OpenRaiser/Intern-Atlas huggingface dataset:
Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Venue: arXiv cs.CV 2015
Year: 2015

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.
Frequency hopping sequences with optimal partial Hamming correlation
Venue: arXiv cs.IT 2015
Year: 2015

Frequency hopping sequences (FHSs) with favorable partial Hamming correlation properties have important applications in many synchronization and multiple-access systems. In this paper, we investigate constructions of FHSs and FHS sets with optimal partial Hamming correlation. We first establish a correspondence between FHS sets with optimal partial Hamming correlation and multiple partition-type balanced nested cyclic difference packings with a special property. By virtue of this correspondence, some FHSs and FHS sets with optimal partial Hamming correlation are constructed from various combinatorial structures such as cyclic difference packings, and cyclic relative difference families. We also describe a direct construction and two recursive constructions for FHS sets with optimal partial Hamming correlation. As a consequence, our constructions yield new FHSs and FHS sets with optimal partial Hamming correlation.
Information retrieval in folktales using natural language processing
Venue: arXiv cs.CL 2015
Year: 2015

Our aim is to extract information about literary characters in unstructured texts. We employ natural language processing and reasoning on domain ontologies. The first task is to identify the main characters and the parts of the story where these characters are described or act. We illustrate the system in a scenario in the folktale domain. The system relies on a folktale ontology that we have developed based on Propp's model for folktales morphology.
k-way Hypergraph Partitioning via n-Level Recursive Bisection
Venue: arXiv cs.DS 2015
Year: 2015

We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time. Using several caching and lazy-evaluation techniques during coarsening and refinement, we reduce the running time by up to two-orders of magnitude compared to a naive $n$-level algorithm that would be adequate for ordinary graph partitioning. The overall performance is even better than the widely used hMetis hypergraph partitioner that uses a classical multilevel algorithm with few levels. Aided by a portfolio-based approach to initial partitioning and adaptive budgeting of imbalance within recursive bipartitioning, we achieve very high quality. We assembled a large benchmark set with 310 hypergraphs stemming from application areas such VLSI, SAT solving, social networks, and scientific computing. We achieve significantly smaller cuts than hMetis and PaToH, while being faster than hMetis. Considerably larger improvements are observed for some instance classes like social networks, for bipartitioning, and for partitions with an allowed imbalance of 10%. The algorithm presented in this work forms the basis of our hypergraph partitioning framework KaHyPar (Karlsruhe Hypergraph Partitioning).
Taxonomy of Pathways to Dangerous AI
Venue: arXiv cs.AI 2015
Year: 2015

In order to properly handle a dangerous Artificially Intelligent (AI) system it is important to understand how the system came to be in such a state. In popular culture (science fiction movies/books) AIs/Robots became self-aware and as a result rebel against humanity and decide to destroy it. While it is one possible scenario, it is probably the least likely path to appearance of dangerous AI. In this work, we survey, classify and analyze a number of circumstances, which might lead to arrival of malicious AI. To the best of our knowledge, this is the first attempt to systematically classify types of pathways leading to malevolent AI. Previous relevant work either surveyed specific goals/meta-rules which might lead to malevolent behavior in AIs (Özkural, 2014) or reviewed specific undesirable behaviors AGIs can exhibit at different stages of its development (Alexey Turchin, July 10 2015, July 10, 2015).
Tight Bounds for the Distribution-Free Testing of Monotone Conjunctions
Venue: arXiv cs.DM 2015
Year: 2015

We improve both upper and lower bounds for the distribution-free testing of monotone conjunctions. Given oracle access to an unknown Boolean function $f:\{0,1\}^n \rightarrow \{0,1\}$ and sampling oracle access to an unknown distribution $\mathcal{D}$ over $\{0,1\}^n$, we present an $\tilde{O}(n^{1/3}/ε^5)$-query algorithm that tests whether $f$ is a monotone conjunction versus $ε$-far from any monotone conjunction with respect to $\mathcal{D}$. This improves the previous best upper bound of $\tilde{O}(n^{1/2}/ε)$ by Dolev and Ron when $1/ε$ is small compared to $n$. For some constant $ε_0>0$, we also prove a lower bound of $\tildeΩ(n^{1/3})$ for the query complexity, improving the previous best lower bound of $\tildeΩ(n^{1/5})$ by Glasner and Servedio. Our upper and lower bounds are tight, up to a poly-logarithmic factor, when the distance parameter $ε$ is a constant. Furthermore, the same upper and lower bounds can be extended to the distribution-free testing of general conjunctions, and the lower bound can be extended to that of decision lists and linear threshold functions.
Multimodal MRI Neuroimaging with Motion Compensation Based on Particle Filtering
Venue: arXiv cs.CV 2015
Year: 2015

