Reviewerly/exHarmony huggingface dataset:
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning

While significant advancements have been made in the field of fair machine learning, the majority of studies focus on scenarios where the decision model operates on a static population. In this paper, we study fairness in dynamic systems where sequential decisions are made. Each decision may shift the underlying distribution of features or user behavior. We model the dynamic system through a Markov Decision Process (MDP). By acknowledging that traditional fairness notions and long-term fairness are distinct requirements that may not necessarily align with one another, we propose an algorithmic framework to integrate various fairness considerations with reinforcement learning using both pre-processing and in-processing approaches. Three case studies show that our method can strike a balance between traditional fairness notions, long-term fairness, and utility.
Topic-Sentiment Analysis of Central Bank Press Conferences: BOJ Case Study

This paper reports our ongoing work on topic-sentiment analysis of central bank press conferences. Central banks rely on communication to implement monetary policy and convey their objectives to the public. Apart from the policies themselves, the sentiment or tone conveyed during a governor’s press conference could also influence both immediate economic indicators and long-term economic trajectories. This study analyzes the Japanese texts from the press conferences of two contrasting Bank of Japan (BOJ) governors: Shirakawa and Kuroda. By employing topic and sentiment analysis techniques, we define a topic-sentiment distribution representing the sentiment of governors’ statements per topic. Our findings highlight distinct divergences in tone between the two governors in relation to salient economic topics. This divergence provides crucial insights into the nuanced relationship between central bank communication styles and resultant economic outcomes.
Analysis of Knowledge Graph Embedding Using Graph Attention Mechanism

Knowledge graphs (KG) represent information in the form of graphs. Because heterogeneous and collaborative designs remain incomplete employing missing links between the entity pairs. KG embedding paradigm tries to reduce incompleteness by representing graphs in vector space. Attention-based learning provides the natural way of KG representation learning by deriving attention to each central entity from its neighbors. The attention mechanism decides which part to attend more to collect the most important information. This paper analyzes the attention-based models used to learn KG latent structure and is utilized to predict the missing link for KG completion. Many of them are neural network-based methods. Experimental results show that DisenKGAT and HOLE outperform rest methods in standard evaluation metrics on the two benchmark FB15k-237 and WN18RR datasets respectively.
PSNR-Aware Quantization for DCT-based Lossy Compression

Recent years have witnessed a wide adoption of various lossy compression techniques to alleviate the burden on high-performance computing (HPC) systems that run large-scale scientific simulations producing large amounts of data. Peak signal-to-noise ratio (PSNR) is considered one of the most important indicators for measuring the distortion between reconstructed and original data and evaluating the performance of lossy compressors. However, the complex interplay between the error introduced during quantization and PSNR requires in-depth exploration to meet maximum compression potential and user-provided PSNR simultaneously. This paper aims to achieve this goal by exploring a novel quantization process to support a fixed-PSNR mode for a transform-based lossy compressor called DCTZ-F. We evaluate DCTZ-F with six real-world scientific datasets from several solvers in FLASH, a multi-physics application code. Our experimental results show that DCTZ-F can generate up to three times the compression ratio than SZ with fixed-PSNR mode while meeting the user-defined PSNR.
A Systematic Review of the Applications of AR/VR in Ameliorating the Symptoms of Neurobiological Disorders

Autism, Down Syndrome, and Attention Deficit Hyperactivity Disorder (ADHD) are the most prevalent neurobiological disorders diagnosed in children across the world. Several pharmacological treatments have been developed through scientific research that have shown positive impact, however these treatments are accompanied by side effects and the possibility of overdose and addiction. Augmented Reality (AR) and Virtual Reality (VR) are modern technologies that create simulated experiences by providing the user with an immersive experience of the virtual world. Through this research study we aim to survey the existing solutions that employ AR and VR technologies in the treatment of Autism, Down Syndrome and ADHD that help improve the common symptoms. It is observed that AR and VR applications improve focus and concentration among people having these disorders thus aiding in their ability to learn and grasp new information. This has the potential for long term positive impact in academic, occupational, social and interpersonal areas of the lives of these individuals. As these technologies become more affordable and accessible to the masses, AR and VR have great potential to be used in treatment of these neurobiological disorders on a larger scale.
Research on Hardware Automatic Inspection System of Computer Big Data Server

