pwc-archive/papers-with-abstracts huggingface dataset:
Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network
Authors: Ilias Papastratis, Kosmas Dimitropoulos, Petros Daras
Date: 2021-04-01 00:00:00
Proceeding: Sensors 2021 4
Tasks: Generative Adversarial Network, Sentence, Sign Language Recognition, Sign Language Translation

Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual features without exploiting text or contextual information to further improve the recognition accuracy. Moreover, the ability of deep generative models to effectively model data distribution has not been investigated yet in the field of sign language recognition. To this end, a novel approach for context-aware continuous sign language recognition using a generative adversarial network architecture, named as Sign Language Recognition Generative Adversarial Network (SLRGAN), is introduced. The proposed network architecture consists of a generator that recognizes sign language glosses by extracting spatial and temporal features from video sequences, as well as a discriminator that evaluates the quality of the generator’s predictions by modeling text information at the sentence and gloss levels. The paper also investigates the importance of contextual information on sign language conversations for both Deaf-to-Deaf and Deaf-to-hearing communication. Contextual information, in the form of hidden states extracted from the previous sentence, is fed into the bidirectional long short-term memory module of the generator to improve the recognition accuracy of the network. At the final stage, sign language translation is performed by a transformer network, which converts sign language glosses to natural language text. Our proposed method achieved word error rates of 23.4%, 2.1% and 2.26% on the RWTH-Phoenix-Weather-2014 and the Chinese Sign Language (CSL) and Greek Sign Language (GSL) Signer Independent (SI) datasets, respectively
Indirect Domain Shift for Single Image Dehazing
Authors: Huan Liu, Jun Chen
Date: 2021-02-05 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

Despite their remarkable expressibility, convolution neural networks (CNNs) still fall short of delivering satisfactory results on single image dehazing, especially in terms of faithful recovery of fine texture details. In this paper, we argue that the inadequacy of conventional CNN-based dehazing methods can be attributed to the fact that the domain of hazy images is too far away from that of clear images, rendering it difficult to train a CNN for learning direct domain shift through an end-to-end manner and recovering texture details simultaneously. To address this issue, we propose to add explicit constraints inside a deep CNN model to guide the restoration process. In contrast to direct learning, the proposed mechanism shifts and narrows the candidate region for the estimation output via multiple confident neighborhoods. Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images. We also propose two different training schemes, including hard IDS and soft IDS, which further reveal the effectiveness of the proposed method. Our extensive experimental results indicate that the dehazing method based on this mechanism outperforms the state-of-the-arts.
科学博士：新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
Authors: 科学博士
Date: 2021-03-04 00:00:00

科学博士：新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
#科学博士​ #新冠疫苗​ #打两针​ #接种两剂​ #COVID19​

★推荐影片★
三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec​

世上居然有三黄蛋！三黄蛋！99.9%的人一生都没有见过！
https://youtu.be/YvkXjYK0fd4​

稀奇的双黄蛋：万分之一的鸡蛋！双黄蛋密码| 科学博士
https://youtu.be/1rDJq_vUl6Q​

哇！透明的双黄蛋，怎么做的？你在家也可以的！科学博士的家庭实验室
https://youtu.be/rlRWZBZ9sf0
Service-based Network Dimensioning for 5G Networks Assisted by Real Data
Authors: M. Umar Khan, J. J Escudero, anà Garcia armada
Date: 2020-06-01 00:00:00

The fifth-generation (5G) of cellular communications is expected to be deployed in the next
years to support a wide range of services with different demands of peak data rates, latency and quality of
experience (QoE). In this work, we propose a novel approach for radio network dimensioning (RND), named
as Heuristic RND (HRND), which uses real open data in the network dimensioning process. This procedure,
named as NetDataDrilling, provides the dimensioning target area by means of network data selection and
visualization from the existing infrastructure. Moreover, the proposed NetDimensioning heuristic considers
the necessary parameters of numerology and bandwidth parts (BWP) supported by New Radio (NR) to
provide a balanced network design mediating among the requirements of coverage, capacity, QoE and cost.
The proposed HRND is based on the novel quality of experience (QoE) parameter ζ by probabilistically
characterizing the 5G radio resource control (rrc) states to ensure the availability of peak data rates for
the MNO’s required percentage of the time. The simulation results show the fulfilment of QoE and load
balancing parameters with significant cost savings compared to the conventional RND methodology.
Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975-1999)
Authors: Mason Youngblood, Karim Baraghith, Patrick E. Savage
Date: 2020-11-04 00:00:00
Tasks: Community Detection, Cultural Vocal Bursts Intensity Prediction, Diversity, Dynamic Community Detection

