OpenRaiser/Intern-Atlas huggingface dataset:
Machine Learning, Deepest Learning: Statistical Data Assimilation Problems
Venue: arXiv cs.AI 2017
Year: 2017

We formulate a strong equivalence between machine learning, artificial intelligence methods and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in the artificial network setting is the analog of time in the data assimilation setting. Within the discussion of this equivalence we show that adding more layers (making the network deeper) is analogous to adding temporal resolution in a data assimilation framework. How one can find a candidate for the global minimum of the cost functions in the machine learning context using a method from data assimilation is discussed. Calculations on simple models from each side of the equivalence are reported. Also discussed is a framework in which the time or layer label is taken to be continuous, providing a differential equation, the Euler-Lagrange equation, which shows that the problem being solved is a two point boundary value problem familiar in the discussion of variational methods. The use of continuous layers is denoted "deepest learning". These problems respect a symplectic symmetry in continuous time/layer phase space. Both Lagrangian versions and Hamiltonian versions of these problems are presented. Their well-studied implementation in a discrete time/layer, while respected the symplectic structure, is addressed. The Hamiltonian version provides a direct rationale for back propagation as a solution method for the canonical momentum.
Theory of the superposition principle for randomized connectionist representations in neural networks
Venue: arXiv cs.NE 2017
Year: 2017

To understand cognitive reasoning in the brain, it has been proposed that symbols and compositions of symbols are represented by activity patterns (vectors) in a large population of neurons. Formal models implementing this idea [Plate 2003], [Kanerva 2009], [Gayler 2003], [Eliasmith 2012] include a reversible superposition operation for representing with a single vector an entire set of symbols or an ordered sequence of symbols. If the representation space is high-dimensional, large sets of symbols can be superposed and individually retrieved. However, crosstalk noise limits the accuracy of retrieval and information capacity. To understand information processing in the brain and to design artificial neural systems for cognitive reasoning, a theory of this superposition operation is essential. Here, such a theory is presented. The superposition operations in different existing models are mapped to linear neural networks with unitary recurrent matrices, in which retrieval accuracy can be analyzed by a single equation. We show that networks representing information in superposition can achieve a channel capacity of about half a bit per neuron, a significant fraction of the total available entropy. Going beyond existing models, superposition operations with recency effects are proposed that avoid catastrophic forgetting when representing the history of infinite data streams. These novel models correspond to recurrent networks with non-unitary matrices or with nonlinear neurons, and can be analyzed and optimized with an extension of our theory.
Like trainer, like bot? Inheritance of bias in algorithmic content moderation
Venue: arXiv cs.CY 2017
Year: 2017

The internet has become a central medium through which `networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.
Transient stability guarantees for ad hoc dc microgrids
Venue: arXiv cs.SY 2017
Year: 2017

Ad hoc electrical networks are formed by connecting power sources and loads without planning the interconnection structure (topology) in advance. They are designed to be installed and operated by individual communities---without central oversight---and as a result are well-suited to addressing the lack of electricity access in rural and developing areas. However, ad hoc networks are not widely used, and a major technical challenge impeding their development (and deployment) is the difficulty of certifying network stability without a priori knowledge of the topology. We develop conditions on individual power sources and loads such that a microgrid comprised of many units will be stable. We use Brayton-Moser potential theory to develop design constraints on individual microgrid components that certify transient stability---guaranteeing that the system will return to a suitable equilibrium after load switching events. Our central result is that stability can be ensured by installing a parallel capacitor at each constant power load, and we derive an expression for the required capacitance.
PathRec: Visual Analysis of Travel Route Recommendations
Venue: arXiv cs.HC 2017
Year: 2017

We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.
Probing Multivariate Indicators for Academic Evaluation
Venue: arXiv cs.DL 2017
Year: 2017

We combine the Integrated Impact Indicator (I3) and the h-index into the I3-type framework and introduce the publication vector X = (X1, X2, X3) and the citation vector Y = (Y1, Y2, Y3) , the publication score I3X=X1+X2+X3 and the citation score I3Y=Y1+Y2+Y3, and alternative indicators based on percentile classes generated by the h-index. These multivariate indicators can be used for academic evaluation. The empirical studies show that the h-core distribution is suitable to evaluate scholars, the X1 and Y1 are applied to measure core impact power of universities, and I3X and I3Y are alternatives of journal impact factor (JIF). The multivariate indicators provide a multidimensional view of academic evaluation with using the advantages of both the h-index and I3.
OpenFlow-compliant Topology Management for SDN-enabled Information Centric Networks
Venue: arXiv cs.NI 2017
Year: 2017

