pwc-archive/papers-with-abstracts huggingface dataset:
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
Authors: Jannik Fischbach, Max Adam, Victor Dzhagatspanyan, Daniel Mendez, Julian Frattini, Oleksandr Kosenkov, Parisa Elahidoost
Date: 2022-12-13 00:00:00
Tasks: Articles

Context: Sustainable corporate behavior is increasingly valued by society and impacts corporate reputation and customer trust. Hence, companies regularly publish sustainability reports to shed light on their impact on environmental, social, and governance (ESG) factors. Problem: Sustainability reports are written by companies themselves and are therefore considered a company-controlled source. Contrary, studies reveal that non-corporate channels (e.g., media coverage) represent the main driver for ESG transparency. However, analysing media coverage regarding ESG factors is challenging since (1) the amount of published news articles grows daily, (2) media coverage data does not necessarily deal with an ESG-relevant topic, meaning that it must be carefully filtered, and (3) the majority of media coverage data is unstructured. Research Goal: We aim to extract ESG-relevant information from textual media reactions automatically to calculate an ESG score for a given company. Our goal is to reduce the cost of ESG data collection and make ESG information available to the general public. Contribution: Our contributions are three-fold: First, we publish a corpus of 432,411 news headlines annotated as being environmental-, governance-, social-related, or ESG-irrelevant. Second, we present our tool-supported approach called ESG-Miner capable of analyzing and evaluating headlines on corporate ESG-performance automatically. Third, we demonstrate the feasibility of our approach in an experiment and apply the ESG-Miner on 3000 manually labeled headlines. Our approach processes 96.7 % of the headlines correctly and shows a great performance in detecting environmental-related headlines along with their correct sentiment. We encourage fellow researchers and practitioners to use the ESG-Miner at https://www.esg-miner.com.
IP2Vec: Learning Similarities Between IP Addresses
Authors: Markus Ring; Alexander Dallmann; Dieter Landes; Andreas Hotho
Date: 2017-11-21 00:00:00
Proceeding: IEEE International Conference on Data Mining Workshops 2017 11

IP Addresses are a central part of packet- and flow-based network data. However, visualization and similarity computation of IP Addresses are challenging to due the missing natural order. This paper presents a novel similarity measure IP2Vec for IP Addresses that builds on ideas from Word2Vec, a popular approach in text mining. The key idea is to learn similarities by extracting available context information from network data. IP Addresses are similar if they appear in similar contexts. Thus, IP2Vec is automatically derived from the given network data set. The proposed approach is evaluated experimentally on two public flow-based data sets. In particular, we demonstrate the effectiveness of clustering IP Addresses within a botnet data set. In addition, we use visualization methods to analyse the learned similarities in more detail. These experiments indicate that IP2Vec is well suited to capture the similarity of IP Addresses based on their network communications.
Spatial-Temporal Pyramid Graph Reasoning for Action Recognition
Authors: Tiantian Geng, Feng Zheng, Xiaorong Hou, Ke Lu, Guo-Jun Qi, Ling Shao
Date: 2022-08-09 00:00:00
Proceeding: TIP 2022 8
Tasks: Action Recognition, Representation Learning, Temporal Action Localization

Spatial-temporal relation reasoning is a significant yet challenging problem for video action recognition. Previous works typically apply local operations like 2D or 3D CNNs to conduct space-time interactions in video sequences, or simply capture space-time long-range relations of a single fixed scale. However, this is inadequate for obtaining a comprehensive action representation. Besides, most models treat all input frames equally for the final classification, without selecting key frames and motion-sensitive regions. This introduces irrelevant video content and hurts the performance of models. In this paper, we propose a generic Spatial-Temporal Pyramid Graph Network (STPG-Net) to adaptively capture long-range spatial-temporal relations in video sequences at multiple scales. Specifically, we design a temporal attention (TA) module and a spatial-temporal attention (STA) module to learn the contribution of each frame and each space-time region to an action at a feature level, respectively. We then apply the selected key information to build spatial-temporal pyramid graphs for long-range relation reasoning and more comprehensive action representation learning.
STPG-Net can be flexibly integrated into 2D and 3D backbone networks in a plug-and-play manner. Extensive experiments show
that it brings consistent improvements over many challenging baselines on several standard action recognition benchmarks (i.e.,
Something-Something V1 & V2, and FineGym), demonstrating the effectiveness of our approach.
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition
Authors: Guoxi Huang, Adrian G. Bors
Date: 2022-07-13 00:00:00
Proceeding: TIP 2022 7
Tasks: Action Recognition

