OpenRaiser/Intern-Atlas huggingface dataset:
Playing Games with Bounded Entropy: Convergence Rate and Approximate Equilibria
Venue: arXiv cs.GT 2019
Year: 2019

We consider zero-sum repeated games in which the players are restricted to strategies that require only a limited amount of randomness. Let $v_n$ be the max-min value of the $n$ stage game; previous works have characterized $\lim_{n\rightarrow\infty}v_n$, i.e., the long-run max-min value. Our first contribution is to study the convergence rate of $v_n$ to its limit. To this end, we provide a new tool for simulation of a source (target source) from another source (coin source). Considering the total variation distance as the measure of precision, this tool offers an upper bound for the precision of simulation, which is vanishing exponentially in the difference of Rényi entropies of the coin and target sources. In the second part of paper, we characterize the set of all approximate Nash equilibria achieved in long run. It turns out that this set is in close relation with the long-run max-min value.
Safe Artificial General Intelligence via Distributed Ledger Technology
Venue: arXiv cs.CY 2019
Year: 2019

Background. Expert observers and artificial intelligence (AI) progression metrics indicate AI will exceed human intelligence within a few decades. Whether general AI that exceeds human capabilities (AGI) will be the single greatest boon in history or a disaster is unknown. No proofs exist that AGI will benefit humans or that AGI will not harm or eliminate humans. Objective. I propose a set of logically distinct conceptual components that are necessary and sufficient to 1) ensure that most known AGI scenarios will not harm humanity and 2) robustly align AGI values and goals with human values. Methods. By systematically addressing each pathway category to malevolent AI we can induce the methods/axioms required to redress the category. Results and Discussion. Distributed ledger technology (DLT, blockchain) is integral to this proposal, e.g. to reduce the probability of hacking, provide an audit trail to detect and correct errors or identify components causing vulnerability or failure and replace them or shut them down remotely and/or automatically, and to separate and balance key AGI components via decentralized apps (dApps). Smart contracts based on DLT are necessary to address evolution of AI that will be too fast for human monitoring and intervention. The proposed axioms. 1) Access to technology by market license. 2) Transparent ethics embodied in DLT. 3) Morality encrypted via DLT. 4) Behavior control structure with values (ethics) at roots. 5) Individual bar-code identification of all critical components. 6) Configuration Item (from business continuity/disaster recovery planning). 7) Identity verification secured via DLT. 8) Smart automated contracts based on DLT. 9) Decentralized applications - AI software code modules encrypted via DLT. 10) Audit trail of component usage stored via DLT. 11) Social ostracism (denial of societal resources) augmented by DLT petitions.
Attaining Fairness in Communication for Omniscience
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies how to attain fairness in communication for omniscience, where a set of users exchange their observations of a discrete multiple random source to attain omniscience---the state that all users recover the entire source. The optimal rate region containing all source coding rate vectors that achieve the omniscience with the minimum sum rate is shown to coincide with the core (the solution set) of a coalitional game. Two game-theoretic fairness solutions are studied: the Shapley value and the egalitarian solution. It is shown that the Shapley value assigns each user the source coding rate measured by his/her remaining information of the multiple source given the common randomness that is shared by all users, while the egalitarian solution simply distributes the rates as evenly as possible in the core. To avoid the exponentially growing complexity of obtaining the Shapley value, a polynomial-time approximation method is proposed by utilizing the fact that the Shapley value is the mean value over all extreme points in the core. In addition, a steepest descent algorithm is proposed which converges in polynomial time to the fractional egalitarian solution in the core that can be implemented by network coding schemes. Finally, it is shown that the game can be decomposed into subgames so that both the Shapley value and the egalitarian solution can be obtained within each subgame in a distributed manner with reduced complexity.
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation
Venue: arXiv cs.CV 2019
Year: 2019

