OpenRaiser/Intern-Atlas huggingface dataset:
BBS: A Blockchain Big-Data Sharing System
Venue: arXiv cs.CR 2021
Year: 2021

Chain of custody is needed to document the sequence of custody of sensitive big data. In this paper, we design a blockchain big-data sharing system (BBS) based on Hyperledger Fabric. We denote the data stored outside of a ledger for sharing as "off-state" and "big data" (referring to extremely large data) is in this category. In our off-state sharing protocol, a sender registers a file with BBS for sharing. To acquire the file, an authenticated and authorized receiver has to use transactions and interacts with BBS in four phases, including the file transfer request, encrypted file transfer, key retrieval, and file decryption. The corresponding transactions are recorded in the ledger and serve as chain of custody to document the trail of the data. Compared with related work, BBS can perform the four phases autonomously. It utilizes the permissioned blockchain, i.e. Hyperledger Fabric, for access control and can defeat dishonest receivers. We design and implement a prototype of BBS for big file sharing. Extensive experiments were performed to validate its feasibility and performance.
Federated Learning for Smart Healthcare: A Survey
Venue: arXiv cs.LG 2021
Year: 2021

Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach
Venue: arXiv cs.LG 2021
Year: 2021

Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers' advertising revenues. We leverage multiple covariates, including not only the publisher's own characteristics but also other publishers' advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
Venue: arXiv cs.AR 2021
Year: 2021

High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level. With the help of HLS, the hardware designers must describe only a high-level behavioral flow of the design. Despite this, it still can take weeks to develop a high-performance architecture mainly because there are many design choices at a higher level that requires more time to explore. It also takes several minutes to hours to get feedback from the HLS tool on the quality of each design candidate. In this paper, we propose to solve this problem by modeling the HLS tool with a graph neural network (GNN) that is trained to be used for a wide range of applications. The experimental results demonstrate that by employing the GNN-based model, we are able to estimate the quality of design in milliseconds with high accuracy which can help us search through the solution space very quickly.
Six Critical Challenges for 6G Wireless Systems
Venue: arXiv cs.IT 2021
Year: 2021

A large number of papers are now appearing on sixth-generation (6G) wireless systems, covering different aspects, ranging from vision, architecture, applications, and technology breakthroughs. With cellular systems in mind, this paper presents six critical, yet fundamental challenges that must be overcome before development and deployment of 6G systems. These include: Opening the sub-terahertz (sub-THz) spectrum for increased bandwidths and the ability to utilize these bandwidths, pushing the limits of semiconductor technologies for operation within the sub-THz bands, transceiver design and architectures to realize the high peak data rates, and realizations of sub-millisecond latencies at the network-level to achieve the 6G key performance indicators. Additionally, since 6G systems will not be introduced in a green fields environment, backwards compatibility with existing systems is discussed. Where possible, we present practical solutions to realize the identified challenges.
GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
Venue: arXiv cs.LG 2021
Year: 2021

As opposed to natural languages, source code understanding is influenced by grammatical relationships between tokens regardless of their identifier name. Graph representations of source code such as Abstract Syntax Tree (AST) can capture relationships between tokens that are not obvious from the source code. We propose a novel method, GN-Transformer to learn end-to-end on a fused sequence and graph modality we call Syntax-Code-Graph (SCG). GN-Transformer expands on Graph Networks (GN) framework using a self-attention mechanism. SCG is the result of the early fusion between a source code snippet and the AST representation. We perform experiments on the structure of SCG, an ablation study on the model design, and the hyper-parameters to conclude that the performance advantage is from the fused representation. The proposed methods achieve state-of-the-art performance in two code summarization datasets and across three automatic code summarization metrics (BLEU, METEOR, ROUGE-L). We further evaluate the human perceived quality of our model and previous work with an expert-user study. Our model outperforms the state-of-the-art in human perceived quality and accuracy.
Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure
Venue: arXiv cs.LG 2021
Year: 2021

Unified understanding of neuro networks (NNs) gets the users into great trouble because they have been puzzled by what kind of rules should be obeyed to optimize the internal structure of NNs. Considering the potential capability of random graphs to alter how computation is performed, we demonstrate that they can serve as architecture generators to optimize the internal structure of NNs. To transform the random graph theory into an NN model with practical meaning and based on clarifying the input-output relationship of each neuron, we complete data feature mapping by calculating Fourier Random Features (FRFs). Under the usage of this low-operation cost approach, neurons are assigned to several groups of which connection relationships can be regarded as uniform representations of random graphs they belong to, and random arrangement fuses those neurons to establish the pattern matrix, markedly reducing manual participation and computational cost without the fixed and deep architecture. Leveraging this single neuromorphic learning model termed random graph-based neuro network (RGNN) we develop a joint classification mechanism involving information interaction between multiple RGNNs and realize significant performance improvements in supervised learning for three benchmark tasks, whereby they effectively avoid the adverse impact of the interpretability of NNs on the structure design and engineering practice.
Protection of SVM Model with Secret Key from Unauthorized Access
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a block-wise image transformation method with a secret key for support vector machine (SVM) models. Models trained by using transformed images offer a poor performance to unauthorized users without a key, while they can offer a high performance to authorized users with a key. The proposed method is demonstrated to be robust enough against unauthorized access even under the use of kernel functions in a facial recognition experiment.
Intelligence Reflecting Surface-Aided Integrated Data and Energy Networking Coexisting D2D Communications
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs' worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.
How a 4-day Work Week affects Agile Software Development Teams
Venue: arXiv cs.SE 2021
Year: 2021

