OpenRaiser/Intern-Atlas huggingface dataset:
Combined Thermal Control and GNC: An Enabling Technology for CubeSat Surface Probes and Small Robots
Venue: arXiv cs.SY 2017
Year: 2017

Advances in GNC, particularly from miniaturized control electronics, reaction-wheels and attitude determination sensors make it possible to design surface probes and small robots to perform surface exploration and science on low-gravity environments. These robots would use their reaction wheels to roll, hop and tumble over rugged surfaces. These robots could provide 'Google Streetview' quality images of off-world surfaces and perform some unique science using penetrometers. These systems can be powered by high-efficiency fuel cells that operate at 60-65 % and utilize hydrogen and oxygen electrolyzed from water. However, one of the major challenges that prevent these probes and robots from performing long duration surface exploration and science is thermal design and control. In the inner solar system, during the day time, there is often enough solar-insolation to keep these robots warm and power these devices, but during eclipse the temperatures falls well below storage temperature. We have developed a thermal control system that utilizes chemicals to store and dispense heat when needed. The system takes waste products, such as water from these robots and transfers them to a thermochemical storage system. These thermochemical storage systems when mixed with water (a waste product from a PEM fuel cell) releases heat. Under eclipse, the heat from the thermochemical storage system is released to keep the probe warm enough to survive. In sunlight, solar photovoltaics are used to electrolyze the water and reheat the thermochemical storage system to release the water. Our research has showed thermochemical storage systems are a feasible solution for use on surface probes and robots for applications on the Moon, Mars and asteroids.
A one-dimensional model for water desalination by flow-through electrode capacitive deionization
Venue: arXiv cs.CE 2017
Year: 2017

Capacitive deionization (CDI) is a fast-emerging water desalination technology in which a small cell voltage of ~1 V across porous carbon electrodes removes salt from feedwaters via electrosorption. In flow-through electrode (FTE) CDI cell architecture, feedwater is pumped through macropores or laser perforated channels in porous electrodes, enabling highly compact cells with parallel flow and electric field, as well as rapid salt removal. We here present a one-dimensional model describing water desalination by FTE CDI, and a comparison to data from a custom-built experimental cell. The model employs simple cell boundary conditions derived via scaling arguments. We show good model-to-data fits with reasonable values for fitting parameters such as the Stern layer capacitance, micropore volume, and attraction energy. Thus, we demonstrate that from an engineering modeling perspective, an FTE CDI cell may be described with simpler one-dimensional models, unlike more typical flow-between electrodes architecture where 2D models are required.
Socio-Affective Agents as Models of Human Behaviour in the Networked Prisoner's Dilemma
Venue: arXiv cs.MA 2017
Year: 2017

Affect Control Theory (ACT) is a powerful and general sociological model of human affective interaction. ACT provides an empirically derived mathematical model of culturally shared sentiments as heuristic guides for human decision making. BayesACT, a variant on classical ACT, combines affective reasoning with cognitive (denotative or logical) reasoning as is traditionally found in AI. Bayes\-ACT allows for the creation of agents that are both emotionally guided and goal-directed. In this work, we simulate BayesACT agents in the Iterated Networked Prisoner's Dilemma (INPD), and we show four out of five known properties of human play in INPD are replicated by these socio-affective agents. In particular, we show how the observed human behaviours of network structure invariance, anti-correlation of cooperation and reward, and player type stratification are all clearly emergent properties of the networked BayesACT agents. We further show that decision hyteresis (Moody Conditional Cooperation) is replicated by BayesACT agents in over $2/3$ of the cases we have considered. In contrast, previously used imitation-based agents are only able to replicate one of the five properties. We discuss the implications of these findings in the development of human-agent societies.
Learning the distribution with largest mean: two bandit frameworks
Venue: arXiv cs.LG 2017
Year: 2017

