OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines
Venue: arXiv cs.AI 2019
Year: 2019

The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism. Index Term
PhotoTwinVR: An Immersive System for Manipulation, Inspection and Dimension Measurements of the 3D Photogrammetric Models of Real-Life Structures in Virtual Reality
Venue: arXiv cs.HC 2019
Year: 2019

Photogrammetry is a science dealing with obtaining reliable information about physical objects using their imagery description. Recent advancements in the development of Virtual Reality (VR) can help to unlock the full potential offered by the digital 3D-reality models generated using the state-of-art photogrammetric technologies. These models are becoming a viable alternative for providing high-quality content for such immersive environment. Simultaneously, their analyses in VR could bring added-value to professionals working in various engineering and non-engineering settings and help in extracting useful information about physical objects. However, there is little research published to date on feasible interaction methods in the VR-based systems augmented with the 3D photogrammetric models, especially concerning gestural input interfaces. Consequently, this paper presents the PhotoTwinVR -- an immersive, gesture-controlled system for manipulation and inspection of 3D photogrammetric models of physical objects in VR. Our system allows the user to perform basic engineering operations on the model subjected to the off-line inspection process. An observational study with a group of three domain-expert participants was completed to verify its feasibility. The system was populated with a 3D photogrammetric model of an existing pipe-rack generated using a commercial software package. The participants were asked to carry out a survey measurement of the object using the measurement toolbox offered by PhotoTwinVR. The study revealed a potential of such immersive tool to be applied in practical real-words cases of off-line inspections of pipelines.
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods
Venue: arXiv cs.AI 2019
Year: 2019

Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowledge for better explainability, however, we were unable to quantify the extent of explainability. In this work, we (1) briefly analyze the definitions of explainability from the perspective of different disciplines (e.g., psychology, social science), properties of explanation, explanation methods, and human-friendly explanations; and (2) propose and formulate an approach to quantify the extent of explainability. Our experimental result suggests a reasonable and model-agnostic way to quantify explainability
Fleet Control using Coregionalized Gaussian Process Policy Iteration
Venue: arXiv cs.LG 2019
Year: 2019

In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their status and environment. Such an infrastructure allows seamless data sharing between fleet members, which could greatly improve the sample-efficiency of reinforcement learning techniques. However in practice, these machines, while almost identical in design, have small discrepancies due to production errors or degradation, preventing control algorithms to simply aggregate and employ all fleet data. We propose a novel reinforcement learning method that learns to transfer knowledge between similar fleet members and creates member-specific dynamics models for control. Our algorithm uses Gaussian processes to establish cross-member covariances. This is significantly different from standard transfer learning methods, as the focus is not on sharing information over tasks, but rather over system specifications. We demonstrate our approach on two benchmarks and a realistic wind farm setting. Our method significantly outperforms two baseline approaches, namely individual learning and joint learning where all samples are aggregated, in terms of the median and variance of the results.
A Data Set of Internet Claims and Comparison of their Sentiments with Credibility
Venue: arXiv cs.IR 2019
Year: 2019

In this modern era, communication has become faster and easier. This means fallacious information can spread as fast as reality. Considering the damage that fake news kindles on the psychology of people and the fact that such news proliferates faster than truth, we need to study the phenomenon that helps spread fake news. An unbiased data set that depends on reality for rating news is necessary to construct predictive models for its classification. This paper describes the methodology to create such a data set. We collect our data from snopes.com which is a fact-checking organization. Furthermore, we intend to create this data set not only for classification of the news but also to find patterns that reason the intent behind misinformation. We also formally define an Internet Claim, its credibility, and the sentiment behind such a claim. We try to realize the relationship between the sentiment of a claim with its credibility. This relationship pours light on the bigger picture behind the propagation of misinformation. We pave the way for further research based on the methodology described in this paper to create the data set and usage of predictive modeling along with research-based on psychology/mentality of people to understand why fake news spreads much faster than reality.
Privacy-preserving parametric inference: a case for robust statistics
Venue: arXiv cs.LG 2019
Year: 2019

Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric inference with differential privacy guarantees. We first obtain differentially private estimators based on bounded influence M-estimators by leveraging their gross-error sensitivity in the calibration of a noise term added to them in order to ensure privacy. We then show how a similar construction can also be applied to construct differentially private test statistics analogous to the Wald, score and likelihood ratio tests. We provide statistical guarantees for all our proposals via an asymptotic analysis. An interesting consequence of our results is to further clarify the connection between differential privacy and robust statistics. In particular, we demonstrate that differential privacy is a weaker stability requirement than infinitesimal robustness, and show that robust M-estimators can be easily randomized in order to guarantee both differential privacy and robustness towards the presence of contaminated data. We illustrate our results both on simulated and real data.
Linear Consistency for Proof-of-Stake Blockchains
Venue: arXiv cs.CR 2019
Year: 2019

