Reviewerly/exHarmony huggingface dataset:
An Ensemble Learning Method for Volunteer Retention Prediction

The task of predicting volunteer retention based on historical participation data poses significant challenges due to the scarcity of high-quality data. In this work, we introduced a machine learning pipeline predicated on ensemble learning for the prediction of volunteer retention. Through the application of categorical encoding methods, we were able to convert textual features into numerical representations. Our predictive model leverages the ensemble of three robust regression models: random forest, gradient boosting, and histogram gradient boosting. Furthermore, we incorporated hyperparameter search coupled with cross-validation in the training set for all three base models. The efficacy of our approach is demonstrated by our performance in the MLSP 2023 Volunteer Retention competition, where we attained the second position.
Machine Masquerades a Poet: Using Unsupervised T5 Transformer for Semantic Style Transformation in Poetry Generation

This paper presents a novel approach to automatically capturing the unique style of various poets and using it to convert given poems into the styles of those poets. The method combines the power of web scraping, and T5 transformer (11 Billion Parameters). A dataset of poems was collected by web scraping popular online libraries, such as Project Gutenberg and Open Library. These poems were then pre-processed to remove using HTML tags and meta data. The pre-trained T5 Transformer-11b was fine tuned on this corpus of text. The results of this study were highly promising. The proposed method accurately captured the styles of various poets, effectively capturing their overall tone, ideologies, and poetic style. By providing a starting poem, the model generated new poems in the style of a specific poet, successfully mimicking their unique writing characteristics. These findings highlight the potential of machine learning algorithms in understanding and reproducing the intricate nuances of poetic styles. This work opens avenues for automated poem generation, enabling individuals to experience the styles and voices of renowned poets in a novel way.
Matrix Multiplication with Straggler Tolerance in Coded Elastic Computing via Lagrange Code

In cloud computing systems, elastic events and stragglers increase the uncertainty of the system, leading to computation delays. Coded elastic computing (CEC) introduced by Yang et al. in 2018 is a framework which mitigates the impact of elastic events using Maximum Distance Separable (MDS) coded storage. It proposed a CEC scheme for both matrix-vector multiplication and general matrix-matrix multiplication applications. However, in these applications, the proposed CEC scheme cannot tolerate stragglers due to the limitations imposed by MDS codes. In this paper we propose a new elastic computing scheme using uncoded storage and Lagrange coded computing approaches. The proposed scheme can effectively mitigate the effects of both elasticity and stragglers. Moreover, it produces a lower complexity and smaller recovery threshold compared to existing coded storage based schemes.
Coded Distributed Image Classification

In this paper, we present a coded computation (CC) scheme for distributed computation of the inference phase of machine learning (ML) tasks, specifically, the task of image classification. Building upon Agrawal et al. 2022, the proposed scheme combines the strengths of deep learning and Lagrange interpolation technique to mitigate the effect of straggling workers, and recovers approximate results with reasonable accuracy using outputs from any R out of N workers, where R$\leq N$. Our proposed scheme guarantees a minimum recovery threshold R for non-polynomial problems, which can be adjusted as a tunable parameter in the system. Moreover, unlike existing schemes, our scheme maintains flexibility with respect to worker availability and system design. We propose two system designs for our CC scheme that allows flexibility in distributing the computational load between the master and the workers based on the accessibility of input data. Our experimental results demonstrate the superiority of our scheme compared to the state-of-the-art CC schemes for image classification tasks, and pave the path for designing new schemes for distributed computation of any general ML classification tasks.
Generalised Active Learning With Annotation Quality Selection

In this paper we promote a general formulation of active learning (AL), wherein the typically binary decision to annotate a point or not is extended to selecting the qualities with which the points should be annotated. By linking the annotation quality to the cost of acquiring the label, we can trade a lower quality for a larger set of training samples, which may improve learning for the same annotation cost. To investigate this AL formulation, we introduce a concrete criterion, based on the mutual information (MI) between model parameters and noisy labels, for selecting annotation qualities for the entire dataset, before any labels are acquired. We illustrate the usefulness of our formulation with examples for both classification and regression and find that MI is a good candidate for a criterion, but its complexity limits its usefulness.
Non-Inducible RF Fingerprint Hiding via Feature Perturbation