A rich video data representation can be realized by
means of spatio-temporal frequency analysis. In this research
study we show that a video can be disentangled, following the learning of video characteristics according to their
spatio-temporal properties, into two complementary information
components, dubbed Busy and Quiet. The Busy information
characterizes the boundaries of moving regions, moving objects,
or regions of change in movement. Meanwhile, the Quiet information encodes global smooth spatio-temporal structures defined by
substantial redundancy. We design a trainable Motion Band-Pass
Module (MBPM) for separating Busy and Quiet-defined information, in raw video data. We model a Busy-Quiet Net (BQN) by
embedding the MBPM into a two-pathway CNN architecture.
The efficiency of BQN is determined by avoiding redundancy
in the feature spaces defined by the two pathways. While
one pathway processes the Busy features, the other processes
Quiet features at lower spatio-temporal resolutions reducing both
memory and computational costs. Through experiments we show
that the proposed MBPM can be used as a plug-in module in
various CNN backbone architectures, significantly boosting their
performance. The proposed BQN is shown to outperform many
recent video models on Something-Something V1, Kinetics400,
UCF101 and HMDB51 datasets.
Action Recognition With Motion Diversification and Dynamic Selection
Authors: Peiqin Zhuang, Yu Guo, Zhipeng Yu, Luping Zhou, Lei Bai, Ding Liang, Zhiyong Wang, Yali Wang, Wanli Ouyang
Date: 2022-07-15 00:00:00
Proceeding: TIP 2022 7
Tasks: Action Recognition, Computational Efficiency, Motion Generation

Motion modeling is crucial in modern action
recognition methods. As motion dynamics like moving tempos
and action amplitude may vary a lot in different video clips,
it poses great challenge on adaptively covering proper motion
information. To address this issue, we introduce a Motion
Diversification and Selection (MoDS) module to generate
diversified spatio-temporal motion features and then select the
suitable motion representation dynamically for categorizing the
input video. To be specific, we first propose a spatio-temporal
motion generation (StMG) module to construct a bank of
diversified motion features with varying spatial neighborhood
and time range. Then, a dynamic motion selection (DMS)
module is leveraged to choose the most discriminative motion
feature both spatially and temporally from the feature bank.
As a result, our proposed method can make full use of
the diversified spatio-temporal motion information, while
maintaining computational efficiency at the inference stage.
Extensive experiments on five widely-used benchmarks,
demonstrate the effectiveness of the method and we achieve
state-of-the-art performance on Something-Something V1 & V2
that are of large motion variation
Global Temporal Difference Network for Action Recognition
Authors: Zhao Xie, Jiansong Chen, Kewei Wu, Dan Guo, Richang Hong
Date: 2022-11-23 00:00:00
Proceeding: TMM 2022 11
Tasks: Action Recognition