A high-efficiency offline integrated hardware detection software suitable for various operating systems was developed. Windows systems that utilize pre-customized complete DLL dynamic link libraries. Use the existing green hardware detection software of Windows to start the test. Combine the reports generated by each inspection software to form a final streamlined total inspection report. Then the function of hardware fault detection and automatic Unix-like operating system server report can be realized across platforms. Through the test and experiment of the program, it is proved that the program has a good operation interface and universality. It allows full hardware inspection of Unix-like servers.
Twitter Sentiment Analysis using Ensemble Based Deep Learning Techniques for Analyzing COVID-19 Tweets

The COVID-19 is a transportable disease which is rapidly spread and numerous peoples are endures and died from this disease. This paper proposed an ensemble based deep learning techniques for examining COVID-19 tweets. The COVID19 Tweets dataset is utilized in this research which consist of 3100 tweets. The preprocessing is accomplished by including the activities like stemming, tokenization and stop words removal. The preprocessed data features are extracted through Team Frequency-Inverse Document Frequency (TF-IDF). Then, it classified through ensemble classifiers such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional RNN (BRNN). The model performance was evaluated through accuracy, precision, recall, sensitivity and f1-score. The obtained result shows that the ensemble classifier attains high accuracy of 0.985, precision of 0.982, recall of 0.971, specificity of 0.974 and f1-score of 0.978 which is comparatively higher than existing techniques like Extra Tree Classifier (ETC) and Heterogeneous Support Vector Machine (H-SVM).
Sailfish: A Dependency-Aware and Resource Efficient Scheduling for Low Latency in Clouds

Efficiently scheduling jobs in clouds is critical for job performance, system throughput and resource utilization. The growing importance of parallel applications in clouds introduces challenges in scheduling data-parallel jobs. Production data-parallel jobs increasingly have complex dependency structure, i.e., complex task dependencies expressed as directed acyclic graphs (DAGs), and heterogeneous resource demands. NP-hard problems are introduced by relaxing either of these challenges (i.e., scheduling of homogeneous tasks with dependency constraints or independent and heterogeneous tasks) for scheduler design. It is challenging to design a scheduler for simultaneously achieving low latency and high resource utilization due to the complex dependency structure and job heterogeneity. In this paper, we propose Sailfish, a dependency-aware and resource efficient scheduling for low latency in clouds. Sailfish first uses the machine learning algorithm to classify jobs into two categories (high priority jobs and low priority jobs) based on the extracted features. Next, Sailfish splits the jobs into tasks and distributes the tasks to the master nodes based on the dependency of tasks and the load of master nodes. Then, Sailfish utilizes the dependency information of tasks to determine tasks’ priority, and packs tasks by leveraging the complementary of tasks’ requirements on different resource types and task dependency. Finally, the master nodes leverage the proposed mutual reinforcement algorithm to distribute tasks to workers in the system based on the resource demands of tasks, the available resources of workers and task dependency. Extensive experimental results based on a real cluster and experiments using real-world Amazon EC2 cloud service show that Sailfish can improve the average resource utilization (by up to 40%) and reduce the latency (the average job completion time) significantly (by up to 91%) compared to the existing schedulers.
Integrating Staleness and Shapley Value Consistency for Efficient K-Asynchronous Federated Learning

In the big data era, Federated Learning (FL), which allows multiple participants to collaboratively train a global model without sharing their raw data, emerges as a promising solution to address the challenges of isolated data silos and privacy protection. Federated learning has two main communication strategies: synchronous and asynchronous. Synchronous FL ensures stable convergence but may encounter model quality degradation and server crash risks. Asynchronous FL avoids the straggler effect and supports more participants, but unstable convergence and non-IID data could affect the model performance. In this paper, inspired by real-world FL scenarios, we propose a highly efficient K-Asynchronous FL framework, KFLBSV, which addresses the limitations of synchronous and asynchronous strategies to some extent, leading to improved model performance and convergence speed. The framework allows clients to upload updates multiple times within the same round instead of blocking after each upload, thereby enhancing training efficiency. To ensure the stability and performance of the global model, we introduce a novel aggregation method. By approximating Shapley value to assess model consistency and balancing client contribution frequency and model staleness, we allocate weights more accurately to each participating client. We extensively conducted experiments on benchmark datasets using three distinct models, and the results show that KFLBSV outperforms existing algorithms in terms of both model performance and convergence speed.
A Max-Min Security Game for Coordinated Backdoor Attacks on Federated Learning

