pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Hybrid Algorithm for Optimized Solutions in Ocean Renewable Energy Industry: Enhancing Power Take-Off Parameters and Site Selection Procedure of Wave Energy Converters
Authors: Hossein Mehdipour, Erfan Amini, Seyed Taghi Naeeni, Mehdi Neshat
Date: 2023-09-19 00:00:00

Ocean renewable energy, particularly wave energy, has emerged as a pivotal component for diversifying the global energy portfolio, reducing dependence on fossil fuels, and mitigating climate change impacts. This study delves into the optimization of power take-off (PTO) parameters and the site selection process for an offshore oscillating surge wave energy converter (OSWEC). However, the intrinsic dynamics of these interactions, coupled with the multi-modal nature of the optimization landscape, make this a daunting challenge. Addressing this, we introduce the novel Hill Climb - Explorative Gray Wolf Optimizer (HC-EGWO). This new methodology blends a local search method with a global optimizer, incorporating dynamic control over exploration and exploitation rates. This balance paves the way for an enhanced exploration of the solution space, ensuring the identification of superior-quality solutions. Further anchoring our approach, a feasibility landscape analysis based on linear water wave theory assumptions and the flap's maximum angular motion is conducted. This ensures the optimized OSWEC consistently operates within safety and efficiency parameters. Our findings hold significant promise for the development of more streamlined OSWEC power take-off systems. They provide insights for selecting the prime offshore site, optimizing power output, and bolstering the overall adoption of ocean renewable energy sources. Impressively, by employing the HC-EGWO method, we achieved an upswing of up to 3.31% in power output compared to other methods. This substantial increment underscores the efficacy of our proposed optimization approach. Conclusively, the outcomes offer invaluable knowledge for deploying OSWECs in the South Caspian Sea, where unique environmental conditions intersect with considerable energy potential.
Logic Design of Neural Networks for High-Throughput and Low-Power Applications
Authors: Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann, Bing Li
Date: 2023-09-19 00:00:00

Neural networks (NNs) have been successfully deployed in various fields. In NNs, a large number of multiplyaccumulate (MAC) operations need to be performed. Most existing digital hardware platforms rely on parallel MAC units to accelerate these MAC operations. However, under a given area constraint, the number of MAC units in such platforms is limited, so MAC units have to be reused to perform MAC operations in a neural network. Accordingly, the throughput in generating classification results is not high, which prevents the application of traditional hardware platforms in extreme-throughput scenarios. Besides, the power consumption of such platforms is also high, mainly due to data movement. To overcome this challenge, in this paper, we propose to flatten and implement all the operations at neurons, e.g., MAC and ReLU, in a neural network with their corresponding logic circuits. To improve the throughput and reduce the power consumption of such logic designs, the weight values are embedded into the MAC units to simplify the logic, which can reduce the delay of the MAC units and the power consumption incurred by weight movement. The retiming technique is further used to improve the throughput of the logic circuits for neural networks. In addition, we propose a hardware-aware training method to reduce the area of logic designs of neural networks. Experimental results demonstrate that the proposed logic designs can achieve high throughput and low power consumption for several high-throughput applications.
Evolving generalist controllers to handle a wide range of morphological variations
Authors: Corinna Triebold, Anil Yaman
Date: 2023-09-18 00:00:00

Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalizability of the controllers. This is achieved by introducing morphological variations during the evolutionary training process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalizability and proposes a method by which to improve these properties.
Information geometric bound on general chemical reaction networks
Authors: Tsuyoshi Mizohata, Tetsuya J. Kobayashi, Louis-S. Bouchard, Hideyuki Miyahara
Date: 2023-09-19 00:00:00

We investigate the dynamics of chemical reaction networks (CRNs) with the goal of deriving an upper bound on their reaction rates. This task is challenging due to the nonlinear nature and discrete structure inherent in CRNs. To address this, we employ an information geometric approach, using the natural gradient, to develop a nonlinear system that yields an upper bound for CRN dynamics. We validate our approach through numerical simulations, demonstrating faster convergence in a specific class of CRNs. This class is characterized by the number of chemicals, the maximum value of stoichiometric coefficients of the chemical reactions, and the number of reactions. We also compare our method to a conventional approach, showing that the latter cannot provide an upper bound on reaction rates of CRNs. While our study focuses on CRNs, the ubiquity of hypergraphs in fields from natural sciences to engineering suggests that our method may find broader applications, including in information science.
Proposal for an Organic Web, The missing link between the Web and the Semantic Web, Part 1
Authors: Mathilde Noual
Date: 2023-09-19 00:00:00