Cultural phylogenies, or "trees" of culture, are typically built using methods from biology that use similarities and differences in artifacts to infer the historical relationships between the populations that produced them. While these methods have yielded important insights, particularly in linguistics, researchers continue to debate the extent to which cultural phylogenies are tree-like or reticulated due to high levels of horizontal transmission. In this study, we propose a novel method for phylogenetic reconstruction using dynamic community detection that explicitly accounts for transmission between lineages. We used data from 1,498,483 collaborative relationships between electronic music artists to construct a cultural phylogeny based on observed population structure. The results suggest that, although the phylogeny is fundamentally tree-like, horizontal transmission is common and populations never become fully isolated from one another. In addition, we found evidence that electronic music diversity has increased between 1975 and 1999. The method used in this study is available as a new R package called DynCommPhylo. Future studies should apply this method to other cultural systems such as academic publishing and film, as well as biological systems where high resolution reproductive data is available, to assess how levels of reticulation in evolution vary across domains.
太神奇了！水中剪玻璃？视频是真的吗？答案揭晓 科学博士
Authors: 科学博士
Date: 2021-04-03 00:00:00

太神奇了！水中剪玻璃？视频是真的吗？答案揭晓
#科学博士​ #水中剪玻璃​ #真的吗​ #科学实验​ #剪玻璃​

★推荐影片★
新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
https://youtu.be/JQpdRHJnVt0​

三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec​

世上居然有三黄蛋！三黄蛋！99.9%的人一生都没有见过！
https://youtu.be/YvkXjYK0fd4​

稀奇的双黄蛋：万分之一的鸡蛋！双黄蛋密码| 科学博士
https://youtu.be/1rDJq_vUl6Q​

哇！透明的双黄蛋，怎么做的？你在家也可以的！科学博士的家庭实验室
https://youtu.be/rlRWZBZ9sf0
肖美丽女士劝止“渣男”室内吸烟，居然被骂神经病并且被泼油？北大博士有话说：为什么不能在室内公共场所吸烟！
Authors: 科学博士
Date: 2021-04-04 00:00:00

肖美丽女士劝止“渣男”室内吸烟，居然被骂神经病并且被泼油？北大博士有话说：为什么不能在室内公共场所吸烟！
#肖美丽​ #室内吸烟​ #泼油​ #被骂神经病​ #四川成都​ #北大博士​

★推荐影片★
太神奇了！水中剪玻璃？视频是真的吗？答案揭晓
https://youtu.be/zWhefpDgy1s​

新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
https://youtu.be/JQpdRHJnVt0​

三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec
毕业季！如何一个月内写出高质量【博士论文】？方法在这里 科学博士
Authors: 科学博士
Date: 2021-04-04 00:00:00

毕业季！如何一个月内写出高质量【博士论文】？方法在这里
#博士论文​ #一个月​ #毕业论文​ #学位论文​ #论文书写​ #北大博士​

★推荐影片★
直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

太神奇了！水中剪玻璃？视频是真的吗？答案揭晓
https://youtu.be/zWhefpDgy1s​

新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
https://youtu.be/JQpdRHJnVt0​

三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

肖美丽女士劝止“渣男”室内吸烟，居然被骂神经病并且被泼油？北大博士有话说：为什么不能在室内公共场所吸烟！
https://youtu.be/lENcBBGxq_E​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec
Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusions
Authors: Vlad Atanasiu, Isabelle Marthot-Santaniello
Date: 2021-03-11 00:00:00
Tasks: Image Enhancement

