pwc-archive/papers-with-abstracts huggingface dataset:
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
Authors: Onkar Krishna, Hiroki Ohashi, Saptarshi Sinha
Date: 2023-09-03 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

Cross-domain object detection is challenging, and it involves aligning labeled source and unlabeled target domains. Previous approaches have used adversarial training to align features at both image-level and instance-level. At the instance level, finding a suitable source sample that aligns with a target sample is crucial. A source sample is considered suitable if it differs from the target sample only in domain, without differences in unimportant characteristics such as orientation and color, which can hinder the model's focus on aligning the domain difference. However, existing instance-level feature alignment methods struggle to find suitable source instances because their search scope is limited to mini-batches. Mini-batches are often so small in size that they do not always contain suitable source instances. The insufficient diversity of mini-batches becomes problematic particularly when the target instances have high intra-class variance. To address this issue, we propose a memory-based instance-level domain adaptation framework. Our method aligns a target instance with the most similar source instance of the same category retrieved from a memory storage. Specifically, we introduce a memory module that dynamically stores the pooled features of all labeled source instances, categorized by their labels. Additionally, we introduce a simple yet effective memory retrieval module that retrieves a set of matching memory slots for target instances. Our experiments on various domain shift scenarios demonstrate that our approach outperforms existing non-memory-based methods significantly.
Visualizing changes to US federal environmental agency websites, 2016–2020
Authors: Eric Nost, Gretchen Gehrke, Grace Poudrier, Aaron Lemelin, Marcy Beck, Sara Wylie, on behalf of the Environmental Data & Governance Initiative
Date: 2021-02-25 00:00:00
Proceeding: PLOS ONE 2021 2
Tasks: Data Visualization

Websites have become the primary means by which the US federal government communicates about its operations and presents information for public consumption. However, the alteration or removal of critical information from these sites is often entirely legal and done without the public’s awareness. Relative to paper records, websites enable governments to shape public understanding in quick, scalable, and permissible ways. During the Trump administration, website changes indicative of climate denial prompted civil society organizations to develop tools for tracking online government information sources. We in the Environmental Data & Governance Initiative (EDGI) illustrate how five data visualization techniques can be used to document and analyze changes to government websites. We examine a large sample of websites of US federal environmental agencies and show that between 2016 and 2020: 1) the use of the term “climate change” decreased by an estimated 38%; 2) access to as much as 20% of the Environmental Protection Agency’s website was removed; 3) changes were made more to Cabinet agencies’ websites and to highly visible pages. In formulating ways to visualize and assess the alteration of websites, our study lays important groundwork for both systematically tracking changes and holding officials more accountable for their web practices. Our techniques enable researchers and watchdog groups alike to operate at the scale necessary to understand the breadth of impact an administration can have on the online face of government.
How Uncertain Are Solar Neutrino Predictions?
Authors: John N. Bahcall, Sarbani Basu, M. H. Pinsonneault
Date: 1998-05-12 00:00:00

Solar neutrino fluxes and sound speeds are calculated using a systematic reevaluation of nuclear fusion rates. The largest uncertainties are identified and their effects on the solar neutrino fluxes are estimated.
Solar Models: current epoch and time dependences, neutrinos, and helioseismological properties
Authors: John N. Bahcall, M. H. Pinsonneault, Sarbani Basu
Date: 2000-10-17 00:00:00

We calculate accurate solar models and report the detailed time dependences of important solar quantities. We use helioseismology to constrain the luminosity evolution of the sun and report the discovery of semi-convection in evolved solar models that include diffusion. In addition, we compare the computed sound speeds with the results of p-mode observations by BiSON, GOLF, GONG, LOWL, and MDI instruments. We contrast the neutrino predictions from a set of eight standard-like solar models and four deviant (or deficient) solar models with the results of solar neutrino experiments. For solar neutrino and for helioseismological applications, we present present-epoch numerical tabulations of characteristics of the standard solar model as a function of solar radius, including the principal physical and composition variables, sound speeds, neutrino fluxes, and functions needed for calculating solar neutrino oscillations.
What do we (not) know theoretically about solar neutrino fluxes?
Authors: John N. Bahcall, M. H. Pinsonneault
Date: 2004-02-04 00:00:00

Solar model predictions of 8B and p-p neutrinos agree with the experimentally-determined fluxes (including oscillations): phi(pp)_{measured} = (1.02 +- 0.02 +- 0.01)phi(pp)_{theory}, and phi(8B)_{measured} =(0.88 +- .04 +- 0.23)phi(8B)_{theory}, 1 sigma experimental and theoretical uncertainties, respectively. We use improved input data for nuclear fusion reactions, the equation of state, and the chemical composition of the Sun. The solar composition is the dominant uncertainty in calculating the 8B and CNO neutrino fluxes; the cross section for the 3He(4He, gamma)7Be reaction is the largest uncertainty for the calculated 7Be neutrino flux.
Up-dated opacities from the Opacity Project
Authors: N. R. Badnell, M. A. Bautista, K. Butler, F. Delahaye, C. Mendoza, P. Palmeri, C. J. Zeippen, M. J. Seaton
Date: 2004-10-29 00:00:00

