OpenRaiser/Intern-Atlas huggingface dataset:
Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems
Venue: arXiv cs.IT 2020
Year: 2020

Power consumption of multi-user (MU) precoding is a major concern in all-digital massive MU multiple-input multiple-output (MIMO) base-stations with hundreds of antenna elements operating at millimeter-wave (mmWave) frequencies. We propose to replace part of the linear Wiener filter (WF) precoding matrix by a finite-alphabet WF precoding (FAWP) matrix, which enables the use of low-precision hardware that consumes low power and area. To minimize the performance loss of our approach, we present methods that efficiently compute FAWP matrices that best mimic the WF precoder. Our results show that FAWP matrices approach infinite-precision error-rate and error-vector magnitude performance with only 3-bit precoding weights, even when operating in realistic mmWave channels. Hence, FAWP is a promising approach to substantially reduce power consumption and silicon area in all-digital mmWave massive MU-MIMO systems.
Simphony: An open-source photonic integrated circuit simulation framework
Venue: arXiv cs.ET 2020
Year: 2020

We present Simphony, a free and open-source software toolbox for abstracting and simulating photonic integrated circuits, implemented in Python. The toolbox is both fast and easily extensible; plugins can be written to provide compatibility with existing layout tools, and device libraries can be easily created without a deep knowledge of programming. We include several examples of photonic circuit simulations with novel features and demonstrate a speedup of more than 20x over a leading commercially available software tool.
Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain
Venue: arXiv cs.RO 2020
Year: 2020

This study proposes an integrated task and motion planning method for dynamic locomotion in partially observable environments with multi-level safety guarantees. This layered planning framework is composed of a high-level symbolic task planner and a low-level phase-space motion planner. A belief abstraction at the task planning level enables belief estimation of dynamic obstacle locations and guarantees navigation safety with collision avoidance. The high-level task planner, i.e., a two-level navigation planner, employs linear temporal logic for a reactive game synthesis between the robot and its environment while incorporating low-level safe keyframe policies into formal task specification design. The synthesized task planner commands a series of locomotion actions including walking step length, step height, and heading angle changes, to the underlying keyframe decision-maker, which further determines the robot center-of-mass apex velocity keyframe. The low-level phase-space planner uses a reduced-order locomotion model to generate non-periodic trajectories meeting balancing safety criteria for straight and steering walking. These criteria are characterized by constraints on locomotion keyframe states, and are used to define keyframe transition policies via viability kernels. Simulation results of a Cassie bipedal robot designed by Agility Robotics demonstrate locomotion maneuvering in a three-dimensional, partially observable environment consisting of dynamic obstacles and uneven terrain.
Can Microtask Programming Work in Industry?
Venue: arXiv cs.SE 2020
Year: 2020

A critical issue in software development projects in IT service companies is finding the right people at the right time. By enabling assignments of tasks to people to be more fluid, the use of crowdsourcing approaches within a company offers a potential solution to this challenge. Inside a company, as multiple system development projects are ongoing separately, developers with slack time on one project might use this time to contribute to other projects. In this paper, we report on a case study of the application of crowdsourcing within an industrial web application system development project in a large telecommunications company. Developers worked with system specifications which were organized into a set of microtasks, offering a set of short and self-contained descriptions. When crowd workers in other projects had slack time, they fetched and completed microtasks. Our results offer initial evidence for the potential value of microtask programming in increasing the fluidity of team assignments within a company. Crowd contributors to the project were able to onboard and contribute to a new project in less than 2 hours. After onboarding, the crowd workers were together able to successfully implement a small program which contained only a small number of defects. Interview and survey data gathered from project participants revealed that crowd workers reported that they perceived onboarding costs to be reduced and did not experience issues with the reduced face to face communication, but experienced challenges with motivation.
An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
Venue: arXiv cs.CV 2020
Year: 2020

While supervised deep learning has achieved great success in a range of applications, relatively little work has studied the discovery of knowledge from unlabeled data. In this paper, we propose an unsupervised deep learning framework to provide a potential solution for the problem that existing deep learning techniques require large labeled data sets for completing the training process. Our proposed introduces a new principle of joint learning on both deep representations and GMM (Gaussian Mixture Model)-based deep modeling, and thus an integrated objective function is proposed to facilitate the principle. In comparison with the existing work in similar areas, our objective function has two learning targets, which are created to be jointly optimized to achieve the best possible unsupervised learning and knowledge discovery from unlabeled data sets. While maximizing the first target enables the GMM to achieve the best possible modeling of the data representations and each Gaussian component corresponds to a compact cluster, maximizing the second term will enhance the separability of the Gaussian components and hence the inter-cluster distances. As a result, the compactness of clusters is significantly enhanced by reducing the intra-cluster distances, and the separability is improved by increasing the inter-cluster distances. Extensive experimental results show that the propose method can improve the clustering performance compared with benchmark methods.
Hierarchical Roofline Performance Analysis for Deep Learning Applications
Venue: arXiv cs.DC 2020
Year: 2020