Over the past few years, the multi-armed bandit model has become increasingly popular in the machine learning community, partly because of applications including online content optimization. This paper reviews two different sequential learning tasks that have been considered in the bandit literature ; they can be formulated as (sequentially) learning which distribution has the highest mean among a set of distributions, with some constraints on the learning process. For both of them (regret minimization and best arm identification) we present recent, asymptotically optimal algorithms. We compare the behaviors of the sampling rule of each algorithm as well as the complexity terms associated to each problem.
EmbedJoin: Efficient Edit Similarity Joins via Embeddings
Venue: arXiv cs.DB 2017
Year: 2017

We study the problem of edit similarity joins, where given a set of strings and a threshold value $K$, we want to output all pairs of strings whose edit distances are at most $K$. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds.
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure
Venue: arXiv cs.CV 2017
Year: 2017

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoG) noise assumption, which is finely in accordance with the noise characteristics possessed by a natural HSI and thus is capable of adapting various noise shapes encountered in real applications. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose a NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is designed to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed-form. Compared with the current techniques, the proposed method performs more robust beyond the state-of-the-arts, as substantiated by our experiments implemented on synthetic and real noisy HSIs.
Info-Clustering: An Efficient Algorithm by Network Information Flow
Venue: arXiv cs.IT 2017
Year: 2017

Motivated by the fact that entities in a social network or biological system often interact by exchanging information, we propose an efficient info-clustering algorithm that can group entities into communities using a parametric max-flow algorithm. This is a meaningful special case of the info-clustering paradigm where the dependency structure is graphical and can be learned readily from data.
The Role of Quality Assurance in Software Development Projects:Project Failures and Business Performance
Venue: arXiv cs.SE 2017
Year: 2017

In the product business still battles with the hard assignment of creating programming applications that meet quality gauges, and spending limitations. The requirement for programming to be without mistake remained a test to the IT business. Hence, the basic role of this study is to answer why officials hesitant to apportion assets to quality confirmation (QA) process amid the procedure of the framework advancement life cycle (SDLC)? This exploration utilized a quantitative study outline to research to what degree the inclusion of QA amid the SDLC procedure diminished programming venture disappointments. The information will be investigate utilizing inductive techniques and was expected to be summed up to the whole IT programming improvement populace. The exploration showed a feeling that incorporation of QA in all periods of SDLC was a great marvel. To gather the essential information, a proficient study will be led through survey structure will be utilized to take the criticisms and perspectives from various programming houses and industry specialists. The last stage will be the elucidating factual investigation by utilizing measurable techniques. Matlab programming will be utilized to gauge the fluctuation and execution by contrasting the execution parameters. As a consequence of this examination, the odds of the venture disappointment amid undertaking advancement will be declines and the business execution of the tasks will be expansions.
Robust Order Scheduling in the Fashion Industry: A Multi-Objective Optimization Approach
Venue: arXiv cs.AI 2017
Year: 2017

In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.
Inferring Conceptual Relationships When Ranking Patients
Venue: arXiv cs.IR 2017
Year: 2017

Searching patients based on the relevance of their medical records is challenging because of the inherent implicit knowledge within the patients' medical records and queries. Such knowledge is known to the medical practitioners but may be hidden from a search system. For example, when searching for the patients with a heart disease, medical practitioners commonly know that patients who are taking the amiodarone medicine are relevant, since this drug is used to combat heart disease. In this article, we argue that leveraging such implicit knowledge improves the retrieval effectiveness, since it provides new evidence to infer the relevance of patients' medical records towards a query. Specifically, built upon existing conceptual representation for both medical records and queries, we proposed a novel expansion of queries that infers additional conceptual relationships from domain-specific resources as well as by extracting informative concepts from the top-ranked patients' medical records. We evaluate the retrieval effectiveness of our proposed approach in the context of the TREC 2011 and 2012 Medical Records track. Our results show the effectiveness of our approach to model the implicit knowledge in patient search, whereby the retrieval performance is significantly improved over both an effective conceptual representation baseline and an existing semantic query expansion baseline. In addition, we provide an analysis of the types of queries that the proposed approach is likely to be effective.
Inkjet printing-based volumetric display projecting multiple full-colour 2D patterns
Venue: arXiv cs.MM 2017
Year: 2017

