pwc-archive/papers-with-abstracts huggingface dataset:
Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products
Authors: Michael Saidani, Harrison Kim, Nawres Ayadhi, Bernard Yannou
Date: 2021-12-20 00:00:00

Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: ``What sustainable design insights can be identified or interpreted from online product reviews?''. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.
A Survey on Image Deblurring
Authors: ChuMiao Li
Date: 2022-01-31 00:00:00
Tasks: Deblurring, Image Deblurring, Survey

With the improvement of social life quality and the real needs of daily work, images are more and more all around us. Image blurring due to camera shake, human movement, etc. has become the key to affecting image quality. How to remove image blur and restore clear image has gradually become an important research direction in the field of computer vision. After more than half a century of unremitting efforts, the majority of scientific and technological workers have made fruitful progress in image deblurring. This article reviews the work of image deblurring and specifically introduces more classic image deblurring methods, which is helpful to understand current research and look forward to future trends. This article reviews the traditional image deblurring methods and depth-represented image deblurring methods, and comprehensively classifies and introduces the corresponding technical methods. This review can provide some guidance for researchers in the field of image deblurring, and at the same time facilitate their subsequent study and research.
DeepMIH: Deep Invertible Network for MultipleImage Hiding
Authors: Zhenyu Guan; Junpeng Jing; Xin Deng; Mai Xu; Lai Jiang; Zhou Zhang; Yipeng Li
Date: 2022-01-10 00:00:00
Proceeding: IEEE TPAMI 2022 1

Multiple image hiding aims to hide multiple secret images into a single cover image, and then recover all secret images perfectly. Such high-capacity hiding may easily lead to contour shadows or color distortion, which makes multiple image hiding a very challenging task. In this paper, we propose a novel multiple image hiding framework based on invertible neural network, namely DeepMIH. Specifically, we develop an invertible hiding neural network (IHNN) to innovatively model the image concealing and revealing as its forward and backward processes, making them fully coupled and reversible. The IHNN is highly flexible, which can be cascaded as many times as required to achieve the hiding of multiple images. To enhance the invisibility, we design an importance map (IM) module to guide the current image hiding based on the previous image hiding results. In addition, we find that the image hidden in the high-frequency sub-bands tends to achieve better hiding performance, and thus propose a low-frequency wavelet loss to constrain that no secret information is hidden in the low-frequency sub-bands. Experimental results show that our DeepMIH significantly outperforms other state-of-the-art methods, in terms of hiding invisibility, security and recovery accuracy on a variety of datasets.
Likelihood Analysis of CMB Temperature and Polarization Power Spectra
Authors: Samira Hamimeche, Antony Lewis
Date: 2008-01-03 00:00:00

Microwave background temperature and polarization observations are a powerful way to constrain cosmological parameters if the likelihood function can be calculated accurately. The temperature and polarization fields are correlated, partial sky coverage correlates power spectrum estimators at different ell, and the likelihood function for a theory spectrum given a set of observed estimators is non-Gaussian. An accurate analysis must model all these properties. Most existing likelihood approximations are good enough for a temperature-only analysis, however they cannot reliably handle a temperature-polarization correlations. We give a new general approximation applicable for correlated Gaussian fields observed on part of the sky. The approximation models the non-Gaussian form exactly in the ideal full-sky limit and is fast to evaluate using a pre-computed covariance matrix and set of power spectrum estimators. We show with simulations that it is good enough to obtain correct results at ell >~ 30 where an exact calculation becomes impossible. We also show that some Gaussian approximations give reliable parameter constraints even though they do not capture the shape of the likelihood function at each ell accurately. Finally we test the approximations on simulations with realistically anisotropic noise and asymmetric foreground mask.
Fs-Net: Filter Selection Network For Hyperspectral Reconstruction
Authors: Liutao Yang; Zhongnian Li; Zongxiang Pei; Daoqiang Zhang
Date: 2021-08-23 00:00:00
Proceeding: IEEE ICIP 2021 2021 8