Recent geometric methods need reliable estimates of 3D motion parameters to procure accurate dense depth map of a complex dynamic scene from monocular images \cite{kumar2017monocular, ranftl2016dense}. Generally, to estimate \textbf{precise} measurements of relative 3D motion parameters and to validate its accuracy using image data is a challenging task. In this work, we propose an alternative approach that circumvents the 3D motion estimation requirement to obtain a dense depth map of a dynamic scene. Given per-pixel optical flow correspondences between two consecutive frames and, the sparse depth prior for the reference frame, we show that, we can effectively recover the dense depth map for the successive frames without solving for 3D motion parameters. Our method assumes a piece-wise planar model of a dynamic scene, which undergoes rigid transformation locally, and as-rigid-as-possible transformation globally between two successive frames. Under our assumption, we can avoid the explicit estimation of 3D rotation and translation to estimate scene depth. In essence, our formulation provides an unconventional way to think and recover the dense depth map of a complex dynamic scene which is incremental and motion free in nature. Our proposed method does not make object level or any other high-level prior assumption about the dynamic scene, as a result, it is applicable to a wide range of scenarios. Experimental results on the benchmarks dataset show the competence of our approach for multiple frames.
Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications
Venue: arXiv cs.LG 2019
Year: 2019

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms
Global Collaboration through Local Interaction in Competitive Learning
Venue: arXiv cs.LG 2019
Year: 2019

Feature maps, that preserve the global topology of arbitrary datasets, can be formed by self-organizing competing agents. So far, it has been presumed that global interaction of agents is necessary for this process. We establish that this is not the case, and that global topology can be uncovered through strictly local interactions. Enforcing uniformity of map quality across all agents, results in an algorithm that is able to consistently uncover the global topology of diversely challenging datasets.The applicability and scalability of this approach is further tested on a large point cloud dataset, revealing a linear relation between map training time and size. The presented work not only reduces algorithmic complexity but also constitutes first step towards a distributed self organizing map.
Verifiable Smart Contract Portability
Venue: arXiv cs.DC 2019
Year: 2019

With the advent of blockchain technologies, the idea of decentralized applications has gained traction. Smart contracts permit the implementation of application logic to foster distributed systems that are capable of removing intermediaries. Hereby, lock in effects originating from isolated data storage and central authorities are mitigated. Yet, smart contracts deployed to a ledger generate dependencies on the underlying blockchain. Over time, requirements regarding contract execution may detach from the utilized chain due to contradicting incentives and security or performance issues. To avoid a novel form of lock in effect towards a host blockchain, we introduce a concept for smart contract portability that permits any user to migrate contract logic and state between blockchains in a flexible and verifiable manner. As the Ethereum Virtual Machine (EVM) is supported by a multitude of blockchain implementations, it poses a common execution environment for smart contracts. We provide a toolbox that facilitates smart contract portability between EVM-compatible blockchains without trust requirements in the entity executing the migration process. To prove the concept's soundness, we transfer token contracts based on the ERC20 standard as well as applications containing dependencies to other smart contracts. Our evaluation shows the validity of ported applications including their current states.
A Turing machine simulation by P systems without charges
Venue: arXiv cs.CC 2019
Year: 2019

It is well known that the kind of P systems involved in the definition of the P conjecture is able to solve problems in the complexity class $\mathbf{P}$ by leveraging the uniformity condition. Here we show that these systems are indeed able to simulate deterministic Turing machines working in polynomial time with a weaker uniformity condition and using only one level of membrane nesting. This allows us to embed this construction into more complex membrane structures, possibly showing that constructions similar to the one performed in [1] for P systems with charges can be carried out also in this case.
Achieving Secure and Efficient Cloud Search Services: Cross-Lingual Multi-Keyword Rank Search over Encrypted Cloud Data
Venue: arXiv cs.CR 2019
Year: 2019

Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searcha-ble encryption (KNN-SE) algorithm, it can mitigate some draw-backs and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this pa-per, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and per-sonalized search through flexible keyword and language prefer-ence settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments.
Merits and Limits: Applying open data to monitor open access publications in bibliometric databases
Venue: arXiv cs.DL 2019
Year: 2019

