pwc-archive/papers-with-abstracts huggingface dataset:
A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations
Authors: Itzcóatl Bueno, Ramón A. Carrasco, Raquel Ureña, Enrique Herrera-Viedma
Date: 2022-04-01 00:00:00
Proceeding: journal 2022 4
Tasks: Decision Making, Marketing, Sentiment Analysis

In the era of social media, consumers interact and exchange information, subjective opin-
ions, feelings or thoughts about a given issue, a product or a service, via Web 2.0 technolo-
gies. As a result, electronic Word of Mouth, eWOM, has gained increasing influence on the

formation of user opinions. Therefore, extracting and analyzing eWOM statements by

means of Sentiment Analysis techniques is currently a hot topic that could potentially pro-
vide companies with key information about their customers. However, among all the avail-
able techniques, choosing the most appropriate one according to the analysis criteria is a

real challenge. In this contribution, we first study the main desirable criteria for a senti-
ment analysis approach and classify the different existing approaches based on these cri-
teria to try and provide a solution to this issue. We then propose a Multi-criteria

Decision-Making methodology that selects the most suitable technique for each business
case by taking their particular criteria into account.
A station-data-based model residual machine learning method for fine-grained meteorological grid prediction
Authors: Chuansai ZHOU1, Haochen LI2, Chen YU1, Jiangjiang XIA3, Pingwen ZHANG1, †
Date: 2021-12-16 00:00:00
Proceeding: Appl. Math. Mech. -Engl. Ed., 43(2), 155–166 (2022) 2021 12
Tasks: BIG-bench Machine Learning, Weather Forecasting

Fine-grained weather forecasting data, i.e., the grid data with high-resolution,
have attracted increasing attention in recent years, especially for some specific applications
such as the Winter Olympic Games. Although European Centre for Medium-Range
Weather Forecasts (ECMWF) provides grid prediction up to 240 hours, the coarse data
are unable to meet high requirements of these major events. In this paper, we propose
a method, called model residual machine learning (MRML), to generate grid prediction
with high-resolution based on high-precision stations forecasting. MRML applies model
output machine learning (MOML) for stations forecasting. Subsequently, MRML utilizes
these forecasts to improve the quality of the grid data by fitting a machine learning (ML)
model to the residuals. We demonstrate that MRML achieves high capability at diverse
meteorological elements, specifically, temperature, relative humidity, and wind speed. In
addition, MRML could be easily extended to other post-processing methods by invoking
different techniques. In our experiments, MRML outperforms the traditional downscaling
methods such as piecewise linear interpolation (PLI) on the testing data.
Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques
Authors: Maria Kremezi, Viktoria Kristollari, Vassilia Karathanassi, Konstantinos Topouzelis, Pol Kolokoussis, Nicolò Taggio, Antonello Aiello, Giulio Ceriola, Enrico Barbone, Paolo Corradi
Date: 2020-07-30 00:00:00
Proceeding: Marine Pollution Bulletin 2022
Tasks: Image Super-Resolution

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m2 and c) SWIR bands are important for marine litter detection.
Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion
Authors: Weng Fei Low, Gim Hee Lee
Date: 2022-07-29 00:00:00
Tasks: Surface Reconstruction

Explicit neural surface representations allow for exact and efficient extraction of the encoded surface at arbitrary precision, as well as analytic derivation of differential geometric properties such as surface normal and curvature. Such desirable properties, which are absent in its implicit counterpart, makes it ideal for various applications in computer vision, graphics and robotics. However, SOTA works are limited in terms of the topology it can effectively describe, distortion it introduces to reconstruct complex surfaces and model efficiency. In this work, we present Minimal Neural Atlas, a novel atlas-based explicit neural surface representation. At its core is a fully learnable parametric domain, given by an implicit probabilistic occupancy field defined on an open square of the parametric space. In contrast, prior works generally predefine the parametric domain. The added flexibility enables charts to admit arbitrary topology and boundary. Thus, our representation can learn a minimal atlas of 3 charts with distortion-minimal parameterization for surfaces of arbitrary topology, including closed and open surfaces with arbitrary connected components. Our experiments support the hypotheses and show that our reconstructions are more accurate in terms of the overall geometry, due to the separation of concerns on topology and geometry.
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content
Authors: Nicholas Muir, Steven James
Date: 2022-07-29 00:00:00
Tasks: Reinforcement Learning (RL), valid