optimizing spectral filters for hyperspectral reconstruction has received increasing attentions recently. However, current filter selection methods suffer from extremely high computational complexity due to exhaustive optimization. In this paper, in order to reduce the computational complexity, we propose a novel Filter Selection Network (FS-Net) to select filters and learn the reconstruction network simultaneously. Specifically, we propose an end-to-end method to embed filter selection in FS-Net by setting spectral response functions as the input layer. Furthermore, we propose a non-negative Ll sparse regularization (NN-LI) to select optical filters automatically by sparsifying the input layer. Besides, we develop a two-stage training strategy for adjusting the number of selected filters. Experiments on public datasets show that our proposed method can considerably improve the reconstruction quality.
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks
Authors: Hao Peng; Renyu Yang; Zheng Wang; Jianxin Li; Lifang He; Philip S. Yu; Albert Y. Zomaya; Rajiv Ranjan
Date: 2021-02-11 00:00:00
Proceeding: IEEE Transactions on Computers 2021 2
Tasks: Anomaly Detection, Fraud Detection, Incremental Learning, Node Classification, Node Clustering

Understanding the interconnected relationships of large-scale information networks like social, scholar and Internet of Things networks is vital for tasks like recommendation and fraud detection. The vast majority of the real-world networks are inherently heterogeneous and dynamic, containing many different types of nodes and edges and can change drastically over time. The dynamicity and heterogeneity make it extremely challenging to reason about the network structure. Unfortunately, existing approaches are inadequate in modeling real-life dynamical networks as they either have strong assumption of a given stochastic process or fail to capture the heterogeneity of network structure, and they all require extensive computational resources. We introduce Lime , a better approach for modeling dynamic and heterogeneous information networks. Lime is designed to extract high-quality network representation with significantly lower memory resources and computational time over the state-of-the-arts. Unlike prior work that uses a vector to encode each network node, we exploit the semantic relationships among network nodes to encode multiple nodes with similar semantics in shared vectors. By using many fewer node vectors, our approach significantly reduces the required memory space for encoding large-scale networks. To effectively trade information sharing for reduced memory footprint, we employ the recursive neural network (RsNN) with carefully designed optimization strategies to explore the node semantics in a novel cuboid space. We then go further by showing, for the first time, how an effective incremental learning approach can be developed – with the help of RsNN, our cuboid structure, and a set of novel optimization techniques – to allow a learning framework to quickly and efficiently adapt to a constantly evolving network. We evaluate Lime by applying it to three representative network-based tasks, node classification, node clustering and anomaly detection, performin...
POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling
Authors: Haoyi Zhou, Hao Peng, Jieqi Peng, Shuai Zhang, JianXin Li
Date: 2021-11-29 00:00:00
Proceeding: TIST 2021 2021 11
Tasks: Decoder

The spatial-temporal modeling on long sequences is of great importance in many real-world applications.
Recent studies have shown the potential of applying the self-attention mechanism to improve capturing
the complex spatial-temporal dependencies. However, the lack of underlying structure information weakens its general performance on long sequence spatial-temporal problem. To overcome this limitation, we
proposed a novel method, named the Proximity-aware Long Sequence Learning framework, and apply it to
the spatial-temporal forecasting task. The model substitutes the canonical self-attention by leveraging the
proximity-aware attention, which enhances local structure clues in building long-range dependencies with
a linear approximation of attention scores. The relief adjacency matrix technique can utilize the historical
global graph information for consistent proximity learning. Meanwhile, the reduced decoder allows for fast
inference in a non-autoregressive manner. Extensive experiments are conducted on five large-scale datasets,
which demonstrate that our method achieves state-of-the-art performance and validates the effectiveness
brought by local structure information.
Learning Meta-path-aware Embeddings for Recommender Systems
Authors: Qianxiu Hao, Qianqian Xu, Zhiyong Yang, Qingming Huang
Date: 2021-10-17 00:00:00
Proceeding: ACM MM 2021 2021 10
Tasks: Recommendation Systems

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance; 2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method.
Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang
Date: 2021-10-17 00:00:00
Proceeding: ACM MM 2021 2021 10
Tasks: Collaborative Filtering, Recommendation Systems

