Reviewerly/exHarmony huggingface dataset:
Efficient Online Decentralized Learning Framework for Social Internet of Things

Online Decentralized Learning (ODL) is suitable for Internet-of-Things (IoT) devices since only parameter updates are exchanged with neighbors to avoid uploading private data to a central server and the training data is allowed to arrive at the devices sequentially. However, the current ODL frameworks cannot support the emerging Social IoT (SIoT) paradigm favorably since the SIoT devices exchange parameter updates with only trust-worthy neighbors based on specific social relations (e.g., parental object relation and ownership object relation). Conversely, sharing parameter updates with untrustworthy neighbors could speed up the training process but may violate social relations. Differential privacy (DP) is thus used to ensure data security while excessive devices engaging DP may downgrade the training performance. However, most research neglects the effect of neighbor selection for each device based on social networks, physical networks, and DP. Thus, in this paper, we innovate an ODL framework ODLF-PDP to allow only a part of devices to engage DP (i.e., partially DP) to improve training performance. Then, an algorithm BeTTa is proposed to build an adequate communication topology based on the interplay among the social networks, physical networks, and DP. Last, the experiment results manifest that ODLF-PDP saves more than 20&#x0025; physical training time compared to the current frameworks via the benchmark of MNIST.
Cosmopolitan localism as a research framework for sustainability in graphic design practices

Graphic design practices in a developing nation have a negative impact on sustainability, with researchers recommending several strategies to minimize it, yet much has not been achieved. Cosmopolitan localism has been proposed as a solution since contextual problems need contextual solutions. This paper attempts to establish a relationship between cosmopolitan localism and sustainability through exploring emerging innovations in cosmopolitan localism and their potential to minimize the impact of graphic design practices on sustainability. Data were gathered through observation, field notes of shared experiences, and unstructured interviews pertaining to designing packagings, labels, advertisement materials and by-products of graphic design production. The emerging innovations reflect three approaches, namely design policy, up-cycling of graphic design production by-products, and social media usage for design product dissemination. Results show how local approaches that are open and connected shape design decisions and reflect a connection to the global flow of ideas that minimize the impact of graphic design practices on sustainability. The paper concludes that cosmopolitan localism is a collaborative mode of production that creates an enabling environment for the practice of sustainability in graphic design, and that promotes distributed economics.
The range of harmful frequency for DNN corruption robustness

Despite having achieved excellent performance on various tasks, deep neural networks have been shown to be vulnerable to image corruptions (noise, blur, etc.). Understanding this vulnerability is a critical step towards robust DNN. Therefore, many researchers analyzed the frequency domain of images and attributed this vulnerability to the high frequency. However, they only qualitatively describe ‘high frequency’ on a relative scale without quantifying the range of harmful high frequency. To fill this gap and obtain a deeper insight into the corruption robustness, we analyze the correlation between the importance of image frequency components for 8 SOTA models and the corruption robustness of them. Results show that about 6/7 high frequency of the images is harmful. Based on our insight, we propose an image augmentation method, which reduces the importance of harmful frequency to DNN by shuffling the harmful frequency of training images. Experiments on ResNet50 show that, compared with SOTA methods, our method achieves comparable DNN corruption robustness with the least time consumption. What’s more, our image augmentation shows a good generalization across DNNs (e.g. ResNet18, VGG16) and corruption benchmarks (e.g. Cifar-10-C, MNIST-C). Our study has enhanced our understanding of the relationship between model robustness and high frequency from a quantitative perspective and shows that by accurately identifying harmful frequency, we can effectively improve DNN corruption robustness without using additional model assistance or multiple transformation combinations like other SOTA methods.
Using Profanity and Negative Sentiments: An Analysis of Ultimate Fighting Championship Fighters’ Trash Talk on Fans’ Social Media Engagement and Viewership Habits

The rise in popularity of combat sports has afforded fighters an enhanced celebrity status, especially across online platforms that provide fans the opportunity to engage with and discuss their favorite athletes. Given this growth, fighters’ behaviors, both inside and outside of the arena, can have a strong influence on fans’ consumption and social media activity. To evaluate this relationship, this study investigated the effect of combat sports fighters’ trash talking on subsequent fans’ behaviors by collecting and analyzing 516 fighter responses during prefight press conferences and 32,360 fan tweets on Twitter during Ultimate Fighting Championship events. Results demonstrated that fights featuring polarizing and popular athletes generated the highest pay-per-view numbers, and higher levels of profanity speech during trash talking were associated with higher engagement in pay-per-view consumption and Twitter usage.
Improved Hybrid Grey Wolf Optimizer Sine Cosine Algorithm (IHGWOSCA) Trained Artificial Neural Network (ANN) for Classification

