OpenRaiser/Intern-Atlas huggingface dataset:
Incremental multi-domain learning with network latent tensor factorization
Venue: arXiv cs.CV 2019
Year: 2019

The prominence of deep learning, large amount of annotated data and increasingly powerful hardware made it possible to reach remarkable performance for supervised classification tasks, in many cases saturating the training sets. However the resulting models are specialized to a single very specific task and domain. Adapting the learned classification to new domains is a hard problem due to at least three reasons: (1) the new domains and the tasks might be drastically different; (2) there might be very limited amount of annotated data on the new domain and (3) full training of a new model for each new task is prohibitive in terms of computation and memory, due to the sheer number of parameters of deep CNNs. In this paper, we present a method to learn new-domains and tasks incrementally, building on prior knowledge from already learned tasks and without catastrophic forgetting. We do so by jointly parametrizing weights across layers using low-rank Tucker structure. The core is task agnostic while a set of task specific factors are learnt on each new domain. We show that leveraging tensor structure enables better performance than simply using matrix operations. Joint tensor modelling also naturally leverages correlations across different layers. Compared with previous methods which have focused on adapting each layer separately, our approach results in more compact representations for each new task/domain. We apply the proposed method to the 10 datasets of the Visual Decathlon Challenge and show that our method offers on average about 7.5x reduction in number of parameters and competitive performance in terms of both classification accuracy and Decathlon score.
Detecting Anemia from Retinal Fundus Images
Venue: arXiv cs.CV 2019
Year: 2019

Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests. Though some non-invasive approaches have been developed, they are less accurate than invasive methods, resulting in an unmet need for more accurate non-invasive methods. Here, we show that deep learning-based algorithms can detect anemia and quantify several related blood measurements using retinal fundus images both in isolation and in combination with basic metadata such as patient demographics. On a validation dataset of 11,388 patients from the UK Biobank, our algorithms achieved a mean absolute error of 0.63 g/dL (95% confidence interval (CI) 0.62-0.64) in quantifying hemoglobin concentration and an area under receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.86-0.89) in detecting anemia. This work shows the potential of automated non-invasive anemia screening based on fundus images, particularly in diabetic patients, who may have regular retinal imaging and are at increased risk of further morbidity and mortality from anemia.
Evaluation of the RIKEN Post-K Processor Simulator
Venue: arXiv cs.DC 2019
Year: 2019

For the purpose of developing applications for Post-K at an early stage, RIKEN has developed a post-K processor simulator. This simulator is based on the general-purpose processor simulator gem5. It does not simulate the actual hardware of a post-K processor. However, we believe that sufficient simulation accuracy can be obtained since it simulates the instruction pipeline of out-of-order execution with cycle-level accuracy along with performing detailed parameter tuning of out-of-order resources and function expansion of cache/memory hierarchy. In this simulator, we aim to estimate the execution cycles of one node application on a post-K processor with accuracy that enables relative evaluation and application tuning. In this paper, we show the details of the implementation of this simulator and verify its accuracy compared with that of a post-K test chip.
Open Science in Software Engineering
Venue: arXiv cs.SE 2019
Year: 2019

Open science describes the movement of making any research artefact available to the public and includes, but is not limited to, open access, open data, and open source. While open science is becoming generally accepted as a norm in other scientific disciplines, in software engineering, we are still struggling in adapting open science to the particularities of our discipline, rendering progress in our scientific community cumbersome. In this chapter, we reflect upon the essentials in open science for software engineering including what open science is, why we should engage in it, and how we should do it. We particularly draw from our experiences made as conference chairs implementing open science initiatives and as researchers actively engaging in open science to critically discuss challenges and pitfalls, and to address more advanced topics such as how and under which conditions to share preprints, what infrastructure and licence model to cover, or how do it within the limitations of different reviewing models, such as double-blind reviewing. Our hope is to help establishing a common ground and to contribute to make open science a norm also in software engineering.
Texture image analysis and texture classification methods - A review
Venue: arXiv cs.CV 2019
Year: 2019

Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately. Texture analysis methods usually are classified into four categories: statistical methods, structural, model-based and transform-based methods. This paper discusses the various methods used for texture or analysis in details. New researches shows the power of combinational methods for texture analysis, which can't be in specific category. This paper provides a review on well known combinational methods in a specific section with details. This paper counts advantages and disadvantages of well-known texture image descriptors in the result part. Main focus in all of the survived methods is on discrimination performance, computational complexity and resistance to challenges such as noise, rotation, etc. A brief review is also made on the common classifiers used for texture image classification. Also, a survey on texture image benchmark datasets is included.
Got: Git, but for Objects
Venue: arXiv cs.PL 2019
Year: 2019