Identifying and monitoring Open Access (OA) publications might seem a trivial task while practical efforts prove otherwise. Contradictory information arise often depending on metadata employed. We strive to assign OA status to publications in Web of Science (WOS) and Scopus while complementing it with different sources of OA information to resolve contradicting cases. We linked publications from WOS and Scopus via DOIs and ISSNs to Unpaywall, Crossref, DOAJ and ROAD. Only about 50% of articles and reviews from WOS and Scopus could be matched via a DOI to Unpaywall. Matching with Crossref brought 56 distinct licences, which define in many cases the legally binding access status of publications. But only 44% of publications hold only a single licence on Crossref, while more than 50% have no licence information submitted to Crossref. Contrasting OA information from Crossref licences with Unpaywall we found contradictory cases overall amounting to more than 25%, which might be partially explained by (ex-)including green OA. A further manual check found about 17% of OA publications that are not accessible and 15% non-OA publications that are accessible through publishers' websites. These preliminary results suggest that identification of OA state of publications denotes a difficult and currently unfulfilled task.
Geometric Multicut
Venue: arXiv cs.CG 2019
Year: 2019

We study the following separation problem: Given a collection of colored objects in the plane, compute a shortest "fence" $F$, i.e., a union of curves of minimum total length, that separates every two objects of different colors. Two objects are separated if $F$ contains a simple closed curve that has one object in the interior and the other in the exterior. We refer to the problem as GEOMETRIC $k$-CUT, where $k$ is the number of different colors, as it can be seen as a geometric analogue to the well-studied multicut problem on graphs. We first give an $O(n^4\log^3 n)$-time algorithm that computes an optimal fence for the case where the input consists of polygons of two colors and $n$ corners in total. We then show that the problem is NP-hard for the case of three colors. Finally, we give a $(2-4/3k)$-approximation algorithm.
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. U-Net is the most prominent deep network in this regard, which has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, from extensive experimentations on challenging datasets, we found out that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Hence, following the modifications we develop a novel architecture MultiResUNet as the potential successor to the successful U-Net architecture. We have compared our proposed architecture MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Albeit slight improvements in the cases of ideal images, a remarkable gain in performance has been attained for challenging images. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively.
Replication Can Improve Prior Results: A GitHub Study of Pull Request Acceptance
Venue: arXiv cs.SE 2019
Year: 2019

Crowdsourcing and data mining can be used to effectively reduce the effort associated with the partial replication and enhancement of qualitative studies. For example, in a primary study, other researchers explored factors influencing the fate of GitHub pull requests using an extensive qualitative analysis of 20 pull requests. Guided by their findings, we mapped some of their qualitative insights onto quantitative questions. To determine how well their findings generalize, we collected much more data (170 additional pull requests from 142 GitHub projects). Using crowdsourcing, that data was augmented with subjective qualitative human opinions about how pull requests extended the original issue. The crowd's answers were then combined with quantitative features and, using data mining, used to build a predictor for whether code would be merged. That predictor was far more accurate that one built from the primary study's qualitative factors (F1=90 vs 68\%), illustrating the value of a mixed-methods approach and replication to improve prior results. To test the generality of this approach, the next step in future work is to conduct other studies that extend qualitative studies with crowdsourcing and data mining.
REAFFIRM: Model-Based Repair of Hybrid Systems for Improving Resiliency
Venue: arXiv cs.SY 2019
Year: 2019

Model-based design offers a promising approach for assisting developers to build reliable and secure cyber-physical systems (CPSs) in a systematic manner. In this methodology, a designer first constructs a model, with mathematically precise semantics, of the system under design, and performs extensive analysis with respect to correctness requirements before generating the implementation from the model. However, as new vulnerabilities are discovered, requirements evolve aimed at ensuring resiliency. There is currently a shortage of an inexpensive, automated mechanism that can effectively repair the initial design, and a model-based system developer regularly needs to redesign and reimplement the system from scratch. In this paper, we propose a new methodology along with a Matlab toolkit called REAFFIRM to facilitate the model-based repair for improving the resiliency of CPSs. REAFFIRM takes the inputs including 1) an original hybrid system modeled as a Simulink/Stateflow diagram, 2) a given resiliency pattern specified as a model transformation script, and 3) a safety requirement expressed as a Signal Temporal Logic formula, and then outputs a repaired model which satisfies the requirement. The overall structure of REAFFIRM contains two main modules, a model transformation, and a model synthesizer built on top of the falsification tool Breach. We introduce a new model transformation language for hybrid systems, which we call HATL to allow a designer to specify resiliency patterns. To evaluate the proposed approach, we use REAFFIRM to automatically synthesize repaired models for an adaptive cruise control (ACC) system under a GPS sensor spoofing attack, for a single-machine infinite-bus (SMIB) system under a sliding-mode switching attack, and for a missile guidance system under gyroscopes sensor attack.
On Residual Networks Learning a Perturbation from Identity
Venue: arXiv cs.NE 2019
Year: 2019