The conventional technique for Artificial Neural Network (ANN) training, that is, the Back Propagation algorithm (BP), require large number of iterations and high computational time. The induction of meta-heuristic algorithms improve the training process by introducing randomness into the architecture. However even these algorithms are inclined to fall into the local minima when cost solution search spaces are at greater dimensions and/or convergence rate is slow. To address these inefficiencies in an artificial neural network, the manuscript presents a novel ANN classifier that updates its weights and biases using the simulation of the improved hybrid grey wolf sine cosine algorithm (IHGWOSCA). The proposed network, improved hybrid grey wolf sine cosine algorithm based neural network, consists of an input and output layer and a fully-connected single hidden layer of 10 neurons. Three University of California Irvine (UCI) database datasets were used as basis for the benchmark test namely; ‘Cryotherapy’, ‘Haberman’ and ‘Blood Transfusion Service Center’, training accuracies for which are 93.33%, 74.01% and 76.75% whereas the testing accuracies are 93.33%, 76.47% and 81.52% respectively. Comparative analysis with Group Teaching Optimization Algorithm-Neural Network (GTOANN), Particle Swarm Optimization-Neural Network (PSONN) and Hybrid Grey Wolf Sine Cosine Optimization Algorithm Neural Network (HGWOSCA-NN) show that the proposed IHGWOSCA-NN outperforms in training accuracies as well as in testing accuracies.
Sentiment Analysis on Telemedicine App Reviews using XGBoost Classifier

Provides a listing of current committee members and society officers.
Comprehensive Visualization of AI Decisions for Early Complication Detection of Cardiac Surgery Patients

Background: Postoperative care of cardiac surgery patients is complex and often fraught with an increased morbidity and mortality due to potential complications. Even with intensive and continuous monitoring of patients it is often difficult to interpret the early signs of life-threatening complications from the vast amount of monitoring data before actual symptoms occur. AI-based monitoring systems can assist medical personal by predicting incipient complications in an early stage, which gains valuable time for treatment. Although the trend in medical AI systems is going toward the use of explainable AI models, the results and type of explanations vary widely and are hard to understand. We introduce a novel dashboard system that interprets the AI decisions and visualizes them in a uniform and comprehensible way, tailored toward medical personal.
Private Computation of Polynomials over Networks

This study concentrates on preserving privacy in a network of agents where each agent desires to evaluate a polynomial function over the private values of its immediate neighbours. We provide an algorithm for the exact evaluation of this function while preserving privacy of the involved agents. The solution is based on two cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme and multiplicative-additive secret sharing. The provided scheme covers a large class of polynomial functions in distributed systems. Moreover, conditions guaranteeing the privacy preservation of the private value of an agent against a set of colluding agents are derived. The simulation results demonstrate that the proposed scheme can be employed in a network to enhance privacy of the involved agents at the cost of extra communication and computation budgets.
Research on a Financial Crisis Early Warning Model Based on a Stochastic Forest Algorithm

To solve the problem of low accuracy of the traditional financial crisis early warning model, this study proposes a financial crisis early warning model based on the random forest algorithm. First, according to the formation mechanism of the financial crisis, from the aspects of operation ability, development ability, profitability, solvency, cash flow, ratio structure and so on, this paper selects the early warning and detection indicators of the financial crisis. Then, the random forest algorithm is used to screen the early warning detection indicators and calculate the weight of financial crisis indicators. Through the comparison of financial crisis detection results and early warning thresholds, financial crisis early warnings are realized. The experimental results show that compared with the traditional model, the false positive rate and false negative rate of this model are lower, and the values of the two indicators are always lower than 5%, indicating that this model has good performance in early warning accuracy.
CE-SGD: Communication-Efficient Distributed Machine Learning

Training large-scale machine learning models usually demands a distributed approach to process the huge amount of training data efficiently. However, the high network communication cost introduced by parallel stochastic gradient descent (SGD) algorithms is a well-known bottleneck. To this end, we propose CE-SGD, a communication-efficient distributed machine learning algorithm that aggressively reduces the amount of gradient data exchanged among the training workers. CE-SGD belongs to the family of gradient sparsification schemes. CE-SGD adaptively adjusts the gradient sparsity according to the model&#x0027;s feedback. It also selectively transmits the gradients based on their degree of participation in the backpropagation. We mathematically prove the convergence of CE-SGD for both convex and non-convex cases and conduct a series of experiments on our CE-SGD implementation. Our experiments reveal that CE-SGD can achieve fast convergence, desirable gradient compression ratio, and high accuracy with low network bandwidth cost compared to state-of-the-art algorithms.
Learning From Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