Using the code AUTOSTRUCTURE, extensive calculations of inner-shell atomic data have been made for the chemical elements He, C, N, O, Ne, Na, Mg, Al, Si, S, Ar, Ca, Cr, Mn, Fe and Ni. The results are used to obtain up-dated opacities from the Opacity Project, OP. A number of other improvements on earlier work have also been included. Rosseland-mean opacities from OP are compared with those from OPAL. Differences of 5 to 10% occur. OP gives the `Z-bump', at log(T)=5.2, to be shifted to slightly higher temperatures. The opacities from OP, as functions of temperature and density, are smoother than those from OPAL. Extensive tests show that the numerical accuracy of the OP opacities following integration over frequency mesh and interpolation on temperature-density mesh is better than 1%. Prior to a number of recent investigations which have indicated a need for a downward revision in the solar abundances of oxygen and other elements, there was good agreement between properties of the sun deduced from helioseismology and from stellar evolution models calculated using OPAL opacities. The revisions in abundances destroy that agreement. In a recent paper Bahcall et al argue that the agreement would be restored if opacities for the regions of the sun between 0.7 and 0.4 solar radii were larger than those given by OPAL by about 10%. In the region concerned, the present results from OP do not differ from those of OPAL by more than 2.5%.
OP data on CD for mean opacities and radiative accelerations
Authors: M. J. Seaton
Date: 2004-10-30 00:00:00
Tasks: All

All monochromatic opacity data from the Opacity Project (OP), together with all codes required for the calculation of mean opacities and radiative accelerations for any required chemical mixture, temperature and mass-density, are being put on a 700 Mb CD which will be made generally available. The present paper gives a concise summary of the contents of the CD. More complete documentation will be provided on the CD itself.
A Comparative Study of Sentiment Analysis on Flipkart Dataset using Naïve Bayes Classifier Algorithm
Authors: Shanmugapriyaa D, Aravind M S, Sri Bhavan Prakath, S.Deivarani, Rajarajeshwari K
Date: 2023-07-28 00:00:00
Proceeding: GRENZE International Journal of Engineering and Technology 2023 7
Tasks: Sentiment Analysis

Sentiment analysis, also referred to as sentiment mining, is the process of utilizing computational methods to extract subjective information such as opinions, attitudes, and feelings from textual data. As social media and other online platforms increasingly serve as outlets for expressing emotions and viewpoints, sentiment analysis has become a crucial area of study. This article presents a comprehensive overview of various sentiment analysis techniques and their applications in different fields, including social media analysis. Through machine learning-based techniques, reviews can be classified as positive or negative, and models can be assessed based on metrics such as accuracy, precision, and recall. The article covers rule-based methods, machine learning-based methods, and hybrid approaches, while also addressing the challenges and limitations of sentiment analysis. This resource is valuable for both researchers and practitioners in the field
Exchanging Goods Using Valuable Money
Authors: J. V. Howard
Date: 2015-10-27 00:00:00
Tasks: All

A group of people wishes to use money to exchange goods efficiently over several time periods. However, there are disadvantages to using any of the goods as money, and in addition fiat money issued in the form of notes or coins will be valueless in the final time period, and hence in all earlier periods. Also, Walrasian market prices are determined only up to an arbitrary rescaling. Nevertheless we show that it is possible to devise a system which uses money to exchange goods and in which money has a determinate positive value. In this system, tokens are initially supplied to all traders by a central authority and recovered by a purchase tax. All trades must be made using tokens or promissory notes for tokens. This mechanism controls the flow rather than the stock of money: it introduces some trading frictions, some redistribution of wealth, and some distortion of prices, but these effects can all be made small.
Sand Cat Swarm Optimization Based on Stochastic Variation With Elite Collaboration
Authors: YIMING LI AND GENCHENG WANG
Date: 2022-08-23 00:00:00
Proceeding: IEEE Access 2022 8
Tasks: Sand

