairness, and professionalism in otherwise unstructured discussions. To evaluate the effectiveness of this agent, we recruited university students to participate in a study involving a series of prompted discussions over the Slack messenger app. The results of this study suggest that the conversational agent is effective at balancing contributions across participants, encouraging a timely consensus and promoting a higher coverage of topics. We believe that the results motivate further investigation into how conversational agents can be used to improve group discussion and cooperation.
IAN

With the rise of Location-Based Social Networks (LBSNs) and their heavy reliance on User-Generated Content, it has become essential to attract and keep more users, which makes the churn prediction problem interesting. Recent research focuses on solving the task by utilizing complex neural networks. However, due to the black-box nature of those proposed deep learning algorithms, it is still a challenge for LBSN managers to interpret the prediction results and design strategies to prevent churning behavior. Therefore, in this paper, we perform the first investigation into the interpretability of the churn prediction in LBSNs. We proposed a novel attention-based deep learning network, Interpretable Attention Network (IAN), to achieve high performance while ensuring interpretability. The network is capable to process the complex temporal multivariate multidimensional user data from LBSN datasets (i.e. Yelp and Foursquare) and provides meaningful explanations of its prediction. We also utilize several visualization techniques to interpret the prediction results. By analyzing the attention output, researchers can intuitively gain insights into which features dominate the model's prediction of churning users. Finally, we expect our model to become a robust and powerful tool to help LBSN applications to understand and analyze user churning behavior and in turn remain users.
Consistency Based Co-segmentation for Multi-view Cardiac MRI Using Vision Transformer

Segmentation of cardiac structures in magnetic resonance imaging is essential to the diagnosis of many cardiovascular diseases. However, sometimes it is challenging to accurately define the right ventricle (RV) structure due to the complex texture. It requires the collaboration of both short-axis (SA) images and long-axis (LA) images. Current deep learning methods trained with single-view data neglect the spatial relations between SA and LA images and could fail at the basal and apex plane of the RV. In order to properly handle the geometrical relations, we proposed a consistency based co-training method that involves an extra penalty for confusing images at the basal and apex plane. At test phase, the anatomical relations are also used for post-processing which further improves the robustness of segmentation. Besides, we incorporate the transformer network in U-Net architecture to enhance its ability to model long-range dependency. We evaluated the proposed method in the M&Ms-2 challenge and obtained promising performance for the segmentation of both SA and LA images.
Beyond Human Eye in Surgical Smart Glasses

Visualizing information onto the lens, collects data over a wireless network, and uses a program to convert it into text or images to be transmitted to the display using smart glasses. This game-changing research finds the utilization of the technology that will serve the clinical business, and subsequently to plan various items as indicated by various implementation, to focus on industry and individuals while addressing the requirements of more users. Accordingly, the plausibility of utilizing smart glasses in a medical procedure was concentrated in this investigation, technology acceptance model was utilized to examine respondents in the clinical field. Blend mode examination of the information was directed through the logical investigation and the cross-sectional investigation, and pertinent information was gathered utilizing an online poll. The investigation found that specialists glasses could improve the achievement pace of a medical procedure. In-depth theoretical and management implications were discussed.
Bangla Hate Speech Detection in Videos Using Machine Learning

Due to the internet and social media advance, people now communicate their ideas and opinions on many topics freely through several channels. However, because of its ethnicity, religion, genders, etc., this freedom of expression is exploited to direct hate to individuals or groups of people. The increase in hate speech led to disputes and cyberbullying, which drove many companies to find ideal solutions to this problem. The strategy we have taken is to use machine learning classification methods to classify videos. We have generated our dataset and tested it with several training models in this paper, utilizing the spoken material of the videos from YouTube. We experimented with some of the machine learning models along with some deep learning techniques. Our experiments concluded that the logistic regression and deep learning GRU model bring out the best accuracy on the current dataset.
Effective Use of E-tutoring System: Social WhatsApp Messenger on Social Identity Development

The present research study outlines the importance of WhatsApp application in the tutoring environment. It analyses utilities related with informative WhatsApp groups. The study additionally investigates how the presence of the tutor in the educational WhatsApp group can have impact on the tutees behaviour and the performance of the tutees. In the study, virtual interviews were convened with computer science first year students. The findings of the study revealed that the significant capacities these WhatsApp educational groups serve are generally for educational purposes. Nevertheless, apart from academic purposes, tutors do utilize WhatsApp groups for sharing career-related vacancies, as well as for entertainment purposes. In addition, the outcomes show that the presence of the tutor has a positive impact on the educational WhatsApp group discussions. Despite the fact that tutees alludes that sometimes these educational WhatsApp groups cause some challenges and can take a great deal of time, but they also accept that educational groups are unavoidable. This research used a qualitative method for data collection and an interpretive phenomenological analysis (IPA) was used to analyse data recorded in the Experience Journal. The results also identify themes that emerged during data analysis.
FederatedTree: A Secure Serverless Algorithm for Federated Learning to Reduce Data Leakage