Traditional robust recommendation methods view atypical user-item interactions as noise and aim to reduce their impact with some kind of noise filtering technique, which often suffers from two challenges. First, in real world, atypical interactions may signal users’ temporary interest different from their general preference. Therefore, simply filtering out the atypical interactions as noise may be inappropriate and degrade the personalization of recommendations. Second, it is hard to acquire the temporary interest since there are no explicit supervision signals to indicate whether an interaction is atypical or not. To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users. Particularly, we propose a reinforcement learning framework containing a recommender agent and an auxiliary classifier agent, which are jointly trained with the objective of maximizing the cumulative return of the recommendations made by the recommender agent. During the joint training process, the classifier agent can judge whether the interaction with an item recommended by the recommender agent is atypical, and the knowledge about learning temporary interest from atypical interactions can be transferred to the recommender agent, which makes the recommender agent able to alone make recommendations that balance the general preference and temporary interest of users. At last, the experiments conducted on real world datasets verify the effectiveness of TIARec.
Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test

The increasingly sophisticated at-home screening systems for obstructive sleep apnea (OSA), integrated with both contactless and contact-based sensing modalities, bring convenience and reliability to remote chronic disease management. However, the device pairing processes between system components are vulnerable to wireless exploitation from a non-compliant user wishing to manipulate the test results. This work presents SIENNA, an insider-resistant context-based pairing protocol. SIENNA leverages JADE-ICA to uniquely identify a user&#x0027;s respiration pattern within a multi-person environment and fuzzy commitment for automatic device pairing, while using friendly jamming technique to prevent an insider with knowledge of respiration patterns from acquiring the pairing key. Our analysis and test results show that SIENNA can achieve reliable (&#x003E; 90&#x0025; success rate) device pairing under a noisy environment and is robust against the attacker with full knowledge of the context information.
Automated Hand Gesture Recognition Using Machine Learning

The hand gesture and sign language are extraordinary means of communication between ordinary people and the deaf-mute people. Although an ordinary person usually does not understand sign language, automated recognition systems can be used to overcome this barrier. In this research, we opted for accurate recognition of hand gestures using four different machine learning models on two different datasets- American Sign Language (ASL) and a general gesture set to classify both static and dynamic gestures for the English Language. All the classifiers used had shown good accuracy on both datasets, and we got even better results after normalization. The first model of Artificial Neural Network (ANN) was proposed by varying the input, hidden and output layers that gave an accuracy of 99.40%. The second model of K-Nearest Neighbors (KNN) acquired an accuracy of 99.14%. The third model of Decision Tree (DT) achieved an accuracy of 94.52%. Finally, we took the ensemble of these models using the Ensemble vote classifier which demonstrated an overall predictive performance and proved to be a much more generalized model with an accuracy of 99.45%. In the case of dynamic gestures, we got 100% accuracy for three gestures.
An Augmented Image Captioning Model: Incorporating Hierarchical Image Information

Despite published accessibility standards many websites remain nan-compliant, containing images lacking accompanying textual descriptions. This leaves visually-impaired individuals unable to fully enjoy the rich wonders of the web. To help address this inequity, our research seeks to improve the ability of autonomous systems to generate accurate, relevant image descriptions. Our model enhances training efficacy by incorporating the use of category labels, high-level object superclasses, which are derivable using modern object-detection models. We show that this simple augmentation to an existing architecture results in a statistically significant improvement in caption quality.
Pri-PGD: Forging privacy-preserving graph towards spectral-based graph neural network

The development of Graph Neural Network (GNN) enables people to explore the value of graph data better. Additionally, graph data often reveals more private information than data in Euclidean space. Some scholars have proposed that differential privacy can be used to add noise to the graph. However, the availability of the data will decrease with the increase of privacy. Some work utilize graph data through federated learning, which is computationally complex and heavily burdened by communication. Therefore, based on the principle of the spectral-based GNN model (Graph convolutional network, GCN), we proposed a method (Pri-PGD) to forge a privacy-preserving graph by disturbing. We set a user-selectable parameter <tex>$m$</tex> to convert the first m-order neighbor nodes to the first-order neighbor for each node. Pri-PGD achieves two goals: preserving convolution basis space and preserving first-order neighbor information of the real graph. This allows Pri-Pgdto guarantee the privacy of the graph data while keeping the availability of it. We evaluated the effectiveness of our algorithm on three classical graph datasets (Cora, Citeseer, Pubmed). The accuracy of our method on the GCN model for the three datasets decreases only by about 1&#x0025; when the parameter <tex>$m$</tex> is 2. This verifies the validity of the forged graph obtained by Pri-PGD. As the experiment shows, our method is also valid for the Graph attention network model that is sensitive to first-order neighbor nodes.
Interactive and Collaborative Activities of the Extra-Curriculum Project Team of Undergraduate Students Under COVID-19 Pandemic Situation and Their Educational Effects

