OpenRaiser/Intern-Atlas huggingface dataset:
80 New Packages to Mine Database Query Logs
Venue: arXiv cs.DB 2017
Year: 2017

The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symbolic objects, not vectors of numbers. Therefore, many popular statistical concepts, such as means, regression, or decision trees do not apply. Most authors limit themselves to ad hoc algorithms or approaches based on neighborhoods, such as k Nearest Neighbors. Our project is to challenge this limitation. We introduce methods to manipulate SQL queries as if they were vectors, thereby unlocking the whole statistical toolbox. We present three families of methods: feature maps, kernel methods, and Bayesian models. The first technique directly encodes queries into vectors. The second one transforms the queries implicitly. The last one exploits probabilistic graphical models as an alternative to vector spaces. We present the benefits and drawbacks of each solution, highlight how they relate to each other, and make the case for future investigation.
All-Path Routing Protocols: Analysis of Scalability and Load Balancing Capabilities for Ethernet Networks
Venue: arXiv cs.NI 2017
Year: 2017

This paper presents a scalability and load balancing study of the All-Path protocols, a family of distributed switching protocols based on path exploration. ARP-Path is the main protocol and it explores every possible path reaching from source to destination by using ARP messages, selecting the lowest latency path. Flow-Path and Bridge-Path are respectively the flow-based and bridge-based versions, instead of the source address-based approach of ARP-Path. While preserving the main advantages of ARP-Path, Flow-Path has the advantages of full independence of flows for path creation, guaranteeing path symmetry and increased path diversity. While Bridge-Path increases scalability by reducing forwarding table entries at core bridges. We compare the characteristics of each protocol and the convenience of using each one depending on the topology and the type of traffic. Finally, we prove their load balancing capabilities analytically and via simulation.
Anonymity Properties of the Bitcoin P2P Network
Venue: arXiv cs.CR 2017
Year: 2017

Bitcoin is a popular alternative to fiat money, widely used for its perceived anonymity properties. However, recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its gossip-based flooding protocols, which are used to ensure global network consistency, may enable user deanonymization---the linkage of a user's IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different protocol, known as diffusion. However, no systematic justification was provided for the change, and it is unclear if diffusion actually improves the system's anonymity. In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both pre- and post-2015. In doing so, we consider new adversarial models and spreading mechanisms that have not been previously studied in the source-finding literature. We theoretically prove that Bitcoin's networking protocols (both pre- and post-2015) offer poor anonymity properties on networks with a regular-tree topology. We validate this claim in simulation on a 2015 snapshot of the real Bitcoin P2P network topology.
Tweet for Behavior Change: Using Social Media for the Dissemination of Public Health Messages
Venue: arXiv cs.SI 2017
Year: 2017

Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change. Objective: The aim of this study was to test the feasibility of designing, implementing, and evaluating a social media-enabled intervention for skin cancer prevention. Conclusions: Social media-disseminated public health messages reached more than 23% of the Northern Ireland population. A Web-based survey suggested that the campaign might have contributed to improved knowledge and attitudes toward skin cancer among the target population. Findings suggested that shocking and humorous messages generated greatest impressions and engagement, but information-based messages were likely to be shared most. The extent of behavioral change as a result of the campaign remains to be explored, however, the change of attitudes and knowledge is promising. Social media is an inexpensive, effective method for delivering public health messages. However, existing and traditional process evaluation methods may not be suitable for social media.
Token-based Function Computation with Memory
Venue: arXiv cs.DC 2017
Year: 2017

In distributed function computation, each node has an initial value and the goal is to compute a function of these values in a distributed manner. In this paper, we propose a novel token-based approach to compute a wide class of target functions to which we refer as "Token-based function Computation with Memory" (TCM) algorithm. In this approach, node values are attached to tokens and travel across the network. Each pair of travelling tokens would coalesce when they meet, forming a token with a new value as a function of the original token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where token movement is governed by random walk, meeting of tokens in our scheme is accelerated by adopting a novel chasing mechanism. We proved that, compared to the CRW algorithm, the TCM algorithm results in a reduction of time complexity by a factor of at least $\sqrt{n/\log(n)}$ in Erdös-Renyi and complete graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks. Simulation results show that there is at least a constant factor improvement in the message complexity of TCM algorithm in all considered topologies. Robustness of the CRW and TCM algorithms in the presence of node failure is analyzed. We show that their robustness can be improved by running multiple instances of the algorithms in parallel.
A new dominance concept and its application to diversity-stability analysis
Venue: arXiv cs.CE 2017
Year: 2017

We introduce a new dominance concept consisting of three new dominance metrics based on Lloyd's (1967) mean crowding index. The new metrics link communities and species, whereas existing ones are applicable only to communities. Our community-level metric is a function of Simpson's diversity index. For species, our metric quantifies the difference between community dominance and the dominance of a virtual community whose mean population size (per species) equals the population size of the focal species. The new metrics have at least two immediate applications: (i) acting as proxies for diversity in diversity-stability modeling (ii) replacing population abundance in reconstructing species dominance networks. The first application is demonstrated here using data from a longitudinal study of the human vaginal microbiome, and provides new insights relevant for microbial community stability and disease etiology.
Learning Simpler Language Models with the Differential State Framework
Venue: arXiv cs.CL 2017
Year: 2017

Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models. DSF models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical, simple recurrent network. Within the DSF framework, a new architecture is presented, the Delta-RNN. In language modeling at the word and character levels, the Delta-RNN outperforms popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, performs comparably to several state-of-the-art baselines. At the subword level, the Delta-RNN's performance is comparable to that of complex gated architectures.
Some new bounds of placement delivery arrays
Venue: arXiv cs.IT 2017
Year: 2017

Coded caching scheme is a technique which reduce the load during peak traffic times in a wireless network system. Placement delivery array (PDA in short) was first introduced by Yan et al.. It can be used to design coded caching scheme. In this paper, we prove some lower bounds of PDA on the element and some lower bounds of PDA on the column. We also give some constructions for optimal PDA.
Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots
Venue: arXiv cs.RO 2017
Year: 2017

Ambiguity and noise in natural language instructions create a significant barrier towards adopting autonomous systems into safety critical workflows involving humans and machines. In this paper, we propose to build on recent advances in electrophysiological monitoring methods and augmented reality technologies, to develop alternative modes of communication between humans and robots involved in large-scale proximal collaborative tasks. We will first introduce augmented reality techniques for projecting a robot's intentions to its human teammate, who can interact with these cues to engage in real-time collaborative plan execution with the robot. We will then look at how electroencephalographic (EEG) feedback can be used to monitor human response to both discrete events, as well as longer term affective states while execution of a plan. These signals can be used by a learning agent, a.k.a an affective robot, to modify its policy. We will present an end-to-end system capable of demonstrating these modalities of interaction. We hope that the proposed system will inspire research in augmenting human-robot interactions with alternative forms of communications in the interests of safety, productivity, and fluency of teaming, particularly in engineered settings such as the factory floor or the assembly line in the manufacturing industry where the use of such wearables can be enforced.
Analysis of Different Approaches of Parallel Block Processing for K-Means Clustering Algorithm
Venue: arXiv cs.DC 2017
Year: 2017

Distributed Computation has been a recent trend in engineering research. Parallel Computation is widely used in different areas of Data Mining, Image Processing, Simulating Models, Aerodynamics and so forth. One of the major usage of Parallel Processing is widely implemented for clustering the satellite images of size more than dimension of 1000x1000 in a legacy system. This paper mainly focuses on the different approaches of parallel block processing such as row-shaped, column-shaped and square-shaped. These approaches are applied for classification problem. These approaches is applied to the K-Means clustering algorithm as this is widely used for the detection of features for high resolution orthoimagery satellite images. The different approaches are analyzed, which lead to reduction in execution time and resulted the influence of improvement in performance measurement compared to sequential K-Means Clustering algorithm.
A Sentence Simplification System for Improving Relation Extraction
Venue: arXiv cs.CL 2017
Year: 2017

In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.
De-anonymization of Social Networks with Communities: When Quantifications Meet Algorithms
Venue: arXiv cs.SI 2017
Year: 2017

A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed without sufficient rationale. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a more realistic social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is new and well justified cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. While proving the general multiplicative inapproximability, we are able to propose two algorithms, which, respectively, enjoy an ε-additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are empirically validated, with a notable dataset extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations also manifest the importance of community information in enhancing privacy inferencing.
Memristor equations: incomplete physics and undefined passivity/activity
Venue: arXiv cs.ET 2017
Year: 2017

In his seminal paper, Chua presented a fundamental physical claim by introducing the memristor, "The missing circuit element". The memristor equations were originally supposed to represent a passive circuit element because, with active circuitry, arbitrary elements can be realized without limitations. Therefore, if the memristor equations do not guarantee that the circuit element can be realized by a passive system, the fundamental physics claim about the memristor as "missing circuit element" loses all its weight. The question of passivity/activity belongs to physics thus we incorporate thermodynamics into the study of this problem. We show that the memristor equations are physically incomplete regarding the problem of passivity/activity. As a consequence, the claim that the present memristor functions describe a passive device lead to unphysical results, such as violating the Second Law of thermodynamics, in infinitely large number of cases. The seminal memristor equations cannot introduce a new physical circuit element without making the model more physical such as providing the Fluctuation Dissipation Theory of memristors.
Theoretical Evaluation of Li et al.'s Approach for Improving a Binary Watermark-Based Scheme in Remote Sensing Data Communications
Venue: arXiv cs.MM 2017
Year: 2017

This letter is about a principal weakness of the published article by Li et al. in 2014. It seems that the mentioned work has a terrible conceptual mistake while presenting its theoretical approach. In fact, the work has tried to design a new attack and its effective solution for a basic watermarking algorithm by Zhu et al. published in 2013, however in practice, we show the Li et al.'s approach is not correct to obtain the aim. For disproof of the incorrect approach, we only apply a numerical example as the counterexample of the Li et al.'s approach.
Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection
Venue: arXiv cs.CV 2017
Year: 2017

Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise local-structure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.
Deep Architectures for Modulation Recognition
Venue: arXiv cs.LG 2017
Year: 2017

We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.
Femoral ROIs and Entropy for Texture-based Detection of Osteoarthritis from High-Resolution Knee Radiographs
Venue: arXiv cs.CV 2017
Year: 2017

The relationship between knee osteoarthritis progression and changes in tibial bone structure has long been recognized and various texture descriptors have been proposed to detect early osteoarthritis (OA) from radiographs. This work aims to investigate (1) femoral textures as an OA indicator and (2) the potential of entropy as a computationally efficient alternative to established texture descriptors. We design a robust semi-automatically placed layout for regions of interest (ROI), compute the Hurst coefficient and the entropy in each ROI, and employ statistical and machine learning methods to evaluate feature combinations. Based on 153 high-resolution radiographs, our results identify medial femur as an effective univariate descriptor, with significance comparable to medial tibia. Entropy is shown to contribute to classification performance. A linear five-feature classifier combining femur, entropic and standard texture descriptors, achieves AUC of 0.85, outperforming the state-of-the-art by roughly 0.1.
Tracing the Use of Practices through Networks of Collaboration
Venue: arXiv cs.SI 2017
Year: 2017

An active line of research has used on-line data to study the ways in which discrete units of information---including messages, photos, product recommendations, group invitations---spread through social networks. There is relatively little understanding, however, of how on-line data might help in studying the diffusion of more complex {\em practices}---roughly, routines or styles of work that are generally handed down from one person to another through collaboration or mentorship. In this work, we propose a framework together with a novel type of data analysis that seeks to study the spread of such practices by tracking their syntactic signatures in large document collections. Central to this framework is the notion of an "inheritance graph" that represents how people pass the practice on to others through collaboration. Our analysis of these inheritance graphs demonstrates that we can trace a significant number of practices over long time-spans, and we show that the structure of these graphs can help in predicting the longevity of collaborations within a field, as well as the fitness of the practices themselves.
On Data Flow Management: the Multilevel Analysis of Data Center Total Cost
Venue: arXiv cs.NI 2017
Year: 2017

Information management is one of the most significant issues in nowadays data centers. Selection of appropriate software, security mechanisms and effective energy consumption management together with caring for the environment enforces a profound analysis of the considered system. Besides these factors, financial analysis of data center maintenance is another important aspect that needs to be considered. Data centers are mission-critical components of all large enterprises and frequently cost hundreds of millions of dollars to build, yet few high-level executives understand the true cost of operating such facilities. Costs are typically spread across the IT, networking, and facilities, which makes management of these costs and assessment of alternatives difficult. This paper deals with a research on multilevel analysis of data center management and presents an approach to estimate the true total costs of operating data center physical facilities, taking into account the proper management of the information flow.
DART: Noise Injection for Robust Imitation Learning
Venue: arXiv cs.LG 2017
Year: 2017

One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy. A known problem with this "off-policy" approach is that the robot's errors compound when drifting away from the supervisor's demonstrations. On-policy, techniques alleviate this by iteratively collecting corrective actions for the current robot policy. However, these techniques can be tedious for human supervisors, add significant computation burden, and may visit dangerous states during training. We propose an off-policy approach that injects noise into the supervisor's policy while demonstrating. This forces the supervisor to demonstrate how to recover from errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot Trajectories), that collects demonstrations with injected noise, and optimizes the noise level to approximate the error of the robot's trained policy during data collection. We compare DART with DAgger and Behavior Cloning in two domains: in simulation with an algorithmic supervisor on the MuJoCo tasks (Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human supervisors training a Toyota HSR robot to perform grasping in clutter. For high dimensional tasks like Humanoid, DART can be up to $3x$ faster in computation time and only decreases the supervisor's cumulative reward by $5\%$ during training, whereas DAgger executes policies that have $80\%$ less cumulative reward than the supervisor. On the grasping in clutter task, DART obtains on average a $62\%$ performance increase over Behavior Cloning.
Discovering Scholarly Orphans Using ORCID
Venue: arXiv cs.DL 2017
Year: 2017

Archival efforts such as (C)LOCKSS and Portico are in place to ensure the longevity of traditional scholarly resources like journal articles. At the same time, researchers are depositing a broad variety of other scholarly artifacts into emerging online portals that are designed to support web-based scholarship. These web-native scholarly objects are largely neglected by current archival practices and hence they become scholarly orphans. We therefore argue for a novel paradigm that is tailored towards archiving these scholarly orphans. We are investigating the feasibility of using Open Researcher and Contributor ID (ORCID) as a supporting infrastructure for the process of discovery of web identities and scholarly orphans for active researchers. We analyze ORCID in terms of coverage of researchers, subjects, and location and assess the richness of its profiles in terms of web identities and scholarly artifacts. We find that ORCID currently lacks in all considered aspects and hence can only be considered in conjunction with other discovery sources. However, ORCID is growing fast so there is potential that it could achieve a satisfactory level of coverage and richness in the near future.