In Federated Learning there have been many op-timization methods that allow flexible local updating such as FedAvg that has become the de facto mechanism for averaging local stochastic gradient descent without sharing the data. Classic FL methods such as FedAvg struggle with trust and data leakage issues. In FedAvg and similar techniques, clients assume the aggregator server is a trusted but curious server. However, even if the server is trusted, the models still leak a lot of data through the weights. Several techniques have been proposed to reduce data leakage. One mechanism involves sharing pieces of the data with the server, but it violates the key privacy assumption of federated learning. Other solutions such as Federated Learning with Differential Privacy aim to reduce data leakage by adding noise to the weights/gradients. However, there is a trade-off between accuracy and the amount of noise added.In this paper, we propose a practical Federated Learning algorithm of deep neural networks on iterative model averaging we called FederatedTree. While FedAvg with differential privacy adds noise to the weights to provide a level of privacy, our algorithm applies a secure sequential averaging without adding noise to the models. FederatedTree solves the trust issue between client-to-client, client-to-server (if exists) and reduces the amount of data leakage without adding noise that lowers the model accuracy. The results show that the FederatedTree algorithm provides a high privacy rate with higher accuracy on popular datasets: MNIST, Fashion MNIST, CIFAR-10. Furthermore, FederatedTree utilizes a binary tree structure to reduce the sequential averaging time and remove the overhead of the excessive communication between the server and the clients.
360HRL: Hierarchical Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

Recently, reinforced adaptive bitrate (ABR) algorithms have achieved remarkable success in tile-based 360-degree video streaming. However, they heavily rely on accurate viewport prediction. To alleviate this issue, we propose a hierarchical reinforcement-learning (RL) based ABR algorithm, dubbed 360HRL. Specifically, 360HRL consists of a top agent and a bottom agent. The former is used to decide whether to download a new segment for continuous playback or re-download an old segment for correcting wrong bitrate decisions caused by inaccurate viewport estimation, and the latter is used to select bitrates for tiles in the chosen segment. In addition, 360HRL adopts a two-stage training methodology. In the first stage, the bottom agent is trained under the environment where the top agent always chooses to download a new segment. In the second stage, the bottom agent is fixed and the top agent is optimized with the help of a heuristic decision rule. Experimental results demonstrate that 360HRL outperforms existing RL-based ABR algorithms across a broad of network conditions and quality of experience (QoE) objectives.
BIRD-QA: A BERT-based Information Retrieval Approach to Domain Specific Question Answering

During recent years, Question Answering (QA) systems have been widely used in many industries to provide round the clock online services to consumers from all over the world. The importance of such services became more evident during the pandemic in online medical services, education, training, marketing, system support and administration. Most of the existing systems apply simple rule-based QA strategy. Human-defined rules are used to apply pattern matching for extracting information from a given data or knowledge base to generate responses to user queries. However, rule-based pattern matching techniques are not intelligent enough to understand the context of the question to always generate appropriate responses and are static. In this work, we explored different data preprocessing strategies and BERT-style pre-trained models to build an information retrieval (IR)-based Domain specific QA framework named BIRD-QA, and created a domain specific knowledge base using website data of a university department. We implemented multiple variations of extended BERT and ALBERT-base models and validated our framework on reading comprehension task using the Stanford Question Answering Dataset (SQuAD) 1.1 and 2.0 datasets. Our extended ALBERT-based model achieved 75.4% Exact Match (EM) score and 78.8% F1 score. We also present a small feasibility test of our framework for departmental QA using data from a university website.
Multi-Layer Heterogeneous Ensemble Model for Human Activity Recognition

Human activity recognition is required in various fields such as sports science, healthcare, elderly care, rehabilitation centers, and other industrial application. In this work, data for five activities of daily living are recorded using three wireless, wearable tri-axis accelerometer sensors. The sensors were placed on the lower limb of the subjects to record multiple observations of the activities for different subjects. First, commonly used machine learning models were tuned to optimize their performance in terms of prediction accuracies. Then, a stacking ensemble was built with the tuned machine learning models as the base learners and three different machine learning models were evaluated when used as metal earners. Further, a novel multi-layer ensemble model was designed by placing the heterogeneous machine learning models in two-stages of meta-learning. The multi-layer stacking improves the prediction performance as compared to the single-layer stacking ensemble as well as the individual classifiers. The average accuracy over all activities obtained using the proposed multi-layer ensemble is 96.1%, which is higher as compared to the other considered models.
Med-3D: 3D Reconstruction of Medical Images based on Structure-from-Motion via Transfer Learning

