pwc-archive/papers-with-abstracts huggingface dataset:
An Interdisciplinary Approach to Optimal Communication and Flight Operation of High-Altitude Long-Endurance Platforms
Authors: Sidrah Javed, Mohamed-Slim Alouini, Zhiguo Ding
Date: 2022-03-01 00:00:00

Aerial communication platforms, stratospheric high-altitude platform stations (HAPS), have the potential to provide/catalyze advanced mobile wireless communication services with its ubiquitous connectivity and ultra-wide coverage radius. Recently, HAPS has gained immense popularity - achieved primarily through self-sufficient energy systems - to render long-endurance characteristics. The photo-voltaic cells mounted on the aircraft harvest solar energy during the day, which can be partially used for communication and station-keeping, whereas, the excess is stored in the rechargeable batteries for the night time sustenance. We carry out an adroit power budgeting to ascertain if the available solar power can simultaneously and efficiently self-sustain the requisite propulsion and communication power expense. We further propose an energy optimum trajectory for station-keeping flight and non-orthogonal multiple access (NOMA) for multi-cell users, which are served by the directional beams from HAPS communication systems. We design optimal power allocation for downlink (DL) NOMA users along with the ideal position and speed of flight with the aim to maximize sum data rate during the day and minimize power expenditure during the night, while ensuring quality of service. Our findings reveal the significance of joint design of communication and aerodynamics parameters for optimum energy utilization and resource allocation.
Joint Beamforming and Reflection Design for RIS-assisted ISAC Systems
Authors: Rang Liu, Ming Li, A. Lee Swindlehurst
Date: 2022-03-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate the potential of employing reconfigurable intelligent surface (RIS) in integrated sensing and communication (ISAC) systems. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communication and target detection. We aim to jointly design the transmit beamforming and receive filter of the BS, and the reflection coefficients of the RIS to maximize the sum-rate of the communication users, while satisfying a worst-case radar output signal-to-noise ratio (SNR), the transmit power constraint, and the unit modulus property of the reflecting coefficients. An efficient iterative algorithm based on fractional programming (FP), majorization-minimization (MM), and alternative direction method of multipliers (ADMM) is developed to solve the complicated non-convex problem. Simulation results verify the advantage of the proposed RIS-assisted ISAC scheme and the effectiveness of the developed algorithm.
When to Crossover from Earth to Space for Lower Latency Data Communications?
Authors: Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2022-03-01 00:00:00

For data communications over long distances, optical wireless satellite networks (OWSNs) can offer lower latency than optical fiber terrestrial networks (OFTNs). However, when is it beneficial to switch or crossover from an OFTN to an OWSN for lower latency data communications? In this work, we introduce a crossover function that enables to find the crossover distance, i.e., a distance between two points on the surface of the Earth beyond which switching or crossing over from an OFTN to an OWSN for data communications between these points is useful in terms of latency. Numerical results reveal that a higher refractive index of optical fiber (or $i$) in an OFTN and a lower altitude of satellites (or $h$) in an OWSN result in a shorter crossover distance. To account for the variation in the end-to-end propagation distance that occurs over the OWSN, we examine the crossover function in four different scenarios. Numerical results indicate that the crossover distance varies with the end-to-end propagation distance over an OWSN and is different for different scenarios. We calculate the average crossover distance over all scenarios for different $h$ and $i$ and use it to evaluate the simulation results. Furthermore, for a comparative analysis of OFTNs and OWSNs in terms of latency, we study three different OFTNs having different refractive indices and three different OWSNs having different satellite altitudes in three different scenarios for long-distance inter-continental data communications, including connections between New York and Dublin, Sao Paulo and London, and Toronto and Sydney.
Carrier-phase and IMU based GNSS Spoofing Detection for Ground Vehicles
Authors: Zachary Clements, James E. Yoder, Todd E. Humphreys
Date: 2022-02-28 00:00:00

This paper develops, implements, and validates a powerful single-antenna carrier-phase-based test to detect Global Navigation Satellite Systems (GNSS) spoofing attacks on ground vehicles equipped with a low-cost inertial measurement unit (IMU). Increasingly-automated ground vehicles require precise positioning that is resilient to unusual natural or accidental events and secure against deliberate attack. This paper's spoofing detection technique capitalizes on the carrier-phase fixed-ambiguity residual cost produced by a well-calibrated carrier-phase-differential GNSS (CDGNSS) estimator that is tightly coupled with a low-cost IMU. The carrier-phase fixed-ambiguity residual cost is sensitive at the sub-centimeter-level to discrepancies between measured carrier phase values and the values predicted by prior measurements and by the dynamics model, which is based on IMU measurements and on vehicle constraints. Such discrepancies will arise in a spoofing attack due to the attacker's practical inability to predict the centimeter-amplitude vehicle movement caused by roadway irregularities. The effectiveness of the developed spoofing detection method is evaluated with data captured by a vehicle-mounted sensor suite in Austin, Texas. The dataset includes both consumer- and industrial-grade IMU data and a diverse set of multipath environments (open sky, shallow urban, and deep urban). Artificial worst-case spoofing attacks injected into the dataset are detected within two seconds.
Wavelet-Based Multi-Class Seizure Type Classification System
Authors: Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta
Date: 2022-02-19 00:00:00
Tasks: Classification, Diagnostic, EEG, Electroencephalogram (EEG), Vocal Bursts Type Prediction

Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and highly-specialized. Moreover, EEG manual evaluation is a process known to have a low inter-rater agreement among experts. This paper presents a novel automatic technique that involves extraction of specific features from EEG signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them. We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with existing state-of-the-art techniques using overall F1-score due to class imbalance seizure types. Our proposed technique achieved the best results of weighted F1-score of 99.1\% and 74.7\% for seizure-wise and patient-wise classification respectively, thereby setting new benchmark results for this dataset.
The evolutionary stability of partial migration with Allee effects
Authors: Yogesh Trivedi, Ram Singh, Anushaya Mohapatra
Date: 2022-03-01 00:00:00
Tasks: Diversity, Unity

An Allee effect occurs when the per-capita growth rate increases at low densities. Here, we investigate the evolutionary stability of a partial migration population with migrant population experiencing Allee effects. Partial migration is a unique form of phenotypic diversity wherein migrant and non-migrant individuals coexist together. It is shown that when Allee effect is incorporated, the population undergoes a bifurcation as the fraction of migrating population increases from zero to unity. Using an evolutionary game theoretic approach, we prove the existence of a unique evolutionary stable strategy (ESS). It is also shown that the ESS is the only ideal free distribution (IFD) that arises in the context of partially migrating population.
AI-based approach for improving the detection of blood doping in sports
Authors: Maxx Richard Rahman, Jacob Bejder, Thomas Christian Bonne, Andreas Breenfeldt Andersen, Jesús Rodríguez Huertas, Reid Aikin, Nikolai Baastrup Nordsborg, Wolfgang Maaß
Date: 2022-02-09 00:00:00
Tasks: Decision Making

Sports officials around the world are facing incredible challenges due to the unfair means of practices performed by the athletes to improve their performance in the game. It includes the intake of hormonal based drugs or transfusion of blood to increase their strength and the result of their training. However, the current direct test of detection of these cases includes the laboratory-based method, which is limited because of the cost factors, availability of medical experts, etc. This leads us to seek for indirect tests. With the growing interest of Artificial Intelligence in healthcare, it is important to propose an algorithm based on blood parameters to improve decision making. In this paper, we proposed a statistical and machine learning-based approach to identify the presence of doping substance rhEPO in blood samples.
Cardiotocogram Biomedical Signal Classification and Interpretation for Fetal Health Evaluation
Authors: Ggaliwango Marvin, . Md. Golam Rabiul Alam
Date: 2022-03-01 00:00:00
Proceeding: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) 2022 3
Tasks: GPU, Interpretable Machine Learning

Maternal and Neonatal health has been greatly constrained by the in-access to essential maternal health care services due to the preventive measures implemented against the spread of covid-19 hence making maternal and fetal monitoring so hard for physicians. Besides maternal toxic stress caused by fear of catching covid-19, affordable mobility of pregnant mothers to skilled health practitioners in limited resource settings is another contributor to maternal and neonatal mortality and morbidity. In this work, we leveraged existing health data to build interpretable Machine Learning (ML) models that allow physicians to offer precision maternal and fetal medicine based on biomedical signal classification results of fetal cardiotocograms (CTGs).We obtained 99%, 100% and 97% accuracy, precision and recall respectively for the LightGBM classification model without any GPU Learning resources. Then we explainably evaluated all built models with ELI5 and comprehensive feature extraction.
A Machine Learning Approach for Predicting Therapeutic Adherence to Osteoporosis Treatment
Authors: Ggaliwango Marvin, Md. Golam Rabiul Alam
Date: 2022-03-01 00:00:00
Proceeding: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) 2022 3
Tasks: BIG-bench Machine Learning, Management