In this study, a method to construct a full-colour volumetric display is presented using a commercially available inkjet printer. Photoreactive luminescence materials are minutely and automatically printed as the volume elements, and volumetric displays are constructed with high resolution using easy-to-fabricate means that exploit inkjet printing technologies. The results experimentally demonstrate the first prototype of an inkjet printing-based volumetric display composed of multiple layers of transparent films that yield a full-colour three-dimensional (3D) image. Moreover, we propose a design algorithm with 3D structures that provide multiple different 2D full-colour patterns when viewed from different directions and experimentally demonstrates prototypes. It is considered that these types of 3D volumetric structures and their fabrication methods based on widely deployed existing printing technologies can be utilised as novel information display devices and systems, including digital signage, media art, entertainment and security.
ImageNet MPEG-7 Visual Descriptors - Technical Report
Venue: arXiv cs.CV 2017
Year: 2017

ImageNet is a large scale and publicly available image database. It currently offers more than 14 millions of images, organised according to the WordNet hierarchy. One of the main objective of the creators is to provide to the research community a relevant database for visual recognition applications such as object recognition, image classification or object localisation. However, only a few visual descriptors of the images are available to be used by the researchers. Only SIFT-based features have been extracted from a subset of the collection. This technical report presents the extraction of some MPEG-7 visual descriptors from the ImageNet database. These descriptors are made publicly available in an effort towards open research.
Communication-Optimal Distributed Clustering
Venue: arXiv cs.DS 2017
Year: 2017

Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site. In this work, we study both graph and geometric clustering problems in two distributed models: (1) a point-to-point model, and (2) a model with a broadcast channel. We give protocols in both models which we show are nearly optimal by proving almost matching communication lower bounds. Our work highlights the surprising power of a broadcast channel for clustering problems; roughly speaking, to spectrally cluster $n$ points or $n$ vertices in a graph distributed across $s$ servers, for a worst-case partitioning the communication complexity in a point-to-point model is $n \cdot s$, while in the broadcast model it is $n + s$. A similar phenomenon holds for the geometric setting as well. We implement our algorithms and demonstrate this phenomenon on real life datasets, showing that our algorithms are also very efficient in practice.
ArchiveWeb: Collaboratively Extending and Exploring Web Archive Collections
Venue: arXiv cs.DL 2017
Year: 2017

Curated web archive collections contain focused digital contents which are collected by archiving organizations to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an iterative evaluation-driven design-based research approach, with considerable user feedback at all stages. This paper describes the functionalities of our current prototype for searching, constructing, exploring and discussing web archive collections, as well as feedback on this prototype from seven archiving organizations, and our plans for improving the next release of the system.
Collision vs non-Collision Distributed Time Synchronization for Dense IoT Deployments
Venue: arXiv cs.IT 2017
Year: 2017

Massive co-located devices require new paradigms to allow proper network connectivity. Internet of things (IoT) is the paradigm that offers a solution for the inter-connectivity of devices, but in dense IoT networks time synchronization is a critical aspect. Further, the scalability is another crucial aspect. This paper focuses on synchronization for uncoordinated dense networks without any external timing reference. Two synchronization methods are proposed and compared: i) conventional synchronization that copes with the high density of nodes by frame collision-avoidance methods (e.g., CSMA/CA) to avoid the superimposition (or collision) of synchronization signals; and ii) distributed synchronization that exploits the frames' collision to drive the network to a global synchronization. The distributed synchronization algorithm allows the network to reach a timing synchronization status based on a common beacon with the same signature broadcasted by every device. The superimposition of beacons from all the other devices enables the network synchronization, rather than preventing it. Numerical analysis evaluates the synchronization performance based on the convergence time and synchronization dispersion, both on collision and non-collision scenario, by investigating the scalability of the network. Results prove that in dense network the ensemble of signatures provides remarkable improvements of synchronization performance compared to conventional master-slave reference.
Attacking the V: On the Resiliency of Adaptive-Horizon MPC
Venue: arXiv cs.SY 2017
Year: 2017

