pwc-archive/papers-with-abstracts huggingface dataset:
Technology Mapping Using WebAI: The Case of 3D Printing
Authors: Julian Schwierzy, Robert Dehghan, Sebastian Schmidt, Elisa Rodepeter, Andreas Stoemmer, Kaan Uctum, Jan Kinne, David Lenz, Hanna Hottenrott
Date: 2022-01-04 00:00:00
Tasks: Language Modeling, Language Modelling

The diffusion of new technologies is crucial for the realization of social and economic returns to innovation. Tracking and mapping technology diffusion is, however, typically limited by the extent to which we can observe technology adoption. This study uses website texts to train a multilingual language model ensemble to map technology diffusion for the case of 3D printing. The study identifies relevant actors and their roles in the diffusion process. The results show that besides manufacturers, service provider, retailers, and information providers play an important role. The geographic distribution of adoption intensity suggests that regional 3D-printing intensity is driven by experienced lead users and the presence of technical universities. The overall adoption intensity varies by sector and firm size. These patterns indicate that the approach of using webAI provides a useful and novel tool for technology mapping which adds to existing measures based on patents or survey data.
Robust Private Supply of a Public Good
Authors: Wanchang Zhang
Date: 2022-01-04 00:00:00

We study the mechanism design problem of selling a public good to a group of agents by a principal in the correlated private value environment. We assume the principal only knows the expectations of the agents' values, but does not know the joint distribution of the values. The principal evaluates a mechanism by the worst-case expected revenue over joint distributions that are consistent with the known expectations. We characterize maxmin public good mechanisms among dominant-strategy incentive compatible and ex-post individually rational mechanisms for the two-agent case and for a special $N$-agent ($N>2$) case.
Adversarial Transformation of Spoofing Attacks for Voice Biometrics
Authors: Alejandro Gomez-Alanis, Jose A. Gonzalez-Lopez, Antonio M. Peinado
Date: 2022-01-04 00:00:00
Tasks: Speaker Verification, Voice Conversion

Voice biometric systems based on automatic speaker verification (ASV) are exposed to \textit{spoofing} attacks which may compromise their security. To increase the robustness against such attacks, anti-spoofing or presentation attack detection (PAD) systems have been proposed for the detection of replay, synthesis and voice conversion based attacks. Recently, the scientific community has shown that PAD systems are also vulnerable to adversarial attacks. However, to the best of our knowledge, no previous work have studied the robustness of full voice biometrics systems (ASV + PAD) to these new types of adversarial \textit{spoofing} attacks. In this work, we develop a new adversarial biometrics transformation network (ABTN) which jointly processes the loss of the PAD and ASV systems in order to generate white-box and black-box adversarial \textit{spoofing} attacks. The core idea of this system is to generate adversarial \textit{spoofing} attacks which are able to fool the PAD system without being detected by the ASV system. The experiments were carried out on the ASVspoof 2019 corpus, including both logical access (LA) and physical access (PA) scenarios. The experimental results show that the proposed ABTN clearly outperforms some well-known adversarial techniques in both white-box and black-box attack scenarios.
Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands
Authors: Ravi Peters, Balázs Dukai, Stelios Vitalis, Jordi van Liempt, Jantien Stoter
Date: 2021-12-30 00:00:00
Tasks: 3D Reconstruction, All

In this paper we present our workflow to automatically reconstruct 3D building models based on 2D building polygons and a LiDAR point cloud. The workflow generates models at different levels of detail (LoDs) to support data requirements of different applications from one consistent source. Specific attention has been paid to make the workflow robust to quickly run a new iteration in case of improvements in an algorithm or in case new input data become available. The quality of the reconstructed data highly depends on the quality of the input data and is monitored in several steps of the process. A 3D viewer has been developed to view and download the openly available 3D data at different LoDs in different formats. The workflow has been applied to all 10 million buildings of The Netherlands. The 3D service will be updated after new input data becomes available.
Cooperative RADAR Sensors for the Digital Test Field A9 (KoRA9): Algorithmic Recap and Lessons Learned
Authors: Sören Kohnert, Julian Stähler, Reinhard Stolle, Florian Geissler
Date: 2022-01-04 00:00:00