We look at one important category of distributed applications characterized by the existence of multiple collaborating, and competing, components sharing mutable, long-lived, replicated objects. The problem addressed by our work is that of object state synchronization among the components. As an organizing principle for replicated objects, we formally specify the Global Object Tracker (GoT) model, an object-oriented programming model based on causal consistency with application-level conflict resolution strategies, whose elements and interfaces mirror those found in decentralized version control systems: a version graph, working data, diffs, commit, checkout, fetch, push, and merge. We have implemented GoT in a framework called Spacetime, written in Python. In its purest form, GoT is impractical for real systems, because of the unbounded growth of the version graph and because passing diff'ed histories over the network makes remote communication too slow. We present our solution to these problems that adds some constraints to GoT applications, but that makes the model feasible in practice. We present a performance analysis of Spacetime for representative workloads, which shows that the additional constraints added to GoT make it not just feasible, but viable for real applications.
Introducing Enumerative Sphere Shaping for Optical Communication Systems with Short Blocklengths
Venue: arXiv cs.IT 2019
Year: 2019

Probabilistic shaping based on constant composition distribution matching (CCDM) has received considerable attention as a way to increase the capacity of fiber optical communication systems. CCDM suffers from significant rate loss at short blocklengths and requires long blocklengths to achieve high shaping gain, which makes its implementation very challenging. In this paper, we propose to use enumerative sphere shaping (ESS) and investigate its performance for the nonlinear fiber optical channel. ESS has lower rate loss than CCDM at the same shaping rate, which makes it a suitable candidate to be implemented in real-time high-speed optical systems. In this paper, we first show that finite blocklength ESS and CCDM exhibit higher effective signal-to-noise ratio than their infinite blocklength counterparts. These results show that for the nonlinear fiber optical channel, large blocklengths should be avoided. We then show that for a 400 Gb/s dual-polarization 64-QAM WDM transmission system, ESS with short blocklengths outperforms both uniform signaling and CCDM. Gains in terms of both bit-metric decoding rate and bit-error rate are presented. ESS with a blocklength of 200 is shown to provide an extension reach of about 200 km in comparison with CCDM with the same blocklength. The obtained reach increase of ESS with a blocklength of 200 over uniform signaling is approximately 450 km (approximately 19%)
Secure Consistency Verification for Untrusted Cloud Storage by Public Blockchains
Venue: arXiv cs.CR 2019
Year: 2019

This work presents ContractChecker, a Blockchain-based security protocol for verifying the storage consistency between the mutually distrusting cloud provider and clients. Unlike existing protocols, the ContractChecker uniquely delegates log auditing to the Blockchain, and has the advantages in reducing client cost and lowering requirements on client availability, lending itself to modern scenarios with mobile and web clients. The ContractChecker collects the logs from both clients and the cloud server, and verifies the consistency by cross-checking the logs. By this means, it does not only detects the attacks from malicious clients and server forging their logs, but also is able to mitigate those attacks and recover the system from them. In addition, we design new attacks against ContractChecker exploiting various limits in real Blockchain systems (e.g., write unavailability, Blockchain forks, contract race conditions). We analyze and harden the security of ContractChecker protocols against the proposed new attacks. For evaluating the cost, we build a functional prototype of the ContractChecker on Ethereum/Solidity. By experiments on private and public Ethereum testnets, we extensively evaluate the cost of the ContractChecker in comparison with that of existing client-based log auditing works. The result shows the ContractChecker can scale to hundreds of clients and save client costs by more than one order of magnitude.
Lunar surface image restoration using U-net based deep neural networks
Venue: arXiv cs.CV 2019
Year: 2019

Image restoration is a technique that reconstructs a feasible estimate of the original image from the noisy observation. In this paper, we present a U-Net based deep neural network model to restore the missing pixels on the lunar surface image in a context-aware fashion, which is often known as image inpainting problem. We use the grayscale image of the lunar surface captured by Multiband Imager (MI) onboard Kaguya satellite for our experiments and the results show that our method can reconstruct the lunar surface image with good visual quality and improved PSNR values.
Joint Seat Allocation 2018: An algorithmic perspective
Venue: arXiv cs.CY 2019
Year: 2019

