OpenRaiser/Intern-Atlas huggingface dataset:
A New Classification of Technologies
Venue: arXiv cs.CY 2017
Year: 2017

This study here suggests a classification of technologies based on taxonomic characteristics of interaction between technologies in complex systems that is not a studied research field in economics of technical change. The proposed taxonomy here categorizes technologies in four typologies, in a broad analogy with the ecology: 1) technological parasitism is a relationship between two technologies T1 and T2 in a complex system S where one technology T1 benefits from the interaction with T2, whereas T2 has a negative side from interaction with T1; 2) technological commensalism is a relationship between two technologies in S where one technology benefits from the other without affecting it; 3) technological mutualism is a relationship in which each technology benefits from the activity of the other within complex systems; 4) technological symbiosis is a long-term interaction between two (or more) technologies that evolve together in complex systems. This taxonomy systematizes the typologies of interactive technologies within complex systems and predicts their evolutionary pathways that generate stepwise coevolutionary processes of complex systems of technology. This study here begins the process of generalizing, as far as possible, critical typologies of interactive technologies that explain the long-run evolution of technology. The theoretical framework developed here opens the black box of the interaction between technologies that affects, with different types of technologies, the evolutionary pathways of complex systems of technology over time and space. Overall, then, this new theoretical framework may be useful for bringing a new perspective to categorize the gradient of benefit to technologies from interaction with other technologies that can be a ground work for development of more sophisticated concepts to clarify technological and economic change in human society.
POSIX-based Operating System in the environment of NVM/SCM memory
Venue: arXiv cs.OS 2017
Year: 2017

Modern Operating Systems are typically POSIX-compliant. The system calls are the fundamental layer of interaction between user-space applications and the OS kernel and its implementation of fundamental abstractions and primitives used in modern computing. The next generation of NVM/SCM memory raises critical questions about the efficiency of modern OS architecture. This paper investigates how the POSIX API drives performance for a system with NVM/SCM memory. We show that OS and metadata related system calls represent the most important area of optimization. However, the synchronization related system calls (poll(), futex(), wait4()) are the most time-consuming overhead that even a RAMdisk platform fails to eliminate. Attempting to preserve the POSIX-based approach will likely result in fundamental inefficiencies for any future applications of NVM/SCM memory.
Bit-Vector Model Counting using Statistical Estimation
Venue: arXiv cs.CR 2017
Year: 2017

Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms.
Skew cyclic codes over $\mathbb{F}_{p}+u\mathbb{F}_{p}$
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study skew cyclic codes with arbitrary length over the ring $R=\mathbb{F}_{p}+u\mathbb{F}_{p}$ where $p$ is an odd prime and $% u^{2}=0$. We characterize all skew cyclic codes of length $n$ as left $% R[x;θ]$-submodules of $R_{n}=R[x;θ]/\langle x^{n}-1\rangle $. We find all generator polynomials for these codes and describe their minimal spanning sets. Moreover, an encoding and decoding algorithm is presented for skew cyclic codes over the ring $R$. Finally, based on the theory we developed in this paper, we provide examples of codes with good parameters over $F_{p}$ with different odd prime $p.$ In fact, example 25 in our paper is a new ternary code in the class of quasi-twisted codes. The other examples we provided are examples of optimal codes.
Towards a Deep Improviser: a prototype deep learning post-tonal free music generator
Venue: arXiv cs.SD 2017
Year: 2017

Two modest-sized symbolic corpora of post-tonal and post-metric keyboard music have been constructed, one algorithmic, the other improvised. Deep learning models of each have been trained and largely optimised. Our purpose is to obtain a model with sufficient generalisation capacity that in response to a small quantity of separate fresh input seed material, it can generate outputs that are distinctive, rather than recreative of the learned corpora or the seed material. This objective has been first assessed statistically, and as judged by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has been generated using the approach, and informal judgements place it roughly on a par with algorithmic and composed music in related forms. Future work will aim to enhance the model such that it can be evaluated in relation to expression, meaning and utility in real-time performance.
ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming
Venue: arXiv cs.SE 2017
Year: 2017

Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
Venue: arXiv cs.CV 2017
Year: 2017

The most common paradigm for vision-based multi-object tracking is tracking-by-detection, due to the availability of reliable detectors for several important object categories such as cars and pedestrians. However, future mobile systems will need a capability to cope with rich human-made environments, in which obtaining detectors for every possible object category would be infeasible. In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects. We present an efficient segmentation mask-based tracker which associates pixel-precise masks reported by the segmentation. Our approach can utilize semantic information whenever it is available for classifying objects at the track level, while retaining the capability to track generic unknown objects in the absence of such information. We demonstrate experimentally that our approach achieves performance comparable to state-of-the-art tracking-by-detection methods for popular object categories such as cars and pedestrians. Additionally, we show that the proposed method can discover and robustly track a large variety of other objects.
A general construction of permutation polynomials of the form $ (x^{2^m}+x+δ)^{i(2^m-1)+1}+x$ over $\F_{2^{2m}}$
Venue: arXiv cs.IT 2017
Year: 2017