In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable of generating diverse levels, but this generation procedure is slow, which is problematic in real-time settings. Reinforcement learning (RL) has also been proposed to tackle the same problem, and while level generation is fast, training time can be prohibitively expensive. We propose a framework to tackle the procedural content generation problem that combines the best of ES and RL. In particular, our approach first uses ES to generate a sequence of levels evolved over time, and then uses behaviour cloning to distil these levels into a policy, which can then be queried to produce new levels quickly. We apply our approach to a maze game and Super Mario Bros, with our results indicating that our approach does in fact decrease the time required for level generation, especially when an increasing number of valid levels are required.
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Authors: Hang Chu, Amir Hosein Khasahmadi, Karl D. D. Willis, Fraser Anderson, Yaoli Mao, Linh Tran, Justin Matejka, Jo Vermeulen
Date: 2022-07-29 00:00:00
Tasks: Data Augmentation, Representation Learning

User modeling is crucial to understanding user behavior and essential for improving user experience and personalized recommendations. When users interact with software, vast amounts of command sequences are generated through logging and analytics systems. These command sequences contain clues to the users' goals and intents. However, these data modalities are highly unstructured and unlabeled, making it difficult for standard predictive systems to learn from. We propose SimCURL, a simple yet effective contrastive self-supervised deep learning framework that learns user representation from unlabeled command sequences. Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation. We train and evaluate our method on a real-world command sequence dataset of more than half a billion commands. Our method shows significant improvement over existing methods when the learned representation is transferred to downstream tasks such as experience and expertise classification.
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
Authors: Nicola Messina, Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Fabrizio Falchi, Giuseppe Amato, Rita Cucchiara
Date: 2022-07-29 00:00:00
Tasks: Cross-Modal Retrieval, Image-text matching, Retrieval, Text Matching

Image-text matching is gaining a leading role among tasks involving the joint understanding of vision and language. In literature, this task is often used as a pre-training objective to forge architectures able to jointly deal with images and texts. Nonetheless, it has a direct downstream application: cross-modal retrieval, which consists in finding images related to a given query text or vice-versa. Solving this task is of critical importance in cross-modal search engines. Many recent methods proposed effective solutions to the image-text matching problem, mostly using recent large vision-language (VL) Transformer networks. However, these models are often computationally expensive, especially at inference time. This prevents their adoption in large-scale cross-modal retrieval scenarios, where results should be provided to the user almost instantaneously. In this paper, we propose to fill in the gap between effectiveness and efficiency by proposing an ALign And DIstill Network (ALADIN). ALADIN first produces high-effective scores by aligning at fine-grained level images and texts. Then, it learns a shared embedding space - where an efficient kNN search can be performed - by distilling the relevance scores obtained from the fine-grained alignments. We obtained remarkable results on MS-COCO, showing that our method can compete with state-of-the-art VL Transformers while being almost 90 times faster. The code for reproducing our results is available at https://github.com/mesnico/ALADIN.
Enhanced Methods for the Weight Constrained Shortest Path Problem
Authors: Saman Ahmadi, Guido Tack, Daniel Harabor, Philip Kilby, Mahdi Jalili
Date: 2022-07-29 00:00:00

The classic problem of constrained pathfinding is a well-studied, yet challenging, topic in AI with a broad range of applications in various areas such as communication and transportation. The Weight Constrained Shortest Path Problem (WCSPP), the base form of constrained pathfinding with only one side constraint, aims to plan a cost-optimum path with limited weight/resource usage. Given the bi-criteria nature of the problem (i.e., dealing with the cost and weight of paths), methods addressing the WCSPP have some common properties with bi-objective search. This paper leverages the recent state-of-the-art techniques in both constrained pathfinding and bi-objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs. We empirically evaluate the performance of our algorithms on a set of large and realistic problem instances and show their advantages over the state-of-the-art algorithms in both time and space metrics. This paper also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomised graphs how bucket-based queues without tie-breaking can effectively improve the algorithmic performance of exhaustive A*-based bi-criteria searches.
End-to-end View Synthesis via NeRF Attention
Authors: Zelin Zhao, Jiaya Jia
Date: 2022-07-29 00:00:00
Tasks: Inductive Bias, NeRF, Novel View Synthesis