A huge amount of information is produced in digital form. The Semantic Web stems from the realisation that dealing efficiently with this production requires getting better at interlinking digital informational resources together. Its focus is on linking data. Linking data isn't enough. We need to provide infrastructural support for linking all sorts of informational resources including resources whose understanding and fine interlinking requires domain-specific human expertise. At times when many problems scale to planetary dimensions, it is essential to scale coordination of information processing and information production, without giving up on expertise and depth of analysis, nor forcing languages and formalisms onto thinkers, decision-makers and innovators that are only suitable to some forms of intelligence. This article makes a proposal in this direction and in line with the idea of interlinking championed by the Semantic Web.
A Digital Forensics Case Study of the DJI Mini 3 Pro and DJI RC
Authors: Aaron Taylor
Date: 2023-09-19 00:00:00

The consumer drone market is rapidly expanding with new drone models featuring unique variations of hardware and software. The rapid development of drone technology and variability in drone systems can make it difficult for digital forensic investigators and tools to keep pace and effectively extract and analyse digital evidence from drones. Furthermore, the growing popularity of drones and their increased use in illegal and harmful activities, such as smuggling, espionage, and even terrorism, has led to an increase in the number of drone forensic cases for authorities to manage. To assist forensic investigators, a static digital forensic case study was conducted on two drone devices recently released by Da-Jiang Innovations (DJI): the Mini 3 Pro drone, and its remote controller, the DJI RC. The study discovered the presence of several digital artefacts on both devices, including recorded media, flight logs, and other information that could help investigators trace the drone's usage and identify its operator. Additionally, this paper explored several methods for extracting and visualising the drone's flight history, and highlights some of the potential methods used to limit, obscure, or remove key types of digital evidence.
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Authors: Ning Wu, Ming Gong, Linjun Shou, Jian Pei, Daxin Jiang
Date: 2023-09-19 00:00:00
Tasks: Contrastive Learning, Recommendation Systems, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sequential Recommendation

Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately under the condition of data sparsity. To address this challenge, some methods have leveraged external user behavior data from multiple platforms to enrich user representation. However, all of these methods require a consistent user ID across platforms and ignore the information from similar users. In this study, we propose RUEL, a novel retrieval-based sequential recommender that can effectively incorporate external anonymous user behavior data from Edge browser logs to enhance recommendation. We first collect and preprocess a large volume of Edge browser logs over a one-year period and link them to target entities that correspond to candidate items in recommendation datasets. We then design a contrastive learning framework with a momentum encoder and a memory bank to retrieve the most relevant and diverse browsing sequences from the full browsing log based on the semantic similarity between user representations. After retrieval, we apply an item-level attentive selector to filter out noisy items and generate refined sequence embeddings for the final predictor. RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets. We conduct extensive experiments on four real datasets for sequential recommendation tasks and demonstrate that RUEL significantly outperforms state-of-the-art baselines. We also conduct ablation studies and qualitative analysis to validate the effectiveness of each component of RUEL and provide additional insights into our method.
Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
Authors: Yi Ren, Ying Du, Bin Wang, Shenzheng Zhang
Date: 2023-09-19 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems

Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data. The typical way of conducting multi-task learning is to establish appropriate parameter sharing across multiple tasks at lower layers while reserving a separate task tower for each task at upper layers. Since the task towers exert direct impact on the prediction results, we argue that the architecture of standalone task towers is sub-optimal for promoting positive knowledge sharing. Accordingly, we propose the framework of Deep Mutual Learning across task towers, which is compatible with various backbone multi-task networks. Extensive offline experiments and online AB tests are conducted to evaluate and verify the proposed approach's effectiveness.
Towards Measuring Fairness in Grid Layout in Recommender Systems
Authors: Amifa Raj, Michael D. Ekstrand
Date: 2023-09-19 00:00:00
Tasks: Fairness, Recommendation Systems

