pwc-archive/papers-with-abstracts huggingface dataset:
北大博士对话高考状元：如何塑造良好学习习惯与生活习惯？助力理想取得成功
Authors: 科学博士
Date: 2021-04-21 00:00:00

北大博士对话高考状元：如何塑造良好学习习惯与生活习惯？助力理想取得成功
#学习习惯​ #生活习惯​ #北大博士对话高考状元​ #科学博士​ #理想与成功​

★推荐影片★
毕业季！如何一个月内写出高质量【博士论文】？方法在这里
https://youtu.be/KpvLQgbWEOI​

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​

成都肖美丽女士劝止“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
MTPSA: Multi Tenant Portable Switch Architecture
Authors: Radostin Stoyanov, Noa Zilberman
Date: 2020-12-01 00:00:00

Virtualized multi-tenant programmable switches enable on-demand support of different users’ protocols and programs. However, supporting multiple tenants on a virtualized switch raises concerns such as resource isolation and security. Truly isolating users is mandatory for virtualized programmable switches to be deployed in production networks. In this paper we propose MTPSA, a Multi
Tenant Portable Switch Architecture. MTPSA offers performance, resource and security isolation. It further introduces roles and privileges within programmable switches. MTPSA is an open-source contribution, implemented over PSA and NetFPGA. Our evaluation shows that it adds minimal overheads, supports line-rate throughput, and scales with the number of users, while providing an isolation of users.
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting
Authors: Zexuan Yin, Paolo Barucca
Date: 2021-04-26 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis, Time Series Forecasting

Time series forecasting based on deep architectures has been gaining popularity in recent years due to their ability to model complex non-linear temporal dynamics. The recurrent neural network is one such model capable of handling variable-length input and output. In this paper, we leverage recent advances in deep generative models and the concept of state space models to propose a stochastic adaptation of the recurrent neural network for multistep-ahead time series forecasting, which is trained with stochastic gradient variational Bayes. In our model design, the transition function of the recurrent neural network, which determines the evolution of the hidden states, is stochastic rather than deterministic as in a regular recurrent neural network; this is achieved by incorporating a latent random variable into the transition process which captures the stochasticity of the temporal dynamics. Our model preserves the architectural workings of a recurrent neural network for which all relevant information is encapsulated in its hidden states, and this flexibility allows our model to be easily integrated into any deep architecture for sequential modelling. We test our model on a wide range of datasets from finance to healthcare; results show that the stochastic recurrent neural network consistently outperforms its deterministic counterpart.
StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible Positional Embeddings
Authors: Gokhan Egri, Todd Zickler
Date: 2021-04-25 00:00:00
Tasks: Decoder

We present a learned, spatially-varying steganography system that allows detecting when and how images have been altered by cropping, splicing or inpainting after publication. The system comprises a learned encoder that imperceptibly hides distinct positional signatures in every local image region before publication, and an accompanying learned decoder that extracts the steganographic signatures to determine, for each local image region, its 2D positional coordinates within the originally-published image. Crop and replacement edits become detectable by the inconsistencies they cause in the hidden positional signatures. Using a prototype system for small $(400 \times 400)$ images, we show experimentally that simple CNN encoder and decoder architectures can be trained jointly to achieve detection that is reliable and robust, without introducing perceptible distortion. This approach could help individuals and image-sharing platforms certify that an image was published by a trusted source, and also know which parts of such an image, if any, have been substantially altered since publication.
A STDP-based Encoding Algorithm for Associative and Composite Data
Authors: Hong-Gyu Yoon, Pilwon Kim
Date: 2021-04-25 00:00:00

Spike-timing-dependent plasticity(STDP) is a biological process of synaptic modification caused by the difference of firing order and timing between neurons. One of the neurodynamical roles of STDP is to form a macroscopic geometrical structure in the neuronal state space in response to a periodic input. This work proposes a practical memory model based on STDP that can store and retrieve high-dimensional associative data. The model combines STDP dynamics with an encoding scheme for distributed representations and can handle multiple composite data in a continuous manner. In the auto-associative memory task where a group of images is continuously streamed to the model, the images are successfully retrieved from an oscillating neural state whenever a proper cue is given. In the second task that deals with semantic memories embedded from sentences, the results show that words can recall multiple sentences simultaneously or one exclusively, depending on their grammatical relations.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Authors: Yukun Zhou, MouCheng Xu, Yipeng Hu, Hongxiang Lin, Joseph Jacob, Pearse A. Keane, Daniel C. Alexander
Date: 2021-04-25 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4\%, 5.1\%, and 4.2\% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentation
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked Objects
Authors: Ricky Ma
Date: 2021-04-26 00:00:00
Tasks: Anomaly Detection, Image Reconstruction, Object Recognition, Out-of-Distribution Detection, Unsupervised Anomaly Detection