Machine learning mechanisms are applied to detect the unique characteristics of the wireless interface or signaler that can distinguish one device's signal pattern from the others, which has been widely researched as the fingerprint for user identification. However, such fingerprinting can also be used for malicious purposes, i.e., identification tracking, undesired positioning, etc., as the unique features of the radio signal from a device is determined at the manufacturing stage and often cannot be easily removed afterward. To prevent privacy leakage from such radio frequency (RF) fingerprinting, in this paper, we propose an adversarial mechanism to hide the fingerprint whereby the device's identification cannot be induced by machine learning models from the preamble. Specially, we apply the adversarial attack method to attack the fingerprinting model by adding optimized adversarial perturbation to the preamble that can mislead the model classification results. To alleviate the adversarial sample's impact on communications and ensure the execution of packet detection at receivers, we improve the identification protection strategy with sparse perturbed features. In order to prevent further fingerprinting of re-training over the perturbed RF feature, we extend our method with the time-varying perturbations to further hide the device's identity. Extensive experiments are conducted, and we show that the proposed method can effectively hide the device identification from both the dedicated fingerprint model and the re-trained one from perturbed signals without disturbing the preamble functionality, which provides a gratifying confirmation of the proposed method.
Enhancing Face Anti-spoofing Systems Through Synthetic Image Generation

This study introduces a strategy for synthetic image generation aimed at enhancing the detection capability of facial authentication systems (FAS). By employing various digital manipulation techniques, new synthetic fake images were generated using existing datasets. Through experiments and result analysis, the impact of using these new fake samples on improving the detection accuracy of FAS systems was evaluated. The findings demonstrated the effectiveness of synthetic image generation in augmenting the diversity and complexity of the training data. Fine-tuning using the enhanced datasets significantly improved the detection accuracy across the evaluated FAS systems. Nonetheless, the degree of improvement varied among systems, indicating varying susceptibility to specific types of attacks.
Coded Reactive Stragglers Mitigation in Distributed Computing Systems

In distributed computing systems, to mitigate the adverse effect of stragglers on the computation time, computation redundancy is used. The redundancy can be added proactively at the beginning, or reactively after some time based on the delay pattern of the workers. While most of the existing work with reactive mitigation strategy only considered task replication, we propose a coded reactive straggler mitigation with an uncoded and a coded phase for distributed matrix-matrix multiplication. Specifically, in the uncoded phase of the proposed reactive strategy, the master distributes the computational job without redundancy among workers and waits for some time. After the waiting time, the master cancels the remaining tasks. It then encodes the remaining tasks and distributes them among the workers that have already completed their computations. The expected execution time of the proposed method is analytically obtained. Furthermore, the optimal waiting time for the uncoded phase and the optimal code rate for the coded phase are investigated. Our simulation results demonstrate that the proposed coded reactive mitigation strategy significantly decreases the execution time in comparison with the proactive mitigation strategy or repetition-based reactive mitigation strategy.
Domain Adaptation in Color-Shifted Datasets Through Adversarial Learning Balancing

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.
HPVES: High Performance Video Encryption Scheme with Cryptographic USB Key

Real time streaming data accounts for a growing proportion of Internet traffic. The demand for security is accordingly increasing, which poses severe burdens on the performance of terminal processing streaming data. In this paper, a high performance video encryption scheme (HPVES) is proposed to speed up cryptographic operations for streaming media data based on cryptographic USB key. In our scheme, the cryptographic key is stored and used in USB key, a dedicated hardware security module (HSM), which avoids the leakage of cryptographic key even when the terminal is compromised. We modify the cos (chip operating system) driver of USB key which is then called HPUK to accelerate data transmission between computer and its' peripherals. A flow control mechanism is also proposed to balance the throughput of keystream and media stream which could (1) make data encryption/decryption maintain at a constant throughput in regardless of the data size and (2) save storage space. We have implemented the proposed scheme and evaluated the performance on different devices. The evaluation demonstrates that HPUK almost doubles the throughput of original USB key on resource-constrained devices at small packet size (no more than 1.5 KB, which suits for real-time UDP transmission). Field experiments also show that HPVES could provide continuous and stable keystream with throughput of 4 MBps.
Rate-Matched Turbo Autoencoder: A Deep Learning Based Multi-Rate Channel Autoencoder

