OpenRaiser/Intern-Atlas huggingface dataset:
A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer
Venue: arXiv cs.NI 2020
Year: 2020

Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by vehicle-to-cloud transmissions will confront the cellular network infrastructure with great capacity-related challenges. A cognitive way for achieving relief without introducing additional physical infrastructure is the application of opportunistic data transfer for delay-tolerant applications. Hereby, the clients schedule their data transmissions in a channel-aware manner in order to avoid retransmissions and interference with other cell users. In this paper, we introduce a novel approach for this type of resourceaware data transfer which brings together supervised learning for network quality prediction with reinforcement learningbased decision making. The performance evaluation is carried out using data-driven network simulation and real world experiments in the public cellular networks of multiple Mobile Network Operators (MNOs) in different scenarios. The proposed transmission scheme significantly outperforms state-of-the-art probabilistic approaches in most scenarios and achieves data rate improvements of up to 181% in uplink and up to 270% in downlink transmission direction in comparison to conventional periodic data transfer.
A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms
Venue: arXiv cs.DC 2020
Year: 2020

Wireless sensor networks (WSNs) contain numerous nodes that their main goals are to monitor and control environments. Also, sensor nodes distribute based on network usage. One of the most significant issues in this type of network is the energy consumption of sensor nodes. In fixed-sink networks, nodes which are near the sink act as an interface to transfer data of other nodes to sink. This causes the energy consumption of sensors reduces rapidly. Therefore, the lifetime of the network declines. Sensor nodes owing to their weaknesses are susceptible to several threats, one of which is denial-of-sleep attack (DoSA) threatening WSN. Hence, the DoSA refers to the energy loss in these nodes by maintaining the nodes from entering energy-saving and sleep mode. In this paper, a hybrid approach is proposed based on mobile sink, firefly algorithm based on leach, and Hopfield neural network (WSN-FAHN). Thus, mobile sink is applied to both improve energy consumption and increase network lifetime. Firefly algorithm is proposed to cluster nodes and authenticate in two levels to prevent from DoSA. In addition, Hopfield neural network detects the direction route of the sink movement to send data of CH. Furthermore, here WSN-FAHN technique is assessed through wide simulations performed in the NS-2 environment. The WSN-FAHN procedure superiority is demonstrated by simulation outcomes in comparison with contemporary schemes based on performance metrics like packet delivery ratio (PDR), average throughput, detection ratio, and network lifetime while decreasing the average residual energy.
The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives
Venue: arXiv cs.DL 2020
Year: 2020

The Archives Unleashed project aims to improve scholarly access to web archives through a multi-pronged strategy involving tool creation, process modeling, and community building - all proceeding concurrently in mutually-reinforcing efforts. As we near the end of our initially-conceived three-year project, we report on our progress and share lessons learned along the way. The main contribution articulated in this paper is a process model that decomposes scholarly inquiries into four main activities: filter, extract, aggregate, and visualize. Based on the insight that these activities can be disaggregated across time, space, and tools, it is possible to generate "derivative products", using our Archives Unleashed Toolkit, that serve as useful starting points for scholarly inquiry. Scholars can download these products from the Archives Unleashed Cloud and manipulate them just like any other dataset, thus providing access to web archives without requiring any specialized knowledge. Over the past few years, our platform has processed over a thousand different collections from about two hundred users, totaling over 280 terabytes of web archives.
Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data
Venue: arXiv cs.SI 2020
Year: 2020

Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation metrics are strongly affected by the interplay between the age distribution of the milestone items and age biases of the evaluated metrics. Outcomes of these metrics are therefore not representative of the metrics' ranking ability. We argue in favor of a modified evaluation procedure that explicitly penalizes biased metrics and allows us to reveal metrics' performance patterns that are consistent across the datasets. PageRank and LeaderRank turn out to be the best-performing ranking metrics when their age bias is suppressed by a simple transformation of the scores that they produce, whereas other popular metrics, including citation count, HITS and Collective Influence, produce significantly worse ranking results.
GPU Tensor Cores for fast Arithmetic Reductions
Venue: arXiv cs.DC 2020
Year: 2020

This work proposes a GPU tensor core approach that encodes the arithmetic reduction of $n$ numbers as a set of chained $m \times m$ matrix multiply accumulate (MMA) operations executed in parallel by GPU tensor cores. The asymptotic running time of the proposed chained tensor core approach is $T(n)=5 log_{m^2}{n}$ and its speedup is $S=\dfrac{4}{5} log_{2}{m^2}$ over the classic $O(n \log n)$ parallel reduction algorithm. Experimental performance results show that the proposed reduction method is $\sim 3.2 \times$ faster than a conventional GPU reduction implementation, and preserves the numerical precision because the sub-results of each chain of $R$ MMAs is kept as a 32-bit floating point value, before being all reduced into as a final 32-bit result. The chained MMA design allows a flexible configuration of thread-blocks; small thread-blocks of 32 or 128 threads can still achieve maximum performance using a chain of $R=4,5$ MMAs per block, while large thread-blocks work best with $R=1$. The results obtained in this work show that tensor cores can indeed provide a significant performance improvement to non-Machine Learning applications such as the arithmetic reduction, which is an integration tool for studying many scientific phenomena.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Venue: arXiv cs.LG 2020
Year: 2020

