Kin Gwn Lore

LLNet: A deep autoencoder approach to natural low-light image enhancement

Abstract In surveillance, monitoring and tactical reconnaissance, gathering visual information from a dynamic environment and accurately processing such data are essential to making informed decisions and ensuring the success of a mission. Camera sensors are often cost-limited to capture clear images or videos taken in a poorly-lit environment. Many applications aim to enhance brightness, contrast and reduce noise content from the images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with a high dynamic range. We show that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded. Results show significant credibility of the approach both visually and by quantitative comparison with various techniques.

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

A new technique for shaping microfluid flow, known as flow sculpting, offers an unprecedented level of passive fluid flow control, with potential breakthrough applications in advancing manufacturing, biology, and chemistry research at the microscale. However, efficiently solving the inverse problem of designing a flow sculpting device for a desired fluid flow shape remains a challenge. Current approaches struggle with the many-to-one design space, requiring substantial user interaction and the necessity of building intuition, all of which are time and resource intensive. Deep learning has emerged as an efficient function approximation technique for high-dimensional spaces, and presents a fast solution to the inverse problem, yet the science of its implementation in similarly defined problems remains largely unexplored. We propose that deep learning methods can completely outpace current approaches for scientific inverse problems while delivering comparable designs. To this end, we show how intelligent sampling of the design space inputs can make deep learning methods more competitive in accuracy, while illustrating their generalization capability to out-of-sample predictions.

Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video.

Recent work has presented max-equivocation as a measure of the resistance of a cryptosystem to attacks when the attacker is aware of the encoder function and message distribution. Here we consider the vulnerability of a cryptosystem in the one-try attack scenario when the attacker has incomplete information about the encoder function and message distribution. We show that encoder functions alone yield information to the attacker, and combined with inferable information about the ciphertexts, information about the message distribution can be discovered. We show that the whole encoder function need not be fixed or shared a priori for an effective cryptosystem, and this can be exploited to increase the equivocation over an a priori shared encoder. Finally we present two algorithms that operate in these scenarios and achieve good equivocation results, ExPad that demonstrates the key concepts, and ShortPad that has less overhead than ExPad.We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.In today’s landscape of more and more software-driven functionalities, spanning more and more fields, model-driven engineering (MDE) promises to ease the development of software. To accomplish this goal, MDE employs domain-specific languages (DSLs). The problem is that, on one hand, DSLs are not easy to create, and, on the other hand, as a result of the increased software-driven functionalities, they need to deal with bigger models. In dealing with these big models, modularity mechanisms are employed regularly by DSLs. These mechanisms need to be introduced over and over again into the developed DSLs, adding to the effort of creating them. To ease the development of DSLs, we propose to introduce a modularisation of models that is independent of the DSLs. We do so via two mechanisms, groups and fragment abstractions, that comprise many modularity use cases found in DSLs. These two mechanisms have been implemented in a prototype tool, MetaMod.This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.Finding commonalities between descriptions of data or knowledge is a fundamental task in Machine Learning. The formal notion characterizing precisely such commonalities is known as least general generalization of descriptions and was introduced by G. Plotkin in the early 70s, in First Order Logic. Identifying least general generalizations has a large scope of database applications ranging from query optimization (e.g., to share commonalities between queries in view selection or multi-query optimization) to recommendation in social networks (e.g., to establish connections between users based on their commonalities between profiles or searches). To the best of our knowledge, this is the first work that re-visits the notion of least general generalizations in the entire Resource Description Framework (RDF) and popular con-junctive fragment of SPARQL, a.k.a. Basic Graph Pattern (BGP) queries. Our contributions include the definition and the computation of least general generalizations in these two settings, which amounts to finding the largest set of com-monalities between incomplete databases and conjunctive queries, under deductive constraints. We also provide an experimental assessment of our technical contributions.Previous studies have shown the efficiency of using quasi-random mutations on the well-know CMA evolution strategy [13]. Quasi-random mutations have many advantages, in particular their application is stable, efficient and easy to use. In this article, we extend this principle by applying quasi-random mutations on several well known continuous evolutionary algorithms (SA, CMSA, CMA) and do it on several old and new test functions, and with several criteria. The results point out a clear improvement compared to the baseline, in all cases, and in particular for moderate computational budget.We extend the definition and study the algebraic properties of the polylogarithm Li(T) , where T is rational series over the alphabet X = {x 0 , x 1 } belonging to suitable subalgebras of rational series.Multi-task algorithms typically use task similarity information as a bias to speed up learning. We argue that, when the classification problem is unbalanced, task dissimilarity information provides a more effective bias, as rare class labels tend to be better separated from the frequent class labels. In particular, we show that a multi-task extension of the label propagation algorithm for graph-based classification works much better on protein function prediction problems when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. CCS Concepts •Computing methodologies → Multi-task learning; Semi-supervised learning settings; •Applied computing → Bioinformatics;This paper proposes an end-to-end convolutional selective autoencoder approach for early detection of combustion instabilities using rapidly arriving flame image frames. The instabilities arising in combustion processes cause significant deterioration and safety issues in various human-engineered systems such as land and air based gas turbine engines. These properties are described as self-sustaining, large amplitude pressure oscillations and show varying spatial scales periodic coherent vortex structure shedding. However, such instability is extremely difficult to detect before a combustion process becomes completely unstable due to its sudden (bifurcation-type) nature. In this context, an autoencoder is trained to selectively mask stable flame and allow unstable flame image frames. In that process, the model learns to identify and extract rich descriptive and explanatory flame shape features. With such a training scheme, the selective autoencoder is shown to be able to detect subtle instability features as a combustion process makes transition from stable to unstable region. As a consequence, the deep learning tool-chain can perform as an early detection framework for combustion instabilities that will have a transformative impact on the safety and performance of modern engines.Content-based publish/subscribe is an attractive option for disseminating event data in cyber-physical systems. To this end, we propose MothPad: a monitoring and visualization tool to demonstrate the performance of various pub/sub solutions within the context of location-based applications. MothPad consists of Mammoth, an online game research framework used as a cyber-physical system simulator, and PADRES, the publish/subscribe dissemination substrate. Both are instrumented and the performance is displayed in real-time using a monitoring client. We show the applicability of our approach through two case studies: network engines for online games and self-evolving subscriptions.This paper presents a multi-modal hoax detection system composed of text, source, and image analysis. As hoax can be very diverse, we want to analyze several modalities to better detect them. This system is applied in the context of the Verifying Multimedia Use task of MediaEval 2016. Experiments show the performance of each separated modality as well as their combination.

