OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Multi-word Compare and Swap
Venue: arXiv cs.DC 2020
Year: 2020

Atomic lock-free multi-word compare-and-swap (MCAS) is a powerful tool for designing concurrent algorithms. Yet, its widespread usage has been limited because lock-free implementations of MCAS make heavy use of expensive compare-and-swap (CAS) instructions. Existing MCAS implementations indeed use at least 2k+1 CASes per k-CAS. This leads to the natural desire to minimize the number of CASes required to implement MCAS. We first prove in this paper that it is impossible to "pack" the information required to perform a k-word CAS (k-CAS) in less than k locations to be CASed. Then we present the first algorithm that requires k+1 CASes per call to k-CAS in the common uncontended case. We implement our algorithm and show that it outperforms a state-of-the-art baseline in a variety of benchmarks in most considered workloads. We also present a durably linearizable (persistent memory friendly) version of our MCAS algorithm using only 2 persistence fences per call, while still only requiring k+1 CASes per k-CAS.
UBER-GNN: A User-Based Embeddings Recommendation based on Graph Neural Networks
Venue: arXiv cs.IR 2020
Year: 2020

The problem of session-based recommendation aims to predict user next actions based on session histories. Previous methods models session histories into sequences and estimate user latent features by RNN and GNN methods to make recommendations. However under massive-scale and complicated financial recommendation scenarios with both virtual and real commodities , such methods are not sufficient to represent accurate user latent features and neglect the long-term characteristics of users. To take long-term preference and dynamic interests into account, we propose a novel method, i.e. User-Based Embeddings Recommendation with Graph Neural Network, UBER-GNN for brevity. UBER-GNN takes advantage of structured data to generate longterm user preferences, and transfers session sequences into graphs to generate graph-based dynamic interests. The final user latent feature is then represented as the composition of the long-term preferences and the dynamic interests using attention mechanism. Extensive experiments conducted on real Ping An scenario show that UBER-GNN outperforms the state-of-the-art session-based recommendation methods.
Reconfigurable Intelligent Surfaces with Reflection Pattern Modulation: Beamforming Design and Performance Analysis
Venue: arXiv cs.IT 2020
Year: 2020

Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beamforming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. We analyze the asymptotic outage probability of the proposed scheme under Rayleigh fading channels, for which a closed-form expression is derived. The achievable rate of the proposed scheme is also investigated for the case where the transmitted symbol is drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate performance. It is shown that the proposed scheme outperforms the conventional RIS-assisted system without information transfer in terms of achievable rate performance.
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumption of a DNN depends on both the number of MAC operations and the energy efficiency of each MAC operation. The former can be estimated at design time; however, the latter depends on the intricate data reuse patterns and underlying hardware architecture. Hence, estimating it at design time is challenging. This work shows that the conventional approach to estimate the data reuse, viz. arithmetic intensity, does not always correctly estimate the degree of data reuse in DNNs since it gives equal importance to all the data types. We propose a novel model, termed "data type aware weighted arithmetic intensity" ($DI$), which accounts for the unequal importance of different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs on two GPUs. We show that our model accurately models data-reuse for all possible data reuse patterns for different types of convolution and different types of layers. We show that our model is a better indicator of the energy efficiency of DNNs. We also show its generality using the central limit theorem.
Fast Approximate Modelling of the Next Combination Result for Stopping the Text Recognition in a Video
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we consider a task of stopping the video stream recognition process of a text field, in which each frame is recognized independently and the individual results are combined together. The video stream recognition stopping problem is an under-researched topic with regards to computer vision, but its relevance for building high-performance video recognition systems is clear. Firstly, we describe an existing method of optimally stopping such a process based on a modelling of the next combined result. Then, we describe approximations and assumptions which allowed us to build an optimized computation scheme and thus obtain a method with reduced computational complexity. The methods were evaluated for the tasks of document text field recognition and arbitrary text recognition in a video. The experimental comparison shows that the introduced approximations do not diminish the quality of the stopping method in terms of the achieved combined result precision, while dramatically reducing the time required to make the stopping decision. The results were consistent for both text recognition tasks.
Silhouette Games: An Interactive One-Way Mirror Approach to Watching Players in VR
Venue: arXiv cs.HC 2020
Year: 2020

Watching others play is a key ingredient of digital games and an important aspect of games user research. However, spectatorship is not very popular in virtual reality, as such games strongly rely on one's feelings of presence. In other words, the head-mounted display creates a barrier between the player and the audience. We contribute an alternative watching approach consisting of two major components: a dynamic view frustum that renders the game scene from the current spectator position and a one-way mirror in front of the screen. This mirror, together with our silhouetting algorithm, allows seeing the player's reflection at the correct position in the virtual world. An exploratory survey emphasizes the overall positive experience of the viewers in our setup. In particular, the participants enjoyed their ability to explore the virtual surrounding via physical repositioning and to observe the blended player during object manipulations. Apart from requesting a larger screen, the participants expressed a strong need to interact with the player. Consequently, we suggest utilizing our technology as a foundation for novel playful experiences with the overarching goal to transform the passive spectator into a collocated player.
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Venue: arXiv cs.AI 2020
Year: 2020

Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated data sets. 2) Non-generalizability that limits deployment to new scanners / institutions. And 3) Inadequate explainability and interpretability. We believe that reinforcement learning can address all three shortcomings, with robust and intuitive algorithms trainable on small datasets. To the best of our knowledge, reinforcement learning has not been directly applied to computer vision tasks for radiological images. In this proof-of-principle work, we train a deep reinforcement learning network to predict brain tumor location. Materials and Methods: Using the BraTS brain tumor imaging database, we trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We did so in concert with image exploration, with rewards and punishments designed to localize lesions. To compare with supervised deep learning, we trained a keypoint detection convolutional neural network on the same 70 images. We applied both approaches to a separate 30 image testing set. Results: Reinforcement learning predictions consistently improved during training, whereas those of supervised deep learning quickly diverged. Reinforcement learning predicted testing set lesion locations with 85% accuracy, compared to roughly 7% accuracy for the supervised deep network. Conclusion: Reinforcement learning predicted lesions with high accuracy, which is unprecedented for such a small training set. We believe that reinforcement learning can propel radiology AI well past the inherent limitations of supervised deep learning, with more clinician-driven research and finally toward true clinical applicability.
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Venue: arXiv cs.CV 2020
Year: 2020

Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this sense, these methods are not really self-supervised. In this paper, we propose a novel self-supervised method, referred to as Exploring Relations in Untrimmed Videos (ERUV), which can be straightforwardly applied to untrimmed videos (real unlabeled) to learn spatio-temporal features. ERUV first generates single-shot videos by shot change detection. Then a designed sampling strategy is used to model relations for video clips. The strategy is saved as our self-supervision signals. Finally, the network learns representations by predicting the category of relations between the video clips. ERUV is able to compare the differences and similarities of videos, which is also an essential procedure for action and video related tasks. We validate our learned models with action recognition and video retrieval tasks with three kinds of 3D CNNs. Experimental results show that ERUV is able to learn richer representations and it outperforms state-of-the-art self-supervised methods with significant margins.
Towards Ranking-based Semantics for Abstract Argumentation using Conditional Logic Semantics
Venue: arXiv cs.AI 2020
Year: 2020

We propose a novel ranking-based semantics for Dung-style argumentation frameworks with the help of conditional logics. Using an intuitive translation for an argumentation framework to generate conditionals, we can apply nonmonotonic inference systems to generate a ranking on possible worlds. With this ranking we construct a ranking for our arguments. With a small extension to this ranking-based semantics we already satisfy some desirable properties for a ranking over arguments.
Internet-Scale Video Streaming over NDN
Venue: arXiv cs.NI 2020
Year: 2020

Research in Information-Centric Networking (ICN) and Named Data Networking (NDN) has produced many protocol designs and software prototypes, but they need to be validated and evaluated by real usage on the Internet, which is also critical to the realization of the ICN/NDN vision in the long-run. This paper presents the first Internet-scale adaptive video streaming service over NDN, which allows regular users to watch videos using NDN technology with no software installation or manual configuration. Since mid-2019, the official NDN website has started using our service to deliver its videos to Internet users over the global NDN testbed, showcasing the feasibility of NDN. We conduct real-world experiments on the NDN testbed to validate the proper implementation of the client, network, and server sides of the proposed system and also evaluate the performance of our video streaming service.
Competitive Allocation of a Mixed Manna
Venue: arXiv cs.GT 2020
Year: 2020

We study the fair division problem of allocating a mixed manna under additively separable piecewise linear concave (SPLC) utilities. A mixed manna contains goods that everyone likes and bads that everyone dislikes, as well as items that some like and others dislike. The seminal work of Bogomolnaia et al. [Econometrica'17] argue why allocating a mixed manna is genuinely more complicated than a good or a bad manna, and why competitive equilibrium is the best mechanism. They also provide the existence of equilibrium and establish its peculiar properties (e.g., non-convex and disconnected set of equilibria even under linear utilities), but leave the problem of computing an equilibrium open. This problem remained unresolved even for only bad manna under linear utilities. Our main result is a simplex-like algorithm based on Lemke's scheme for computing a competitive allocation of a mixed manna under SPLC utilities, a strict generalization of linear. Experimental results on randomly generated instances suggest that our algorithm will be fast in practice. The problem is known to be PPAD-hard for the case of good manna, and we also show a similar result for the case of bad manna. Given these PPAD-hardness results, designing such an algorithm is the only non-brute-force (non-enumerative) option known, e.g., the classic Lemke-Howson algorithm (1964) for computing a Nash equilibrium in a 2-player game is still one of the most widely used algorithms in practice. Our algorithm also yields several new structural properties as simple corollaries. We obtain a (constructive) proof of existence for a far more general setting, membership of the problem in PPAD, rational-valued solution, and odd number of solutions property. The last property also settles the conjecture of Bogomolnaia et al. in the affirmative.
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
Venue: arXiv cs.CV 2020
Year: 2020