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems.
Design of Trusted Market Platforms using Permissioned Blockchains and Game Theory
Venue: arXiv cs.GT 2020
Year: 2020

The blockchain concept forms the backbone of a new wave technology that promises to be deployed extensively in a wide variety of industrial and societal applications. Governments, financial institutions, banks, industrial supply chains, service companies, and even educational institutions and hospitals are investing in a substantial manner in the hope of improving business efficiency and operational robustness through deployment of blockchain technology. This thesis work is concerned with designing trustworthy business-to-business (B2B) market platforms drawing upon blockchain technology and game theory. The proposed platform is built upon three key ideas. First, we use permissioned blockchains with smart contracts as a technically sound approach for building the B2B platform. The blockchain deploys smart contracts that govern the interactions of enterprise buyers and sellers. Second, the smart contracts are designed using a rigorous analysis of a repeated game model of the strategic interactions between buyers and sellers. We show that such smart contracts induce honest behavior from buyers and sellers. Third, we embed cryptographic regulation protocols into the permissioned blockchain to ensure that business sensitive information is not revealed to the competitors. We believe our work is an important step in the direction of building a powerful B2B platform that maximizes social welfare and enables trusted collaboration between strategic enterprise agents.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Venue: arXiv cs.CV 2020
Year: 2020

3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set. We also use the stochastic information from the Kalman Filter in the data association step by measuring the Mahalanobis distance between the predicted object states and current object detections. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric.
Candidate Software Process Flaws for the Boeing 737 Max MCAS Algorithm and Risks for a Proposed Upgrade
Venue: arXiv cs.CY 2020
Year: 2020

By reasoning about the claims and speculations promised as part of the public discourse, we analyze the hypothesis that flaws in software engineering played a critical role in the Boeing 737 MCAS incidents. We use promise-based reasoning to discuss how, from an outsider's perspective, one may assemble clues about what went wrong. Rather than looking for a Rational Alternative Design (RAD), as suggested by Wendel, we look for candidate flaws in the software process. We describe four such potential flaws. Recently, Boeing has circulated information on its envisaged MCAS algorithm upgrade. We cast this as a promise to resolve the flaws, i.e. to provide a RAD for the B737 Max. We offer an assessment of B-Max-New based on the public discourse.
Cache-Aided Modulation for Heterogeneous Coded Caching over a Gaussian Broadcast Channel
Venue: arXiv cs.IT 2020
Year: 2020

Coded caching is an information theoretic scheme to reduce high peak hours traffic by partially prefetching files in the users local storage during low peak hours. This paper considers heterogeneous decentralized caching systems where cache of users and content library files may have distinct sizes. The server communicates with the users through a Gaussian broadcast channel. The main contribution of this paper is a novel modulation strategy to map the multicast messages generated in the coded caching delivery phase to the symbols of a signal constellation, such that users can leverage their cached content to demodulate the desired symbols with higher reliability. For the sake of simplicity, in this paper we focus only on uncoded modulation and symbol-by-symbol error probability. However, our scheme in conjunction with multilevel coded modulation can be extended to channel coding over a larger block lengths.
Human Action Recognition and Assessment via Deep Neural Network Self-Organization
Venue: arXiv cs.CV 2020
Year: 2020

The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility, robustness, and scalability needed to operate in natural HRI scenarios which require the continuous acquisition of sensory information as well as the classification or assessment of human body patterns in real time. In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization. A particularity of these models is the use of growing self-organizing networks that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learning from temporally correlated input.
Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Pictures
Venue: arXiv cs.CV 2020
Year: 2020

Small satellite constellations provide daily global coverage of the earth's landmass, but image enrichment relies on automating key tasks like change detection or feature searches. For example, to extract text annotations from raw pixels requires two dependent machine learning models, one to analyze the overhead image and the other to generate a descriptive caption. We evaluate seven models on the previously largest benchmark for satellite image captions. We extend the labeled image samples five-fold, then augment, correct and prune the vocabulary to approach a rough min-max (minimum word, maximum description). This outcome compares favorably to previous work with large pre-trained image models but offers a hundred-fold reduction in model size without sacrificing overall accuracy (when measured with log entropy loss). These smaller models provide new deployment opportunities, particularly when pushed to edge processors, on-board satellites, or distributed ground stations. To quantify a caption's descriptiveness, we introduce a novel multi-class confusion or error matrix to score both human-labeled test data and never-labeled images that include bounding box detection but lack full sentence captions. This work suggests future captioning strategies, particularly ones that can enrich the class coverage beyond land use applications and that lessen color-centered and adjacency adjectives ("green", "near", "between", etc.). Many modern language transformers present novel and exploitable models with world knowledge gleaned from training from their vast online corpus. One interesting, but easy example might learn the word association between wind and waves, thus enriching a beach scene with more than just color descriptions that otherwise might be accessed from raw pixels without text annotation.
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
Venue: arXiv cs.CV 2020
Year: 2020