This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs. It discusses the extension of the Empirical Roofline Toolkit for broader support of a range of data precisions and Tensor Core support and introduces a Nsight Compute based method to accurately collect application performance information. This methodology allows for automated machine characterization and application characterization for Roofline analysis across the entire memory hierarchy on NVIDIA GPUs, and it is validated by a complex deep learning application used for climate image segmentation. We use two versions of the code, in TensorFlow and PyTorch respectively, to demonstrate the use and effectiveness of this methodology. We highlight how the application utilizes the compute and memory capabilities on the GPU and how the implementation and performance differ in two deep learning frameworks.
The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs
Venue: arXiv cs.SE 2020
Year: 2020

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet for short. PiaNet consists of a feature-extraction module and a prediction module. The former module is constructed by introducing pyramid multiscale source connections into a contracting-expanding structure. The latter module includes a bounding-box regressor and a classifier that are employed to simultaneously recognize GGO nodules and estimate bounding boxes at multiple scales. To train the proposed PiaNet, a two-stage transfer learning strategy is developed. In the first stage, the feature-extraction module is embedded into a classifier network that is trained on a large data set of GGO and non-GGO patches, which are generated by performing data augmentation from a small number of annotated CT scans. In the second stage, the pretrained feature-extraction module is loaded into PiaNet, and then PiaNet is fine-tuned using the annotated CT scans. We evaluate the proposed PiaNet on the LIDC-IDRI data set. The experimental results demonstrate that our method outperforms state-of-the-art counterparts, including the Subsolid CAD and Aidence systems and S4ND and GA-SSD methods. PiaNet achieves a sensitivity of 91.75% with only one false positive per scan
Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity
Venue: arXiv cs.AI 2020
Year: 2020

This article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system with heterogeneous information as ideas and knowledge, after the results about an ontology to describe the meaning of these ideas. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas. During the creative workshop many creative activities and collaborative creative methods are used by roles immersed in this creativity workshop event where they share knowledge. The collaboration of these roles is physically distant, their interactions might be synchrony or asynchrony, and the information of the ideas are heterogeneous, so we can say that the process is distributed. Those ideas are writing in natural language by participants which have a role and the ideas are heterogeneous since some of them are described by schema, text or scenario of use. This paper presents first, our MAS and second our Ontology design.
Attribute-conditioned Layout GAN for Automatic Graphic Design
Venue: arXiv cs.CV 2020
Year: 2020

Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and sizes of design elements usually involves constraints with respect to element attributes, such as area, aspect ratio and reading-order. Automating attribute conditional graphic layouts remains a complex and unsolved problem. In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions. Due to the complexity of graphic designs, we further propose an element dropout method to make the discriminator look at partial lists of elements and learn their local patterns. In addition, we introduce various loss designs following different design principles for layout optimization. We demonstrate that the proposed method can synthesize graphic layouts conditioned on different element attributes. It can also adjust well-designed layouts to new sizes while retaining elements' original reading-orders. The effectiveness of our method is validated through a user study.
Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
Venue: arXiv cs.CV 2020
Year: 2020

We propose a self-supervised method for partial point set registration. While recent proposed learning-based methods have achieved impressive registration performance on the full shape observations, these methods mostly suffer from performance degradation when dealing with partial shapes. To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process. To achieve this, we design a latent code for each pair of shapes, which can be regarded as a geometric encoding of the target shape. By doing so, our model does need an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion network and the point set registration network take the shared latent codes as input, which are optimized along with the parameters of two decoder networks in the training process. Therefore, the point set registration process can thus benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shape instead of partial ones. In the inference stage, we fix the network parameter and optimize the latent codes to get the optimal shape completion and registration results. Our proposed method is pure unsupervised and does not need any ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration.
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN
Venue: arXiv cs.CV 2020
Year: 2020

Smart cameras are increasingly used in surveillance solutions in public spaces. Contemporary computer vision applications can be used to recognize events that require intervention by emergency services. Smart cameras can be mounted in locations where citizens feel particularly unsafe, e.g., pathways and underpasses with a history of incidents. One promising approach for smart cameras is edge AI, i.e., deploying AI technology on IoT devices. However, implementing resource-demanding technology such as image recognition using deep neural networks (DNN) on constrained devices is a substantial challenge. In this paper, we explore two approaches to reduce the need for compute in contemporary image recognition in an underpass. First, we showcase successful neural network pruning, i.e., we retain comparable classification accuracy with only 1.1\% of the neurons remaining from the state-of-the-art DNN architecture. Second, we demonstrate how a CycleGAN can be used to transform out-of-distribution images to the operational design domain. We posit that both pruning and CycleGANs are promising enablers for efficient edge AI in smart cameras.
A Smart Home System based on Internet of Things
Venue: arXiv cs.CR 2020
Year: 2020