We introduce the concept of a V-formation game between a controller and an attacker, where controller's goal is to maneuver the plant (a simple model of flocking dynamics) into a V-formation, and the goal of the attacker is to prevent the controller from doing so. Controllers in V-formation games utilize a new formulation of model-predictive control we call Adaptive-Horizon MPC (AMPC), giving them extraordinary power: we prove that under certain controllability assumptions, an AMPC controller is able to attain V-formation with probability 1. We define several classes of attackers, including those that in one move can remove R birds from the flock, or introduce random displacement into flock dynamics. We consider both naive attackers, whose strategies are purely probabilistic, and AMPC-enabled attackers, putting them on par strategically with the controllers. While an AMPC-enabled controller is expected to win every game with probability 1, in practice, it is resource-constrained: its maximum prediction horizon and the maximum number of game execution steps are fixed. Under these conditions, an attacker has a much better chance of winning a V-formation game. Our extensive performance evaluation of V-formation games uses statistical model checking to estimate the probability an attacker can thwart the controller. Our results show that for the bird-removal game with R = 1, the controller almost always wins (restores the flock to a V-formation). For R = 2, the game outcome critically depends on which two birds are removed. For the displacement game, our results again demonstrate that an intelligent attacker, i.e. one that uses AMPC in this case, significantly outperforms its naive counterpart that randomly executes its attack.
Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the problem of large scale image retrieval, with the aim of accurately ranking the similarity of a large number of images to a given query image. To achieve this, we propose a novel Siamese network. This network consists of two computational strands, each comprising of a CNN component followed by a Fisher vector component. The CNN component produces dense, deep convolutional descriptors that are then aggregated by the Fisher Vector method. Crucially, we propose to simultaneously learn both the CNN filter weights and Fisher Vector model parameters. This allows us to account for the evolving distribution of deep descriptors over the course of the learning process. We show that the proposed approach gives significant improvements over the state-of-the-art methods on the Oxford and Paris image retrieval datasets. Additionally, we provide a baseline performance measure for both these datasets with the inclusion of 1 million distractors.
BFR: a Bloom Filter-based Routing Approach for Information-Centric Networks
Venue: arXiv cs.NI 2017
Year: 2017

Locating the demanded content is one of the major challenges in Information-Centric Networking (ICN). This process is known as content discovery. To facilitate content discovery, in this paper we focus on Named Data Networking (NDN) and propose a novel routing scheme for content discovery, called Bloom Filter-based Routing (BFR), which is fully distributed, content oriented, and topology agnostic at the intra-domain level. In BFR, origin servers advertise their content objects using Bloom filters. We compare the performance of the proposed BFR with flooding and shortest path content discovery approaches. BFR outperforms its counterparts in terms of the average round-trip delay, while it is shown to be very robust to false positive reports from Bloom filters. Also, BFR is much more robust than shortest path routing to topology changes. BFR strongly outperforms flooding and performs almost equal with shortest path routing with respect to the normalized communication costs for data retrieval and total communication overhead for forwarding Interests. All the three approaches achieve similar mean hit distance. The signalling overhead for content advertisement in BFR is much lower than the signalling overhead for calculating shortest paths in the shortest path approach. Finally, BFR requires small storage overhead for maintaining content advertisements.
Data Modelling for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms
Venue: arXiv cs.NI 2017
Year: 2017

To conduct a more realistic evaluation on Virtualized Network Functions resource allocation algorithms, researches needed data on: (1) potential NFs chains (policies), (2) traffic flows passing through these NFs chains, (3) how the dynamic traffic changes affect the NFs (scale out/in) and (4) different data center architectures for the NFC. However, there are no publicly available real data sets on NF chains and traffic that pass through NF chains. Therefore we have used data from previous empirical analyses and made some assumptions to derive the required data to evaluate resource allocation algorithms for VNFs. We developed four programs to model the gathered data and generate the required data. All gathered data and data modelling programs are publicly available at github repository.