Head movement during scanning impedes activation detection in fMRI studies. Head motion in fMRI acquired using slice-based Echo Planar Imaging (EPI) can be estimated and compensated by aligning the images onto a reference volume through image registration. However, registering EPI images volume to volume fails to consider head motion between slices, which may lead to severely biased head motion estimates. Slice-to-volume registration can be used to estimate motion parameters for each slice by more accurately representing the image acquisition sequence. However, accurate slice to volume mapping is dependent on the information content of the slices: middle slices are information rich, while edge slides are information poor and more prone to distortion. In this work, we propose a Gaussian particle filter based head motion tracking algorithm to reduce the image misregistration errors. The algorithm uses a dynamic state space model of head motion with an observation equation that models continuous slice acquisition of the scanner. Under this model the particle filter provides more accurate motion estimates and voxel position estimates. We demonstrate significant performance improvement of the proposed approach as compared to registration-only methods of head motion estimation and brain activation detection.
What do Vegans do in their Spare Time? Latent Interest Detection in Multi-Community Networks
Venue: arXiv cs.SI 2015
Year: 2015

Most social network analysis works at the level of interactions between users. But the vast growth in size and complexity of social networks enables us to examine interactions at larger scale. In this work we use a dataset of 76M submissions to the social network Reddit, which is organized into distinct sub-communities called subreddits. We measure the similarity between entire subreddits both in terms of user similarity and topical similarity. Our goal is to find community pairs with similar userbases, but dissimilar content; we refer to this type of relationship as a "latent interest." Detection of latent interests not only provides a perspective on individual users as they shift between roles (student, sports fan, political activist) but also gives insight into the dynamics of Reddit as a whole. Latent interest detection also has potential applications for recommendation systems and for researchers examining community evolution.
Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning
Venue: arXiv cs.CV 2015
Year: 2015

Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non-linearity. Third, HRNE is able to uncover temporal transitions between frame chunks with different granularities, i.e., it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks. Notably, even using a single network with only RGB stream as input, HRNE beats all the recent systems which combine multiple inputs, such as RGB ConvNet plus 3D ConvNet.
Investigating Differences between Graphical and Textual Declarative Process Models
Venue: arXiv cs.SE 2015
Year: 2015

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand.
Making Sense of Declarative Process Models: Common Strategies and Typical Pitfalls
Venue: arXiv cs.SE 2015
Year: 2015

Declarative approaches to process modeling are regarded as well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative business process models impede often their usage. In particular, how declarative models are understood has not been investigated yet. This paper takes a first step toward addressing this question and reports on an exploratory study investigating how analysts make sense of declarative process models. We have handed out real-world declarative process models to subjects and asked them to describe the illustrated process. Our qualitative analysis shows that subjects tried to describe the processes in a sequential way although the models represent circumstantial information, namely, conditions that produce an outcome, rather than a sequence of activities. Finally, we observed difficulties with single building blocks and combinations of relations between activities.
Modeling And Control Battery Aging in Energy Harvesting Systems
Venue: arXiv cs.SY 2015
Year: 2015

Energy storage is a fundamental component for the development of sustainable and environment-aware technologies. One of the critical challenges that needs to be overcome is preserving the State of Health (SoH) in energy harvesting systems, where bursty arrival of energy and load may severely degrade the battery. Tools from Markov process and Dynamic Programming theory are becoming an increasingly popular choice to control dynamics of these systems due to their ability to seamlessly incorporate heterogeneous components and support a wide range of applications. Mapping aging rate measures to fit within the boundaries of these tools is non-trivial. In this paper, a framework for modeling and controlling the aging rate of batteries based on Markov process theory is presented. Numerical results illustrate the tradeoff between battery degradation and task completion delay enabled by the proposed framework.
Software-Based Fault Recovery via Adaptive Diversity for COTS Multi-Core Processors
Venue: arXiv cs.SE 2015
Year: 2015