—Temporal modeling still remains as a challenge
for action recognition. Most existing temporal models focus on
learning local variation between neighbor frames. There exists
obvious deviations between local and global variations, such as
subtle and notable motion variations. In this paper, we propose a
global temporal difference module for action recognition, which
consists of two sub-modules, i.e., a global aggregation module and
a global difference module. These two sub-modules cooperate
following the idea of using prior knowledge from the global
view (i.e., global motion variation) to guide local learning at
each moment. In the global aggregation module, the global
prior knowledge is learned by aggregating the visual feature
sequence of video into a global vector. In the global difference
module, we prepare the difference vector sequence of video
by subtracting each local vector from the global vector. Our
method performs as a contextual guidance with a global view.
The sequential dependency between these difference vectors is
exploited with a channel-wise self-attention operation. Finally,
the difference vectors at each timestamp are further used to
enhance the semantics of the original local features. The enhanced
features endow the action recognition has less deviation to
understand the variation in the video globally. We instantiate
the global temporal difference module into the ResNet block to
form a global temporal difference network (GTDNet). Exhaustive
experiments are conducted and our method achieves competitive
performance at small FLOPs on Something-Something V1 & V2
and Kinetics-400.
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding
Authors: Bin Wang, Chunsheng Liu, Faliang Chang, Wenqian Wang and Nanjun Li
Date: 2022-07-21 00:00:00
Proceeding: TMM 2022 7
Tasks: Action Recognition, Video Understanding

Action recognition in video understanding is a challenging task, largely because of the complexity and difficulty in
temporal modeling, making it suffer from motion information loss
and misalignment of temporal attention in spatial dimensions. To
overcome these difficulties, we propose a novel temporal modeling
method called Adjoint Enhancement Network (AE-Net), which
can fully explore clues of motion and time in the long-range
structure. The AE-Net mainly consists of two new modules:
the Initial Adjoint Enhancement Module (IAE-Module), which
deals with shallow features; and the Global Adjoint Enhancement
Module (GAE-Module), which deals with global features. With
a novel mechanism of parallel spatio-temporal convolution and
difference fusion, the IAE-Module is to enhance the degree of
motion transformation in shallow network features, exciting the
potential of motion flow and avoiding motion information loss.
The GAE-Module is proposed to improve the local temporal
representation in long-range structures by feeding the enhanced
feature differences into a spatial cascade module with residuals
to resolve the misalignment of temporal attention in the spatial
dimension.The experimental results show that our AE-Net can
achieve state-of-the-art results in Something-Something V1, UCF101 and HMDB-51 datasets.
LightFace: A Hybrid Deep Face Recognition Framework
Authors: Sefik Ilkin Serengil, Alper Ozpinar
Date: 2020-11-23 00:00:00
Proceeding: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) 2020 11
Tasks: Face Recognition, Face Swapping

Face recognition constitutes a relatively a popular area which has emerged from the rulers of the social media to top universities in the world. Those frontiers and rule makers recently designed deep learning based custom face recognition models. A modern face recognition pipeline consists of four common stages: detecting, alignment, representation and verification. However, face recognition studies mainly mention the representation stage of a pipeline. In this paper, first of all a review face recognition has been done and then the description of the developed lightweight hybrid high performance face recognition framework has been made. Its hybrid feature enables to switch face recognition models among state-of-the-art ones.
Quantum Clustering with k-Means: a Hybrid Approach
Authors: Alessandro Poggiali, Alessandro Berti, Anna Bernasconi, Gianna M. Del Corso, Riccardo Guidotti
Date: 2022-12-13 00:00:00
Tasks: Clustering

Quantum computing is a promising paradigm based on quantum theory for performing fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degree of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version, still obtaining comparable clustering results.
Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception
Authors: Lizhao You, Zhirong Tang, Pengbo Wang, Zhaorui Wang, Haipeng Dai, Liqun Fu
Date: 2022-12-13 00:00:00
Tasks: Decoder

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$ in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$ for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.
Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning
Authors: Kouki Wakita, Youhei Akimoto, Dimas M. Rachman, Yoshiki Miyauchi, Umeda Naoya, Atsuo Maki
Date: 2022-12-13 00:00:00

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.
Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels
Authors: Shuang Wu, Xiaoqiang Ren, Qing-Shan Jia, Karl Henrik Johansson, Ling Shi
Date: 2022-12-13 00:00:00
Tasks: Decision Making, Scheduling

Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink scheduling for an access point (AP) over multiple unknown channels with heterogeneous AoI requirements defined by AoI-dependent costs. The AP serves $N$ users by using $M$ channels without the channel state information. Each channel serves only one user in each decision epoch. The optimization objective is to minimize the time-averaged AoI-dependent costs plus additional communication transmission costs over an infinite horizon. This decision-making problem can be formulated as a Markov decision process, but it is computationally intractable because the size of the state space grows exponentially with respect to the number of users. To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle's index theory to design an index-based scheduling policy algorithm. We derive an analytic formula for the indices, which reduces the computational overhead and facilitates online adaptation. Our numerical examples show that our index-based scheduling policy achieves comparable performance to the optimal policy and outperforms several other heuristics.
Validation of Neural Network Controllers for Uncertain Systems Through Keep-Close Approach: Robustness Analysis and Safety Verification
Authors: Abdelhafid Zenati, Nabil Aouf
Date: 2022-12-13 00:00:00

Among the major challenges in neural control system technology is the validation and certification of the safety and robustness of neural network (NN) controllers against various uncertainties including unmodelled dynamics, nonlinearities, and time delays. One way in providing such validation guarantees is to maintain the closed-loop system output with a NN controller when its input changes within a bounded set, close to the output of a robustly performing closed-loop reference model. This paper presents a novel approach to analysing the performance and robustness of uncertain feedback systems with NN controllers. Due to the complexity of analysing such systems, the problem is reformulated as the problem of dynamical tracking errors between the closed-loop system with a neural controller and an ideal closed-loop reference model. Then, the approximation of the controller error is characterised by adopting the differential mean value theorem (DMV) and the Integral Quadratic Constraints (IQCs) technique. Moreover, the Relative Integral Square Error (RISE) and the Supreme Square Error (SSE) bounded set are derived for the output of the error dynamical system. The analysis is then performed by integrating Lyapunov theory with the IQCs-based technique. The resulting worst-case analysis provides the user a prior knowledge about the worst case of RISE and SSE between the reference closed-loop model and the uncertain system controlled by the neural controller.
Even Order Explicit Symplectic Geometric Algorithms for Solving Quaternions in Guidance Navigation and Control via Diagonal Padé Approximation and Cayley Transform
Authors: Hong-Yan Zhang, Fei Liu, Yu Zhou, Man Liang
Date: 2022-12-13 00:00:00
Tasks: LEMMA

Quaternion kinematical differential equation (QKDE) plays a key role in navigation, control and guidance systems. Although explicit symplectic geometric algorithms (ESGA) for this problem are available, there is a lack of a unified way for constructing high order symplectic difference schemes with configurable order parameter and the fractional interval sampling problem should be treated carefully. We present even order explicit symplectic geometric algorithms to solve the QKDE with diagonal Pad\'{e} approximation via a four-step strategy. Firstly, the Pad\'{e}-Cayley lemma is proved and used to simplify the symplectic Pad\'{e} approximation for the linear Hamiltonian system with infinitesimal symplectic structure. Secondly, both parallel and alternative iterative methods are proposed to construct the symplectic difference schemes with even order accuracy. Thirdly, the symplecity, orthogonality and invertibility of the single-step transition matrices are proved rigorously. Finally, the explicit symplectic geometric algorithms are designed for both the linear time-invariant and linear time-varying QKDE. The maximum absolute error for solving the QKDE is $\mathcal{O}((t_f-t_0)\tau^{2\ell})$ where $\tau$ is the time step, $\ell$ is the order parameter and $[t_0,t_f]$ is the time span. The linear time complexity and constant space complexity of computation as well as the simple algorithmic structure show that our algorithms are appropriate for real-time applications in aeronautics, astronautics, robotics and so on. The performance of the proposed algorithms are verified and validated by mathematical analysis and numerical simulation.
An activity-based spatial-temporal community electricity vulnerability assessment framework
Authors: Chen Xia, Yuqing Hu, Jianli Chen
Date: 2022-12-13 00:00:00
Tasks: Management

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk management