pwc-archive/papers-with-abstracts huggingface dataset:
Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Cases
Authors: Michael Sheinman Orenstrakh, Oscar Karnalim, Carlos Anibal Suarez, Michael Liut
Date: 2023-07-10 00:00:00

Due to the recent improvements and wide availability of Large Language Models (LLMs), they have posed a serious threat to academic integrity in education. Modern LLM-generated text detectors attempt to combat the problem by offering educators with services to assess whether some text is LLM-generated. In this work, we have collected 124 submissions from computer science students before the creation of ChatGPT. We then generated 40 ChatGPT submissions. We used this data to evaluate eight publicly-available LLM-generated text detectors through the measures of accuracy, false positives, and resilience. The purpose of this work is to inform the community of what LLM-generated text detectors work and which do not, but also to provide insights for educators to better maintain academic integrity in their courses. Our results find that CopyLeaks is the most accurate LLM-generated text detector, GPTKit is the best LLM-generated text detector to reduce false positives, and GLTR is the most resilient LLM-generated text detector. We also express concerns over 52 false positives (of 114 human written submissions) generated by GPTZero. Finally, we note that all LLM-generated text detectors are less accurate with code, other languages (aside from English), and after the use of paraphrasing tools (like QuillBot). Modern detectors are still in need of improvements so that they can offer a full-proof solution to help maintain academic integrity. Further, their usability can be improved by facilitating a smooth API integration, providing clear documentation of their features and the understandability of their model(s), and supporting more commonly used languages.
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes
Authors: Kausik Lakkaraju, Sai Krishna Revanth Vuruma, Vishal Pallagani, Bharath Muppasani, Biplav Srivastava
Date: 2023-07-08 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model

Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT and Bard, are becoming available to users that have the potential to revolutionize the quality of decision-making achieved by the public. In this context, we set out to investigate how such systems perform in the personal finance domain, where financial inclusion has been an overarching stated aim of banks for decades. We asked 13 questions representing banking products in personal finance: bank account, credit card, and certificate of deposits and their inter-product interactions, and decisions related to high-value purchases, payment of bank dues, and investment advice, and in different dialects and languages (English, African American Vernacular English, and Telugu). We find that although the outputs of the chatbots are fluent and plausible, there are still critical gaps in providing accurate and reliable financial information using LLM-based chatbots.
Long Short-term Memory with Two-Compartment Spiking Neuron
Authors: Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu, Haizhou Li, Kay Chen Tan
Date: 2023-07-14 00:00:00

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates an extended memory capacity. To address this challenge, we propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our model incorporates carefully designed somatic and dendritic compartments that are tailored to retain short- and long-term memories. The theoretical analysis further confirms its effectiveness in addressing the notorious vanishing gradient problem. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
STCSNN: High energy efficiency spike-train level spiking neural networks with spatio-temporal conversion
Authors: Changqing Xu, Yi Liu, YinTang Yang
Date: 2023-07-14 00:00:00

Brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest due to their low power features, high biological plausibility, and strong spatiotemporal information processing capability. Although adopting a surrogate gradient (SG) makes the non-differentiability SNN trainable, achieving comparable accuracy for ANNs and keeping low-power features simultaneously is still tricky. In this paper, we proposed an energy-efficient spike-train level spiking neural network with spatio-temporal conversion, which has low computational cost and high accuracy. In the STCSNN, spatio-temporal conversion blocks (STCBs) are proposed to keep the low power features of SNNs and improve accuracy. However, STCSNN cannot adopt backpropagation algorithms directly due to the non-differentiability nature of spike trains. We proposed a suitable learning rule for STCSNNs by deducing the equivalent gradient of STCB. We evaluate the proposed STCSNN on static and neuromorphic datasets, including Fashion-Mnist, Cifar10, Cifar100, TinyImageNet, and DVS-Cifar10. The experiment results show that our proposed STCSNN outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time steps and being highly energy-efficient.
A Hybrid Genetic Algorithm for the min-max Multiple Traveling Salesman Problem
Authors: Sasan Mahmoudinazlou, Changhyun Kwon
Date: 2023-07-14 00:00:00
Tasks: Diversity, Traveling Salesman Problem

This paper proposes a hybrid genetic algorithm for solving the Multiple Traveling Salesman Problem (mTSP) to minimize the length of the longest tour. The genetic algorithm utilizes a TSP sequence as the representation of each individual, and a dynamic programming algorithm is employed to evaluate the individual and find the optimal mTSP solution for the given sequence of cities. A novel crossover operator is designed to combine similar tours from two parents and offers great diversity for the population. For some of the generated offspring, we detect and remove intersections between tours to obtain a solution with no intersections. This is particularly useful for the min-max mTSP. The generated offspring are also improved by a self-adaptive random local search and a thorough neighborhood search. Our algorithm outperforms all existing algorithms on average, with similar cutoff time thresholds, when tested against multiple benchmark sets found in the literature. Additionally, we improve the best-known solutions for $21$ out of $89$ instances on four benchmark sets.
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
Authors: Qi-Wei Wang, Hongyu Lu, Yu Chen, Da-Wei Zhou, De-Chuan Zhan, Ming Chen, Han-Jia Ye
Date: 2023-07-14 00:00:00
Tasks: Click-Through Rate Prediction, Prediction, Recommendation Systems

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems.
Digital Health Discussion Through Articles Published Until the Year 2021: A Digital Topic Modeling Approach
Authors: Junhyoun Sung, Hyungsook Kim
Date: 2023-07-14 00:00:00
Tasks: Articles, Electrical Engineering

The digital health industry has grown in popularity since the 2010s, but there has been limited analysis of the topics discussed in the field across academic disciplines. This study aims to analyze the research trends of digital health-related articles published on the Web of Science until 2021, in order to understand the concentration, scope, and characteristics of the research. 15,950 digital health-related papers from the top 10 academic fields were analyzed using the Web of Science. The papers were grouped into three domains: public health, medicine, and electrical engineering and computer science (EECS). Two time periods (2012-2016 and 2017-2021) were compared using Latent Dirichlet Allocation (LDA) for topic modeling. The number of topics was determined based on coherence score, and topic compositions were compared using a homogeneity test. The number of optimal topics varied across domains and time periods. For public health, the first and second halves had 13 and 19 topics, respectively. Medicine had 14 and 25 topics, and EECS had 7 and 21 topics. Text analysis revealed shared topics among the domains, but with variations in composition. The homogeneity test confirmed significant differences between the groups (adjusted p-value<0.05). Six dominant themes emerged, including journal article methodology, information technology, medical issues, population demographics, social phenomena, and healthcare. Digital health research is expanding and evolving, particularly in relation to Covid-19, where topics such as depression and mental disorders, education, and physical activity have gained prominence. There was no bias in topic composition among the three domains, but other fields like kinesiology or psychology could contribute to future digital health research. Exploring expanded topics that reflect people's needs for digital health over time will be crucial.
NS4AR: A new, focused on sampling areas sampling method in graphical recommendation Systems
Authors: Xiangqi Wang, Dilinuer Aishan, Qi Liu
Date: 2023-07-13 00:00:00
Tasks: Recommendation Systems

The effectiveness of graphical recommender system depends on the quantity and quality of negative sampling. This paper selects some typical recommender system models, as well as some latest negative sampling strategies on the models as baseline. Based on typical graphical recommender model, we divide sample region into assigned-n areas and use AdaSim to give different weight to these areas to form positive set and negative set. Because of the volume and significance of negative items, we also proposed a subset selection model to narrow the core negative samples.
Analysis Issues for Large CMB Data Sets
Authors: K. M. Gorski, E. Hivon, B. D. Wandelt
Date: 1998-12-18 00:00:00

Multi-frequency, high resolution, full sky measurements of the anisotropy in both temperature and polarisation of the cosmic microwave background radiation are the goals of the satellite missions MAP (NASA) and Planck (ESA). The ultimate data products of these missions - multiple microwave sky maps, each of which will have to comprise more than 10^6 pixels in order to render the angular resolution of the instruments - will present serious challenges to those involved in the analysis and scientific exploitation of the results of both surveys. Some considerations of the relevant aspects of the mathematical structure of future CMB data sets are presented in this contribution. >>> for better on-screen rendition of the figures see <<< http://www.tac.dk/~healpix or http://www.mpa-garching.mpg.de/~cosmo/contributions.html
Deep residual inception encoder–decoder network for medical imaging synthesis
Authors: Fei Gao; Teresa Wu; Xianghua Chu; Hyunsoo Yoon; Yanzhe Xu; Bhavika Patel
Date: 2019-04-22 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2019 4
Tasks: Decoder, Image Generation

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder- decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with two models from the literature: synthetic CT deep convolutional neural network (sCT-DCNN) and shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimer's Disease Neuroimaging Initiative. Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index, mean absolute percent error, and peak signal-to-noise ratio.
Two-person interaction detection using body-pose features and multiple instance learning
Authors: Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, Dimitris Samaras
Date: 2012-07-16 00:00:00
Proceeding: International Workshop on Human Activity Understanding from 3D Data at Conference on Computer Vision and Pattern Recognition (HAU3D-CVPRW) 2012 7
Tasks: Activity Recognition, Human Activity Recognition, Multiple Instance Learning, Retrieval, Video Retrieval