Osteoporosis is a great disability burden with an expected cost increase of almost 50% by 2025. Due to its long term treatment, 50–70% of the patients withdraw from their osteoporosis medications within the first year of initiation. This necessitates an urgent need for improved osteoporosis and pharmacologic management tools most especially for pregnant women, postmenopausal women and the elderly to ensure therapeutic adherence of the patients during treatment. In this paper, we developed and tested accuracy of Machine Learning Models for predicting therapeutic adherence of patients to enable health professionals to compatibly decide on the therapeutic treatments and approaches for osteoporosis treatment and pharmacologic management of their patients. We were the first to develop and test Machine Learning Models for Predicting Therapeutic Adherence treatments. The ML Model accuracy results are summarized as classical metrics where the ExtraTree Model exhibited the highest accuracy of 100%, 85.0%, 94.5% on the training, testing and overall dataset respectively using Synthetic Minority Over-sampling Technique Support Vector Machine Learning (SMOTE-SVM).
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
Authors: Fangqiang Ding, Zhijun Pan, Yimin Deng, Jianning Deng, Chris Xiaoxuan Lu
Date: 2022-03-02 00:00:00
Tasks: Autonomous Vehicles, Motion Segmentation, Scene Flow Estimation, Self-Supervised Learning, Self-supervised Scene Flow Estimation

Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popular automotive sensor for its robustness against adverse weather and lighting conditions. Compared with the LiDAR point clouds, radar data are drastically sparser, noisier and in much lower resolution. Annotated datasets for radar scene flow are also in absence and costly to acquire in the real world. These factors jointly pose the radar scene flow estimation as a challenging problem. This work aims to address the above challenges and estimate scene flow from 4-D radar point clouds by leveraging self-supervised learning. A robust scene flow estimation architecture and three novel losses are bespoken designed to cope with intractable radar data. Real-world experimental results validate that our method is able to robustly estimate the radar scene flow in the wild and effectively supports the downstream task of motion segmentation.
Pattern Recognition and Event Detection on IoT Data-streams
Authors: Christos Karras, Aristeidis Karras, Spyros Sioutas
Date: 2022-03-02 00:00:00
Tasks: Event Detection, Knowledge Graphs

Big data streams are possibly one of the most essential underlying notions. However, data streams are often challenging to handle owing to their rapid pace and limited information lifetime. It is difficult to collect and communicate stream samples while storing, transmitting and computing a function across the whole stream or even a large segment of it. In answer to this research issue, many streaming-specific solutions were developed. Stream techniques imply a limited capacity of one or more resources such as computing power and memory, as well as time or accuracy limits. Reservoir sampling algorithms choose and store results that are probabilistically significant. A weighted random sampling approach using a generalised sampling algorithmic framework to detect unique events is the key research goal of this work. Briefly, a gradually developed estimate of the joint stream distribution across all feasible components keeps k stream elements judged representative for the full stream. Once estimate confidence is high, k samples are chosen evenly. The complexity is O(min(k,n-k)), where n is the number of items inspected. Due to the fact that events are usually considered outliers, it is sufficient to extract element patterns and push them to an alternate version of k-means as proposed here. The suggested technique calculates the sum of squared errors (SSE) for each cluster, and this is utilised not only as a measure of convergence, but also as a quantification and an indirect assessment of the element distribution's approximation accuracy. This clustering enables for the detection of outliers in the stream based on their distance from the usual event centroids. The findings reveal that weighted sampling and res-means outperform typical approaches for stream event identification. Detected events are shown as knowledge graphs, along with typical clusters of events.
An Accelerated Stochastic Algorithm for Solving the Optimal Transport Problem
Authors: Yiling Xie, Yiling Luo, Xiaoming Huo
Date: 2022-03-02 00:00:00

A primal-dual accelerated stochastic gradient descent with variance reduction algorithm (PDASGD) is proposed to solve linear-constrained optimization problems. PDASGD could be applied to solve the discrete optimal transport (OT) problem and enjoys the best-known computational complexity -- $\widetilde{\mathcal{O}}(n^2/\epsilon)$, where $n$ is the number of atoms, and $\epsilon>0$ is the accuracy. In the literature, some primal-dual accelerated first-order algorithms, e.g., APDAGD, have been proposed and have the order of $\widetilde{\mathcal{O}}(n^{2.5}/\epsilon)$ for solving the OT problem. To understand why our proposed algorithm could improve the rate by a factor of $\widetilde{\mathcal{O}}(\sqrt{n})$, the conditions under which our stochastic algorithm has a lower order of computational complexity for solving linear-constrained optimization problems are discussed. It is demonstrated that the OT problem could satisfy the aforementioned conditions. Numerical experiments demonstrate superior practical performances of the proposed PDASGD algorithm for solving the OT problem.
An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking and Ranking Models
Authors: Qi Zhang, Zijian Yang, Yilun Huang, Jiarong He, Lixiang Wang
Date: 2022-03-02 00:00:00
Tasks: feature selection, Recommendation Systems

The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets. Finally, our team OPDAI won the first place with NDCG@10 score of 0.6773 on the leaderboard. Our solution to this task will be detailed in this paper. To better transform information from source markets to target markets, we adopt two stages of ranking. In pre-ranking stage, we adopt diverse pre-ranking methods or models to do feature generation. After elaborate feature analysis and feature selection, we train LightGBM with 10-fold bagging to do the final ranking.
Exploiting Long-Term Dependencies for Generating Dynamic Scene Graphs
Authors: Shengyu Feng, Subarna Tripathi, Hesham Mostafa, Marcel Nassar, Somdeb Majumdar
Date: 2021-12-18 00:00:00
Tasks: Graph Generation, Object, Scene Graph Detection, Scene Graph Generation, Video Understanding