Context: Agile software development (ASD) sets social aspects like communication and collaboration in focus. Thus, one may assume that the specific work organization of companies impacts the work of ASD teams. A major change in work organization is the switch to a 4-day work week, which some companies investigated in experiments. Also, recent studies show that ASD teams are affected by the switch to remote work since the Covid 19 pandemic outbreak in 2020. Objective: Our study presents empirical findings on the effects on ASD teams operating remote in a 4-day work week organization. Method: We performed a qualitative single case study and conducted seven semi-structured interviews, observed 14 agile practices and screened eight project documents and protocols of agile practices. Results: We found, that the teams adapted the agile method in use due to the change to a 4-day work week environment and the switch to remote work. The productivity of the two ASD teams did not decrease. Although the stress level of the ASD team member increased due to the 4-day work week, we found that the job satisfaction of the individual ASD team members is affected positively. Finally, we point to affects on social facets of the ASD teams. Conclusions: The research community benefits from our results as the current state of research dealing with the effects of a 4-day work week on ASD teams is limited. Also, our findings provide several practical implications for ASD teams working remote in a 4-day work week.
Generating Unrestricted 3D Adversarial Point Clouds
Venue: arXiv cs.CV 2021
Year: 2021

Utilizing 3D point cloud data has become an urgent need for the deployment of artificial intelligence in many areas like facial recognition and self-driving. However, deep learning for 3D point clouds is still vulnerable to adversarial attacks, e.g., iterative attacks, point transformation attacks, and generative attacks. These attacks need to restrict perturbations of adversarial examples within a strict bound, leading to the unrealistic adversarial 3D point clouds. In this paper, we propose an Adversarial Graph-Convolutional Generative Adversarial Network (AdvGCGAN) to generate visually realistic adversarial 3D point clouds from scratch. Specifically, we use a graph convolutional generator and a discriminator with an auxiliary classifier to generate realistic point clouds, which learn the latent distribution from the real 3D data. The unrestricted adversarial attack loss is incorporated in the special adversarial training of GAN, which enables the generator to generate the adversarial examples to spoof the target network. Compared with the existing state-of-art attack methods, the experiment results demonstrate the effectiveness of our unrestricted adversarial attack methods with a higher attack success rate and visual quality. Additionally, the proposed AdvGCGAN can achieve better performance against defense models and better transferability than existing attack methods with strong camouflage.
An Improved Random Shift Algorithm for Spanners and Low Diameter Decompositions
Venue: arXiv cs.DS 2021
Year: 2021

Spanners have been shown to be a powerful tool in graph algorithms. Many spanner constructions use a certain type of clustering at their core, where each cluster has small diameter and there are relatively few spanner edges between clusters. In this paper, we provide a clustering algorithm that, given $k\geq 2$, can be used to compute a spanner of stretch $2k-1$ and expected size $O(n^{1+1/k})$ in $k$ rounds in the CONGEST model. This improves upon the state of the art (by Elkin, and Neiman [TALG'19]) by making the bounds on both running time and stretch independent of the random choices of the algorithm, whereas they only hold with high probability in previous results. Spanners are used in certain synchronizers, thus our improvement directly carries over to such synchronizers. Furthermore, for keeping the \emph{total} number of inter-cluster edges small in low diameter decompositions, our clustering algorithm provides the following guarantees. Given $β\in (0,1]$, we compute a low diameter decomposition with diameter bound $O\left(\frac{\log n}β\right)$ such that each edge $e\in E$ is an inter-cluster edge with probability at most $β\cdot w(e)$ in $O\left(\frac{\log n}β\right)$ rounds in the CONGEST model. Again, this improves upon the state of the art (by Miller, Peng, and Xu [SPAA'13]) by making the bounds on both running time and diameter independent of the random choices of the algorithm, whereas they only hold with high probability in previous results.
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, the usage of ensemble learning in applications has grown significantly due to increasing computational power allowing the training of large ensembles in reasonable time frames. Many applications, e.g., malware detection, face recognition, or financial decision-making, use a finite set of learning algorithms and do aggregate them in a way that a better predictive performance is obtained than any other of the individual learning algorithms. In the field of Post-Silicon Validation for semiconductor devices (PSV), data sets are typically provided that consist of various devices like, e.g., chips of different manufacturing lines. In PSV, the task is to approximate the underlying function of the data with multiple learning algorithms, each trained on a device-specific subset, instead of improving the performance of arbitrary classifiers on the entire data set. Furthermore, the expectation is that an unknown number of subsets describe functions showing very different characteristics. Corresponding ensemble members, which are called outliers, can heavily influence the approximation. Our method aims to find a suitable approximation that is robust to outliers and represents the best or worst case in a way that will apply to as many types as possible. A 'soft-max' or 'soft-min' function is used in place of a maximum or minimum operator. A Neural Network (NN) is trained to learn this 'soft-function' in a two-stage process. First, we select a subset of ensemble members that is representative of the best or worst case. Second, we combine these members and define a weighting that uses the properties of the Local Outlier Factor (LOF) to increase the influence of non-outliers and to decrease outliers. The weighting ensures robustness to outliers and makes sure that approximations are suitable for most types.
An efficient two-dimensional heat transfer model for building envelopes
Venue: arXiv cs.CE 2021
Year: 2021

