pwc-archive/papers-with-abstracts huggingface dataset:
FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models
Authors: Yuwei Yin, Yazheng Yang, Jian Yang, Qi Liu
Date: 2023-07-22 00:00:00

Financial risk prediction plays a crucial role in the financial sector. Machine learning methods have been widely applied for automatically detecting potential risks and thus saving the cost of labor. However, the development in this field is lagging behind in recent years by the following two facts: 1) the algorithms used are somewhat outdated, especially in the context of the fast advance of generative AI and large language models (LLMs); 2) the lack of a unified and open-sourced financial benchmark has impeded the related research for years. To tackle these issues, we propose FinPT and FinBench: the former is a novel approach for financial risk prediction that conduct Profile Tuning on large pretrained foundation models, and the latter is a set of high-quality datasets on financial risks such as default, fraud, and churn. In FinPT, we fill the financial tabular data into the pre-defined instruction template, obtain natural-language customer profiles by prompting LLMs, and fine-tune large foundation models with the profile text to make predictions. We demonstrate the effectiveness of the proposed FinPT by experimenting with a range of representative strong baselines on FinBench. The analytical studies further deepen the understanding of LLMs for financial risk prediction.
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Authors: Seongsik Park, Jeonghee Jo, Jongkil Park, YeonJoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Inho Kim, Jong-Keuk Park, Kyeong Seok Lee, Gye Weon Hwang, Hyun Jae Jang
Date: 2023-08-01 00:00:00
Tasks: image-classification, Image Classification

Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation (STBP) with surrogate gradient was proposed. Although deep SNNs have been successfully trained with STBP, they cannot fully utilize spike information. In this work, we proposed gradient scaling with local spike information, which is the relation between pre- and post-synaptic spikes. Considering the causality between spikes, we could enhance the training performance of deep SNNs. According to our experiments, we could achieve higher accuracy with lower spikes by adopting the gradient scaling on image classification tasks, such as CIFAR10 and CIFAR100.
An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM
Authors: Hemn Barzan Abdalla, Awder Ahmed, Bahtiyar Mehmed, Mehdi Gheisari, Maryam Cheraghy, Yang Liu
Date: 2023-07-31 00:00:00
Tasks: Graph Embedding, Product Recommendation, Recommendation Systems

Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on Passer Learning Optimization-enhanced Bi-LSTM classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. It achieves as low as 1.24% MSE on the baby dataset. This lifts it as high as 88.58%. Besides, there is also robust performance of the system on digital music and patio lawn garden datasets at F1 of 88.46% and 92.51%, correspondingly. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters, establish the suitability of the proposed model in various e-commerce environments.
The physics of optical computing
Authors: Peter L. McMahon
Date: 2023-07-31 00:00:00

There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has been a topic of periodic study for over 50 years, including for neural networks three decades ago, and a wide variety of optical-computing schemes and architectures have been proposed. In this paper we provide a systematic explanation of why and how optics might be able to give speed or energy-efficiency benefits over electronics for computing, enumerating 11 features of optics that can be harnessed when designing an optical computer. One often-mentioned motivation for optical computing -- that the speed of light $c$ is fast -- is not a key differentiating physical property of optics for computing; understanding where an advantage could come from is more subtle. We discuss how gaining an advantage over state-of-the-art electronic processors will likely only be achievable by careful design that harnesses more than one of the 11 features, while avoiding a number of pitfalls that we describe.
TimePool: Visually Answer "Which and When" Questions On Univariate Time Series
Authors: Tinghao Feng, Yueqi Hu, Jing Yang, Tom Polk, Ye Zhao, Shixia Liu, Zhaocong Yang
Date: 2023-08-01 00:00:00
Tasks: Time Series, Time Series Analysis

When exploring time series datasets, analysts often pose "which and when" questions. For example, with world life expectancy data over one hundred years, they may inquire about the top 10 countries in life expectancy and the time period when they achieved this status, or which countries have had longer life expectancy than Ireland and when. This paper proposes TimePool, a new visualization prototype, to address this need for univariate time series analysis. It allows users to construct interactive "which and when" queries and visually explore the results for insights.
On the Effects of Regional Spelling Conventions in Retrieval Models
Authors: Andreas Chari, Sean MacAvaney, Iadh Ounis
Date: 2023-08-01 00:00:00
Tasks: Retrieval