Dynamic scene graph generation from a video is challenging due to the temporal dynamics of the scene and the inherent temporal fluctuations of predictions. We hypothesize that capturing long-term temporal dependencies is the key to effective generation of dynamic scene graphs. We propose to learn the long-term dependencies in a video by capturing the object-level consistency and inter-object relationship dynamics over object-level long-term tracklets using transformers. Experimental results demonstrate that our Dynamic Scene Graph Detection Transformer (DSG-DETR) outperforms state-of-the-art methods by a significant margin on the benchmark dataset Action Genome. Our ablation studies validate the effectiveness of each component of the proposed approach. The source code is available at https://github.com/Shengyu-Feng/DSG-DETR.
I-CNet: Leveraging Involution and Convolution for Image Classification
Authors: GUIHUANG LIANG ; AND HAOXIANG WANG
Date: 2021-11-30 00:00:00
Proceeding: IEEE Access 2021 11
Tasks: Classification, image-classification, Image Classification

Convolution is widely adapted in deep learning models on image classification tasks for
extracting hidden spatial-domain representations. However, as convolution is channel-specific, the potential
cross-channel correlations in images are often neglected. This paper proposes a novel model, namely
I-CNet, which leverages involution and convolution for improving the accuracy of image classification tasks,
by extracting feature representations on both channel domain and spatial domain. The proposed I-CNet has
been experimented on three image classification datasets. The experimental results show that involution
component employed in I-CNet model can effectively represent cross-channel features in images and I-CNet
is superior to other comparatives with higher classification accuracy achieved on all the three datasets.
High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning
Authors: Paul Pu Liang, Yiwei Lyu, Xiang Fan, Jeffrey Tsaw, Yudong Liu, Shentong Mo, Dani Yogatama, Louis-Philippe Morency, Ruslan Salakhutdinov
Date: 2022-03-02 00:00:00
Tasks: Representation Learning, Time Series Analysis, Transfer Learning, Vocal Bursts Intensity Prediction

Many real-world problems are inherently multimodal, from spoken language, gestures, and paralinguistics humans use to communicate, to force, proprioception, and visual sensors on robots. While there has been an explosion of interest in multimodal learning, these methods are focused on a small set of modalities primarily in language, vision, and audio. In order to accelerate generalization towards diverse and understudied modalities, this paper studies efficient representation learning for high-modality scenarios involving a large set of diverse modalities. Since adding new models for every new modality becomes prohibitively expensive, a critical technical challenge is heterogeneity quantification: how can we measure which modalities encode similar information and interactions in order to permit parameter sharing with previous modalities? This paper proposes two new information theoretic metrics for heterogeneity quantification: (1) modality heterogeneity studies how similar 2 modalities {X1,X2} are by measuring how much information can be transferred from X1 to X2, while (2) interaction heterogeneity studies how similarly pairs of modalities {X1,X2}, {X3,X4} interact by measuring how much information can be transferred from fusing {X1,X2} to {X3,X4}. We show the importance of these 2 proposed metrics as a way to automatically prioritize the fusion of modalities that contain unique information or interactions. The result is a single model, HighMMT, that scales up to 10 modalities (text, image, audio, video, sensors, proprioception, speech, time-series, sets, and tables) and 15 tasks from 5 research areas. Not only does HighMMT outperform prior methods on the tradeoff between performance and efficiency, it also demonstrates a crucial scaling behavior: performance continues to improve with each modality added, and it transfers to entirely new modalities and tasks during fine-tuning.
Counterfactually Evaluating Explanations in Recommender Systems
Authors: Yuanshun Yao, Chong Wang, Hang Li
Date: 2022-03-02 00:00:00
Tasks: counterfactual, Recommendation Systems

Modern recommender systems face an increasing need to explain their recommendations. Despite considerable progress in this area, evaluating the quality of explanations remains a significant challenge for researchers and practitioners. Prior work mainly conducts human study to evaluate explanation quality, which is usually expensive, time-consuming, and prone to human bias. In this paper, we propose an offline evaluation method that can be computed without human involvement. To evaluate an explanation, our method quantifies its counterfactual impact on the recommendation. To validate the effectiveness of our method, we carry out an online user study. We show that, compared to conventional methods, our method can produce evaluation scores more correlated with the real human judgments, and therefore can serve as a better proxy for human evaluation. In addition, we show that explanations with high evaluation scores are considered better by humans. Our findings highlight the promising direction of using the counterfactual approach as one possible way to evaluate recommendation explanations.