There has been significant research in the last five years on ensuring the providers of items in a recommender system are treated fairly, particularly in terms of the exposure the system provides to their work through its results. However, the metrics developed to date have all been designed and tested for linear ranked lists. It is unknown whether and how existing fair ranking metrics for linear layouts can be applied to grid-based displays. Moreover, depending on the device (phone, tab, or laptop) users use to interact with systems, column size is adjusted using column reduction approaches in a grid-view. The visibility or exposure of recommended items in grid layouts varies based on column sizes and column reduction approaches as well. In this paper, we extend existing fair ranking concepts and metrics to study provider-side group fairness in grid layouts, present an analysis of the behavior of these grid adaptations of fair ranking metrics, and study how their behavior changes across different grid ranking layout designs and geometries. We examine how fairness scores change with different ranking layouts to yield insights into (1) the consistency of fair ranking measurements across layouts; (2) whether rankings optimized for fairness in a linear ranking remain fair when the results are displayed in a grid; and (3) the impact of column reduction approaches to support different device geometries on fairness measurement. This work highlights the need to use layout-specific user attention models when measuring fairness of rankings, and provide practitioners with a first set of insights on what to expect when translating existing fair ranking metrics to the grid layouts in wide use today.
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Authors: Bo Peng, Srinivasan Parthasarathy, Xia Ning
Date: 2023-09-18 00:00:00
Tasks: Sequential Recommendation

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promising, in this paper, we show that existing methods suffer from the notorious negative transfer issue, where the model adapted from the pre-trained model results in worse performance compared to the model learned from scratch in the task of interest (i.e., target task). To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from related tasks (i.e., auxiliary tasks); and 2) better capturing task-specific knowledge in the target task using a re-learning-based adaptation strategy. We evaluate ANT against eight state-of-the-art baseline methods on five target tasks. Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks. The results also demonstrate that ANT substantially outperforms baseline methods in the target tasks with an improvement of as much as 15.2%. Our analysis highlights the superior effectiveness of our re-learning-based strategy compared to fine-tuning on the target tasks.
BDEC:Brain Deep Embedded Clustering model
Authors: Xiaoxiao Ma, Chunzhi Yi, Zhicai Zhong, Hui Zhou, Baichun Wei, Haiqi Zhu, Feng Jiang
Date: 2023-09-12 00:00:00
Tasks: Clustering, Dimensionality Reduction, model

An essential premise for neuroscience brain network analysis is the successful segmentation of the cerebral cortex into functionally homogeneous regions. Resting-state functional magnetic resonance imaging (rs-fMRI), capturing the spontaneous activities of the brain, provides the potential for cortical parcellation. Previous parcellation methods can be roughly categorized into three groups, mainly employing either local gradient, global similarity, or a combination of both. The traditional clustering algorithms, such as "K-means" and "Spectral clustering" may affect the reproducibility or the biological interpretation of parcellations; The region growing-based methods influence the expression of functional homogeneity in the brain at a large scale; The parcellation method based on probabilistic graph models inevitably introduce model assumption biases. In this work, we develop an assumption-free model called as BDEC, which leverages the robust data fitting capability of deep learning. To the best of our knowledge, this is the first study that uses deep learning algorithm for rs-fMRI-based parcellation. By comparing with nine commonly used brain parcellation methods, the BDEC model demonstrates significantly superior performance in various functional homogeneity indicators. Furthermore, it exhibits favorable results in terms of validity, network analysis, task homogeneity, and generalization capability. These results suggest that the BDEC parcellation captures the functional characteristics of the brain and holds promise for future voxel-wise brain network analysis in the dimensionality reduction of fMRI data.
Two-stage coarse-to-fine image anomaly segmentation and detection model
Authors: Rizwan Ali Shah, Odilbek Urmonov, HyungWon Kim
Date: 2023-09-19 00:00:00
Proceeding: Image and Vision Computing 2023 9
Tasks: Anomaly Detection, Anomaly Segmentation, Segmentation