A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the \DF ~approach that provides an explicit scheme with a relaxed stability condition. The model is first validated using an analytical solution and then compared to three other standard schemes. Results show that the proposed model offers a good compromise in terms of high accuracy and reduced computational efforts. Then, a more complex case study is investigated, considering non-uniform shading effects due to the neighboring buildings. In addition, the surface heat transfer coefficient varies with wind velocity and height, which imposes an addition non-uniform boundary condition. After showing the reliability of the model prediction, a comparison over almost $120$ cities in France is carried out between the two- and the one-dimensional approaches of the current building simulation programs. Important discrepancies are observed for regions with high magnitudes of solar radiation and wind velocity. Last, a sensitivity analysis is carried out using a derivative-based approach. It enables to assess the variability of the solution according to the modeling of the two-dimensional boundary conditions. Moreover, the proposed model computes efficiently the solution and its sensitivity to the modeling of the urban environment.
Interpretable Models via Pairwise permutations algorithm
Venue: arXiv cs.LG 2021
Year: 2021

One of the most common pitfalls often found in high dimensional biological data sets are correlations between the features. This may lead to statistical and machine learning methodologies overvaluing or undervaluing these correlated predictors, while the truly relevant ones are ignored. In this paper, we will define a new method called \textit{pairwise permutation algorithm} (PPA) with the aim of mitigating the correlation bias in feature importance values. Firstly, we provide a theoretical foundation, which builds upon previous work on permutation importance. PPA is then applied to a toy data set, where we demonstrate its ability to correct the correlation effect. We further test PPA on a microbiome shotgun dataset, to show that the PPA is already able to obtain biological relevant biomarkers.
Top-k Dynamic Service Composition in Skyway Networks
Venue: arXiv cs.MA 2021
Year: 2021

We propose a novel top-k service composition framework for drone services under a dynamic environment. We develop a system model for formal modelling of drone services in a skyway network. The composition process is accomplished in two phases, i.e., computing top-k compositions and extending and ranking top-k compositions using probabilistic wait and recharge times under congestion conditions. We propose a top-k composition algorithm to compute the best service composition plan meeting user's requirements. A set of experiments with a real dataset is conducted to demonstrate the effectiveness of the proposed approach.
Information Freshness in Multi-Hop Wireless Networks
Venue: arXiv cs.NI 2021
Year: 2021

We consider the problem of minimizing age of information in multihop wireless networks and propose three classes of policies to solve the problem - stationary randomized, age difference, and age debt. For the unicast setting with fixed routes between each source-destination pair, we first develop a procedure to find age optimal Stationary Randomized policies. These policies are easy to implement and allow us to derive closed-form expression for average AoI. Next, for the same unicast setting, we develop a class of heuristic policies, called Age Difference, based on the idea that if neighboring nodes try to reduce their age differential then all nodes will have fresher updates. This approach is useful in practice since it relies only on the local age differential between nodes to make scheduling decisions. Finally, we propose the class of policies called Age Debt, which can handle 1) non-linear AoI cost functions; 2) unicast, multicast and broadcast flows; and 3) no fixed routes specified per flow beforehand. Here, we convert AoI optimization problems into equivalent network stability problems and use Lyapunov drift to find scheduling and routing schemes that stabilize the network. We also provide numerical results comparing our proposed classes of policies with the best known scheduling and routing schemes available in the literature for a wide variety of network settings.
An Experimental Study of Latency for IEEE 802.11be Multi-link Operation
Venue: arXiv cs.NI 2021
Year: 2021

Will Multi-Link Operation (MLO) be able to improve the latency of Wi-Fi networks? MLO is one of the most disruptive MAC-layer techniques included in the IEEE 802.11be amendment. It allows a device to use multiple radios simultaneously and in a coordinated way, providing a new framework to improve the WLAN throughput and latency. In this paper, we investigate the potential latency benefits of MLO by using a large dataset containing 5 GHz spectrum occupancy measurements. Experimental results show that when the channels are symmetrically occupied, MLO can improve latency by one order of magnitude. In contrast, in asymmetrically occupied channels, MLO can sometimes be detrimental and increase latency. To address this case, we introduce Opportunistic Simultaneous Transmit and Receive (STR+) channel access and study its benefits.