Multimodal spatiotemporal information fusion using neural-symbolic modeling for early detection of combustion instabilities

Detection and prediction of combustion instabilities are of interest to the gas turbine engine community with many practical applications. This paper presents a dynamic data-driven approach to accurately detect precursors to the combustion instability phenomena. In particular, grey-scale images of combustion flames have been used in combination with pressure time-series data for information fusion to detect and predict flame instabilities in the combustion process. These grey-scale images are analyzed using deep belief network (DBN). The cross-dependencies between the features extracted by the DBN and the symbolic sequences generated from pressure time-series are then analyzed using ×D-Markov (pronounced cross D-Markov) models that are constructed by a combination of state-splitting and cross-entropy rate; this leads to the development of a variable-memory cross-model as a representation of the underlying physical process. These cross-models are then used for detection and prediction of combustion instability phenomena. The proposed concept is validated on experimental data collected from a laboratory-scale swirl-stabilized combustor apparatus, where the instability phenomena are induced by typical protocols leading to unstable flames.

A deep learning framework for causal shape transformation

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element.

Topology control in mobile sensor networks using information space feedback

Time-varying network topology plays a key role in mobile sensor networks for detection of an event of interest and subsequent awareness propagation within a monitoring and surveillance framework. While physical space parameters such as communication range and mobility characteristics directly drive the network structure, feedback from the information space can be useful to improve network topology and facilitate efficient information management. In this context, the paper proposes a feedback control scheme for tuning key network topology parameters, such as average degree and degree distribution under the recently proposed generalized gossip framework for distributed belief/awareness propagation in mobile sensor networks. The crux of this decentralized control policy is to modify the timelines of the asynchronous belief update protocol depending on the node-level belief/awareness. Using a proximity network representation for a mobile sensor network, the paper presents both analytic and numerical results associated with topology control scheme as well as its impacts on belief/awareness propagation characteristics.