Until 2014, admissions to the Indian Institutes of Technology (IITs) were conducted under one umbrella, whereas the admissions to the non-IIT Centrally Funded Government Institutes (CFTIs) were conducted under a different umbrella, the Central Seat Allocation Board. In 2015, a new Multi-Round Multi-Run Deferred Acceptance joint seat allocation process was implemented, improving the efficiency and productivity of concerned stakeholders. The process brings all CFTIs under one umbrella for admissions: 100 institutes and approximately 39000 seats in 2018. In this scheme, each candidate submits a single choice list over all available programs, and receives no more than a single seat from the system, based on the choices and the ranks in the relevant merit lists. Significantly, overbooking of seats is forbidden. In this report, we provide details of our safe, fair and optimal algorithm. Novel features include the ability to handle multiple merit lists, seat guarantee across multiple rounds, implementing reservation, and de-reservation rules, handling escalation of ranks due to a revision of marks by state boards during the allocation process, and dealing with last minute de-recognition of other backward caste categories. A notable rule required the allocation of supernumerary seats to females, provided the program did not have a sufficient desired percentage, while, at the same time, not reducing the number of seats available to non-females. Looking forward, we posit first that it is inevitable that different colleges will prefer different mechanisms of judging merit, and assigning relative rank. We believe the ability of our algorithm to gracefully handle multiple merit lists gives us hope to express optimism that all undergraduate admissions in the country, beyond the CFTIs, can beneficially use the suggested scheme.
Approximating the noise sensitivity of a monotone Boolean function
Venue: arXiv cs.DS 2019
Year: 2019

The noise sensitivity of a Boolean function $f: \{0,1\}^n \rightarrow \{0,1\}$ is one of its fundamental properties. A function of a positive noise parameter $δ$, it is denoted as $NS_δ[f]$. Here we study the algorithmic problem of approximating it for monotone $f$, such that $NS_δ[f] \geq 1/n^{C}$ for constant $C$, and where $δ$ satisfies $1/n \leq δ\leq 1/2$. For such $f$ and $δ$, we give a randomized algorithm performing $O\left(\frac{\min(1,\sqrt{n} δ\log^{1.5} n) }{NS_δ[f]} \text{poly}\left(\frac{1}ε\right)\right)$ queries and approximating $NS_δ[f]$ to within a multiplicative factor of $(1\pm ε)$. Given the same constraints on $f$ and $δ$, we also prove a lower bound of $Ω\left(\frac{\min(1,\sqrt{n} δ)}{NS_δ[f] \cdot n^ξ}\right)$ on the query complexity of any algorithm that approximates $NS_δ[f]$ to within any constant factor, where $ξ$ can be any positive constant. Thus, our algorithm's query complexity is close to optimal in terms of its dependence on $n$. We introduce a novel descending-ascending view of noise sensitivity, and use it as a central tool for the analysis of our algorithm. To prove lower bounds on query complexity, we develop a technique that reduces computational questions about query complexity to combinatorial questions about the existence of "thin" functions with certain properties. The existence of such "thin" functions is proved using the probabilistic method. These techniques also yield previously unknown lower bounds on the query complexity of approximating other fundamental properties of Boolean functions: the total influence and the bias.
Personalized Re-ranking for Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual influence between items, as well as the differences of users' preferences or intents. Therefore, we propose a personalized re-ranking model for recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after any ranking algorithm, by directly using the existing ranking feature vectors. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. We confirm that the performance can be further improved by introducing pre-trained embedding to learn personalized encoding functions for different users. Experimental results on both offline benchmarks and real-world online e-commerce systems demonstrate the significant improvements of the proposed re-ranking model.
Performance Models for Data Transfers: A Case Study with Molecular Chemistry Kernels
Venue: arXiv cs.PF 2019
Year: 2019

With increasing complexity of hardwares, systems with different memory nodes are ubiquitous in High Performance Computing (HPC). It is paramount to develop strategies to overlap the data transfers between memory nodes with computations in order to exploit the full potential of these systems. In this article, we consider the problem of deciding the order of data transfers between two memory nodes for a set of independent tasks with the objective to minimize the makespan. We prove that with limited memory capacity, obtaining the optimal order of data transfers is a NP-complete problem. We propose several heuristics for this problem and provide details about their favorable situations. We present an analysis of our heuristics on traces, obtained by running 2 molecular chemistry kernels, namely, Hartree-Fock (HF) and Coupled Cluster Single Double (CCSD) on 10 nodes of an HPC system. Our results show that some of our heuristics achieve significant overlap for moderate memory capacities and are very close to the lower bound of makespan.
Taking Linear Logic Apart
Venue: arXiv cs.LO 2019
Year: 2019