The problem of 3D reconstruction of medical images is very challenging due to the disadvantages of less texture and serious speckle noise. In this paper, we propose a medical image 3D reconstruction framework based on transfer learning and Structure-from-Motion. Unlike some existing methods, which need special hardware devices, or be effective for a particular modal of images, our unified framework does not depend on any special equipment and can be applied to diverse modalities of medical images, such as ultrasonic image, computed tomography (CT) etc. Its contribution covers three aspects: First, it uses Structure-From-Motion(SFM) to estimate the camera structure and pose of consecutive frames end-to-end, which is suitable for clinical auxiliary diagnosis. Second, strong and robust photogrammetry knowledge, including depth, the camera structure and pose, can be pre-learned from the natural images with the richer texture, and then, are transferred to the medical images learning. Besides, Med-3D is proposed, a GAN model based on self-supervised learning framework, in the generator, the spatial coplanar characteristics to constrain the cloud points reconstructed from a single captured frame or adjacent frames are considered for learning more precise features. Experimental results on the medical data of ultrasound images, and CT in the open clinical library, demonstrate that our method can reconstruct geometric structures from 2D slices and has excellent accuracy in terms of reconstruction effects.
Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study

Older adults are at an increased risk of falls with the consequent impacts on the health of the individual and health expenditure for the population. Smartwatch apps have been developed to detect a fall, but their sensitivity and specificity have not been subjected to blinded assessment nor have the factors that influence the effectiveness of fall detection been fully identified.This study aims to assess accuracy metrics for a novel fall detection smartwatch algorithm.We performed a cross-sectional study of 22 healthy adults comparing the detection of induced forward, side (left and right), and backward falls and near falls provided by a smartwatch threshold-based algorithm, with a video record of induced falls serving as the gold standard; a blinded assessor compared the two. Three different smartwatches with two different operating systems were used. There were 226 falls: 64 were backward, 51 forward, 55 left sided, and 56 right sided.The overall smartwatch app sensitivity for falls was 77%, the specificity was 99%, the false-positive rate was 1.7%, and the false-negative rate was 16.4%. The positive and negative predictive values were 98% and 84%, respectively, while the accuracy was 89%. There were 249 near falls: the sensitivity was 89%, the specificity was 100%, there were no false positives, 11% were false negatives, the positive predictive value was 100%, the false-negative predictive value was 83%, and the accuracy was 93%.Falls were more likely to be detected if the fall was on the same side as the wrist with the smartwatch. There was a trend toward some smartwatches and operating systems having superior sensitivity, but these did not reach statistical significance. The effectiveness data and modifying factors pertaining to this smartwatch app can serve as a reference point for other similar smartwatch apps.
Benchmarking Classification Algorithms for Measuring the Performance on Maintainable Applications

Classification is one of the most important techniques, is applicable to handle a large amount of data. It classifies each element of a data set into a set of predetermined classes. Various classification algorithms are being used to organize various data sets. This paper provides a study of various classification methods, including their advantages and disadvantages. Three different types of classification methods were considered: Decision Tree, K-Nearest Neighbor, and Naive Bayes. The study compares the classification methods in terms of accuracy, speed, performance, efficiency, scalability, and other criteria.
A survey on user privacy preserving blockchain for health insurance using Ethereum smart contract

Blockchain, the foundation of Ethereum, has received greater attention on recent years as a decentralised and distributed public ledger technology that offers different properties like security, privacy, scalability and immutability during online data transactions, reduces cost of transactions, and enables verification and efficient exchange of personnel information across many computers without any trusted parties. The goal of this survey is to propel some methods in preserving user privacy during data transactions using Ethereum smart contract. We analysed the privacy challenges in blockchain and figured out the existing cryptographic mechanisms in preserving the transaction privacy. We also addressed some technique for simplifying claim settlement process in healthcare insurance. By using Ethereum blockchain via smart contract, current challenges in insurance such as being time-consuming, can be avoided thereby preventing insurance frauds and thus improving transparency to all members in-network. It also helps to avoid paper-based contracts, which are prone to errors, and removes the intermediaries. Furthermore, there is still a need to examine and consider future research in order to overcome privacy attacks when using blockchain.
Analysis Password-based Authentication Systems with Password Policy

Passwords are the most common form of authentication used to control access to information, ranging from the personal identification numbers we use for automatic teller machines, credit cards, telephone calling cards, and voice mail systems to the more complex alphanumeric passwords that protect access to files, computers, and network servers. Passwords are widely used because they are simple, inexpensive, and convenient mechanisms to use and implement. At the same time, passwords are also recognized as being an extremely poor form of protection. In this paper, password based authentication systems are analyzed with password policy and recommendation is given to improve password related problems.
Achieving food security in Ghana: Does governance matter?

This study complements the extant literature by assessing the role of governance dynamics in food security in Ghana for the period 1980-2019. The empirical evidence is based on the Fully Modified Ordinary Least Squares (FMOLS) technique and governance is categorized into: political (entailing political stability and voice & accountability), economic (consisting of regulatory quality and government effectiveness) and institutional (entailing corruption-control and the rule of law) governance dynamics. The study finds that the engaged governance dynamics improve food security in Ghana. Policy implications are discussed with specific emphasis on the sustainable development goals.