The purpose of this work is to test and study the hypothesis that residual networks are learning a perturbation from identity. Residual networks are enormously important deep learning models, with many theories attempting to explain how they function; learning a perturbation from identity is one such theory. In order to answer this question, the magnitudes of the perturbations are measured in both an absolute sense as well as in a scaled sense, with each form having its relative benefits and drawbacks. Additionally, a stopping rule is developed that can be used to decide the depth of the residual network based on the average perturbation magnitude being less than a given epsilon. With this analysis a better understanding of how residual networks process and transform data from input to output is formed. Parallel experiments are conducted on MNIST as well as CIFAR10 for various sized residual networks with between 6 and 300 residual blocks. It is found that, in this setting, the average scaled perturbation magnitude is roughly inversely proportional to increasing the number of residual blocks, and from this it follows that for sufficiently large residual networks, they are learning a perturbation from identity.
Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and an Application for Face Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Max-C and min-D projection autoassociative fuzzy morphological memories (max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural networks able to implement an associative memory designed for the storage and retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we address the main features of these autoassociative memories, which include unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and an excellent tolerance to either dilative noise or erosive noise. Particular attention is given to the so-called PAFMM of Zadeh which, besides performing no floating-point operations, exhibit the largest noise tolerance among max-C and min-D PAFMMs. Computational experiments reveal that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with strong potential for face recognition.
Occupancy-map-based rate distortion optimization for video-based point cloud compression
Venue: arXiv cs.MM 2019
Year: 2019

The state-of-the-art video-based point cloud compression scheme projects the 3D point cloud to 2D patch by patch and organizes the patches into frames to compress them using the efficient video compression scheme. Such a scheme shows a good trade-off between the number of points projected and the video continuity to utilize the video compression scheme. However, some unoccupied pixels between different patches are compressed using almost the same quality with the occupied pixels, which will lead to the waste of lots of bits since the unoccupied pixels are useless for the reconstructed point cloud. In this paper, we propose to consider only the rate instead of the rate distortion cost for the unoccupied pixels during the rate distortion optimization process. The proposed scheme can be applied to both the geometry and attribute frames. The experimental results show that the proposed algorithm can achieve an average of 11.9% and 15.4% bitrate savings for the geometry and attribute, respectively.
Domain Constraint Approximation based Semi Supervision
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning for supervised learning has achieved astonishing performance in various machine learning applications. However, annotated data is expensive and rare. In practice, only a small portion of data samples are annotated. Pseudo-ensembling-based approaches have achieved state-of-the-art results in computer vision related tasks. However, it still relies on the quality of an initial model built by labeled data. Less labeled data may degrade model performance a lot. Domain constraint is another way regularize the posterior but has some limitation. In this paper, we proposed a fuzzy domain-constraint-based framework which loses the requirement of traditional constraint learning and enhances the model quality for semi supervision. Simulations results show the effectiveness of our design.
Applying Quantum Hardware to non-Scientific Problems: Grover's Algorithm and Rule-based Algorithmic Music Composition
Venue: arXiv cs.AI 2019
Year: 2019

Of all novel computing methods, quantum computation (QC) is currently the most likely to move from the realm of the unconventional into the conventional. As a result some initial work has been done on applications of QC outside of science: for example music. The small amount of arts research done in hardware or with actual physical systems has not utilized any of the advantages of quantum computation (QC): the main advantage being the potential speed increase of quantum algorithms. This paper introduces a way of utilizing Grover's algorithm - which has been shown to provide a quadratic speed-up over its classical equivalent - in algorithmic rule-based music composition. The system introduced - qgMuse - is simple but scalable. Example melodies are composed using qgMuse using the ibmqx4 quantum hardware. The paper concludes with discussion on how such an approach can grow with the improvement of quantum computer hardware and software.