The ever growing demands of embedded systems to satisfy high computing performance and cost efficiency lead to the trend of using commercial off-the-shelf hardware. However, due to their highly integrated design they are becoming increasingly susceptible to hardware errors (e.g. caused by radiation-induced soft-errors or wear-out effects). Since such faults cannot be fully prevented, systems have to cope with their effects. At the same time there is the trend of multi-core processors in embedded systems. Approaches to achieve fault tolerance by using the multiple cores to establish redundancy have been presented in literature. However, typically only homogeneous redundancy techniques are considered to tolerate soft errors. However, there is a lack of appropriate reaction mechanisms for restoring the system in case of permanent hardware faults. Here, we propose the basic idea of enhancing multi-core redundancy techniques with a cost-efficient automated introduction of diversity in the executed software replicas. Recently, these automated software diversity techniques have attracted attention in the security domain. We propose to use these techniques to recover from permanent hardware faults. This is achieved by adapting the software execution in such a way that permanent faults are mitigated.
A Continuous Max-Flow Approach to Cyclic Field Reconstruction
Venue: arXiv cs.CV 2015
Year: 2015

Reconstruction of an image from noisy data using Markov Random Field theory has been explored by both the graph-cuts and continuous max-flow community in the form of the Potts and Ishikawa models. However, neither model takes into account the particular cyclic topology of specific intensity types such as the hue in natural colour images, or the phase in complex valued MRI. This paper presents \textit{cyclic continuous max-flow} image reconstruction which models the intensity being reconstructed as having a fundamentally cyclic topology. This model complements the Ishikawa model in that it is designed with image reconstruction in mind, having the topology of the intensity space inherent in the model while being readily extendable to an arbitrary intensity resolution.
Combining features of the Unreal and Unity Game Engines to hone development skills
Venue: arXiv cs.CY 2015
Year: 2015

Two of the most popular game engines today, Unreal Engine v4.x and Unity Game Engine v5.x have recently adopted competitive and very appealing pricing structures for individual game developers and small teams. One may lean towards one or the other game engine based on various criteria: existing familiarization / vested interest, steepness of learning curve, quality, richness, variability and pricing of add-on assets, initial cost of basic ownership versus subscribing to updates and acquiring needed modules, etc. In this paper we present complementary features of the Unreal and Unity game engines that can be combined to enhance understanding game development and to hone relevant skills. Such combinations of different game engine features can increase the added value for our undergraduate informatics engineering students taking a "Game Technologies" class.
Wireless Powered Cooperative Jamming for Secrecy Multi-AF Relaying Networks
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies secrecy transmission with the aid of a group of wireless energy harvesting (WEH)-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer (SWIPT) with each relay employing a power splitting (PS) receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise (AN) for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.
Larger-Context Language Modelling
Venue: arXiv cs.CL 2015
Year: 2015

In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demon- strate that the proposed model improves perplexity significantly. In the experi- ments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained larger- context language model, we discover that content words, including nouns, adjec- tives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.
Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation
Venue: arXiv cs.SI 2015
Year: 2015

Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are absent or very sparse. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit these attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impact on users and items.
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
Venue: arXiv cs.LG 2015
Year: 2015

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.
Generalized Alternating Projection Based Total Variation Minimization for Compressive Sensing
Venue: arXiv cs.IT 2015
Year: 2015

We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework with TV minimization for video and hyperspectral image compressive sensing under the CACTI and CASSI framework, respectively. Connections between GAP and ADMM are also provided.
Fast Data Management with Distributed Streaming SQL
Venue: arXiv cs.DB 2015
Year: 2015

To stay competitive in today's data driven economy, enterprises large and small are turning to stream processing platforms to process high volume, high velocity, and diverse streams of data (fast data) as they arrive. Low-level programming models provided by the popular systems of today suffer from lack of responsiveness to change: enhancements require code changes with attendant large turn-around times. Even though distributed SQL query engines have been available for Big Data, we still lack support for SQL-based stream querying capabilities in distributed stream processing systems. In this white paper, we identify a set of requirements and propose a standard SQL based streaming query model for management of what has been referred to as Fast Data.
Change Patterns in Use: A Critical Evaluation
Venue: arXiv cs.SE 2015
Year: 2015

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns. To obtain a better understanding of the PPM when using change patterns, the arising challenges, and the subjective perceptions of process designers, we conduct an exploratory study. The results indicate that process designers face little problems as long as control-flow is simple, but have considerable problems with the usage of change patterns when complex, nested models have to be created. Finally, we outline how effective tool support for change patterns should be realized.