pwc-archive/papers-with-abstracts huggingface dataset:
Updating velocities in heterogeneous comprehensive learning particle swarm optimization with low-discrepancy sequences
Authors: Yuelin Zhao, Feng Wu, Jianhua Pang, Wanxie Zhong
Date: 2022-09-20 00:00:00

Heterogeneous comprehensive learning particle swarm optimization (HCLPSO) is a type of evolutionary algorithm with enhanced exploration and exploitation capabilities. The low-discrepancy sequence (LDS) is more uniform in covering the search space than random sequences. In this paper, making use of the good uniformity of LDS to improve HCLPSO is researched. Numerical experiments are performed to show that it is impossible to effectively improve the search ability of HCLPSO by only using LDS to generate the initial population. However, if we properly choose some random sequences from the HCLPSO velocities updating formula and replace them with the deterministic LDS, we can obtain a more efficient algorithm. Compared with the original HCLPSO under the same accuracy requirement, the HCLPSO updating the velocities with the deterministic LDS can significantly reduce the iterations required for finding the optimal solution, without decreasing the success rate.
An accurate and flexible analog emulation of AdEx neuron dynamics in silicon
Authors: Sebastian Billaudelle, Johannes Weis, Philipp Dauer, Johannes Schemmel
Date: 2022-09-19 00:00:00

Analog neuromorphic hardware promises fast brain emulation on the one hand and an efficient implementation of novel, brain-inspired computing paradigms on the other. Bridging this spectrum requires flexibly configurable circuits with reliable and reproducible dynamics fostered by an accurate implementation of the targeted neuron and synapse models. This manuscript presents the analog neuron circuits of the mixed-signal accelerated neuromorphic system BrainScaleS-2. They are capable of flexibly and accurately emulating the adaptive exponential leaky integrate-and-fire model equations in combination with both current- and conductance-based synapses, as demonstrated by precisely replicating a wide range of complex neuronal dynamics and firing patterns.
The boosted HP filter is more general than you might think
Authors: Ziwei Mei, Peter C. B. Phillips, Zhentao Shi
Date: 2022-09-20 00:00:00
Tasks: Time Series, Time Series Analysis, Unity

The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.
Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones
Authors: Oliver Y. Chén, Florian Lipsmeier, Huy Phan, Frank Dondelinger, Andrew Creagh, Christian Gossens, Michael Lindemann, Maarten De Vos
Date: 2022-09-20 00:00:00
Tasks: Imputation, Missing Values

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
Authors: Shaochuan Lin, Yicong Yu, Xiyu Ji, Taotao Zhou, Hengxu He, Zisen Sang, Jia Jia, Guodong Cao, Ning Hu
Date: 2022-09-20 00:00:00
Tasks: Attribute, Click-Through Rate Prediction

In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i.e., in different geographical locations and at different times, user click behavior will change significantly. Appropriate spatiotemporal enhancement modeling of user click behavior and large-scale sparse attributes is key to building an LBS model. Although most of existing methods have been proved to be effective, they are difficult to apply to takeaway scenarios due to insufficient modeling of spatiotemporal information. In this paper, we address this challenge by seeking to explicitly model the timing and locations of interactions and proposing a Spatiotemporal-Enhanced Network, namely StEN. In particular, StEN applies a Spatiotemporal Profile Activation module to capture common spatiotemporal preference through attribute features. A Spatiotemporal Preference Activation is further applied to model the personalized spatiotemporal preference embodied by behaviors in detail. Moreover, a Spatiotemporal-aware Target Attention mechanism is adopted to generate different parameters for target attention at different locations and times, thereby improving the personalized spatiotemporal awareness of the model.Comprehensive experiments are conducted on three large-scale industrial datasets, and the results demonstrate the state-of-the-art performance of our methods. In addition, we have also released an industrial dataset for takeaway industry to make up for the lack of public datasets in this community.
Scaling and compressing melodies using geometric similarity measures
Authors: Luis Evaristo Caraballo, José Miguel Díaz-Báñez, Fabio Rodríguez, Vanesa Sánchez-Canales, Inmaculada Ventura
Date: 2022-09-19 00:00:00
Tasks: Audio Compression, Geometric Matching, Information Retrieval, Music Information Retrieval, Retrieval