This paper presents a novel framework for unsupervised anomaly detection on masked objects called ODDObjects, which stands for Out-of-Distribution Detection on Objects. ODDObjects is designed to detect anomalies of various categories using unsupervised autoencoders trained on COCO-style datasets. The method utilizes autoencoder-based image reconstruction, where high reconstruction error indicates the possibility of an anomaly. The framework extends previous work on anomaly detection with autoencoders, comparing state-of-the-art models trained on object recognition datasets. Various model architectures were compared, and experimental results show that memory-augmented deep convolutional autoencoders perform the best at detecting out-of-distribution objects.
3D Adversarial Attacks Beyond Point Cloud
Authors: Jinlai Zhang, Lyujie Chen, Binbin Liu, Bo Ouyang, Qizhi Xie, Jihong Zhu, Weiming Li, Yanmei Meng
Date: 2021-04-25 00:00:00
Tasks: Adversarial Attack

Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud need to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code is available at: https://github.com/cuge1995/Mesh-Attack.
Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining
Authors: Xiang Chen, Yufeng Huang, Lei Xu
Date: 2021-04-25 00:00:00
Tasks: Rain Removal, Single Image Deraining

Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network (MH2F-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass Extraction Block (MHEB) is proposed to get local and global features across different scales through down- and up-sample process. Besides, a Hierarchical Attentive Distillation Block (HADB) then employs the dual attention feature responses to adaptively recalibrate the hierarchical features and eliminate the redundant ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy to progressively discriminate feature learning and aggregate different features instead of directly concatenating or adding. Extensive experiments on both synthetic and real rainy datasets demonstrate the effectiveness of the designed MH2F-Net by comparing with recent state-of-the-art deraining algorithms. Our source code will be available on the GitHub: https://github.com/cxtalk/MH2F-Net.
Music Embedding: A Tool for Incorporating Music Theory into Computational Music Applications
Authors: SeyyedPooya HekmatiAthar, Mohd Anwar
Date: 2021-04-24 00:00:00

Advancements in the digital technologies have enabled researchers to develop a variety of Computational Music applications. Such applications are required to capture, process, and generate data related to music. Therefore, it is important to digitally represent music in a music theoretic and concise manner. Existing approaches for representing music are ineffective in terms of utilizing music theory. In this paper, we address the disjoint of music theory and computational music by developing an opensource representation tool based on music theory. Through the wide range of use cases, we run an analysis on the classical music pieces to show the usefulness of the developed music embedding.
Single Stage Class Agnostic Common Object Detection: A Simple Baseline
Authors: Chuong H. Nguyen, Thuy C. Nguyen, Anh H. Vo, Yamazaki Masayuki
Date: 2021-04-25 00:00:00
Tasks: Object, object-detection, Object Detection

This paper addresses the problem of common object detection, which aims to detect objects of similar categories from a set of images. Although it shares some similarities with the standard object detection and co-segmentation, common object detection, recently promoted by \cite{Jiang2019a}, has some unique advantages and challenges. First, it is designed to work on both closed-set and open-set conditions, a.k.a. known and unknown objects. Second, it must be able to match objects of the same category but not restricted to the same instance, texture, or posture. Third, it can distinguish multiple objects. In this work, we introduce the Single Stage Common Object Detection (SSCOD) to detect class-agnostic common objects from an image set. The proposed method is built upon the standard single-stage object detector. Furthermore, an embedded branch is introduced to generate the object's representation feature, and their similarity is measured by cosine distance. Experiments are conducted on PASCAL VOC 2007 and COCO 2014 datasets. While being simple and flexible, our proposed SSCOD built upon ATSSNet performs significantly better than the baseline of the standard object detection, while still be able to match objects of unknown categories. Our source code can be found at \href{https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection}{(URL)}
Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps
Authors: Mohammad Rahimzadeh, AmirAli Askari, Soroush Parvin, Elnaz Safi, Mohammad Reza Mohammadi
Date: 2021-04-26 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

One of the main challenges since the advancement of convolutional neural networks is how to connect the extracted feature map to the final classification layer. VGG models used two sets of fully connected layers for the classification part of their architectures, which significantly increased the number of models' weights. ResNet and the next deep convolutional models used the Global Average Pooling (GAP) layer to compress the feature map and feed it to the classification layer. Although using the GAP layer reduces the computational cost, but also causes losing spatial resolution of the feature map, which results in decreasing learning efficiency. In this paper, we aim to tackle this problem by replacing the GAP layer with a new architecture called Wise-SrNet. It is inspired by the depthwise convolutional idea and is designed for processing spatial resolution while not increasing computational cost. We have evaluated our method using three different datasets: Intel Image Classification Challenge, MIT Indoors Scenes, and a part of the ImageNet dataset. We investigated the implementation of our architecture on several models of the Inception, ResNet, and DenseNet families. Applying our architecture has revealed a significant effect on increasing convergence speed and accuracy. Our Experiments on images with 224*224 resolution increased the Top-1 accuracy between 2% to 8% on different datasets and models. Running our models on 512*512 resolution images of the MIT Indoors Scenes dataset showed a notable result of improving the Top-1 accuracy within 3% to 26%. We will also demonstrate the GAP layer's disadvantage when the input images are large and the number of classes is not few. In this circumstance, our proposed architecture can do a great help in enhancing classification results. The code is shared at https://github.com/mr7495/image-classification-spatial.
Easy and Efficient Transformer : Scalable Inference Solution For large NLP model
Authors: Gongzheng li, Yadong Xi, Jingzhen Ding, Duan Wang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
Date: 2021-04-26 00:00:00
Tasks: Decoder, GPU, Inference Optimization, Text Generation

Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels. First, we design highly optimized kernels for long inputs and large hidden sizes. Second, we propose a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. Compared with the state-of-the-art transformer inference library (Faster Transformer v4.0), EET can achieve an average of 1.40-4.20x speedup on the transformer decoder layer with an A100 GPU
Cloud computing as a platform for monetizing data services: A two-sided game business model
Authors: Ahmed Saleh Bataineh, Jamal Bentahar, Rabeb Mizouni, Omar Abdel Wahab, Gaith Rjoub, May El Barachi
Date: 2021-04-26 00:00:00
Tasks: Cloud Computing

With the unprecedented reliance on cloud computing as the backbone for storing today's big data, we argue in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing the big data through Artificial Intelligence (AI) services. The objective is to enable the cloud to be an active platform that can help big data service providers reach a wider set of customers and cloud users (i.e., data consumers) to be exposed to a larger and richer variety of data to run their data analytic tasks. To achieve this vision, we propose a novel game theoretical model, which consists of a mix of cooperative and competitive strategies. The players of the game are the big data service providers, cloud computing platform, and cloud users. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects among involved parties, while integrating the externalities between the cloud resources and consumer demands into the design of the game. Simulations conducted using Amazon and google clustered data show that the proposed model improves the total surplus of all the involved parties in terms of cloud resources provision and monetary profits compared to the current merchant model.
Instance-wise Causal Feature Selection for Model Interpretation
Authors: Pranoy Panda, Sai Srinivas Kancheti, Vineeth N Balasubramanian
Date: 2021-04-26 00:00:00
Tasks: feature selection, model

We formulate a causal extension to the recently introduced paradigm of instance-wise feature selection to explain black-box visual classifiers. Our method selects a subset of input features that has the greatest causal effect on the models output. We quantify the causal influence of a subset of features by the Relative Entropy Distance measure. Under certain assumptions this is equivalent to the conditional mutual information between the selected subset and the output variable. The resulting causal selections are sparser and cover salient objects in the scene. We show the efficacy of our approach on multiple vision datasets by measuring the post-hoc accuracy and Average Causal Effect of selected features on the models output.
TrustyAI Explainability Toolkit
Authors: Rob Geada, Tommaso Teofili, Rui Vieira, Rebecca Whitworth, Daniele Zonca
Date: 2021-04-26 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Artificial intelligence (AI) is becoming increasingly more popular and can be found in workplaces and homes around the world. The decisions made by such "black box" systems are often opaque; that is, so complex as to be functionally impossible to understand. How do we ensure that these systems are behaving as desired? TrustyAI is an initiative which looks into explainable artificial intelligence (XAI) solutions to address this issue of explainability in the context of both AI models and decision services. This paper presents the TrustyAI Explainability Toolkit, a Java and Python library that provides XAI explanations of decision services and predictive models for both enterprise and data science use-cases. We describe the TrustyAI implementations and extensions to techniques such as LIME, SHAP and counterfactuals, which are benchmarked against existing implementations in a variety of experiments.
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
Authors: Jialin Yu, Laila Alrajhi, Anoushka Harit, Zhongtian Sun, Alexandra I. Cristea, Lei Shi
Date: 2021-04-26 00:00:00
Tasks: Variational Inference

Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. However, due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This problem has been studied as a Natural Language Processing (NLP) problem recently, and is known to be challenging, due to the imbalance of the data and the complex nature of the task. In this paper, we explore for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference, as a new solution to assessing the need of instructor interventions for a learner's post. We compare models based on our proposed methods with probabilistic modelling to its baseline non-Bayesian models under similar circumstances, for different cases of applying prediction. The results suggest that Bayesian deep learning offers a critical uncertainty measure that is not supplied by traditional neural networks. This adds more explainability, trust and robustness to AI, which is crucial in education-based applications. Additionally, it can achieve similar or better performance compared to non-probabilistic neural networks, as well as grant lower variance.
An Algorithm to Effect Prompt Termination of Myopic Local Search on Kauffman-s NK Landscape
Authors: Sasanka Sekhar Chanda
Date: 2021-04-26 00:00:00

In Kauffman-s NK model, myopic local search involves flipping one randomly-chosen bit of an N-bit decision string in every time step and accepting the new configuration if that has higher fitness. One issue is that, this algorithm consumes the full extent of computational resources allocated - given by the number of alternative configurations inspected - even though search is expected to terminate the moment there are no neighbors having higher fitness. Otherwise, the algorithm must compute the fitness of all N neighbors in every time step, consuming a high amount of resources. In order to get around this problem, I describe an algorithm that allows search to logically terminate relatively early, without having to evaluate fitness of all N neighbors at every time step. I further suggest that when the efficacy of two algorithms need to be compared head to head, imposing a common limit on the number of alternatives evaluated - metering - provides the necessary level field.