The extra-curriculum project team "Rika-Kobo", organized by university undergraduate students, has actively performed various science communication activities in local community over more than 15 years for the main purpose of stimulating interests of children and other generations into various fields of sciences and technologies. The author has acted as the supervisor of the project team. The activities of the project team are performed in extra-curriculum basis, which means that the student members of the team are not involved in the activities so as to improve their GPA score, but instead, they are intrinsically motivated in the activities. As a result, they are likely to have the same level of motivations and enthusiasms, leading to generally high qualities in the activities. Due to the current COVID-19 pandemic situations, normal interactive activities of the project team in local community has been largely limited. Nevertheless, the team still had some opportunities of providing science classes/events for children both in face-to-face and online styles. While observing their activities in such difficult situations, it has been impressive to see that the student members were trying to make necessary revisions and improvements in their performances so as to be suited in the difficult pandemic situations. Such behaviors look like real Project-based-learning activities, and conducting science communication activities in the pandemic situations are believed to have certain advantageous effects for self-growth of the student members of the project team.
A Novel Logo-Inspired Chaotic Random Number Generator

Lakshman, Apoorva Bayari, Parkala Vishnu Bharadwaj Bhatnagar, GauravThis paper proposes a novel yet simple pseudo-random number generation technique using a sequence of one-dimensional discrete chaotic maps and a logo image. The core idea is to generate a sequence of real numbers between zero and one by combining the sequences generated by chaotic maps and a logo image, defining a relation between the two. This combination has generated numbers showing good sensitivity even to slight changes in the underlying keys. To evaluate the proposed generator, a set of statistical and security analyses are considered to demonstrate the efficiency of the proposed generator.
Predicting Disasters from Tweets Using GloVe Embeddings and BERT Layer Classification

Twitter, a social media platform, has quickly become one of the most reliable sources of news and other information. With an ever-increasing number of users and tweets, it's feasible to use Twitter data to learn about a variety of interesting things that are happening around us. During a disaster, people can get real-time information from Twitter to give and receive help. This paper deals with the extraction of Twitter data in order to identify the tweets that give information about disasters. This is essentially a binary classification problem. GloVe Global Vectors for Word Representation embeddings have been implemented to convert the tweets into vectors which are then trained using the BERT (Bidirectional Encoder Representations from Transformers) model to classify the tweets into two categories: tweets related to disasters and tweets unrelated to disasters. This can be helpful to know how many tweets are related to disasters and are truly informative. Existing research focus on using LSTM, classification models (Random forest, Decision trees, Naive Bayes) which do not give accurate results. The results obtained in the proposed solution got an accuracy of around 87% in both training and validation parts. Thus, the BERT model is better as compared to other models.
An Event-Based Framework for Facilitating Real-Time Sentiment Analysis in Educational Contexts

Sentiment analysis has been a hot topic nowadays that has been broadly applied in various disciplines such as media and finance, but its application to the education domain is limited to generating insights by applying existing methods to a selected corpus at the individual record level. Many educational data like student forum posts and ongoing course evaluation responses can be categorised as event data. However, insufficient attention is paid to the temporal and influential features of event data in these educational corpora. This paper proposes a novel event-based framework for addressing the complexity of the sentiment analysis process in the context of education. The framework features an event data model for educational sentiment analysis and an architecture that harnesses both sentiment analysis algorithms and the complex event processing technology, aiming to achieve timely warning and action on defined complex events. To validate the framework, a prototype is implemented and applied to detecting student emergency occurrences from university student forum posts.
Blockchain for Islamic HRM: Potentials and Challenges on Psychological Work Contract

Blockchain technology has evolved and has now entered the non-finance function since some organizations have begun to use blockchain to facilitate human resources management practices. One of them is to make psychological work contract formation process more efficient and effective. This paper analyzes the potential and challenges of using Blockchain-based application for Islamic human resources management practices. Drawing from the current literature review existence, the paper discussed various interesting mechanism to support the formation and maintenance of psychological work contract that can bring some potentials and benefits. The paper also underlined the challenges facing in applying blockchains in Islamic HRM and some strategies to face the challenges. We argue that blockchain has the ability to harmonize differing sharia-compliance in work contract or work agreement between employee and employers.