The blockchain data structure maintained via the longest-chain rule---popularized by Bitcoin---is a powerful algorithmic tool for consensus algorithms. Such algorithms achieve consistency for blocks in the chain as a function of their depth from the end of the chain. While the analysis of Bitcoin guarantees consistency with error $2^{-k}$ for blocks of depth $O(k)$, the state-of-the-art of proof-of-stake (PoS) blockchains suffers from a quadratic dependence on $k$: these protocols, exemplified by Ouroboros (Crypto 2017), Ouroboros Praos (Eurocrypt 2018) and Sleepy Consensus (Asiacrypt 2017), can only establish that depth $Θ(k^2)$ is sufficient. Whether this quadratic gap is an intrinsic limitation of PoS---due to issues such as the nothing-at-stake problem---has been an urgent open question, as deployed PoS blockchains further rely on consistency for protocol correctness. We give an axiomatic theory of blockchain dynamics that permits rigorous reasoning about the longest-chain rule and achieve, in broad generality, $Θ(k)$ dependence on depth in order to achieve consistency error $2^{-k}$. In particular, for the first time, we show that PoS protocols can match proof-of-work protocols for linear consistency. We analyze the associated stochastic process, give a recursive relation for the critical functionals of this process, and derive tail bounds in both i.i.d. and martingale settings via associated generating functions.
A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution
Venue: arXiv cs.LG 2019
Year: 2019

As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation. To address these problems, we propose a Domain-Adaptive Density Clustering (DADC) algorithm, which consists of three steps: domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble. For data with VDD features, clusters in sparse regions are often neglected by using uniform density peak thresholds, which results in the loss of sparse clusters. We define a domain-adaptive density measurement method based on K-Nearest Neighbors (KNN) to adaptively detect the density peaks of different density regions. We treat each data point and its KNN neighborhood as a subgroup to better reflect its density distribution in a domain view. In addition, for data with ED or MDDM features, a large number of density peaks with similar values can be identified, which results in cluster fragmentation. We propose a cluster center self-identification and cluster self-ensemble method to automatically extract the initial cluster centers and merge the fragmented clusters. Experimental results demonstrate that compared with other comparative algorithms, the proposed DADC algorithm can obtain more reasonable clustering results on data with VDD, ED and MDDM features. Benefitting from a few parameter requirements and non-iterative nature, DADC achieves low computational complexity and is suitable for large-scale data clustering.
Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
Venue: arXiv cs.LG 2019
Year: 2019

The correspondence between residual networks and dynamical systems motivates researchers to unravel the physics of ResNets with well-developed tools in numeral methods of ODE systems. The Runge-Kutta-Fehlberg method is an adaptive time stepping that renders a good trade-off between the stability and efficiency. Can we also have an adaptive time stepping for ResNets to ensure both stability and performance? In this study, we analyze the effects of time stepping on the Euler method and ResNets. We establish a stability condition for ResNets with step sizes and weight parameters, and point out the effects of step sizes on the stability and performance. Inspired by our analyses, we develop an adaptive time stepping controller that is dependent on the parameters of the current step, and aware of previous steps. The controller is jointly optimized with the network training so that variable step sizes and evolution time can be adaptively adjusted. We conduct experiments on ImageNet and CIFAR to demonstrate the effectiveness. It is shown that our proposed method is able to improve both stability and accuracy without introducing additional overhead in inference phase.
PlantDoc: A Dataset for Visual Plant Disease Detection
Venue: arXiv cs.CV 2019
Year: 2019

India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.
Compressing Representations for Embedded Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices. To address that issue, we envision sharing the computational costs of inference between local devices and the cloud, taking advantage of the compression performed by the first layers of the networks to reduce communication costs. Inference in such distributed setting would allow new applications, but requires balancing a triple trade-off between computation cost, communication bandwidth, and model accuracy. We explore that trade-off by studying the compressibility of representations at different stages of MobileNetV2, showing those results agree with theoretical intuitions about deep learning, and that an optimal splitting layer for network can be found with a simple PCA-based compression scheme.
A New Model in Firefighting Theory
Venue: arXiv cs.CG 2019
Year: 2019

Continuous and discrete models for firefighting problems are well-studied in Theoretical Computer Science. We introduce a new, discrete, and more general framework based on a hexagonal cell graph to study firefighting problems in varied terrains. We present three different firefighting problems in the context of this model; for two of which, we provide efficient polynomial time algorithms and for the third, we show NP-completeness. We also discuss possible extensions of the model and their implications on the computational complexity.
Arsenal of Hardware Prefetchers
Venue: arXiv cs.AR 2019
Year: 2019