Recently, there has been a lot of work on constructions of permutation polynomials of the form $(x^{2^m}+x+δ)^{s}+x$ over the finite field $\F_{2^{2m}}$, especially in the case when $s$ is of the form $s=i(2^m-1)+1$ (Niho exponent). In this paper, we further investigate permutation polynomials with this form. Instead of seeking for sporadic constructions of the parameter $i$, we give a general sufficient condition on $i$ such that $(x^{2^m}+x+δ)^{i(2^m-1)+1}+x$ permutes $\F_{2^{2m}}$, that is, $(2^k+1)i \equiv 1 ~\textrm{or}~ 2^k~(\textrm{mod}~ 2^m+1)$, where $1 \leq k \leq m-1$ is any integer. This generalizes a recent result obtained by Gupta and Sharma who actually dealt with the case $k=2$. It turns out that most of previous constructions of the parameter $i$ are covered by our result, and it yields many new classes of permutation polynomials as well.
Assessing public health interventions using Web content
Venue: arXiv cs.CY 2017
Year: 2017

Public health interventions are a fundamental tool for mitigating the spread of an infectious disease. However, it is not always possible to obtain a conclusive estimate for the impact of an intervention, especially in situations where the effects are fragmented in population parts that are under-represented within traditional public health surveillance schemes. To this end, online user activity can be used as a complementary sensor to establish alternative measures. Here, we provide a summary of our research on formulating statistical frameworks for assessing public health interventions based on data from social media and search engines (Lampos et al., 2015 [20]; Wagner et al., 2017 [37]). Our methodology has been applied in two real-world case studies: the 2013/14 and 2014/15 flu vaccination campaigns in England, where school-age children were vaccinated in a number of locations aiming to reduce the overall transmission of the virus. Disease models from online data combined with historical patterns of disease prevalence across different areas allowed us to quantify the impact of the intervention. In addition, a qualitative evaluation of our impact estimates demonstrated that they were in line with independent assessments from public health authorities.
Linear centralization classifier
Venue: arXiv cs.LG 2017
Year: 2017

A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced. The algorithm seeks to find a transformation that best maps instances from the feature space to a space where they concentrate towards the center of their own classes, while maximimizing the distance between class centers. We formulate the classifier as a quadratic program with quadratic constraints. We then simplify this formulation to a linear program that can be solved effectively using a linear programming solver (e.g., simplex-dual). We extend the formulation for LCC to enable the use of kernel functions for non-linear classification applications. We compare our method with two standard classification methods (support vector machine and linear discriminant analysis) and four state-of-the-art classification methods when they are applied to eight standard classification datasets. Our experimental results show that LCC is able to classify instances more accurately (based on the area under the receiver operating characteristic) in comparison to other tested methods on the chosen datasets. We also report the results for LCC with a particular kernel to solve for synthetic non-linear classification problems.
Virtual Sensor Modelling using Neural Networks with Coefficient-based Adaptive Weights and Biases Search Algorithm for Diesel Engines
Venue: arXiv cs.NE 2017
Year: 2017

With the explosion in the field of Big Data and introduction of more stringent emission norms every three to five years, automotive companies must not only continue to enhance the fuel economy ratings of their products, but also provide valued services to their customers such as delivering engine performance and health reports at regular intervals. A reasonable solution to both issues is installing a variety of sensors on the engine. Sensor data can be used to develop fuel economy features and will directly indicate engine performance. However, mounting a plethora of sensors is impractical in a very cost-sensitive industry. Thus, virtual sensors can replace physical sensors by reducing cost while capturing essential engine data.
Towards Software Development For Social Robotics Systems
Venue: arXiv cs.RO 2017
Year: 2017

In this paper we introduce the core results of the project on software development for social robotics systems. The usability of maintenance and control features is crucial for many kinds of systems, but in the case of social robotics we also have to take into account that (1) the humanoid robot physically interacts with humans, (2) the conversation with children might have different requirements in comparison to the conversation with adults. The results of our work were implement for the humanoid PAL REEM robot, but their core ideas can be applied for other types of humanoid robots. We developed a web-based solution that supports the management of robot-guided tours, provides recommendations for the users as well as allows for a visual analysis of the data on previous tours.
Predicting Triple Scoring with Crowdsourcing-specific Features - The fiddlehead Triple Scorer at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

The Triple Scoring Task at the WSDM Cup 2017 involves the prediction of the relevance scores between persons and professions/nationalities. The ground truth of the relevance scores was obtained by counting the vote of seven crowdworkers. I confirmed that features related to task difficulty correlate with the discrepancy among crowdworkers' judgement. This means such features are useful for predicting whether a score is in the middle or not. Hence, the features were incorporated into the prediction model of the crowdsourced relevance scores. The introduced features improve the average score difference of the prediction. The final ranking of my prediction was 4th for average score difference and 12th for both accuracy and Kendall's tau.
Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
Venue: arXiv cs.LG 2017
Year: 2017