Turbo Autoencoder (TAE) is a deep-learning based channel code that demonstrates promising error correction performance. This paper studies the rate-matching problem for TAE and proposes a rate-matched TAE framework. The rate-matched TAE is a single auto-encoder model that can be used for multiple code rates. It matches the code rate of a mother <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(3k, k)$</tex> TAE to a desired code of parameters ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n^{\ast}, k^{\ast}$</tex> ) by using a combination of freezing message bits, repeating code symbols, and puncturing code symbols. We refer to the conventional TAE with message word length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> and code length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3k$</tex> as mismatched TAE. The rate-matched TAE shares the same encoder and decoder structure with mismatched TAE but is trained to jointly optimize the performance across multiple rates. We study two important hyper-parameters for the rate-matched TAE: puncturing pattern and training signal-to-noise ratio (SNR) for constituent rates. Three puncturing patterns, namely, head, tail, and uniform puncturing are proposed and evaluated. Training SNRs are determined according to a heuristic method that uses test loss as a performance metric. Our simulation results show that the rate-matched TAE for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k= 100$</tex> and rates <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r\in \{0.1, 0.2, \ldots, 0.9\}$</tex> significantly outperforms the mismatched TAE when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r\geq 0.4$</tex> .
Applying Decision Aid Science to Support Customer Choices for Web Services Operations

The growing popularity of Web services has been accompanied by a significant increase in the number of Web services operations. For a given need: Several Web services operations may exist. The customer is satisfied. He has many choices, but at the same time, he feels lost: what is the best choice to make? In this work, we try to support the client's decision. We are not trying to decide in its place but to justify his choices. We seek to rank the list of returned operations (Web services operations) from the best to the least good according to customer preferences using Multiple-criteria decision analysis, more precisely the PROMETHEE II method.
Multi-Task Semantic Communications: An Extended Rate-Distortion Theory Based Scheme

Semantic communication is a new communication paradigm, which aims at transmitting semantic symbols for artificial intelligence (AI) tasks. When there are multiple target AI tasks, existing studies typically require extracting and transmitting dedicated semantics for each AI task. Considering the fact that each AI task may share common semantics, this paper proposes a joint source-channel coding scheme for multi-task semantic communication systems, which can extract the common semantics required by multiple AI tasks, so that the overall transmitted semantics can be reduced. To this end, we first formulate the multi-task semantic communication problem as an rate-distortion problem, which simultaneously takes the rate of the extracted semantics and the distortion of multiple AI tasks into consideration. Then, we derive a new form of rate-distortion, coined as extended rate-distortion, which can guide the extraction of the compact semantics of multiple AI tasks simultaneously. To guarantee the appropriate trade-off between the rate and distortion of multiple AI tasks, we further propose a rate adjustment module that can dynamically adjust the rate according to channel conditions. Experimental results show that the proposed method can achieve 7.6% multi-task performance gain with 40% reduction in transmission overhead compared with existing single-task oriented communication systems.
3D Human Pose Estimation with Dilated Sampled Frames

Three-dimensional (3D) human pose estimation (HPE) targets to produce the 3D spatial coordinates of the human pose from 2D images. 3D HPE is a basic computer vision task for many intelligent industrial applications. Commonly, the coordinates of the predicted 3D human pose joints are calculated through the 2D keypoint from the ground truth provided by the datasets or generated by a classical and robust 2D human pose estimator. With the development of transformer-based methods, the methods with a sequence of monocular images have achieved great success in 2D-to-3D lifting human pose estimation. In this paper, the sampled frames with a dilated ratio are given as the input of the 3D human pose estimator. Extensive experiments on the public benchmark Human 3.6M demonstrate the significance and effectiveness of the proposed method.
Networked Personalized Federated Learning Using Reinforcement Learning