Infrastructure sensing systems in combination with Infrastructure-to-Vehicle communication can be used to enhance sensor data obtained from the perspective of a vehicle, only. This paper presents a system consisting of a radar sensor network installed at the side of the street, together with an Edge Processing Unit to fuse the data of different sensors. Measurements taken by the demonstrator are shown, the system architecture is discussed, and some lessons learned are presented.
Partially latent factors based multi-view subspace learning
Authors: Run-kun Lu, Jian-wei Liu, Ze-Yu Liu, Jin-zhong Chen
Date: 2022-01-04 00:00:00
Tasks: Clustering, Multi-view Subspace Clustering, Representation Learning

Multi-view subspace clustering always performs well in high-dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering. This paper first propose a novel matrix factorization method that can separate the coupling consistent and complementary information from observations of multiple views. Based on the obtained latent representations, we further propose two subspace clustering strategies: feature-level fusion and subspace-level hierarchical strategy. Feature-level method concatenates all kinds of latent representations from multiple views, and the original problem therefore degenerates to a single-view subspace clustering process. Subspace-level hierarchical method performs different self-expressive reconstruction processes on the corresponding complementary and consistent latent representations coming from each view, i.e. the prior constraints imposed on different types of subspace representations are related to the appropriate input factors. Finally, extensive experimental results on real-world datasets demonstrate the superiority of our proposed methods by comparing against some state-of-the-art subspace clustering algorithms.
Compressed Training for Dual-Wideband Time-Varying Sub-Terahertz Massive MIMO
Authors: Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
Date: 2022-01-04 00:00:00
Tasks: compressed sensing

6G operators may use millimeter wave (mmWave) and sub-terahertz (sub-THz) bands to meet the ever-increasing demand for wireless access. Sub-THz communication comes with many existing challenges of mmWave communication and adds new challenges associated with the wider bandwidths, more antennas, and harsher propagations. Notably, the frequency- and spatial-wideband (dual-wideband) effects are significant at sub-THz. This paper presents a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels. A set of frequency-dependent array response matrices are constructed, enabling channel recovery from multiple observations across subcarriers via multiple measurement vectors (MMV). Using the temporal correlation, MMV least squares (LS) is designed to estimate the channel based on the previous beam support, and MMV compressed sensing (CS) is applied to the residual signal. We refer to this as the MMV-LS-CS framework. Two-stage (TS) and MMV FISTA-based (M-FISTA) algorithms are proposed for the MMV-LS-CS framework. Leveraging the spreading loss structure, a channel refinement algorithm is proposed to estimate the path coefficients and time delays of the dominant paths. To reduce the computational complexity and enhance the beam resolution, a sequential search method using hierarchical codebooks is developed. Numerical results demonstrate the improved channel estimation accuracy of MMV-LS-CS over state-of-the-art techniques.
Continuous-time Markowitz's mean-variance model under different borrowing and saving rates
Authors: Chonghu Guan, Xiaomin Shi, Zuo Quan Xu
Date: 2022-01-04 00:00:00

We study Markowitz's mean-variance portfolio selection problem in a continuous-time Black-Scholes market with different borrowing and saving rates. The associated Hamilton-Jacobi-Bellman equation is fully nonlinear. Using a delicate partial differential equation and verification argument, the value function is proven to be $C^{3,2}$ smooth. It is also shown that there are a borrowing boundary and a saving barrier which divide the entire trading area into a borrowing-money region, an all-in-stock region, and a saving-money region in ascending order. The optimal trading strategy is a mixture of continuous-time strategy (as suggested by most continuous-time models) and discontinuous-time strategy (as suggested by models with transaction costs): one should put all her wealth in the stock in the middle all-in-stock region, and continuously trade it in the other two regions in a feedback form of wealth and time. It is never optimal to short sale the stock. Numerical examples are also presented to verify the theoretical results and to give more financial insights beyond them.
How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model
Authors: Liao Wang, Jin Yao, Xiaowei Zhang
Date: 2022-01-04 00:00:00