The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning nature of SVMs. Based on the insight that perturbing the SVM kernel may help in diversifying SVMs, we propose two kernel perturbation based boosting schemes where the kernel is modified in each round so as to increase the resolution of the kernel-induced Reimannian metric in the vicinity of the datapoints misclassified in the previous round. We propose a method for identifying the disjuncts in a dataset, dispelling the dependence on rule-based learning methods for identifying the disjuncts. We also present a new performance measure called Geometric Small Disjunct Index (GSDI) to quantify the performance on small disjuncts for balanced as well as class imbalanced datasets. Experimental comparison with a variety of state-of-the-art algorithms is carried out using the best classifiers of each type selected by a new approach inspired by multi-criteria decision making. The proposed method is found to outperform the contending state-of-the-art methods on different datasets (ranging from mildly imbalanced to highly imbalanced and characterized by varying number of disjuncts) in terms of three different performance indices (including the proposed GSDI).
Scalable Load Balancing in Networked Systems: Universality Properties and Stochastic Coupling Methods
Venue: arXiv cs.PF 2017
Year: 2017

We present an overview of scalable load balancing algorithms which provide favorable delay performance in large-scale systems, and yet only require minimal implementation overhead. Aimed at a broad audience, the paper starts with an introduction to the basic load balancing scenario, consisting of a single dispatcher where tasks arrive that must immediately be forwarded to one of $N$ single-server queues. A popular class of load balancing algorithms are so-called power-of-$d$ or JSQ($d$) policies, where an incoming task is assigned to a server with the shortest queue among $d$ servers selected uniformly at random. This class includes the Join-the-Shortest-Queue (JSQ) policy as a special case ($d = N$), which has strong stochastic optimality properties and yields a mean waiting time that vanishes as $N$ grows large for any fixed subcritical load. However, a nominal implementation of the JSQ policy involves a prohibitive communication burden in large-scale deployments. In contrast, a random assignment policy ($d = 1$) does not entail any communication overhead, but the mean waiting time remains constant as $N$ grows large for any fixed positive load. In order to examine the fundamental trade-off between performance and implementation overhead, we consider an asymptotic regime where $d(N)$ depends on $N$. We investigate what growth rate of $d(N)$ is required to match the performance of the JSQ policy on fluid and diffusion scale. The results demonstrate that the asymptotics for the JSQ($d(N)$) policy are insensitive to the exact growth rate of $d(N)$, as long as the latter is sufficiently fast, implying that the optimality of the JSQ policy can asymptotically be preserved while dramatically reducing the communication overhead. We additionally show how the communication overhead can be reduced yet further by the so-called Join-the-Idle-Queue scheme, leveraging memory at the dispatcher.
Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration
Venue: arXiv cs.AI 2017
Year: 2017

Discovery of an accurate causal Bayesian network structure from observational data can be useful in many areas of science. Often the discoveries are made under uncertainty, which can be expressed as probabilities. To guide the use of such discoveries, including directing further investigation, it is important that those probabilities be well-calibrated. In this paper, we introduce a novel framework to derive calibrated probabilities of causal relationships from observational data. The framework consists of three components: (1) an approximate method for generating initial probability estimates of the edge types for each pair of variables, (2) the availability of a relatively small number of the causal relationships in the network for which the truth status is known, which we call a calibration training set, and (3) a calibration method for using the approximate probability estimates and the calibration training set to generate calibrated probabilities for the many remaining pairs of variables. We also introduce a new calibration method based on a shallow neural network. Our experiments on simulated data support that the proposed approach improves the calibration of causal edge predictions. The results also support that the approach often improves the precision and recall of predictions.
WNOS: An Optimization-based Wireless Network Operating System
Venue: arXiv cs.NI 2017
Year: 2017

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware. We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.
Online Forecasting Matrix Factorization
Venue: arXiv cs.LG 2017
Year: 2017

In this paper the problem of forecasting high dimensional time series is considered. Such time series can be modeled as matrices where each column denotes a measurement. In addition, when missing values are present, low rank matrix factorization approaches are suitable for predicting future values. This paper formally defines and analyzes the forecasting problem in the online setting, i.e. where the data arrives as a stream and only a single pass is allowed. We present and analyze novel matrix factorization techniques which can learn low-dimensional embeddings effectively in an online manner. Based on these embeddings a recursive minimum mean square error estimator is derived, which learns an autoregressive model on them. Experiments with two real datasets with tens of millions of measurements show the benefits of the proposed approach.
Complete MDP convolutional codes
Venue: arXiv cs.IT 2017
Year: 2017

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond that the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. The first main result of this paper is to show the existence and genericity of complete MDP convolutional codes for all code parameters. The second main contribution is the presentation of two concrete construction techniques to obtain complete MDP convolutional codes. These constructions work for all code parameters but require that the size of the underlying base field is (sufficiently) large.