Personalized federated learning enables every edge device or group of edge devices within the distributed network to learn a device- or cluster-specific model tailored to their local needs. Data scarcity, however, makes it difficult to learn such individual models, resulting in performance degradation. Since the device- or cluster-specific tasks are distinct but often related, leveraging these similarities through inter-cluster learning alleviates data shortage and enhances learning performance. Although inter-cluster learning can boost performance, uncontrolled intercluster learning may lead to performance degradation due to over- or under-usage of local similarity enforcement. In light of this issue, an intelligent mechanism that performs inter-cluster learning based on device-specific needs is required. To this end, this paper proposes adopting reinforcement learning principles to control device-specific inter-cluster learning in real-time. We propose networked personalized federated learning using reinforcement learning (NPFed-RL) as a general framework and then demonstrate its feasibility by applying it to the ridge regression problem. We conduct numerical experiments to compare the proposed method with the state-of-the-art. The proposed method successfully controls device-specific parameters and offers better learning performance than existing solutions.
Construct New Graphs Using Information Bottleneck Against Property Inference Attacks

Graphs provide a unique representation of real- world data. However, recent studies found that inference attacks can extract private property information of graph data from trained graph neural networks (GNNs), which arouses privacy concerns about graph data, especially in collaborative learning systems where model information is more accessible. While there has been a few research efforts on the property inference attacks against GNNs, how to defend against such attacks has seldom been studied. In this paper, we propose to leverage the information bottleneck (IB) principle to defend against the property inference attacks. Particularly, we involve a threat model, where the attacker can extract graph property from the graph embedding developed by GNNs. To defend against the attacks, we use IB to construct new graph structures from the original graphs. The change in graph structures enables the new graphs to contain less information related to the property information of the original graphs, making it harder for attackers to infer property information of the original graphs from the graph embeddings. Meantime, the IB principle enables task-relevant information to be sufficiently contained in the new graph, enabling GNNs to develop accurate predictions. The experimental results demonstrate the efficacy of the proposed approach in resisting property inference attacks and developing accurate predictions.
A Truthful Auction for Green Continuous Task Allocation and Pricing in Edge Computing

With the advent of edge computing, more and more tasks are offloaded to edge servers, but the computing and storage capabilities of edge servers are limited. Although some works propose efficient schemes for task allocation and pricing, they may ignore users' preferences for continuous tasks. However, the combinatorial preference causes high computational complexity. In this paper, we propose a dominant-strategy incentive compatibility (DSIC) and computationally efficient mechanism for green continuous task allocation based on the combinatorial auction. Besides, the activity on edge (AOE) network is introduced to describe the continuity of tasks. The proposed mechanism gives an approximate solution to the winner determination problem (WDP) in polynomial time and a pricing strategy that can guarantee the truthfulness and individual rationality of auction participants. We demonstrate the approximate ratio of the proposed algorithm through theoretical analysis. Experimental results show that the proposed mechanism achieves truthfulness, individual rationality, and high computational efficiency while considering green continuous task allocation.
Establishment of Automatic Scoring System for Multiple-Choice Test Using a Tablet

With the development of the mobile computer market, classes using tablets in schools are gradually expanding. It proposes a system that can automatically score test papers when taking multiple- choice written tests using a tablet. Test takers can open a test paper file on a tablet to fill out the problem solving and fill out multiple-choice answers in the box placed for each question. At the top of all the pages of the test paper, you can fill in the student number and the page number is written, so even if there are several multiple pages, you can score them in order and by student. The system proposed in this paper allows students to score test papers using a computer vision-based model and then save answers and scores written by students in an Excel file.
Anime Recommendation System Using Bert and Cosine Similarity

With the popularity of anime over time, the number of available shows, genres, titles, and thematic elements make it challenging for users to find content that matches their tastes. Because of the many things, users also feel confused in finding which anime suits their tastes because most of the anime in circulation have types that are like one another. Still, other elements differentiate, so classification is needed. This paper presented a recommendation system for anime by implementing BERT, an advanced approach to NLP tasks. The Anime Recommendation System is designed to provide multiple suggestions for anime titles based on their similarities. In this research, the data used are anime titles and genres to compare their similarities with other anime. The anime title and genre data will be featured and extracted using the BERT method and compared with the cosine similarity method.