We address in this paper the challenge of data poisoning attacks on Federated Learning. We consider a particularly challenging attack scenario in which a single poisoning attack is coordinated over a set of clients to complicate its detection. In response, the federated learning server assigns a weight to each client’s model update with the aim of mitigating the effects of the poisoning on the global model. To address this challenge, we first design a trust mechanism that enables the federated learning server to assess the trustworthiness of each client on the basis of the client’s adherence to the federated learning protocol and the quality of data contributed by the client. Capitalizing on the trust mechanism, we model the interactions between the attacker and federated learning server as a security max-min game. The outcome of the game guides the server on the optimal weight assignment strategy over the set of clients’ model updates, so as to minimize the effects of the data poisoning on the global model. Simulations conducted on the MNIST and CIFAR-10 datasets suggest that our proposed solution decreases the coordinated attack success rate, as well as the false positive and false negative percentages compared to two baseline solutions.
IoT Botnet-Traffic Classification Using Few-Shot Learning

The Internet of Things (IoT) is experiencing a constant expansion, embedding connectivity into everyday objects for increased efficiency. Despite this, security vulnerabilities pose a growing concern because IoT devices often lack robust security measures, leaving room for IoT botnet malware action and underlining the critical need for increased IoT security. During the last years, Machine Learning (ML) and Deep Learning (DL) have offered effective tools against IoT attacks, but these solutions struggle with identifying novel threats. In fact, the dynamic nature of IoT ecosystems requires data-driven systems capable of responding promptly to emerging threats, characterized by the limited availability of samples for training.In this context, we exploit Few-Shot Learning (FSL) to effectively identify emerging network attacks within the traffic generated by IoT devices by performing botnet-traffic classification. In detail, FSL enables ML and DL models to recognize and adapt to novel classes of attack traffic with minimal available samples, tackling class imbalance issues between high-frequency and lowfrequency attacks (which generate high and low network traffic, respectively). This strategic integration of FSL is crucial in enhancing overall IoT security, providing a proactive approach to handle dynamic and imbalanced scenarios, and ensuring the resilience of interconnected systems. The experimental evaluation is conducted on the publicly available IoT-23 dataset. The results highlight that the best FSL approach obtains the highest performance figures with just 3 shots, scoring 92% F1-score when discriminating low-frequency botnet malware. Noteworthy, satisfactory performance (up to 93% F1-score) is achieved also in misuse detection, proving the capability to distinguish between legitimate and malicious traffic.
Deciphering Student Coding Behavior: Interpretable Keystroke Features and Ensemble Strategies for Grade Prediction

Keystroke data in programming reveals intricate patterns that reflect the behavior of programmers. These patterns hold promise for predicting grades and other applications, providing insights into the skills of both proficient and less proficient programmers. Analyzing these patterns can yield tailored feedback for students who need support, enabling effective interventions. Our study utilizes a keystroke dataset from the CS1 (Introduction to Computer Science) course at Utah State University. We developed novel features by combining elements like key presses, timestamps, source locations, and programming terminology, drawing on prior research, our insights, and an analysis of programming behavior. An ensemble-based feature selection method identifies key features, which are then used in hyperparameter optimization and grade prediction with six classification and three regression algorithms. We categorized grades into three levels: Low, Average, and High. Despite challenges such as class imbalance, plagiarism, limited data per assignment, and the ceiling effect, we attained a notable weighted F1 score of 78%. We also introduce an ensemble classification strategy, merging Isolation Forest outlier detection with a refined Random Forest classifier, achieving 80% accuracy on our test set. Additionally, we provide a detailed interpretation of our features, supported by results and a case study of our dataset. This research aims to enhance computer science education at the undergraduate level, focusing on improving its overall quality. Code and data are available https://github.com/DSAatUSU/Student-Coding-Behavior.git.
BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems

Federated recommendation systems (FRecSys) alleviate the privacy issues of recommendation systems (RecSys) by distributing model training tasks onto users’ local devices. However, they still need a single global server to aggregate training results from users and, thereby are vulnerable to server malfunctioning. Besides, they assume all users voluntarily use their data and computing resources to train recommendation model gradients, which is usually impractical. This paper aims to address the aforementioned problems of FRecSys using blockchain. A blockchain-based federated matrix factorization is designed and realized for RecSys, named BFRecSys. It eliminates the need for a single central server by storing the items and user matrices of the matrix factorization in a blockchain. An incentive mechanism is designed and implemented via smart contracts to record participants’ contributions. Besides, to further enhance the fairness of the incentive mechanism, a fake gradients detection mechanism based on an unsupervised cluster is designed to evict fake gradients in each iteration. The prototype of BFRecSys is realized, and experiments are carried out on public MovieLens datasets and private Ethereum blockchain. The results show that BFRecSys can significantly improve recommendation performance in terms of training accuracy.
Improve interpretability of Information Bottlenecks for Attribution with Layer-wise Relevance Propagation