One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i.e. where two words have similar or identical meanings. In this paper, we investigate and quantify how well various ranking models perform in a clear-cut case of synonymity: when words are simply expressed in different surface forms due to regional differences in spelling conventions (e.g., color vs colour). We first explore the prevalence of American and British English spelling conventions in datasets used for the pre-training, training and evaluation of neural retrieval methods, and find that American spelling conventions are far more prevalent. Despite these biases in the training data, we find that retrieval models often generalise well in this case of synonymity. We explore the effect of document spelling normalisation in retrieval and observe that all models are affected by normalising the document's spelling. While they all experience a drop in performance when normalised to a different spelling convention than that of the query, we observe varied behaviour when the document is normalised to share the query spelling convention: lexical models show improvements, dense retrievers remain unaffected, and re-rankers exhibit contradictory behaviour.
Generative Query Reformulation for Effective Adhoc Search
Authors: Xiao Wang, Sean MacAvaney, Craig Macdonald, Iadh Ounis
Date: 2023-08-01 00:00:00
Tasks: Information Retrieval, Retrieval

Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance feedback approaches, which expand the query in order to alleviate the vocabulary mismatch problem. Recent advancements in generative language models have demonstrated their ability in generating responses that are relevant to a given prompt. In light of this success, we seek to study the capacity of such models to perform query reformulation and how they compare with long-standing query reformulation methods that use pseudo-relevance feedback. In particular, we investigate two representative query reformulation frameworks, GenQR and GenPRF. GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information. For each reformulation method, we leverage different techniques, including fine-tuning and direct prompting, to harness the knowledge of language models. The reformulated queries produced by the generative models are demonstrated to markedly benefit the effectiveness of a state-of-the-art retrieval pipeline on four TREC test collections (varying from TREC 2004 Robust to the TREC 2019 Deep Learning). Furthermore, our results indicate that our studied generative models can outperform various statistical query expansion approaches while remaining comparable to other existing complex neural query reformulation models, with the added benefit of being simpler to implement.
Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model
Authors: Sanjay Chakraborty, Saroj Kumar Pandey, Saikat Maity, Lopamudra Dey
Date: 2023-07-29 00:00:00

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network-devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats.
Mining Reviews in Open Source Code for Developers Trail: A Process Mining Approach
Authors: Patrick Mukala
Date: 2023-07-16 00:00:00
Tasks: Management

Audit trails are evidential indications of activities performers in any logs. Modern reactive systems such as transaction processing systems, management information systems, decision support systems and even executive management systems log activities of users as they perform their daily tasks for a number of reasons and perhaps one of the most important is security. In order to efficiently monitor and manage privacy and access to information, the trails as captured and recorded in these logs play a pivotal role in this regard. In Open Source realm, however, this is not the case. Although the objective with free software is to allow for access, free distribution and the rights to modify coding, having such audit trails can help to trace and understand how active members of these communities are and the type of activities they perform. In this paper, we propose using process mining to construct logs using as much data as can be found in open source repositories in order to produce a process model, also called a workflow net that graphical depicts the sequential occurrence of developers activities. Our method is exhibited through a simple algorithm called Act-Trace.
PDBImages: A Command Line Tool for Automated Macromolecular Structure Visualization
Authors: Adam Midlik, Sreenath Nair, Stephen Anyango, Mandar Deshpande, David Sehnal, Mihaly Varadi, Sameer Velankar
Date: 2023-08-01 00:00:00

Summary: PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. Availability and Implementation: PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images. Contact: mvaradi@ebi.ac.uk, sameer@ebi.ac.uk
NIPUNA: A Novel Optimizer Activation Function for Deep Neural Networks
Authors: Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam M Zawbaa, Ali Wagdy Mohamed
Date: 2023-02-28 00:00:00
Proceeding: Axioms 2023 2
Tasks: Medical Diagnosis

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles, and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in bias. However, unlike ReLU activation functions, Swish activation functions do not remain stable or move in a single direction. This research proposes a new activation function named NIPUNA for deep neural networks. We test this activation by training on customized convolutional neural networks (CCNN). On benchmark datasets (Fashion MNIST images of clothes, MNIST dataset of handwritten digits), the contributions are examined and compared to various activation functions. The proposed activation function can outperform traditional activation functions.
Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval
Authors: Wenyu Zhao, Dong Zhou, Buqing Cao, Kai Zhang, Jinjun Chen
Date: 2023-03-08 00:00:00
Proceeding: IEEE Transactions on Artificial Intelligence 2023 3
Tasks: Contrastive Learning, Cross-Modal Retrieval, Retrieval, Triplet

The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this non-trivial problem relies on graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However, there exist the following issues: 1) the cross-modal attention mechanism is only in favor of text representations and can not provide helpful information for molecule representations. 2) the GCN-based molecule encoder ignores edge features and the importance of various substructures of a molecule. 3) the retrieval learning loss function is rather simplistic. This paper further investigates the Text2Mol problem and proposes a novel Adversarial Modality Alignment Network (AMAN)-based method to sufficiently learn both description and molecule information. Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations. Then an adversarial network is used to align these modalities interactively. Meanwhile, a triplet loss function is leveraged to perform retrieval learning and further enhance the modality alignment. Experiments on the ChEBI-20 dataset show the effectiveness of our AMAN method compared with baselines.
A Permissioned Blockchain-Based Platform for Education Certificate Verification
Authors: Hanlei Cheng, Jing Lu, Zhiyu Xiang & Bin Song
Date: 2020-11-12 00:00:00
Proceeding: International Conference on Blockchain and Trustworthy Systems 2020 11
Tasks: Management