Purpose: This article develops theoretical, algorithmic, perceptual, and interaction aspects of script legibility enhancement in the visible light spectrum for the purpose of scholarly editing of papyri texts. - Methods: Novel legibility enhancement algorithms based on color processing and visual illusions are compared to classic methods in a user experience experiment. - Results: (1) The proposed methods outperformed the comparison methods. (2) Users exhibited a broad behavioral spectrum, under the influence of factors such as personality and social conditioning, tasks and application domains, expertise level and image quality, and affordances of software, hardware, and interfaces. No single enhancement method satisfied all factor configurations. Therefore, it is suggested to offer users a broad choice of methods to facilitate personalization, contextualization, and complementarity. (3) A distinction is made between casual and critical vision on the basis of signal ambiguity and error consequences. The criteria of a paradigm for enhancing images for critical applications comprise: interpreting images skeptically; approaching enhancement as a system problem; considering all image structures as potential information; and making uncertainty and alternative interpretations explicit, both visually and numerically.
Bridging Global Context Interactions for High-Fidelity Image Completion
Authors: Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai, Dinh Phung
Date: 2021-04-02 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Inpainting, Vocal Bursts Intensity Prediction

Bridging global context interactions correctly is important for high-fidelity image completion with large masks. Previous methods attempting this via deep or large receptive field (RF) convolutions cannot escape from the dominance of nearby interactions, which may be inferior. In this paper, we propose to treat image completion as a directionless sequence-to-sequence prediction task, and deploy a transformer to directly capture long-range dependence in the encoder. Crucially, we employ a restrictive CNN with small and non-overlapping RF for weighted token representation, which allows the transformer to explicitly model the long-range visible context relations with equal importance in all layers, without implicitly confounding neighboring tokens when larger RFs are used. To improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced to better exploit distantly related high-frequency features. Overall, extensive experiments demonstrate superior performance compared to state-of-the-art methods on several datasets.
The Spatially-Correlative Loss for Various Image Translation Tasks
Authors: Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
Date: 2021-04-02 00:00:00
Proceeding: CVPR 2021 1
Tasks: Self-Supervised Learning, Translation

We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation. Previous methods attempt this by using pixel-level cycle-consistency or feature-level matching losses, but the domain-specific nature of these losses hinder translation across large domain gaps. To address this, we exploit the spatial patterns of self-similarity as a means of defining scene structure. Our spatially-correlative loss is geared towards only capturing spatial relationships within an image rather than domain appearance. We also introduce a new self-supervised learning method to explicitly learn spatially-correlative maps for each specific translation task. We show distinct improvement over baseline models in all three modes of unpaired I2I translation: single-modal, multi-modal, and even single-image translation. This new loss can easily be integrated into existing network architectures and thus allows wide applicability.
Active Learning for Entity Filtering in Microblog Streams
Authors: Damiano Spina, Maria-Hendrike Peetz, Maarten de Rijke
Date: 2015-08-01 00:00:00
Tasks: Active Learning

Monitoring the reputation of entities such as companies or brands in microblog streams (e.g., Twitter) starts by selecting mentions that are related to the entity of interest. Entities are often ambiguous (e.g., "Jaguar'' or "Ford'') and effective methods for selectively removing non-relevant mentions often use background knowledge obtained from domain experts. Manual annotations by experts, however, are costly. We therefore approach the problem of entity filtering with active learning, thereby reducing the annotation load for experts. To this end, we use a strong passive baseline and analyze different sampling methods for selecting samples for annotation. We find that margin sampling--an informative type of sampling that considers the distance to the hyperplane used for class separation--can effectively be used for entity filtering and can significantly reduce the cost of annotating initial training data.
Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning
Authors: Satvik Garg
Date: 2021-03-24 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems, Sentiment Analysis

Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists, healthcare workers, lack of proper equipment and medicines. The entire medical fraternity is in distress, which results in numerous individuals demise. Due to unavailability, people started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TFIDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TFIDF vectorization outperforms all other models with 93% accuracy.
Bottleneck Congestion And Work Starting Time Distribution Considering Household Travels
Authors: Qida Su, David Z. W. Wang
Date: 2021-04-02 00:00:00
Tasks: Decision Making, Management