Researchers have developed various visualization techniques, such as attribution maps, to understand which parts of an input contribute most to a model’s decision. However, existing methods often produce disparate results and may lack human-perceptual interpretability. In this work, we propose Relevance-IBA, a novel approach that combines the strengths of Information Bottleneck Attribution (IBA) and Layer-wise Relevance Propagation’s (LRP) method to estimate more accurate and human-perceptually interpretable attribution maps. Our method accentuates the contours and subtle details of the identified object, making the model’s decisions more intuitively understandable. Additionally, we introduce a segmentation-oriented evaluation technique, which assesses the capacity of interpretability methods by emphasizing the most important pixels within an object’s boundaries. We benchmark Relevance-IBA against various methodologies, including DeepLIFT, Integrated Gradients, Guided-BP, Guided-GradCAM, IBA, and InputIBA. Our results indicate that Relevance-IBA not only boosts attribution accuracy but also prioritizes human-perceptual clarity, making it a valuable tool for interpreting complex model behaviors.
Analyzing Convolutional Neural Networks as feature extractors for Video Regression

This literature study and experimentation aim to study the current best performing architectures in the domain of image classification. In particular, we investigate the approaches for characterising the Convolutional Neural Network (CNN) architectures for extracting features from frames for our task of video regression. Weather Forecasting and wind speed are important factors for many economic, business and management sectors. Generalizability was successfully demonstrated by the network by accurately predicting based on recorded wind speeds based from other flags in different geographical locations and a controlled wind tunnel test. The accurate measurement of wind speed helps in assisting the prediction of weather fore-cast conditions. In this review, we undertake a comprehensive comparison of various CNN architectures and present a novel and accurate prediction model for wind speed prediction. We specifically explore existing methodology under image classification architectures, data sources, pre-processing methods, for extraction of features, and the variety of evaluation metrics and approaches. The relevance of extracting characteristics is highlighted in this review from individual frames to capture a better representation of information, including the difficulties that the researchers confront during the procedure and the research gaps that exist in this subject.
Automatic Fake News Detection on Social Networks using Multimodal Approach of BERT and ResNet110

With the rapid development of online content-sharing and social media platforms has exploded for spreading fake news which create people trusting in unfavorable stories. This scenario allows to determine public opinion and produce chaos and panic through population. Therefore, detecting fake news has one of most significant topics which aims to identify a certain news is real or fake. The solution of fake news detection is split into three various classes such as content, social context and knowledge-based techniques. In this paper, a multimodal approach is proposed for detecting fake news for text and image, as well as classifying the news is real or fake. The Bidirectional Encoder Representations from Transformers (BERT) is used to extract text features and ResNet110 is used for image feature extraction. This model is estimated on Buzzfeed and ISOT dataset and attains better results using accuracy, precision, recall, specificity, and F1-score. The obtained result shows that the BERT+ResNet110 model achieves better accuracy of 98.51% on Buzzfeed dataset and 99.68% on ISOT dataset which ensures accurate detection compared to other existing methods like Random Forest (RF), Multi Support Vector Machine (MSVM) and Long Short-term Memory-Levy Flight (LSTM-LF).
TASA : A Virtual AI Assistant with Multilevel Authentication Using Face Recognition

Virtual assistants are becoming increasingly popular in our daily lives as they provide convenience and efficiency in completing various tasks. While virtual assistants offer numerous benefits, such as hands-free control and personalized assistance, they also present security risks that must be addressed to protect sensitive user information. TASA (Trusted Assistant with Secure Access) is an intelligent virtual assistant that validates users in real-time using face recognition before providing access to its key functionalities. The system can differentiate between the owner (admin), sub-admin, and guest users based on their access allowance and act accordingly. Existing virtual assistants lack user authentication, making them vulnerable to attacks that compromise sensitive information. TASA addresses this problem by providing full authority to its owner/admin, ensuring data safety, trust, and transparency among users. TASA employs a Convolutional Neural Network (CNN) that implements the Histogram of Oriented Gradients (HOG) algorithm as a feature descriptor. The model is trained on the user's images to register them with the system. TASA captures the video feed and extracts the user's face by capturing images from the video and eliminating unnecessary information to mitigate the risk of fake access using static images. Additionally, TASA provides an extra layer of security on top of the face recognition system by assigning a unique secret passphrase to each user to prevent unauthorized access. TASA showcases high accuracy with over 90% in user authentication, ensuring secure and convenient user interactions. TASA's hardware components exhibit reliability and efficiency, enabling fast and accurate processing of data inputs and outputs. Additionally, the software components, including the API for database operations, facilitate seamless storage, retrieval, and management of user information.