Melodic similarity measurement is of key importance in music information retrieval. In this paper, we use geometric matching techniques to measure the similarity between two melodies. We represent music as sets of points or sets of horizontal line segments in the Euclidean plane and propose efficient algorithms for optimization problems inspired in two operations on melodies; linear scaling and audio compression. In the scaling problem, an incoming query melody is scaled forward until the similarity measure between the query and a reference melody is minimized. The compression problem asks for a subset of notes of a given melody such that the matching cost between the selected notes and the reference melody is minimized.
Modern Tontine with Transaction Costs
Authors: Lin He, Zongxia Liang, Sheng Wang
Date: 2022-09-20 00:00:00

In this paper, we propose a new type of reversible modern tontine with transaction costs. The wealth of the retiree is divided into a bequest account and a tontine account. And consumption can only be withdrawn from the bequest account. Each transaction between the two accounts incurs fixed and proportional transaction costs depending on the transaction volume. The retiree dynamically controls the allocation policy between the two accounts and the consumption policy to maximize the consumption and bequest utilities. We formulate the optimization problem as a combined stochastic and impulse control problem with infinite time horizon, and characterize the value function as the unique viscosity solution of a HJBQVI (Hamilton-Jacobi-Bellmen quasi-variational inequality). The numerical results exhibit the V-shaped transaction region which consists of two stages. In the former stage, since longevity credits increase gradually, the retiree decreases the proportion of wealth in the tontine account to smooth the wealth and reduce the volatility. However, in the latter stage, the retiree increases the proportion of wealth in the tontine account to gamble for the considerable longevity credits.
An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions
Authors: Vipin Venugopal, Justin Joseph, M. Vipin Das, Malaya Kumar Nath
Date: 2022-07-17 00:00:00
Proceeding: Computer Methods and Programs in Biomedicine 2022 7
Tasks: Lesion Segmentation, Local Color Enhancement, Skin Lesion Segmentation, Transfer Learning

Background and objective: During the initial stages, skin lesions may not have sufficient intensity difference or contrast from the background region on dermatological macro-images. The lack of proper light exposure at the time of capturing the image also reduces the contrast. Low contrast between lesion and background regions adversely impacts segmentation. Enhancement techniques for improving the contrast between lesion and background skin on dermatological macro-images are limited in the literature. An EfficientNet-based modified sigmoid transform for enhancing the contrast on dermatological macroimages is proposed to address this issue.
Methods: A modified sigmoid transform is applied in the HSV color space. The crossover point in the modified sigmoid transform that divides the macro-image into lesion and background is predicted using a modified EfficientNet regressor to exclude manual intervention and subjectivity. The Modified EfficientNet regressor is constructed by replacing the classifier layer in the conventional EfficientNet with a regression layer. Transfer learning is employed to reduce the training time and size of the dataset required to train
the modified EfficientNet regressor. For training the modified EfficientNet regressor, a set of value components extracted from the HSV color space representation of the macro-images in the training dataset is fed as input. The corresponding set of ideal crossover points at which the values of Dice similarity coefficient (DSC) between the ground-truth images and the segmented output images obtained from Otsu’s thresholding are maximum is defined as the target.
Results: On images enhanced with the proposed framework, the DSC of segmented results obtained by Otsu’s thresholding increased from 0.68 ± 0.34 to 0.81 ± 0.17.
Conclusions: The proposed algorithm could consistently improve the contrast between lesion and background on a comprehensive set of test images, justifying its applications in automated analysis of dermatological macro-images.
A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network
Authors: Mozhde Firoozi Pouyani, Mansour Vali, Mohammad Amin Ghasemi
Date: 2022-09-20 00:00:00

Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the signal, however, in these methods, it is necessary to calculate the SNR value to determine the appropriate threshold for noise removal. To solve this problem, a combined model based on EMD and Artificial Neural Network (ANN) trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this research. The model can denoise white and pink noises in the range of -2 to 20dB without thresholding or even estimating SNR, and at the same time, keep the main content of the LS signal well. The proposed method is also compared with the EMD-custom method, and the results obtained from the SNR, and fit criteria indicate the absolute superiority of the proposed method. For example, at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for white and pink noises, respectively, while the corresponding values are respectively 5.89 and 4.31dB for the EMD-Custom method.
Sim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
Authors: Ruoyu Chen, Jingzhi Li, Hua Zhang, Changchong Sheng, Li Liu, Xiaochun Cao
Date: 2022-09-08 00:00:00
Proceeding: ACM Transactions on Multimedia Computing, Communications and Applications 2022 9
Tasks: Attribute, counterfactual, Counterfactual Explanation, Decision Making, Explainable Models, Explanation Generation, Face Verification, Similarity Explanation

Recently, we have witnessed substantial success using the deep neural network in many tasks. While there still exists concerns about the explainability of decision-making, it is beneficial for users to discern the defects in the deployed deep models. Existing explainable models either provide the image-level visualization of attention weights or generate textual descriptions as post-hoc justifications. Different from existing models, in this paper, we propose a new interpretation method that explains the image similarity models by salience maps and attribute words. Our interpretation model contains visual salience maps generation and the counterfactual explanation generation. The former has two branches: global identity relevant region discovery and multi-attribute semantic region discovery. Branch one aims to capture the visual evidence supporting the similarity score, which is achieved by computing counterfactual feature maps. Branch two aims to discover semantic regions supporting different attributes, which helps to understand which attributes in an image might change the similarity score. Then, by fusing visual evidence from two branches, we can obtain the salience maps indicating important response evidence. The latter will generate the attribute words that best explain the similarity using the proposed erasing model. The effectiveness of our model is evaluated on the classical face verification task. Experiments conducted on two benchmarks VGG-Face2 and Celeb-A demonstrate that our model can provide convincing interpretable explanations for the similarity. Moreover, our algorithm can be applied to evidential learning cases, e.g. finding the most characteristic attributes in a set of face images and we verify its effectiveness on the VGGFace2 dataset.
Detection of Malicious Websites Using Machine Learning Techniques
Authors: Adebayo Oshingbesan, Courage Ekoh, Chukwuemeka Okobi, Aime Munezero, Kagame Richard
Date: 2022-09-13 00:00:00

In detecting malicious websites, a common approach is the use of blacklists which are not exhaustive in themselves and are unable to generalize to new malicious sites. Detecting newly encountered malicious websites automatically will help reduce the vulnerability to this form of attack. In this study, we explored the use of ten machine learning models to classify malicious websites based on lexical features and understand how they generalize across datasets. Specifically, we trained, validated, and tested these models on different sets of datasets and then carried out a cross-datasets analysis. From our analysis, we found that K-Nearest Neighbor is the only model that performs consistently high across datasets. Other models such as Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines also consistently outperform a baseline model of predicting every link as malicious across all metrics and datasets. Also, we found no evidence that any subset of lexical features generalizes across models or datasets. This research should be relevant to cybersecurity professionals and academic researchers as it could form the basis for real-life detection systems or further research work.
Rationality and correctness in n-player games
Authors: Lorenzo Bastianello, Mehmet S. Ismail
Date: 2022-09-20 00:00:00

There are two well-known sufficient conditions for Nash equilibrium in two-player games: mutual knowledge of rationality (MKR) and mutual knowledge of conjectures. MKR assumes that the concept of rationality is mutually known. In contrast, mutual knowledge of conjectures assumes that a given profile of conjectures is mutually known, which has long been recognized as a strong assumption. In this note, we introduce a notion of "mutual assumption of rationality and correctness" (MARC), which conceptually aligns more closely with the MKR assumption. We present two main results. Our first result establishes that MARC holds in every two-person zero-sum game. In our second theorem, we show that MARC does not in general hold in n-player games.
A Closer Look at Weakly-Supervised Audio-Visual Source Localization
Authors: Shentong Mo, Pedro Morgado
Date: 2022-08-30 00:00:00
Tasks: Sound Source Localization