The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many new applications in content creation. In this paper, we provide an overview of GANs with a special focus on algorithms and applications for visual synthesis. We cover several important techniques to stabilize GAN training, which has a reputation for being notoriously difficult. We also discuss its applications to image translation, image processing, video synthesis, and neural rendering.
Integration of the 3D Environment for UAV Onboard Visual Object Tracking
Venue: arXiv cs.CV 2020
Year: 2020

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.
Fatigue Assessment using ECG and Actigraphy Sensors
Venue: arXiv cs.LG 2020
Year: 2020

Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for interpretable solution, we proposed a feature selection approach which can select less correlated and high informative features for better understanding system's decision-making process. For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment. Extensive experiments were conducted, and very promising results were achieved.
Quality Classification of Defective Parts from Injection Moulding
Venue: arXiv cs.OH 2020
Year: 2020

This report examines machine learning algorithms for detecting short forming and weaving in plastic parts produced by injection moulding. Transfer learning was implemented by using pretrained models and finetuning them on our dataset of 494 samples of 150 by 150 pixels images. The models tested were Xception, InceptionV3 and Resnet-50. Xception showed the highest overall accuracy (86.66%), followed by InceptionV3 (82.47%) and Resnet-50 (80.41%). Short forming was the easiest fault to identify, with the highest F1 score for each model.
Investigating Coordinated 'Social' Targeting of High-Profile Twitter Accounts
Venue: arXiv cs.SI 2020
Year: 2020

Following the 2016 US presidential election, there has been an increased focus on politically-motivated manipulation of mass-user behavior on social media platforms. Since a large volume of political discussion occurs on these platforms, identifying malicious activity and coordinated campaigns is essential to ensuring a robust democratic environment. Twitter has become a critical communication channel for politicians and other public figures, enabling them to maintain a direct relationship with supporters. However, the platform has been fertile ground for large-scale malicious activity. As the 2020 U.S. presidential election approaches, we have developed tools to monitor follower dynamics of some of the most prominent Twitter users, including U.S. presidential candidates. We investigate numerous, strange phenomena, such as dramatic spike and saw-tooth waveforms on follower-count charts; cohorts of user accounts which 'circulate', i.e., re-follow high profile accounts numerous times; and other 'resurrected' accounts, which have recently re-engaged on Twitter after years of non-activity. So through various analyses in these contexts, we reveal multiple, coordinated 'social' targeting campaigns aimed at affecting the outcomes of socially critical events through the use of networks of social automations (bots), often optimizing their social capital through 'compromised' accounts, which have--unbeknownst to the greater world--been hijacked.
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

Zero-shot learning (ZSL) makes object recognition in images possible in absence of visual training data for a part of the classes from a dataset. When the number of classes is large, classes are usually represented by semantic class prototypes learned automatically from unannotated text collections. This typically leads to much lower performances than with manually designed semantic prototypes such as attributes. While most ZSL works focus on the visual aspect and reuse standard semantic prototypes learned from generic text collections, we focus on the problem of semantic class prototype design for large scale ZSL. More specifically, we investigate the use of noisy textual metadata associated to photos as text collections, as we hypothesize they are likely to provide more plausible semantic embeddings for visual classes if exploited appropriately. We thus make use of a source-based voting strategy to improve the robustness of semantic prototypes. Evaluation on the large scale ImageNet dataset shows a significant improvement in ZSL performances over two strong baselines, and over usual semantic embeddings used in previous works. We show that this improvement is obtained for several embedding methods, leading to state of the art results when one uses automatically created visual and text features.
Diagnosis of Autism in Children using Facial Analysis and Deep Learning
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Although the disease is considered to be genetic, the highest rates of accurate diagnosis occur when the child is tested on behavioral characteristics and facial features. Patients have a common pattern of distinct facial deformities, allowing researchers to analyze only an image of the child to determine if the child has the disease. While there are other techniques and models used for facial analysis and autism classification on their own, our proposal bridges these two ideas allowing classification in a cheaper, more efficient method. Our deep learning model uses MobileNet and two dense layers in order to perform feature extraction and image classification. The model is trained and tested using 3,014 images, evenly split between children with autism and children without it. 90% of the data is used for training, and 10% is used for testing. Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture. Additionally, there may be other diseases that are similarly diagnosable.
A Sub-linear Time Algorithm for Approximating k-Nearest-Neighbor with Full Quality Guarantee
Venue: arXiv cs.CG 2020
Year: 2020

In this paper we propose an algorithm for the approximate k-Nearest-Neighbors problem. According to the existing researches, there are two kinds of approximation criterion. One is the distance criteria, and the other is the recall criteria. All former algorithms suffer the problem that there are no theoretical guarantees for the two approximation criterion. The algorithm proposed in this paper unifies the two kinds of approximation criterion, and has full theoretical guarantees. Furthermore, the query time of the algorithm is sub-linear. As far as we know, it is the first algorithm that achieves both sub-linear query time and full theoretical approximation guarantee.