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, car makers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and ``virtual production quantity'' (VPQ). (The latter determines the service level, i.e., the demand fulfillment probability.) We find that hedging generally reduces the optimal price {when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return}, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit.
Runway Extraction and Improved Mapping from Space Imagery
Authors: David A. Noever
Date: 2021-12-30 00:00:00
Tasks: Change Detection, Style Transfer

Change detection methods applied to monitoring key infrastructure like airport runways represent an important capability for disaster relief and urban planning. The present work identifies two generative adversarial networks (GAN) architectures that translate reversibly between plausible runway maps and satellite imagery. We illustrate the training capability using paired images (satellite-map) from the same point of view and using the Pix2Pix architecture or conditional GANs. In the absence of available pairs, we likewise show that CycleGAN architectures with four network heads (discriminator-generator pairs) can also provide effective style transfer from raw image pixels to outline or feature maps. To emphasize the runway and tarmac boundaries, we experimentally show that the traditional grey-tan map palette is not a required training input but can be augmented by higher contrast mapping palettes (red-black) for sharper runway boundaries. We preview a potentially novel use case (called "sketch2satellite") where a human roughly draws the current runway boundaries and automates the machine output of plausible satellite images. Finally, we identify examples of faulty runway maps where the published satellite and mapped runways disagree but an automated update renders the correct map using GANs.
Correlation based feature selection with clustering for high dimensional data
Authors: Smita Chormunge, Sudarson Jena
Date: 2018-01-31 00:00:00
Proceeding: Journal of Electrical Systems and Information Technology 2018 1
Tasks: Clustering, feature selection, Vocal Bursts Intensity Prediction

Feature selection is an essential technique to reduce the dimensionality problem in data mining task. Traditional feature selection
algorithms are fail to scale on large space. This paper proposes a new method to solve dimensionality problem where clustering
is integrating with correlation measure to produce good feature subset. First Irrelevant features are eliminated by using k-means
clustering method and then non-redundant features are selected by correlation measure from each cluster. The proposed method is
evaluate on Microarray and Text datasets and the results are compared with other renowned feature selection methods using Naïve
Bayes classifier. To verify the accuracy of the proposed method with different number of relevant features, percentagewise criteria
is used. The experimental results reveal the efficiency and accuracy of the proposed method.
Graph Representation Learning via Contrasting Cluster Assignments
Authors: ChunYang Zhang, Hongyu Yao, C. L. Philip Chen, Yuena Lin
Date: 2021-12-15 00:00:00
Tasks: Clustering, Contrastive Learning, Graph Representation Learning, Representation Learning

With the rise of contrastive learning, unsupervised graph representation learning has been booming recently, even surpassing the supervised counterparts in some machine learning tasks. Most of existing contrastive models for graph representation learning either focus on maximizing mutual information between local and global embeddings, or primarily depend on contrasting embeddings at node level. However, they are still not exquisite enough to comprehensively explore the local and global views of network topology. Although the former considers local-global relationship, its coarse global information leads to grudging cooperation between local and global views. The latter pays attention to node-level feature alignment, so that the role of global view appears inconspicuous. To avoid falling into these two extreme cases, we propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. This not only facilitates the contrastive effect, but also provides the more high-quality graph information. Meanwhile, GRCCA further excavates cluster-level information, which make it get insight to the elusive association between nodes beyond graph topology. Specifically, we first generate two augmented graphs with distinct graph augmentation strategies, then employ clustering algorithms to obtain their cluster assignments and prototypes respectively. The proposed GRCCA further compels the identical nodes from different augmented graphs to recognize their cluster assignments mutually by minimizing a cross entropy loss. To demonstrate its effectiveness, we compare with the state-of-the-art models in three different downstream tasks. The experimental results show that GRCCA has strong competitiveness in most tasks.
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention
Authors: Dezhong Xu, HENG FU, Lifang Wu, Meng Jian, Dong Wang, AND XU LIU
Date: 2020-03-10 00:00:00
Proceeding: IEEE Access 2020 3
Tasks: Activity Recognition, Autonomous Vehicles, Group Activity Recognition, Optical Flow Estimation, Relation, Video Summarization