Due to privacy concerns, there is a rising favor in Recommender System community for the One-class Collaborative Filtering (OCCF) framework, which predicts user preferences only based on binary implicit feedback (e.g., click or not-click, rated or unrated). The major challenge in OCCF problem stems from the inherent noise in implicit interaction. Previous approaches have taken into account the noise in unobserved interactions (i.e., not-click only means a missing value, rather than negative feedback). However, they generally ignore the noise in observed interactions (i.e., click does not necessarily represent positive feedback), which might induce performance degradation. To attack this issue, we propose a novel iteratively relabeling framework to jointly mitigate the noise in both observed and unobserved interactions. As the core of the framework, the iterative relabeling module exploits the self-training principle to dynamically generate pseudo labels for user preferences. The downstream module for a recommendation task is then trained with the refreshed labels where the noisy patterns are largely alleviated. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed methods.
A Novel Visual Representation on Text Using Diverse Conditional GAN for Visual Recognition
Authors: Tao Hu; Chengjiang Long; Chunxia Xiao
Date: 2021-03-05 00:00:00
Proceeding: IEEE TIP 2021 2021 3
Tasks: Generative Adversarial Network, Semantic Segmentation

Abstract— Automatic image visual recognition can make full
use of largely available images with text descriptions on social
media platforms to build large-scale image labeled datasets.
In this paper, we propose a novel visual text representation,
named DG-VRT (Diverse GAN-Visual Representation on Text),
which extracts visual features from synthetic images generated by
a diverse conditional Generative Adversarial Network (DCGAN)
on the text, for visual recognition. The DCGAN incorporates
the current state-of-the-art text-to-image GANs and generates
multiple synthetic images with various prior noises conditioned
on a text. Then we extract deep visual features from the generated
synthetic images to explore the underlying visual concepts and
provide a visual transformation on text in feature space. Finally,
we combine image-level visual features, text-level features and
visual features based on synthetic images together to recognize
the images, and we also extend the proposed work to semantic
segmentation. We conduct extensive experiments on two benchmark datasets and the experimental results demonstrate the efficacy of our proposed representation on text for visual recognition.
Index Terms— Visual representation, diverse conditional GAN,
visual recognition.
MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion
Authors: Zhiyi Jiang, Jianliang Gao, Xinqi Lv
Date: 2021-07-11 00:00:00
Proceeding: SIGIR 2021 2021 7
Tasks: Information Retrieval, Knowledge Graph Completion, Knowledge Graphs, Relation, Retrieval

Knowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus.
Illumination-Aware Image Quality Assessment for Enhanced Low-light Image
Authors: Sigan Yao，Yiqin Zhu，Lingyu Liang，Tao Wang
Date: 2021-10-22 00:00:00
Proceeding: PRCV 2021 2021 10
Tasks: Image Enhancement, Image Quality Assessment, Intrinsic Image Decomposition, Low-Light Image Enhancement

Images captured in a dark environment may suffer from low visibility, which may degrade the visual aesthetics of images and the performance of vision-based systems. Extensive studies have focused on the low-light image enhancement (LIE) problem. However, we observe that even though the state-of-the-art LIE methods may oversharpen the low-light image and introduce visual artifacts. To reduce the overshoot effects of LIE, this paper proposes an illumination-aware image quality assessment, called LIE-IQA, for the enhanced low-light images. Since directly using the IQA of degraded image may fail to perform well, the proposed LIE-IQA is an illumination-aware and learnable metric. At first, the reflectance and shading components of both the enhanced low-light image and reference image are extracted by intrinsic image decomposition. Then, we use the weighted similarity between the VGG-based feature of enhanced low-light image and reference image to obtain LIE-IQA, where the weight of the measurement can be learned from pairs of data on benchmark dataset. Qualitative and quantitative experiments illustrate the superiority of the LIE-IQA to measure the image quality of LIE on different datasets, including a new IQA dataset built for LIE. We also use the LIE-IQA as a regularization of a loss function to optimize an end-to-end LIE method, and the results indicate the potential of the optimization framework with LIE-IQA to reduce the overshoot effects of low-light enhancement.
Implementation of Naïve Bayes and Gini Index for Spam Email Classification
Authors: Fikri Rozan Imadudin, Danang Triantoro Murdiansyah, Adiwijaya
Date: 2021-05-03 00:00:00
Proceeding: Computational and Simulation  Vol. 6 No. 1 (2021): April, 2020 2021 5
Tasks: feature selection

Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipient. In this study, we show the performance and accuracy of Multinomial Naïve Bayes (MNNB) and Complete Gini-Index Text (GIT) for use in spam email filtering. In this study, we used 6 cross-validations as testers for the built classification machines. We found that the average yield can exceed Multinomial Naïve Bayes without using feature selection which only uses 80000 features with a difference of 0.39%. Feature selection also increases speed during classification and can reduce features that are less relevant to the category to be classified.
LTE Cell Traffic Grow and Congestion Forecasting
Authors: Chmieliauskas, D., Gursnys, D
Date: 2019-04-01 00:00:00
Proceeding: 2019 Open Conference of Electrical, Electronic and Information Sciences 2019 4

Recent popularity grow of predictive analysis is growing in many fields. Importance to forecast mobile traffic for each LTE cell can bring mobile network planning advantages for the operator. It can help operators to spend minimum investing for new sites and cells but be able to guarantee excellent service experience for mobile broadband users. In this paper study of mobile traffic forecasting feasibility using fbProphet algorithm developed by Facebook is presented. Target is to have short term forecasting from which the operator can proactively consider network expansion if the load is too high to satisfy user throughput demands. Five months of daily traffic data used to train model and one month used for forecasting and model testing. Also, 30 days of hourly data were used for busy hour traffic forecasting. In the last part relation between data traffic carried in the LTE cell to cell load explained.
SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning
Authors: Omid Mirzaei, Roman Vasilenko, Engin Kirda, Long Lu, Amin Kharraz
Date: 2021-07-09 00:00:00
Proceeding: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment 2021 7
Tasks: BIG-bench Machine Learning, Code Reuse Detection, Malware Analysis

Growing numbers of advanced malware-based attacks against governments and corporations, for political, financial and scientific gains, have taken security breaches to the next level. In response to such attacks, both academia and industry have investigated techniques to model and reconstruct these attacks and to defend against them. While such efforts have been all useful in mitigating the effects of modern attacks, automated malware code reuse inspection and campaign attribution have received less attention. In this paper, we present an automated system, called SCRUTINIZER, to identify code reuse in malware via a novel machine learning-based encoding mechanism at the function-level. By creating a large knowledge base of previously observed and tagged malware campaigns, we can compare unknown samples against this knowledge base and determine how much overlap exists. SCRUTINIZER leverages an unsupervised learning approach to filter out irrelevant functions before code reuse detection. It provides two valuable capabilities. First, it identifies ties between an unknown sample and those malware specimens that are known to be used by a specific campaign. Second, it inspects if specific tools or functionalities are used by a campaign. Using SCRUTINIZER, we were able to identify 12 samples that were previously unknown to us and that we were able to correctly assign to well-known APT campaigns.
AndrODet: An Adaptive Android Obfuscation Detector
Authors: Omid Mirzaei, Jose Maria de Fuentes, Juan Tapiador, Lorena Gonzáles-Manzano
Date: 2019-01-01 00:00:00
Proceeding: Future Generation Computer Systems 2019 1
Tasks: Android Malware Detection, Malware Analysis, Obfuscation Detection

Obfuscation techniques modify an app’s source (or machine) code in order to make it more difficult to analyze. This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case malware. Since malware analysis often requires considerable resource investment, detecting the particular obfuscation technique used may contribute to apply the right analysis tools, thus leading to some savings. In this paper, we propose AndrODet, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation.
AndrODet leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner. We compare our results with a batch learning algorithm using a dataset of 34,962 apps from both malware and benign apps. Experimental results show that online learning approaches are not only able to compete with batch learning methods in terms of accuracy, but they also save significant amount of time and computational resources. Particularly, AndrODet achieves an accuracy of 92.02% for identifier renaming detection, 81.41% for string encryption detection, and 68.32% for control flow obfuscation detection, on average. Also, the overall accuracy of the system when apps might be obfuscated with more than one technique is around 80.66%.