Flextime is one of the efficient approaches in travel demand management to reduce peak hour congestion and encourage social distancing in epidemic prevention. Previous literature has developed bi-level models of the work starting time choice considering both labor output and urban mobility. Yet, most analytical studies assume the single trip purpose in peak hours (to work) only and do not consider the household travels (daycare drop-off/pick-up). In fact, as one of the main reasons to adopt flextime, household travel plays an influential role in travelers' decision making on work schedule selection. On this account, we incorporate household travels into the work starting time choice model in this study. Both short-run travel behaviours and long-run work start time selection of heterogenous commuters are examined under agglomeration economies. If flextime is not flexible enough, commuters tend to agglomerate in work schedule choice at long-run equilibrium. Further, we analyze optimal schedule choices with two system performance indicators. For total commuting cost, it is found that the rigid school schedule for households may impede the benefits of flextime in commuting cost saving. In terms of total net benefit, while work schedule agglomeration of all commuters leads to the maximum in some cases, the polarized agglomeration of the two heterogenous groups can never achieve the optimum.
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels
Authors: Tiago Santos, Florian Lemmerich, Denis Helic
Date: 2021-04-02 00:00:00
Tasks: Time Series, Time Series Analysis

As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay have seen widespread adoption across many application domains, such as predicting the occurrence time of the next earthquake or stock market spike. However, practical applications of Hawkes processes face a noteworthy challenge: There is substantial and often unquantified variance in decay parameter estimations, especially in the case of a small number of observations or when the dynamics behind the observed data suddenly change. We empirically study the cause of these practical challenges and we develop an approach to surface and thereby mitigate them. In particular, our inspections of the Hawkes process likelihood function uncover the properties of the uncertainty when fitting the decay parameter. We thus propose to explicitly capture this uncertainty within a Bayesian framework. With a series of experiments with synthetic and real-world data from domains such as "classical" earthquake modeling or the manifestation of collective emotions on Twitter, we demonstrate that our proposed approach helps to quantify uncertainty and thereby to understand and fit Hawkes processes in practice.
Semantic Labeling in Remote Sensing Corpora Using Feature Fusion-Based Enhanced Global Convolutional Network with High-Resolution Representations and Depthwise Atrous Convolution
Authors: Teerapong Panboonyuen
Date: 2020-04-12 00:00:00
Tasks: Segmentation, Semantic Segmentation

One of the fundamental tasks in remote sensing is the semantic segmentation on the aerial and satellite images. It plays a vital role in applications, such as agriculture planning, map updates, route optimization, and navigation. The state-of-the-art model is the Enhanced Global Convolutional Network (GCN152-TL-A) from our previous work. It composes two main components: (i) the backbone network to extract features and ( ii ) the segmentation network to annotate labels. However, the accuracy can be further improved, since the deep learning network is not designed for recovering low-level features (e.g., river, low vegetation). In this paper, we aim to improve the semantic segmentation network in three aspects, designed explicitly for the remotely sensed domain. First, we propose to employ a modern backbone network called “High-Resolution Representation (HR)” to extract features with higher quality. It repeatedly fuses the representations generated by the high-to-low subnetworks with the restoration of the low-resolution representations to the same depth and level. Second, “Feature Fusion (FF)” is added to our network to capture low-level features (e.g., lines, dots, or gradient orientation). It fuses between the features from the backbone and the segmentation models, which helps to prevent the loss of these low-level features. Finally, “Depthwise Atrous Convolution (DA)” is introduced to refine the extracted features by using four multi-resolution layers in collaboration with a dilated convolution strategy. The experiment was conducted on three data sets: two private corpora from Landsat-8 satellite and one public benchmark from the “ISPRS Vaihingen” challenge. There are two baseline models: the Deep Encoder-Decoder Network (DCED) and our previous model. The results show that the proposed model significantly outperforms all baselines. It is the winner in all data sets and exceeds more than 90% of F1 : 0.9114, 0.9362, and 0.9111 in two Landsat-8 and ISPRS Vaihingen data sets, respectively. Furthermore, it achieves an accuracy beyond 90% on almost all classes.
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning
Authors: Teerapong Panboonyuen
Date: 2019-01-04 00:00:00
Tasks: Segmentation, Semantic Segmentation, Transfer Learning

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc., on raster images. A deep convolutional encoder–decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, “channel attention” is presented in our network in order to select the most discriminative filters (features). Third, “domain-specific transfer learning” is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of F1 for 17.48% and 2.49% on medium and very high resolution corpora, respectively.