Hardware prefetching is one of the latency tolerance optimization techniques that tolerate costly DRAM accesses. Though hardware prefetching is one of the fundamental mechanisms prevalent on most of the commercial machines, there is no prefetching technique that works well across all the access patterns and different types of workloads. Through this paper, we propose Arsenal, a prefetching framework which allows the advantages provided by different data prefetchers to be combined, by dynamically selecting the best-suited prefetcher for the current workload. Thus effectively improving the versatility of the prefetching system. It bases on the classic Sandbox prefetcher that dynamically adapts and utilizes multiple offsets for sequential prefetchers. We take it to the next step by switching between prefetchers like Multi look Ahead Offset Prefetching and Timing SKID Prefetcher on the run. Arsenal utilizes a space-efficient pooling filter, Bloom filters, that keeps track of useful prefetches of each of these component prefetchers and thus helps to maintain a score for each of the component prefetchers. This approach is shown to provide better speedup than anyone prefetcher alone. Arsenal provides a performance improvement of 44.29% on the single-core mixes and 19.5% for some of the selected 25 representative multi-core mixes.
Seamless Object-Oriented Requirements
Venue: arXiv cs.SE 2019
Year: 2019

Design by Contract enables seamless software development by unifying software requirements with their implementations. In its pure form, however, Design by Contract leaves some problems with contracts' expressiveness, verifiability, and reusability open. These problems significantly reduce practical applicability of seamless development. The present article introduces seamless object-oriented requirements - a novel approach to seamless development that builds upon Design by Contract and now-available advanced program proving tools. The article explains and illustrates the new approach, concluding with a quantitative evaluation of the extent to which the approach fixes the problems of traditional contracts.
Hardness of some variants of the graph coloring game
Venue: arXiv cs.DM 2019
Year: 2019

Very recently, a long-standing open question proposed by Bodlaender in 1991 was answered: the graph coloring game is PSPACE-complete. In 2019, Andres and Lock proposed five variants of the graph coloring game and left open the question of PSPACE-hardness related to them. In this paper, we prove that these variants are PSPACE-complete for the graph coloring game and also for the greedy coloring game, even if the number of colors is the chromatic number. Finally, we also prove that a connected version of the graph coloring game, proposed by Charpentier et al. in 2019, is PSPACE-complete.
OFDM-Based Optical Spatial Modulation
Venue: arXiv cs.IT 2019
Year: 2019

Spatial modulation (SM) has proven to be a promising multiple-input-multiple-output (MIMO) technique which provides high energy efficiency and reduces system complexity. In SM, only one transmitter is active at any given time while the rest of them remain silent. The index of the active transmitter carries information. This spatial information is in addition to the data carried by the constellation symbols in the signal domain. Therefore, SM increases the transmission rate of the communication system compared to single-input-single-output and space-time block coding (STBC)-MIMO. For signal domain data encoding, orthogonal frequency division multiplexing (OFDM) has been widely adopted. The key benefits in multi-carrier intensity-modulation and direct-detection (IM/DD) systems are: i) the capability to achieve high spectral efficiency and ii) the ability to effectively mitigate direct-current (DC) wander effects and the impact of ambient light. However, current off-the shelf light emitting diodes (LEDs) which are used as transmit entities are primarily bandwidth limited. Thus, there are benefits of combining SM and OFDM to enhance transmission speeds while maintaining low complexity. In this paper, the two most common OFDM-based SM types, namely frequency domain SM (FD-SM) and time domain SM (TD-SM), are investigated for optical wireless communications (OWC). Moreover, proof-ofconcept experimental results are presented to showcase practical feasibility of both techniques. The obtained results are also compared with Monte Carlo simulations. The results show that TDSM with an optimal maximum-a-posteriori-probability (MAP) detector significantly outperforms FD-SM. It can be inferred from the results that TD SM is a strong candidate among OFDM-based optical SM systems for future optical IM/DD wireless communication systems.
Smoothed complexity of local Max-Cut and binary Max-CSP
Venue: arXiv cs.DS 2019
Year: 2019

We show that the smoothed complexity of the FLIP algorithm for local Max-Cut is at most $\smash{ϕn^{O(\sqrt{\log n})}}$, where $n$ is the number of nodes in the graph and $ϕ$ is a parameter that measures the magnitude of perturbations applied on its edge weights. This improves the previously best upper bound of $ϕn^{O(\log n)}$ by Etscheid and Röglin. Our result is based on an analysis of long sequences of flips, which shows~that~it is very unlikely for every flip in a long sequence to incur a positive but small improvement in the cut weight. We also extend the same upper bound on the smoothed complexity of FLIP to all binary Maximum Constraint Satisfaction Problems.
Controlling the Amount of Verbatim Copying in Abstractive Summarization
Venue: arXiv cs.CL 2019
Year: 2019

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.
The Bouquet Algorithm for Model Checking Unbounded Until
Venue: arXiv cs.LO 2019
Year: 2019

The problem of verifying the "Unbounded Until" fragment in temporal logic formulas has been studied extensively in the past, especially in the context of statistical model checking. Statistical model checking, a computationally inexpensive sampling based alternative to the more expensive numerical model checking technique, presents the following decision dilemma -- what length of the sample is enough in general? In this paper, we discuss an algorithm for this problem that combines ideas from graph theory, statistical model checking and numerical model checking. We analyze the algorithm and show through experiments that this approach outperforms the standard statistical model checking algorithm for verifying unbounded until for low density Discrete Time Markov Chains.