Human activity recognition has potential to impact a wide range of applications from surveillance to human computer interfaces to content based video retrieval. Recently, the rapid development of inexpensive depth sensors (e.g. Microsoft Kinect) provides adequate accuracy for real-time full-body human tracking for activity recognition applications. In this paper, we create a complex human activity dataset depicting two person interactions, including synchronized video, depth and motion capture data. Moreover, we use our dataset to evaluate various features typically used for indexing and retrieval of motion capture data, in the context of real-time detection of interaction activities via Support Vector Machines (SVMs). Experimentally, we find that the geometric relational features based on distance between all pairs of joints outperforms other feature choices. For whole sequence classification, we also explore techniques related to Multiple Instance Learning (MIL) in which the sequence is represented by a bag of body-pose features. We find that the MIL based classifier outperforms SVMs when the sequences extend temporally around the interaction of interest.
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognition
Authors: Qin Cheng, Jun Cheng, Ziliang Ren, Qieshi Zhang, Jianming Liu
Date: 2023-05-12 00:00:00
Proceeding: journal 2023 5
Tasks: Action Recognition, Skeleton Based Action Recognition

The skeleton data convey significant information for action recognition since they can robustly against cluttered backgrounds and illumination variation. In recent years, due to the limited ability to extract spatial–temporal features from skeleton data, the methods based on convolutional neural network (CNN) or recurrent neural network are inferior in recognition accuracy. A series of methods based on graph convolutional networks (GCN) have achieved remarkable performance and gradually become dominant. However, the computational cost of GCN-based methods is quite heavy, several works even over 100 GFLOPs. This is contrary to the highly condensed attributes of skeleton data. In this paper, a novel multi-scale spatial–temporal convolutional (MSST) module is proposed to take the implicit complementary advantages across spatial–temporal representations with different scales. Instead of converting skeleton data into pseudo-images like some previous CNN-based methods or using complex graph convolution, we take full use of multi-scale convolutions on temporal and spatial dimensions to capture comprehensive dependencies of skeleton joints. Unifying the MSST module, a multi-scale spatial–temporal convolutional neural network (MSSTNet) is proposed to capture high-level spatial–temporal semantic features for action recognition. Unlike previous methods which boost performance at the cost of computation, MSSTNet can be easily implemented with light model size and fast inference. Moreover, MSSTNet is used in a four-stream framework to fuse data of different modalities, providing notable improvement to recognition accuracy. On NTU RGB+D 60, NTU RGB+D 120, UAV-Human and Northwestern-UCLA datasets, the proposed MSSTNet achieves competitive performance with much less computational cost than state-of-the-art methods.
Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
Authors: Tao Lei, Xinzhe Geng, Hailong Ning, Zhiyong Lv, Maoguo Gong, Yaochu Jin, Asoke K. Nandi
Date: 2023-03-24 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 3
Tasks: Building change detection for remote sensing images, Change Detection, Change detection for remote sensing images

Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial–spectral features and generate 3-D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultralightweight spatial–spectral feature cooperation network (USSFC-Net) is proposed for CD in this article. The proposed USSFC-Net has two main advantages. First, a multiscale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multiscale features of changed objects using cyclic multiscale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial–spectral feature cooperation (SSFC) strategy is introduced to obtain richer features. The SSFC differs from the existing 2-D attention mechanisms since it learns 3-D spatial–spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most CNNs-based methods and requires lower computational costs and fewer parameters, even it is superior to some Transformer-based methods. The code is available at https://github.com/SUST-reynole/USSFC-Net.
Improved Decomposition Strategy for Joint Entity and Relation Extraction
Authors: Van-Hien Tran, Van-Thuy Phi, Akihiko Kato, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto
Date: 2021-12-15 00:00:00
Proceeding: Journal of Natural Language Processing 2021 12
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction, valid

The joint entity and relation extraction task detects entity pairs along with their relations to extract relational triplets. A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity. However, this strategy suffers from two major problems. First, if the HE detection task fails to find a valid head-entity, the model will then miss all related triplets containing this head-entity in the head role. Second, as Yu et al. (2020) stated, their model cannot solve the entity pair overlap (EPO) problem. For a given head-entity, the TER extraction task predicts only a single relation between the head-entity and a tail-entity, even though this entity pair can hold multiple relations. To address these problems, we propose an improved decomposition strategy that considers each extracted entity in two roles (head and tail) and allows a model to predict multiple relations (if any) of an entity pair. In addition, a corresponding model framework is presented to deploy our new decomposition strategy. Experimental results showed that our approach significantly outperformed the previous approach of Yu et al. (2020) and achieved state-of-the-art performance on two benchmark datasets.
Prediction of Future Terrorist Activities Using Deep Neural Networks
Authors: M. Irfan Uddin, Nazir Zada, Furqan Aziz, Yousaf Saeed, Asim Zeb, Syed Atif Ali Shah, Mahmoud Ahmad Al-Khasawneh, Marwan Mahmoud
Date: 2020-04-22 00:00:00
Proceeding: Hindawi 2020 4
Tasks: Prediction

One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.