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.
Assessing Robustness of Deep learning Methods in Dermatological Workflow
Venue: arXiv cs.CV 2020
Year: 2020

This paper aims to evaluate the suitability of current deep learning methods for clinical workflow especially by focusing on dermatology. Although deep learning methods have been attempted to get dermatologist level accuracy in several individual conditions, it has not been rigorously tested for common clinical complaints. Most projects involve data acquired in well-controlled laboratory conditions. This may not reflect regular clinical evaluation where corresponding image quality is not always ideal. We test the robustness of deep learning methods by simulating non-ideal characteristics on user submitted images of ten classes of diseases. Assessing via imitated conditions, we have found the overall accuracy to drop and individual predictions change significantly in many cases despite of robust training.
Assigning credit to scientific datasets using article citation networks
Venue: arXiv cs.IR 2020
Year: 2020

A citation is a well-established mechanism for connecting scientific artifacts. Citation networks are used by citation analysis for a variety of reasons, prominently to give credit to scientists' work. However, because of current citation practices, scientists tend to cite only publications, leaving out other types of artifacts such as datasets. Datasets then do not get appropriate credit even though they are increasingly reused and experimented with. We develop a network flow measure, called DataRank, aimed at solving this gap. DataRank assigns a relative value to each node in the network based on how citations flow through the graph, differentiating publication and dataset flow rates. We evaluate the quality of DataRank by estimating its accuracy at predicting the usage of real datasets: web visits to GenBank and downloads of Figshare datasets. We show that DataRank is better at predicting this usage compared to alternatives while offering additional interpretable outcomes. We discuss improvements to citation behavior and algorithms to properly track and assign credit to datasets.
A New Software Framework for Traffic Engineering: Path Cardinality and the Effect of Multipath on Residual Capacity
Venue: arXiv cs.NI 2020
Year: 2020

In this paper, we present a new traffic engineering (TE) software framework to analyze, configure, and optimize (with the aid of a linear programming solver) a network for service provisioning. The developed software tool is based on our new data-driven traffic engineering approach that analyzes a large volume of network configuration data generated given the user input. By analyzing the data, one can then make efficient decisions later when designing a traffic engineering solution. We focus on three well-known traffic engineering objective functions: minimum cost routing (MCR), load balancing (LB), and average delay (AD). With this new tool, one can answer numerous traffic engineering questions. For example, what are the differences among the three objective functions? What is the impact of an objective function on link utilization? How many candidate paths are enough to achieve optimality or near-optimality with respect to a specific objective. This new software tool allows us to conveniently perform various experiments and visualize the results for performance analysis. As case studies, this paper presents examples that answer the questions for two traffic engineering problems: (1) how many paths are required to obtain a solution that is within a few percent from the optimal solution and whether that number is fixed for any network size? (2) how the choice of single-path/multi-path routing affects the load in the network? For the first problem, it turns out that the number of paths needed to achieve optimality increases as the number of links in the network increases.
Generalised Pattern Matching Revisited
Venue: arXiv cs.DS 2020
Year: 2020

In the problem of $\texttt{Generalised Pattern Matching}\ (\texttt{GPM})$ [STOC'94, Muthukrishnan and Palem], we are given a text $T$ of length $n$ over an alphabet $Σ_T$, a pattern $P$ of length $m$ over an alphabet $Σ_P$, and a matching relationship $\subseteq Σ_T \times Σ_P$, and must return all substrings of $T$ that match $P$ (reporting) or the number of mismatches between each substring of $T$ of length $m$ and $P$ (counting). In this work, we improve over all previously known algorithms for this problem for various parameters describing the input instance: * $\mathcal{D}\,$ being the maximum number of characters that match a fixed character, * $\mathcal{S}\,$ being the number of pairs of matching characters, * $\mathcal{I}\,$ being the total number of disjoint intervals of characters that match the $m$ characters of the pattern $P$. At the heart of our new deterministic upper bounds for $\mathcal{D}\,$ and $\mathcal{S}\,$ lies a faster construction of superimposed codes, which solves an open problem posed in [FOCS'97, Indyk] and can be of independent interest. To conclude, we demonstrate first lower bounds for $\texttt{GPM}$. We start by showing that any deterministic or Monte Carlo algorithm for $\texttt{GPM}$ must use $Ω(\mathcal{S})$ time, and then proceed to show higher lower bounds for combinatorial algorithms. These bounds show that our algorithms are almost optimal, unless a radically new approach is developed.