Process calculi based on logic, such as $π$DILL and CP, provide a foundation for deadlock-free concurrent programming. However, in previous work, there is a mismatch between the rules for constructing proofs and the term constructors of the $π$-calculus: the fundamental operator for parallel composition does not correspond to any rule of linear logic. Kokke et al. (2019) introduced Hypersequent Classical Processes (HCP), which addresses this mismatch using hypersequents (collections of sequents) to register parallelism in the typing judgements. However, the step from CP to HCP is a big one. As of yet, HCP does not have reduction semantics, and the addition of delayed actions means that CP processes interpreted as HCP processes do not behave as they would in CP. We introduce HCP-, a variant of HCP with reduction semantics and without delayed actions. We prove progress, preservation, and termination, and show that HCP- supports the same communication protocols as CP.
A Security Reference Architecture for Blockchains
Venue: arXiv cs.CR 2019
Year: 2019

Due to their interesting features, blockchains have become popular in recent years. They are full-stack systems where security is a critical factor for their success. The main focus of this work is to systematize knowledge about security and privacy issues of blockchains. To this end, we propose a security reference architecture based on models that demonstrate the stacked hierarchy of various threats (similar to the ISO/OSI hierarchy) as well as threat-risk assessment using ISO/IEC 15408. In contrast to the previous surveys, we focus on the categorization of security incidents based on their origins and using the proposed architecture we present existing prevention and mitigation techniques. The scope of our work mainly covers aspects related to the decentralized nature of blockchains, while we mention common operational security issues and countermeasures only tangentially.
Scaling Networking Education with Open Educational Resources
Venue: arXiv cs.CY 2019
Year: 2019

To reflect the key role played in our society by the network technologies, the networking courses have moved to Bachelor degrees where they are taught to large classes. We report our experience in developing an open-source ebook that targets those introductory networking courses and a series of open educational resources that complement the ebook.
Learning Whole-Image Descriptors for Real-time Loop Detection andKidnap Recovery under Large Viewpoint Difference
Venue: arXiv cs.RO 2019
Year: 2019

We present a real-time stereo visual-inertial-SLAM system which is able to recover from complicatedkidnap scenarios and failures online in realtime. We propose to learn the whole-image-descriptorin a weakly supervised manner based on NetVLAD and decoupled convolutions. We analyse thetraining difficulties in using standard loss formulations and propose an allpairloss and show itseffect through extensive experiments. Compared to standard NetVLAD, our network takes an orderof magnitude fewer computations and model parameters, as a result runs about three times faster.We evaluate the representation power of our descriptor on standard datasets with precision-recall.Unlike previous loop detection methods which have been evaluated only on fronto-parallel revisits,we evaluate the performace of our method with competing methods on scenarios involving largeviewpoint difference. Finally, we present the fully functional system with relative computation andhandling of multiple world co-ordinate system which is able to reduce odometry drift, recover fromcomplicated kidnap scenarios and random odometry failures. We open source our fully functional system as an add-on for the popular VINS-Fusion.
Adapted and constrained Dijkstra for elastic optical networks
Venue: arXiv cs.NI 2019
Year: 2019

We present an optimal and efficient algorithm for finding a shortest path in an elastic optical network. The algorithm is an adaptation of the Dijkstra shortest path algorithm, where we take into account the spectrum continuity and contiguity constraints, and a limit on the path length. The adaptation redefines the node label in the Dijkstra algorithm, allows for revisiting nodes even at a higher cost for different slices, avoids loops, and prunes worse labels. The algorithm is generic and agnostic of a specific spectrum allocation policy, as it finds the largest set of available slices from which slices can be allocated in any way. We describe and motivate the algorithm design, and point to our freely-available implementation using the Boost Graph Library. We carried out 8100 simulation runs for large, random and realistic networks, and found that the probability of establishing a connection using the proposed algorithm can be even twice as large as the probability of establishing a connection using the edge-disjoint shortest paths, and the Yen K shortest paths.
SMT-based Probabilistic Analysis of Timing Constraints in Cyber-Physical Systems
Venue: arXiv cs.SE 2019
Year: 2019

Modeling and analysis of timing constraints is crucial in cyber-physical systems (CPS). EAST-ADL is an architectural language dedicated to safety-critical embedded system design. SIMULINK/STATEFLOW (S/S) is a widely used industrial tool for modeling and analysis of embedded systems. In most cases, a bounded number of violations of timing constraints in systems would not lead to system failures when the results of the violations are negligible, called Weakly-Hard (WH). We have previously defined a probabilistic extension of Clock Constraint Specification Language (CCSL), called PrCCSL, for formal specification of EAST-ADL timing constraints in the context of WH. In this paper, we propose an SMT-based approach for probabilistic analysis of EAST-ADL timing constraints in CPS modeled in S/S: an automatic transformation from S/S models to the input language of SMT solver is provided; timing constraints specified in PrCCSL are encoded into SMT formulas and the probabilistic analysis of timing constraints is reduced to the validity checking of the resulting SMT encodings. Our approach is demonstrated a cooperative automotive system case study.