Existing Convolutional Neural Network (CNN) based anomaly detection and segmentation approaches are overly
sensitive or not sensitive enough to noise, resulting in anomaly patterns, partially detected in the testing stage.
The previous methods may also differentiate normal and abnormal images, but they cannot identify the location
of anomaly presented in test images with high accuracy. To address this issue, we propose a two-stage CNN
model for coarse-to-fine anomaly segmentation and detection called (TASAD). In both stages of TASAD, we train
our model on a mixture of normal and abnormal training samples. The abnormal images are obtained by insert-
ing pseudo-anomaly patterns that are automatically generated from anomaly source images. We use a novel and
sophisticated anomaly insertion technique to generate various anomalous samples. In the first stage, we design a
coarse anomaly segmentation (CAS) model that takes a whole image as an input, while in the second stage, we
train a fine anomaly segmentation (FAS) model on image patches. FAS model improves detection and segmenta-
tion performance by refining anomaly patterns partially detected by CAS model. We train our framework on
MVTec dataset and compare it with state-of-the-art (SOTA) methods. The proposed architecture leads to a com-
pact model size – four times smaller than the SOTA method, while exhibiting better pixel-level accuracy. TASAD
can also be applied to SOTAs to further improve their anomaly detection performance. Our experiments demon-
strate that when applied to the latest SOTAs, TASAD improves the average precision (AP) performance of previ-
ous methods by 6.2%. For reproducibility of the results, code is provided at https://github.com/RizwanAliQau/
tasad.git.
Triangular code: Near-optimal linear time fountain code
Authors: Jalaluddin Qureshi, Chuan Heng Foh
Date: 2023-08-30 00:00:00
Proceeding: Digital Communications and Networks 2023 8
Tasks: Decoder

In this paper, we propose Triangular Code (TC), a new class of fountain code with near-zero redundancy and linear encoding and decoding computational complexities of OðLklog kÞ, where k is the packet batch size and L is the packet data length. Different from previous works where the optimal performance of codes has been shown under asymptotic assumption, TC enjoys near-zero redundancy even under non-asymptotic settings for smallmoderate number of packets. These features make TC suitable for practical implementation in batteryconstrained devices in IoT, D2D and M2M network paradigms to achieve scalable reliability, and minimize latency due to its low decoding delay. TC is a non-linear code, which is encoded using the simple shift and XOR addition operations, and decoded using the simple back-substitution algorithm. Although it is nonlinear code at the packet level, it remains linear code when atomized at the bit level. We use this property to show that the backsubstitution decoder of TC is equivalent to the Belief Propagation (BP) decoder of LT code. Therefore, TC can benefit from rich prolific literature published on LT code, to design efficient code for various applications. Despite the equivalency between the decoders of TC and LT code, we show that compared to state-of-the-art optimized LT code, TC reduces the redundancy of LT code by 68%–99% for k reaching 1024.
Dual Contrastive Prediction for Incomplete Multi-view Representation Learning
Authors: Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, Xi Peng
Date: 2023-04-01 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 4
Tasks: Action Recognition, Contrastive Learning, MULTI-VIEW LEARNING, Prediction, Representation Learning, Temporal Action Localization

In this article, we propose a unified framework to solve the following two challenging problems in incomplete multi-view representation learning: i) how to learn a consistent representation unifying different views, and ii) how to recover the missing views. To address the challenges, we provide an information theoretical framework under which the consistency learning and data recovery are treated as a whole. With the theoretical framework, we propose a novel objective function which jointly solves the aforementioned two problems and achieves a provable sufficient and minimal representation. In detail, the consistency learning is performed by maximizing the mutual information of different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy through dual prediction. To the best of our knowledge, this is one of the first works to theoretically unify the cross-view consistency learning and data recovery for representation learning. Extensive experimental results show that the proposed method remarkably outperforms 20 competitive multi-view learning methods on six datasets in terms of clustering, classification, and human action recognition. The code could be accessed from https://github.com/XLearning-SCU/2022-TPAMI-DCP.
A Central Asian Food Dataset for Personalized Dietary Interventions
Authors: Aknur Karabay, Arman Bolatov, Huseyin Atakan Varol, Mei-Yen Chan
Date: 2023-03-31 00:00:00
Proceeding: MDPI: Nutrients 2023 3
Tasks: Food Recognition

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.