In this paper, we present a simple seq2seq formulation for view synthesis where we take a set of ray points as input and output colors corresponding to the rays. Directly applying a standard transformer on this seq2seq formulation has two limitations. First, the standard attention cannot successfully fit the volumetric rendering procedure, and therefore high-frequency components are missing in the synthesized views. Second, applying global attention to all rays and pixels is extremely inefficient. Inspired by the neural radiance field (NeRF), we propose the NeRF attention (NeRFA) to address the above problems. On the one hand, NeRFA considers the volumetric rendering equation as a soft feature modulation procedure. In this way, the feature modulation enhances the transformers with the NeRF-like inductive bias. On the other hand, NeRFA performs multi-stage attention to reduce the computational overhead. Furthermore, the NeRFA model adopts the ray and pixel transformers to learn the interactions between rays and pixels. NeRFA demonstrates superior performance over NeRF and NerFormer on four datasets: DeepVoxels, Blender, LLFF, and CO3D. Besides, NeRFA establishes a new state-of-the-art under two settings: the single-scene view synthesis and the category-centric novel view synthesis.
Rating the Crisis of Online Public Opinion Using a Multi-Level Index System
Authors: Fanqi Meng, Xixi Xiao, Jingdong Wang
Date: 2022-07-29 00:00:00
Tasks: Blocking

Online public opinion usually spreads rapidly and widely, thus a small incident probably evolves into a large social crisis in a very short time, and results in a heavy loss in credit or economic aspects. We propose a method to rate the crisis of online public opinion based on a multi-level index system to evaluate the impact of events objectively. Firstly, the dissemination mechanism of online public opinion is explained from the perspective of information ecology. According to the mechanism, some evaluation indexes are selected through correlation analysis and principal component analysis. Then, a classification model of text emotion is created via the training by deep learning to achieve the accurate quantification of the emotional indexes in the index system. Finally, based on the multi-level evaluation index system and grey correlation analysis, we propose a method to rate the crisis of online public opinion. The experiment with the real-time incident show that this method can objectively evaluate the emotional tendency of Internet users and rate the crisis in different dissemination stages of online public opinion. It is helpful to realizing the crisis warning of online public opinion and timely blocking the further spread of the crisis.
Meta Reinforcement Learning with Successor Feature Based Context
Authors: Xu Han, Feng Wu
Date: 2022-07-29 00:00:00
Tasks: continuous-control, Continuous Control, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most reinforcement learning (RL) methods only focus on learning a single task from scratch and are not able to use prior knowledge to learn other tasks more effectively. Context-based meta RL techniques are recently proposed as a possible solution to tackle this. However, they are usually less efficient than conventional RL and may require many trial-and-errors during training. To address this, we propose a novel meta-RL approach that achieves competitive performance comparing to existing meta-RL algorithms, while requires significantly fewer environmental interactions. By combining context variables with the idea of decomposing reward in successor feature framework, our method does not only learn high-quality policies for multiple tasks simultaneously but also can quickly adapt to new tasks with a small amount of training. Compared with state-of-the-art meta-RL baselines, we empirically show the effectiveness and data efficiency of our method on several continuous control tasks.
Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards
Authors: Yixiang Wang, Yujing Hu, Feng Wu, Yingfeng Chen
Date: 2022-07-29 00:00:00

Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the designer and how well the signal assesses progress in reaching that goal. In many cases, the extrinsic rewards provided by the environment (e.g., win or loss of a game) are very sparse and make it difficult to train agents directly. Researchers usually assist the learning of agents by adding some auxiliary rewards in practice. However, designing auxiliary rewards is often turned to a trial-and-error search for reward settings that produces acceptable results. In this paper, we propose to automatically generate goal-consistent intrinsic rewards for the agent to learn, by maximizing which the expected accumulative extrinsic rewards can be maximized. To this end, we introduce the concept of motivation which captures the underlying goal of maximizing certain rewards and propose the motivation based reward design method. The basic idea is to shape the intrinsic rewards by minimizing the distance between the intrinsic and extrinsic motivations. We conduct extensive experiments and show that our method performs better than the state-of-the-art methods in handling problems of delayed reward, exploration, and credit assignment.
Forensic License Plate Recognition with Compression-Informed Transformers
Authors: Denise Moussa, Anatol Maier, Andreas Spruck, Jürgen Seiler, Christian Riess
Date: 2022-07-29 00:00:00
Tasks: License Plate Recognition

