OpenRaiser/Intern-Atlas huggingface dataset:
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets
Venue: arXiv cs.CV 2016
Year: 2016

We propose an image representation scheme combining the local and nonlocal characterization of patches in an image. Our representation scheme can be shown to be equivalent to a tight frame constructed from convolving local bases (e.g. wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g. spectral basis induced by nonlinear dimension reduction on patches), and we call the resulting frame elements {\it convolution framelets}. Insight gained from analyzing the proposed representation leads to a novel interpretation of a recent high-performance patch-based image inpainting algorithm using Point Integral Method (PIM) and Low Dimension Manifold Model (LDMM) [Osher, Shi and Zhu, 2016]. In particular, we show that LDMM is a weighted $\ell_2$-regularization on the coefficients obtained by decomposing images into linear combinations of convolution framelets; based on this understanding, we extend the original LDMM to a reweighted version that yields further improved inpainting results. In addition, we establish the energy concentration property of convolution framelet coefficients for the setting where the local basis is constructed from a given nonlocal basis via a linear reconstruction framework; a generalization of this framework to unions of local embeddings can provide a natural setting for interpreting BM3D, one of the state-of-the-art image denoising algorithms.
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments
Venue: arXiv cs.CV 2016
Year: 2016

Significant performance gains in deep learning coupled with the exponential growth of image and video data on the Internet have resulted in the recent emergence of automated image captioning systems. Ensuring scalability of automated image captioning systems with respect to the ever increasing volume of image and video data is a significant challenge. This paper provides a valuable insight in that the detection of a few significant (top) objects in an image allows one to extract other relevant information such as actions (verbs) in the image. We expect this insight to be useful in the design of scalable image captioning systems. We address two parameters by which the scalability of image captioning systems could be quantified, i.e., the traditional algorithmic time complexity which is important given the resource limitations of the user device and the system development time since the programmers' time is a critical resource constraint in many real-world scenarios. Additionally, we address the issue of how word embeddings could be used to infer the verb (action) from the nouns (objects) in a given image in a zero-shot manner. Our results show that it is possible to attain reasonably good performance on predicting actions and captioning images using our approaches with the added advantage of simplicity of implementation.
Operational Aspects of C/C++ Concurrency
Venue: arXiv cs.PL 2016
Year: 2016

In this work, we present a family of operational semantics that gradually approximates the realistic program behaviors in the C/C++11 memory model. Each semantics in our framework is built by elaborating and combining two simple ingredients: viewfronts and operation buffers. Viewfronts allow us to express the spatial aspect of thread interaction, i.e., which values a thread can read, while operation buffers enable manipulation with the temporal execution aspect, i.e., determining the order in which the results of certain operations can be observed by concurrently running threads. Starting from a simple abstract state machine, through a series of gradual refinements of the abstract state, we capture such language aspects and synchronization primitives as release/acquire atomics, sequentially-consistent and non-atomic memory accesses, also providing a semantics for relaxed atomics, while avoiding the Out-of-Thin-Air problem. To the best of our knowledge, this is the first formal and executable operational semantics of C11 capable of expressing all essential concurrent aspects of the standard. We illustrate our approach via a number of characteristic examples, relating the observed behaviors to those of standard litmus test programs from the literature. We provide an executable implementation of the semantics in PLT Redex, along with a number of implemented litmus tests and examples, and showcase our prototype on a large case study: randomized testing and debugging of a realistic Read-Copy-Update data structure.
Conceptual Knowledge Relationship Model of Enterprise Architecture and Top Management Roles
Venue: arXiv cs.CY 2016
Year: 2016

Enterprise Architecture (EA) continues to gain global recognition as a management tool that would improve the organizations performance. However, based on literature, it was found that the lack of EA knowledge of top management (TM) hinders the assimiliation of EA. This study aims to gauge EA knowledge required for TM for providing a comprehensive support towards EA assimiliation. A semi-structured interviews were conducted with several personnels in an organization. Data were analyzed using the constant comparative method. This study found two sets of EA knowledge of TM; business knowledge and technology knowledge. A conceptual knowledge relationship model is proposed through synthesizing the roles of EA and TM in supporting the TM in identifying the EA knowledge required. The findings gave contribution to academicians and practitioners with knowledge of how to improve EA assimiliation and a structured roadmap for understanding EA to achieve high business value.
Understanding Knowledge Leakage & BYOD (Bring Your Own Device): A Mobile Worker Perspective
Venue: arXiv cs.CY 2016
Year: 2016