Group activity recognition has received a great deal of interest because of its broader applications in sports analysis, autonomous vehicles, CCTV surveillance systems and video summarization systems. Most existing methods typically use appearance features and they seldom consider underlying interaction information. In this work, a technology of novel group activity recognition is proposed based on multi-modal relation representation with temporal-spatial attention. First, we introduce an object relation module, which processes all objects in a scene simultaneously through an interaction between their appearance feature and geometry, thus allowing the modeling of their relations. Second, to extract effective motion features, an optical flow network is fine-tuned by using the action loss as the supervised signal. Then, we propose two types of inference models, opt-GRU and relation-GRU, which are used to encode the object relationship and motion representation effectively, and form the discriminative frame-level feature representation. Finally, an attention-based temporal aggregation layer is proposed to integrate frame-level features with different weights and form effective video-level representations. We have performed extensive experiments on two popular datasets, and both have achieved state-of-the-art performance. The datasets are the Volleyball dataset and the Collective Activity dataset, respectively
Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing
Authors: Hunmin Lee, Jaya Krishna Mandivarapu, Nahom Ogbazghi, Yingshu Li
Date: 2022-01-05 00:00:00
Tasks: Gesture Recognition

Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.
The intersection probability: betting with probability intervals
Authors: Fabio Cuzzolin
Date: 2022-01-05 00:00:00
Tasks: Decision Making

Probability intervals are an attractive tool for reasoning under uncertainty. Unlike belief functions, though, they lack a natural probability transformation to be used for decision making in a utility theory framework. In this paper we propose the use of the intersection probability, a transform derived originally for belief functions in the framework of the geometric approach to uncertainty, as the most natural such transformation. We recall its rationale and definition, compare it with other candidate representives of systems of probability intervals, discuss its credal rationale as focus of a pair of simplices in the probability simplex, and outline a possible decision making framework for probability intervals, analogous to the Transferable Belief Model for belief functions.
Multi Document Reading Comprehension
Authors: Avi Chawla
Date: 2022-01-05 00:00:00
Tasks: Question Answering, Reading Comprehension

Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.
Dynamic GPU Energy Optimization for Machine Learning Training Workloads
Authors: Farui Wang, Weizhe Zhang, Shichao Lai, Meng Hao, Zheng Wang
Date: 2022-01-05 00:00:00
Tasks: BIG-bench Machine Learning, GPU, Scheduling

GPUs are widely used to accelerate the training of machine learning workloads. As modern machine learning models become increasingly larger, they require a longer time to train, leading to higher GPU energy consumption. This paper presents GPOEO, an online GPU energy optimization framework for machine learning training workloads. GPOEO dynamically determines the optimal energy configuration by employing novel techniques for online measurement, multi-objective prediction modeling, and search optimization. To characterize the target workload behavior, GPOEO utilizes GPU performance counters. To reduce the performance counter profiling overhead, it uses an analytical model to detect the training iteration change and only collects performance counter data when an iteration shift is detected. GPOEO employs multi-objective models based on gradient boosting and a local search algorithm to find a trade-off between execution time and energy consumption. We evaluate the GPOEO by applying it to 71 machine learning workloads from two AI benchmark suites running on an NVIDIA RTX3080Ti GPU. Compared with the NVIDIA default scheduling strategy, GPOEO delivers a mean energy saving of 16.2% with a modest average execution time increase of 5.1%.