Forensic license plate recognition (FLPR) remains an open challenge in legal contexts such as criminal investigations, where unreadable license plates (LPs) need to be deciphered from highly compressed and/or low resolution footage, e.g., from surveillance cameras. In this work, we propose a side-informed Transformer architecture that embeds knowledge on the input compression level to improve recognition under strong compression. We show the effectiveness of Transformers for license plate recognition (LPR) on a low-quality real-world dataset. We also provide a synthetic dataset that includes strongly degraded, illegible LP images and analyze the impact of knowledge embedding on it. The network outperforms existing FLPR methods and standard state-of-the art image recognition models while requiring less parameters. For the severest degraded images, we can improve recognition by up to 8.9 percent points.
Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks
Authors: Denise Moussa, Germans Hirsch, Christian Riess
Date: 2022-07-29 00:00:00
Tasks: Human Detection, Misinformation

Freely available and easy-to-use audio editing tools make it straightforward to perform audio splicing. Convincing forgeries can be created by combining various speech samples from the same person. Detection of such splices is important both in the public sector when considering misinformation, and in a legal context to verify the integrity of evidence. Unfortunately, most existing detection algorithms for audio splicing use handcrafted features and make specific assumptions. However, criminal investigators are often faced with audio samples from unconstrained sources with unknown characteristics, which raises the need for more generally applicable methods. With this work, we aim to take a first step towards unconstrained audio splicing detection to address this need. We simulate various attack scenarios in the form of post-processing operations that may disguise splicing. We propose a Transformer sequence-to-sequence (seq2seq) network for splicing detection and localization. Our extensive evaluation shows that the proposed method outperforms existing dedicated approaches for splicing detection [3, 10] as well as the general-purpose networks EfficientNet [28] and RegNet [25].
Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria
Authors: Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine, Hashim Ibrahim Bisallah
Date: 2022-07-29 00:00:00

The term Big Data has been coined to refer to the gargantuan bulk of data that cannot be dealt with by traditional data-handling techniques. Big Data is still a novel concept, and in the following literature, we intend to elaborate on it in a palpable fashion. It commences with the concept of the subject in itself, along with its properties and the two general approaches to dealing with it. Big Data provides an opportunity for educational Institutions to use their Information Technology resources strategically to improve educational quality, guide students to higher completion rates and improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing the adoption of big data and analytics in learning institutions, and seeks to establish the limiting factors hindering the use of big data in Institutions of higher learning. A survey research design was adopted in conducting this research, and Questionnaires were the instrument employed for data collection.
KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach
Authors: Adil Bahaj, Safae Lhazmir, Mounir Ghogho
Date: 2022-07-29 00:00:00
Tasks: Knowledge Graph Completion, Link Prediction

Knowledge Graph (KG) completion is an important task that greatly benefits knowledge discovery in many fields (e.g. biomedical research). In recent years, learning KG embeddings to perform this task has received considerable attention. Despite the success of KG embedding methods, they predominantly use negative sampling, resulting in increased computational complexity as well as biased predictions due to the closed world assumption. To overcome these limitations, we propose \textbf{KG-NSF}, a negative sampling-free framework for learning KG embeddings based on the cross-correlation matrices of embedding vectors. It is shown that the proposed method achieves comparable link prediction performance to negative sampling-based methods while converging much faster.
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
Authors: Tegg Taekyong Sung, Bo Ryu
Date: 2022-07-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neural schedulers based on deep reinforcement learning (DRL) have shown considerable potential for solving real-world resource allocation problems, as they have demonstrated significant performance gain in the domain of cluster computing. In this paper, we investigate the feasibility of neural schedulers for the domain of System-on-Chip (SoC) resource allocation through extensive experiments and comparison with non-neural, heuristic schedulers. The key finding is three-fold. First, neural schedulers designed for cluster computing domain do not work well for SoC due to i) heterogeneity of SoC computing resources and ii) variable action set caused by randomness in incoming jobs. Second, our novel neural scheduler technique, Eclectic Interaction Matching (EIM), overcomes the above challenges, thus significantly improving the existing neural schedulers. Specifically, we rationalize the underlying reasons behind the performance gain by the EIM-based neural scheduler. Third, we discover that the ratio of the average processing elements (PE) switching delay and the average PE computation time significantly impacts the performance of neural SoC schedulers even with EIM. Consequently, future neural SoC scheduler design must consider this metric as well as its implementation overhead for practical utility.