Audio-visual source localization is a challenging task that aims to predict the location of visual sound sources in a video. Since collecting ground-truth annotations of sounding objects can be costly, a plethora of weakly-supervised localization methods that can learn from datasets with no bounding-box annotations have been proposed in recent years, by leveraging the natural co-occurrence of audio and visual signals. Despite significant interest, popular evaluation protocols have two major flaws. First, they allow for the use of a fully annotated dataset to perform early stopping, thus significantly increasing the annotation effort required for training. Second, current evaluation metrics assume the presence of sound sources at all times. This is of course an unrealistic assumption, and thus better metrics are necessary to capture the model's performance on (negative) samples with no visible sound sources. To accomplish this, we extend the test set of popular benchmarks, Flickr SoundNet and VGG-Sound Sources, in order to include negative samples, and measure performance using metrics that balance localization accuracy and recall. Using the new protocol, we conducted an extensive evaluation of prior methods, and found that most prior works are not capable of identifying negatives and suffer from significant overfitting problems (rely heavily on early stopping for best results). We also propose a new approach for visual sound source localization that addresses both these problems. In particular, we found that, through extreme visual dropout and the use of momentum encoders, the proposed approach combats overfitting effectively, and establishes a new state-of-the-art performance on both Flickr SoundNet and VGG-Sound Source. Code and pre-trained models are available at https://github.com/stoneMo/SLAVC.
Multi-robot Implicit Control of Massive Herds
Authors: Eduardo Sebastian, Eduardo Montijano, Carlos Sagues
Date: 2022-09-20 00:00:00

This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.
LEMURS: Learning Distributed Multi-Robot Interactions
Authors: Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano, Carlos Sagues
Date: 2022-09-20 00:00:00

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations. We propose a port-Hamiltonian description of the multi-robot system to exploit universal physical constraints in interconnected systems and achieve closed-loop stability. We represent a multi-robot control policy using an architecture that combines self-attention mechanisms and neural ordinary differential equations. The former handles time-varying communication in the robot team, while the latter respects the continuous-time robot dynamics. Our representation is distributed by construction, enabling the learned control policies to be deployed in robot teams of different sizes. We demonstrate that LEMURS can learn interactions and cooperative behaviors from demonstrations of multi-agent navigation and flocking tasks.
PoxVerifi: An Information Verification System to Combat Monkeypox Misinformation
Authors: Akaash Kolluri, Kami Vinton, Dhiraj Murthy
Date: 2022-09-09 00:00:00
Tasks: Fact Checking, Misinformation, Navigate

Following recent outbreaks, monkeypox-related misinformation continues to rapidly spread online. This negatively impacts response strategies and disproportionately harms LGBTQ+ communities in the short-term, and ultimately undermines the overall effectiveness of public health responses. In an attempt to combat monkeypox-related misinformation, we present PoxVerifi, an open-source, extensible tool that provides a comprehensive approach to assessing the accuracy of monkeypox related claims. Leveraging information from existing fact checking sources and published World Health Organization (WHO) information, we created an open-sourced corpus of 225 rated monkeypox claims. Additionally, we trained an open-sourced BERT-based machine learning model for specifically classifying monkeypox information, which achieved 96% cross-validation accuracy. PoxVerifi is a Google Chrome browser extension designed to empower users to navigate through monkeypox-related misinformation. Specifically, PoxVerifi provides users with a comprehensive toolkit to assess the veracity of headlines on any webpage across the Internet without having to visit an external site. Users can view an automated accuracy review from our trained machine learning model, a user-generated accuracy review based on community-member votes, and have the ability to see similar, vetted, claims. Besides PoxVerifi's comprehensive approach to claim-testing, our platform provides an efficient and accessible method to crowdsource accuracy ratings on monkeypox related-claims, which can be aggregated to create new labeled misinformation datasets.