Knowledge sharing drives innovation and the opportunity to develop a sustainable competitive advantage. However, in the extant knowledge management and information security literature, leakage from sharing activities is neglected. The risk of knowledge leakage is exacerbated with the pervasive use of mobile devices and the adoption of BYOD (Bring Your Own Device). Thus, this research-in-progress paper examines the role of the behavior of mobile workers that engage in accidental knowledge leakage through the use of BYOD. We use the Decomposed Theory of Planned Behavior (DTPB) to explain the causes behind this phenomenon and how it negatively impacts organization's competitive advantage. The contributions of this study are the following. First, it posits that the reasons of knowledge leakage by mobile workers through BYOD can be explained using DTPB. Second, the paper proposes a conceptual model for research based on DTPB constructs whilst adding other variables such as BYOD and mobile device usage context. Finally, the conceptual study outlines the potential contributions and implications of this research.
Open Food Network: the Role of ICT to Support Regional Food Supply Chains in Australia
Venue: arXiv cs.CY 2016
Year: 2016

Many organizations have introduced various ICT-enabled innovations to improve economic, environmental and social performance. The Open Food Network (OFN) is an example of ICT-enabled innovation that has the potential to enhance the sustainability of regional food supply chain by improving farmers access to local and regional markets and consumers access to fresh local produce, as well as optimizing the regional food distribution and improving local community welfare. OFN has just been recently launched in Australia and currently there is a limited understanding of the actual impacts and appropriate strategies to encourage wider adoption. This research in progress aims to evaluate the effectiveness of the OFN system in connecting and supporting the sustainability of regional food supply chain communities in Australia that will help devise strategies for expanding the use beyond Australia. Through a preliminary focus group with a number of early adopters of the OFN, this study identifies the actual use of OFN, benefits to main stakeholders and a number of challenges. The findings contribute to a longer term research program that investigates how ICT can support sustainability initiatives within organizations and supply chains.
Approximately Envy-Free Spectrum Allocation with Complementarities
Venue: arXiv cs.GT 2016
Year: 2016

With spectrum auctions as our prime motivation, in this paper we analyze combinatorial auctions where agents' valuations exhibit complementarities. Assuming that the agents only value bundles of size at most $k$ and also assuming that we can assess prices, we present a mechanism that is efficient, approximately envy-free, asymptotically strategy-proof and that has polynomial-time complexity. Modifying an iterative rounding procedure from assignment problems, we use the primal and dual optimal solutions to the linear programming relaxation of the auction problem to construct a lottery for the allocations and to assess the prices to bundles. The allocations in the lottery over-allocate goods by at most $k-1$ units, and the dual prices are shown to be (approximately) envy-free irrespective of the allocation chosen. We conclude with a detailed numerical investigation of a specific spectrum allocation problem.
Efficient tensor completion for color image and video recovery: Low-rank tensor train
Venue: arXiv cs.NA 2016
Year: 2016

This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via tensor train (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via tensor train (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher-orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Venue: arXiv cs.CV 2016
Year: 2016

(This paper was written in November 2011 and never published. It is posted on arXiv.org in its original form in June 2016). Many recent object recognition systems have proposed using a two phase training procedure to learn sparse convolutional feature hierarchies: unsupervised pre-training followed by supervised fine-tuning. Recent results suggest that these methods provide little improvement over purely supervised systems when the appropriate nonlinearities are included. This paper presents an empirical exploration of the space of learning procedures for sparse convolutional networks to assess which method produces the best performance. In our study, we introduce an augmentation of the Predictive Sparse Decomposition method that includes a discriminative term (DPSD). We also introduce a new single phase supervised learning procedure that places an L1 penalty on the output state of each layer of the network. This forces the network to produce sparse codes without the expensive pre-training phase. Using DPSD with a new, complex predictor that incorporates lateral inhibition, combined with multi-scale feature pooling, and supervised refinement, the system achieves a 70.6\% recognition rate on Caltech-101. With the addition of convolutional training, a 77\% recognition was obtained on the CIfAR-10 dataset.
OpenAI Gym
Venue: arXiv cs.LG 2016
Year: 2016

OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.
Permanence and Community Structure in Complex Networks
Venue: arXiv cs.SI 2016
Year: 2016

The goal of community detection algorithms is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily driven to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this paper, we argue that the belongingness of nodes in a community is not uniform. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -- (i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading. We demonstrate that the process of maximizing permanence produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we show that the communities obtained by this method are (i) less affected by the changes in vertex-ordering, and (ii) more resilient to resolution limit, degeneracy of solutions and asymptotic growth of values.
Shallow Networks for High-Accuracy Road Object-Detection
Venue: arXiv cs.CV 2016
Year: 2016

The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques for this task are based on R-CNN or one of its faster variants. In the research community, much emphasis has been applied to using 3D vision or complex R-CNN variants to achieve higher accuracy. However, are there more straightforward modifications that could deliver higher accuracy? Yes. We show that increasing input image resolution (i.e. upsampling) offers up to 12 percentage-points higher accuracy compared to an off-the-shelf baseline. We also find situations where earlier/shallower layers of CNN provide higher accuracy than later/deeper layers. We further show that shallow models and upsampled images yield competitive accuracy. Our findings contrast with the current trend towards deeper and larger models to achieve high accuracy in domain specific detection tasks.
CG-OoO: Energy-Efficient Coarse-Grain Out-of-Order Execution
Venue: arXiv cs.AR 2016
Year: 2016

We introduce the Coarse-Grain Out-of-Order (CG- OoO) general purpose processor designed to achieve close to In-Order processor energy while maintaining Out-of-Order (OoO) performance. CG-OoO is an energy-performance proportional general purpose architecture that scales according to the program load. Block-level code processing is at the heart of the this architecture; CG-OoO speculates, fetches, schedules, and commits code at block-level granularity. It eliminates unnecessary accesses to energy consuming tables, and turns large tables into smaller and distributed tables that are cheaper to access. CG-OoO leverages compiler-level code optimizations to deliver efficient static code, and exploits dynamic instruction-level parallelism and block-level parallelism. CG-OoO introduces Skipahead issue, a complexity effective, limited out-of-order instruction scheduling model. Through the energy efficiency techniques applied to the compiler and processor pipeline stages, CG-OoO closes 64% of the average energy gap between the In-Order and Out-of-Order baseline processors at the performance of the OoO baseline. This makes CG-OoO 1.9x more efficient than the OoO on the energy-delay product inverse metric.
An introduction to Differential Linear Logic: proof-nets, models and antiderivatives
Venue: arXiv cs.LO 2016
Year: 2016

Differential Linear Logic enriches Linear Logic with additional logical rules for the exponential connectives, dual to the usual rules of dereliction, weakening and contraction. We present a proof-net syntax for Differential Linear Logic and a categorical axiomatization of its denotational models. We also introduce a simple categorical condition on these models under which a general antiderivative operation becomes available. Last we briefly describe the model of sets and relations and give a more detailed account of the model of finiteness spaces and linear and continuous functions.
On the Synchronization of Intermittently Powered Wireless Embedded Systems
Venue: arXiv cs.NI 2016
Year: 2016

Battery-free computational RFID platforms, such as WISP (Wireless Identification and Sensing Platform), are emerging intermittently powered devices designed for replacing existing battery-powered sensor networks. As their applications become increasingly complex, we anticipate that synchronization (among others) to appear as one of crucial building blocks for collaborative and coordinated actions. With this paper we aim at providing initial observations regarding the synchronization of intermittently powered systems. In particular, we design and implement the first and very initial synchronization protocol for the WISP platform that provides explicit synchronization among individual WISPs that reside inside the communication range of a common RFID reader. Evaluations in our testbed showed that with our mechanism a synchronization error of approximately 1.5 milliseconds can be ensured between the RFID reader and a WISP tag.
Proof nets for the Displacement calculus
Venue: arXiv cs.LO 2016
Year: 2016