The Internet of Things (IoT) describes a network infrastructure of identifiable things that share data through the Internet. A smart home is one of the applications for the Internet of Things. In a smart home, household appliances could be monitored and controlled remotely. This raises a demand for reliable security solutions for IoT systems. Authorization and authentication are challenging IoT security operations that need to be considered. For instance, unauthorized access, such as cyber-attacks, to a smart home system could cause danger by controlling sensors and actuators, opening the doors for a thief. This paper applies an extra layer of security of multi-factor authentication to act as a prevention method for mitigating unauthorized access. One of those factors is face recognition, as it has recently become popular due to its non-invasive biometric techniques, which is easy to use with cameras attached to most trending computers and smartphones. In this paper, the gaps in existing IoT smart home systems have been analyzed, and we have suggested improvements for overcoming them by including necessary system modules and enhancing user registration and log-in authentication. We propose software architecture for implementing such a system. To the best of our knowledge, the existing IoT smart home management research does not support face recognition and liveness detection within the authentication operation of their suggested software architectures.
Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
Venue: arXiv cs.LG 2020
Year: 2020

The history of deep learning has shown that human-designed problem-specific networks can greatly improve the classification performance of general neural models. In most practical cases, however, choosing the optimal architecture for a given task remains a challenging problem. Recent architecture-search methods are able to automatically build neural models with strong performance but fail to fully appreciate the interaction between neural architecture and weights. This work investigates the problem of disentangling the role of the neural structure and its edge weights, by showing that well-trained architectures may not need any link-specific fine-tuning of the weights. We compare the performance of such weight-free networks (in our case these are binary networks with {0, 1}-valued weights) with random, weight-agnostic, pruned and standard fully connected networks. To find the optimal weight-agnostic network, we use a novel and computationally efficient method that translates the hard architecture-search problem into a feasible optimization problem.More specifically, we look at the optimal task-specific architectures as the optimal configuration of binary networks with {0, 1}-valued weights, which can be found through an approximate gradient descent strategy. Theoretical convergence guarantees of the proposed algorithm are obtained by bounding the error in the gradient approximation and its practical performance is evaluated on two real-world data sets. For measuring the structural similarities between different architectures, we use a novel spectral approach that allows us to underline the intrinsic differences between real-valued networks and weight-free architectures.
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
Venue: arXiv cs.NE 2020
Year: 2020

The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formulated in a manner suitable for implementation in neural circuitry. The primary challenge is to ensure that any candidate formulation uses only local information, rather than relying on global signals as in standard backprop. Recently several algorithms for approximating backprop using only local signals have been proposed. However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes, or multiple sequential backwards phases with information being stored across phases. Here, we propose a novel algorithm, Activation Relaxation (AR), which is motivated by constructing the backpropagation gradient as the equilibrium point of a dynamical system. Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single parallel backwards relaxation phase, and can operate on arbitrary computation graphs. We illustrate these properties by training deep neural networks on visual classification tasks, and describe simplifications to the algorithm which remove further obstacles to neurobiological implementation (for example, the weight-transport problem, and the use of nonlinear derivatives), while preserving performance.
How reliable and useful is Cabell's Blacklist ? A data-driven analysis
Venue: arXiv cs.DL 2020
Year: 2020

In scholarly publishing, blacklists aim to register fraudulent or deceptive journals and publishers, also known as "predatory", to minimise the spread of unreliable research and the growing of fake publishing outlets. However, blacklisting remains a very controversial activity for several reasons: there is no consensus regarding the criteria used to determine fraudulent journals, the criteria used may not always be transparent or relevant, and blacklists are rarely updated regularly. Cabell's paywalled blacklist service attempts to overcome some of these issues in reviewing fraudulent journals on the basis of transparent criteria and in providing allegedly up-to-date information at the journal entry level. We tested Cabell's blacklist to analyse whether or not it could be adopted as a reliable tool by stakeholders in scholarly communication, including our own academic library. To do so, we used a copy of Walt Crawford's Gray Open Access dataset (2012-2016) to assess the coverage of Cabell's blacklist and get insights on their methodology. Out of the 10,123 journals that we tested, 4,681 are included in Cabell's blacklist. Out of this number of journals included in the blacklist, 3,229 are empty journals, i.e. journals in which no single article has ever been published. Other collected data points to questionable weighing and reviewing methods and shows a lack of rigour in how Cabell applies its own procedures: some journals are blacklisted on the basis of 1 to 3 criteria, identical criteria are recorded multiple times in individual journal entries, discrepancies exist between reviewing dates and the criteria version used and recorded by Cabell, reviewing dates are missing, and we observed two journals blacklisted twice with a different number of violations. Based on these observations, we conclude with recommendations and suggestions that could help improve Cabell's blacklist service.
Multi-Central Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their privacy. A popular alternative is the local model in which the aggregator is untrusted and instead each individual is responsible for their own privacy. The decentralized privacy guarantee of the local model comes at a high price in statistical utility or computational complexity. Thus intermediate models such as the shuffled model and pan privacy have been studied in an attempt to attain the best of both worlds. In this note, we propose an intermediate trust model for differential privacy, which we call the multi-central model. Here there are multiple aggregators and we only assume that they do not collude nefariously. This model relaxes the trust requirements of the central model while avoiding the price of the local model. We motivate this model and provide some simple and efficient algorithms for it. We argue that this model is a promising direction for further research.