The sand cat swarm optimization (SCSO) is a new heuristic optimization algorithm that
simulates the behavior of sand cat groups in the desert using hearing to hunt. To address the shortcomings
of low accuracy of SCSO solution, slow convergence in late iterations and easy convergence stagnation,
a sand cat swarm optimization based on stochastic variation and elite collaboration (SE-SCSO) is pro-
posed. SE-SCSO first introduces a nonlinear periodic adjustment mechanism to balance the exploration
and local exploitation ability of the algorithm and accelerate the convergence of the algorithm.Secondly, the
pseudo-opposition and pseudo-reflection learning mechanisms are used to speed up the optimization-seeking
efficiency of the SCSO algorithm and improve the global convergence capability. Designing elite collab-
orative strategies with random variation to enable the algorithm to jump away from the local extrema,
further improving the algorithm’s optimization-seeking accuracy and convergence speed. In the simulation
experiments, SE-SCSO is compared with Sand Cat Swarm Optimization (SCSO), Sine Cosine Algorithm
(CSA), Circle Search Algorithm (SCA), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO),
Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO) are tested for compari-
son. The experimental results validate the effectiveness of the proposed improvement strategies. Finally,
SE-SCSO is applied to three engineering optimization problems. The results show that the improved
strategy can effectively improve the performance performance of the algorithm, which gives SE-SCSO the
advantages of high convergence accuracy, fast convergence, and the ability to jump out of local optimal
solutions.
Personality Trait Classification Using CNN-LSTM Model
Authors: Joffin George, Koshy M Varkey, Vidul Venogopalan, Rahul G Ms.Neema George
Date: 2023-06-11 00:00:00
Proceeding: IJERT 2023 6
Tasks: Classification, model, Sentiment Analysis

Cognitive base sentiment analysis for
understanding the behaviour of the user on the basis of tweets
post by them in their social media has become a common
technique nowadays. These techniques are not showing accurate
result. In our paper we come forward with a hybrid based deep
learning model called convolution neural network with LSTM to
improve the efficiency of current technique. We classify the
personality trait into 8 types they are introversion,
extroversion,intuition,sensing,thinking,feeling,judging,perceiving
.This type of method have shown better result in comparison with
the existing system. This technology is helpful in recruiting
people for various jobs. We also introduce the concept of audio to
text conversion where user give their audio to the system and our
system will tell the emotions based on given audio.
Human Personality Prediction by Text Analysis Using CNN
Authors: Dutta Sreevalli2, Gujavarthi Lokeshwa Reddy3, Dhatri Gogineni 4, Basava Harsha5, Aradala Mohan Sai6
Date: 2022-12-12 00:00:00
Proceeding: IJFANS 2022 12
Tasks: Prediction

In recent years, predicting an individual's MBTI type using multiple data sources has
become a major research subject. In this paper, we explore the use of machine learning
algorithms for MBTI prediction based on text data. Recently, many firms have begun
shortlisting individuals based on their personality, as this increases job efficiency because
the person would be able to work on what he is good at rather than what he is compelled to
do.The personality model used in this model is Myers-Briggs Personality Type Indicator.
The Myers-Briggs Type Indicator (MBTI) is a popular personality evaluation tool which
separates people into 16 personality types based on their preferences in four domains.
Recent study has revealed that machine learning algorithms can be used to identify an
individual's MBTI type based on writing samples.In this paper, we propose a methodology to
predict an individual's MBTI type using machine learning algorithms such as Convolutional
Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Text Analysis. We also
link an individual's MBTI type to job roles to aid in the recruitment process.
A Method for MBTI Classification Based on Impact of Class Components
Authors: Ninoslav Cerkez, Boris Vrdoljak, Sandro Skansi
Date: 2021-10-18 00:00:00
Proceeding: IEEE 2021 10
Tasks: Classification, text-classification, Text Classification

Predicting the personality type of text authors has a well-known usage in psychology with practical applications in business. From the data science perspective, we can look at this problem as a text classification task that can be tackled using natural language processing (NLP) and deep learning. This paper proposes a method and a novel loss function for multiclass classification using the Myers–Briggs Type Indicator (MBTI) approach for predicting the author’s personality type. Furthermore, this paper proposes an approach that improves the current results of the MBTI multiclass classification because it considers components of compound class labels as supportive elements for better classification according to MBTI. As such, it also provides a new perspective on this classification problem. The experimental results on long short-term memory (LSTM) and convolutional neural network (CNN) models outperform baseline models for multiclass classification, related research on multiclass classification, and most research with four binary approaches to MBTI classification. Moreover, other classification problems that target compound class labels and label parts with binary mutually exclusive values can benefit from this approach.
A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads
Authors: Zixuan Li, Haiying Lin, Zhangyu Wang, Huazhi Li, Miao Yu, Jie Wang
Date: 2023-09-15 00:00:00
Tasks: Point Cloud Registration, Segmentation

Ground segmentation, as the basic task of unmanned intelligent perception, provides an important support for the target detection task. Unstructured road scenes represented by open-pit mines have irregular boundary lines and uneven road surfaces, which lead to segmentation errors in current ground segmentation methods. To solve this problem, a ground segmentation method based on point cloud map is proposed, which involves three parts: region of interest extraction, point cloud registration and background subtraction. Firstly, establishing boundary semantic associations to obtain regions of interest in unstructured roads. Secondly, establishing the location association between point cloud map and the real-time point cloud of region of interest by semantics information. Thirdly, establishing a background model based on Gaussian distribution according to location association, and segments the ground in real-time point cloud by the background substraction method. Experimental results show that the correct segmentation rate of ground points is 99.95%, and the running time is 26ms. Compared with state of the art ground segmentation algorithm Patchwork++, the average accuracy of ground point segmentation is increased by 7.43%, and the running time is increased by 17ms. Furthermore, the proposed method is practically applied to unstructured road scenarios represented by open pit mines.
Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context
Authors: Wei Kang, Xiaoyu Yang, Zengwei Yao, Fangjun Kuang, Yifan Yang, Liyong Guo, Long Lin, Daniel Povey
Date: 2023-09-15 00:00:00

In this paper, we introduce Libriheavy, a large-scale ASR corpus consisting of 50,000 hours of read English speech derived from LibriVox. To the best of our knowledge, Libriheavy is the largest freely-available corpus of speech with supervisions. Different from other open-sourced datasets that only provide normalized transcriptions, Libriheavy contains richer information such as punctuation, casing and text context, which brings more flexibility for system building. Specifically, we propose a general and efficient pipeline to locate, align and segment the audios in previously published Librilight to its corresponding texts. The same as Librilight, Libriheavy also has three training subsets small, medium, large of the sizes 500h, 5000h, 50000h respectively. We also extract the dev and test evaluation sets from the aligned audios and guarantee there is no overlapping speakers and books in training sets. Baseline systems are built on the popular CTC-Attention and transducer models. Additionally, we open-source our dataset creatation pipeline which can also be used to other audio alignment tasks.
Encoded Summarization: Summarizing Documents into Continuous Vector Space for Legal Case Retrieval
Authors: Vu Tran, Minh Le Nguyen, Satoshi Tojo, Ken Satoh
Date: 2023-09-15 00:00:00
Tasks: Retrieval

We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks.
Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown Multi-Class Ensemble of CNNs
Authors: Md Awsafur Rahman, Bishmoy Paul, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Shaikh Anowarul Fattah, Mohammad Saquib
Date: 2023-09-15 00:00:00
Tasks: Attribute

With the huge technological advances introduced by deep learning in audio & speech processing, many novel synthetic speech techniques achieved incredible realistic results. As these methods generate realistic fake human voices, they can be used in malicious acts such as people imitation, fake news, spreading, spoofing, media manipulations, etc. Hence, the ability to detect synthetic or natural speech has become an urgent necessity. Moreover, being able to tell which algorithm has been used to generate a synthetic speech track can be of preeminent importance to track down the culprit. In this paper, a novel strategy is proposed to attribute a synthetic speech track to the generator that is used to synthesize it. The proposed detector transforms the audio into log-mel spectrogram, extracts features using CNN, and classifies it between five known and unknown algorithms, utilizing semi-supervision and ensemble to improve its robustness and generalizability significantly. The proposed detector is validated on two evaluation datasets consisting of a total of 18,000 weakly perturbed (Eval 1) & 10,000 strongly perturbed (Eval 2) synthetic speeches. The proposed method outperforms other top teams in accuracy by 12-13% on Eval 2 and 1-2% on Eval 1, in the IEEE SP Cup challenge at ICASSP 2022.
M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection
Authors: Yao Yuan, Pan Gao, Xiaoyang Tan
Date: 2023-09-15 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Most existing salient object detection methods mostly use U-Net or feature pyramid structure, which simply aggregates feature maps of different scales, ignoring the uniqueness and interdependence of them and their respective contributions to the final prediction. To overcome these, we propose the M$^3$Net, i.e., the Multilevel, Mixed and Multistage attention network for Salient Object Detection (SOD). Firstly, we propose Multiscale Interaction Block which innovatively introduces the cross-attention approach to achieve the interaction between multilevel features, allowing high-level features to guide low-level feature learning and thus enhancing salient regions. Secondly, considering the fact that previous Transformer based SOD methods locate salient regions only using global self-attention while inevitably overlooking the details of complex objects, we propose the Mixed Attention Block. This block combines global self-attention and window self-attention, aiming at modeling context at both global and local levels to further improve the accuracy of the prediction map. Finally, we proposed a multilevel supervision strategy to optimize the aggregated feature stage-by-stage. Experiments on six challenging datasets demonstrate that the proposed M$^3$Net surpasses recent CNN and Transformer-based SOD arts in terms of four metrics. Codes are available at https://github.com/I2-Multimedia-Lab/M3Net.