We present a proof net calculus for the Displacement calculus and show its correctness. This is the first proof net calculus which models the Displacement calculus directly and not by some sort of translation into another formalism. The proof net calculus opens up new possibilities for parsing and proof search with the Displacement calculus.
Adaptive Distributed Top-k Query Processing
Venue: arXiv cs.DB 2016
Year: 2016

ADiT is an adaptive approach for processing distributed top-$k$ queries over peer-to-peer networks optimizing both system load and query response time. This approach considers the size of the peer to peer network, the amount $k$ of searched objects, the network capabilities of a connected peer, i.e. the transmission rate, the amount of objects stored on each peer, and the speed of a peer in processing a local top-$k$ query. In extensive experiments with a variety of scenarios we could show that ADiT outperforms state of the art distributed query processing techniques.
Analyzing Distributed Join-Idle-Queue: A Fluid Limit Approach
Venue: arXiv cs.DC 2016
Year: 2016

In the context of load balancing, Lu et al. introduced the distributed Join-Idle-Queue algorithm, where a group of dispatchers distribute jobs to a cluster of parallel servers. Each dispatcher maintains a queue of idle servers; when a job arrives to a dispatcher, it sends it to a server on its queue, or to a random server if the queue is empty. In turn, when a server has no jobs, it requests to be placed on the idle queue of a randomly chosen dispatcher. Although this algorithm was shown to be quite effective, the original asymptotic analysis makes simplifying assumptions that become increasingly inaccurate as the system load increases. Further, the analysis does not naturally generalize to interesting variations, such as having a server request to be placed on the idle queue of a dispatcher before it has completed all jobs, which can be beneficial under high loads. We provide a new asymptotic analysis of Join-Idle-Queue systems based on mean field fluid limit methods, deriving families of differential equations that describe these systems. Our analysis avoids previous simplifying assumptions, is empirically more accurate, and generalizes naturally to the variation described above, as well as other simple variations. Our theoretical and empirical analyses shed further light on the performance of Join-Idle-Queue, including potential performance pitfalls under high load.
Hierarchical Self-Assembly of Fractals with Signal-Passing Tiles
Venue: arXiv cs.ET 2016
Year: 2016

In this paper, we present high-level overviews of tile-based self-assembling systems capable of producing complex, infinite, aperiodic structures known as discrete self-similar fractals. Fractals have a variety of interesting mathematical and structural properties, and by utilizing the bottom-up growth paradigm of self-assembly to create them we not only learn important techniques for building such complex structures, we also gain insight into how similar structural complexity arises in natural self-assembling systems. Our results fundamentally leverage hierarchical assembly processes, and use as our building blocks square "tile" components which are capable of activating and deactivating their binding "glues" a constant number of times each, based only on local interactions. We provide the first constructions capable of building arbitrary discrete self-similar fractals at scale factor 1, and many at temperature 1 (i.e. "non-cooperatively"), including the Sierpinski triangle.
Build It, Break It, Fix It: Contesting Secure Development
Venue: arXiv cs.CR 2016
Year: 2016

Typical security contests focus on breaking or mitigating the impact of buggy systems. We present the Build-it Break-it Fix-it BIBIFI contest which aims to assess the ability to securely build software not just break it. In BIBIFI teams build specified software with the goal of maximizing correctness performance and security. The latter is tested when teams attempt to break other teams submissions. Winners are chosen from among the best builders and the best breakers. BIBIFI was designed to be open-ended - teams can use any language tool process etc. that they like. As such contest outcomes shed light on factors that correlate with successfully building secure software and breaking insecure software. During we ran three contests involving a total of teams and two different programming problems. Quantitative analysis from these contests found that the most efficient build-it submissions used CC but submissions coded in a statically-typed language were less likely to have a security flaw build-it teams with diverse programming-language knowledge also produced more secure code. Shorter programs correlated with better scores. Break-it teams that were also build-it teams were significantly better at finding security bugs.