Distance education has become an important learning method for students. Nevertheless, online teaching is difficult to ensure the traceability of student activities. The difficulty in managing the non-instantaneous diploma verification and digital/paper files have always been severe problems in education management. The education information stored within the current centralized system is simply leaked and cannot be guaranteed in terms of impartiality and authenticity. In the present paper, a digital education certificate prototype is designed utilizing the permissioned blockchain of PKI-CA, digest algorithm, and interactive data authentication by digital signatures. The digital certificates can be issued by the system realizing the instant certificate verification between students and third parties via QR codes or dynamic authorization codes. The test results indicated that 100% correct work of the prototype, with the significant throughputs of getting and creating transactions are respectively 1982.6 tps and 263.9 tps on-chain during the test cycle.
Safety Control of Uncertain MIMO Systems Using Dynamic Output Feedback Barrier Pairs
Authors: Binghan He, Takashi Tanaka
Date: 2023-08-01 00:00:00

Safety control of dynamical systems using barrier functions relies on knowing the full state information. This paper introduces a novel approach for safety control in uncertain MIMO systems with partial state information. The proposed method combines the synthesis of a vector norm barrier function and a dynamic output feedback safety controller to ensure robust safety enforcement. The safety controller guarantees the invariance of the barrier function under uncertain dynamics and disturbances. To address the challenges associated with safety verification using partial state information, a barrier function estimator is developed. This estimator employs an identifier-based state estimator to obtain a state estimate that is affine in the uncertain model parameters of the system. By incorporating a priori knowledge of the limits of the uncertain model parameters and disturbances, the state estimate provides a robust upper bound for the barrier function. Comparative analysis with existing control barrier function based methods shows the advantage of the proposed approach in enforcing safety constraints under tight input constraints and the utilization of estimated state information.
On the use of deep learning for phase recovery
Authors: Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao, Jiazhen Dou, Jianglei Di, George Barbastathis, Renjie Zhou, Jianlin Zhao, Edmund Y. Lam
Date: 2023-08-02 00:00:00
Tasks: Deep Learning

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.
Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking
Authors: Mingzhan Yang, Guangxin Han, Bin Yan, Wenhua Zhang, Jinqing Qi, Huchuan Lu, Dong Wang
Date: 2023-08-01 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object Tracking, Online Multi-Object Tracking

Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. Most methods accomplish the task by explicitly or implicitly leveraging strong cues (i.e., spatial and appearance information), which exhibit powerful instance-level discrimination. However, when object occlusion and clustering occur, spatial and appearance information will become ambiguous simultaneously due to the high overlap among objects. In this paper, we demonstrate this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues to compensate for strong cues. Along with velocity direction, we introduce the confidence and height state as potential weak cues. With superior performance, our method still maintains Simple, Online and Real-Time (SORT) characteristics. Also, our method shows strong generalization for diverse trackers and scenarios in a plug-and-play and training-free manner. Significant and consistent improvements are observed when applying our method to 5 different representative trackers. Further, with both strong and weak cues, our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where interaction and severe occlusion frequently happen with complex motions. The code and models are available at https://github.com/ymzis69/HybridSORT.
PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Authors: Bang An, Sicheng Zhu, Michael-Andrei Panaitescu-Liess, Chaithanya Kumar Mummadi, Furong Huang
Date: 2023-08-02 00:00:00
Tasks: Classification, image-classification, Image Classification, Language Modelling, Object, zero-shot-classification, Zero-Shot Image Classification, Zero-Shot Learning

Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unprecedented human-like understanding capabilities to achieve better performance is still an open question. This paper draws inspiration from the human visual perception process: when classifying an object, humans first infer contextual attributes (e.g., background and orientation) which help separate the foreground object from the background, and then classify the object based on this information. Inspired by it, we observe that providing CLIP with contextual attributes improves zero-shot image classification and mitigates reliance on spurious features. We also observe that CLIP itself can reasonably infer the attributes from an image. With these observations, we propose a training-free, two-step zero-shot classification method PerceptionCLIP. Given an image, it first infers contextual attributes (e.g., background) and then performs object classification conditioning on them. Our experiments show that PerceptionCLIP achieves better generalization, group robustness, and interoperability. Our code is available at https://github.com/umd-huang-lab/perceptionCLIP