Information-Centric Networking (ICN) has emerged as an interesting approach to overcome many of the limitations of legacy IP-based networks. However, the drastic changes to legacy infrastructure required to realise an ICN have significantly hindered its adoption by network operators. As a result, alternative deployment strategies are investigated, with Software-Defined Networking (SDN) arising as a solution compatible with legacy infrastructure, thus opening new possibilities for integrating ICN concepts in operators' networks. This paper discusses the seamless integration of these two architectural paradigms and suggests a scalable and dynamic network topology bootstrapping and management framework to deploy and operate ICN topologies over SDN-enabled operator networks. We describe the designed protocol and supporting mechanisms, as well as the minimum required implementation to realize this inter-operability. A proof-of-concept prototype has been implemented to validate the feasibility of the approach. Results show that topology bootstrapping time is not significantly affected by the topology size, substantially facilitating the intelligent management of an ICN-enabled network.
Pipelined Parallel FFT Architecture
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, an optimized efficient VLSI architecture of a pipeline Fast Fourier transform (FFT) processor capable of producing the reverse output order sequence is presented. Paper presents Radix-2 multipath delay architecture for FFT calculation. The implementation of FFT in hardware is very critical because for calculation of FFT number of butterfly operations i.e. number of multipliers requires due to which hardware gets increased means indirectly cost of hardware is automatically gets increased. Also multiplier operations are slow that's why it limits the speed of operation of architecture. The optimized VLSI implementation of FFT algorithm is presented in this paper. Here architecture is pipelined to optimize it and to increase the speed of operation. Also to increase the speed of operation 2 levels parallel processing is used.
Application of Fuzzy Assessing for Reliability Decision Making
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes a new fuzzy assessing procedure with application in management decision making. The proposed fuzzy approach build the membership functions for system characteristics of a standby repairable system. This method is used to extract a family of conventional crisp intervals from the fuzzy repairable system for the desired system characteristics. This can be determined with a set of nonlinear parametric programing using the membership functions. When system characteristics are governed by the membership functions, more information is provided for use by management, and because the redundant system is extended to the fuzzy environment, general repairable systems are represented more accurately and the analytic results are more useful for designers and practitioners. Also beside standby, active redundancy systems are used in many cases so this article has many practical instances. Different from other studies, our model provides, a good estimated value based on uncertain environments, a comparison discussion of using fuzzy theory and conventional method and also a comparison between parallel (active redundancy) and series system in fuzzy world when we have standby redundancy. When the membership function intervals cannot be inverted explicitly, system management or designers can specify the system characteristics of interest, perform numerical calculations, examine the corresponding α-cuts, and use this information to develop or improve system processes.
High Resilience Diverse Domain Multilevel Audio Watermarking with Adaptive Threshold
Venue: arXiv cs.MM 2017
Year: 2017

A novel diverse domain (DCT-SVD & DWT-SVD) watermarking scheme is proposed in this paper. Here, the watermark is embedded simultaneously onto the two domains. It is shown that an audio signal watermarked using this scheme has better subjective and objective quality when compared with other watermarking schemes. Also proposed are two novel watermark detection algorithms viz., AOT (Adaptively Optimised Threshold) and AOTx (AOT eXtended). The fundamental idea behind both is finding an optimum threshold for detecting a known character embedded along with the actual watermarks in a known location, with the constraint that the Bit Error Rate (BER) is minimum. This optimum threshold is used for detecting the other characters in the watermarks. This approach is shown to make the watermarking scheme less susceptible to various signal processing attacks, thus making the watermarks more robust.
Fast Compressed Self-Indexes with Deterministic Linear-Time Construction
Venue: arXiv cs.DS 2017
Year: 2017

We introduce a compressed suffix array representation that, on a text $T$ of length $n$ over an alphabet of size $σ$, can be built in $O(n)$ deterministic time, within $O(n\logσ)$ bits of working space, and counts the number of occurrences of any pattern $P$ in $T$ in time $O(|P| + \log\log_w σ)$ on a RAM machine of $w=Ω(\log n)$-bit words. This new index outperforms all the other compressed indexes that can be built in linear deterministic time, and some others. The only faster indexes can be built in linear time only in expectation, or require $Θ(n\log n)$ bits. We also show that, by using $O(n\logσ)$ bits, we can build in linear time an index that counts in time $O(|P|/\log_σn + \log n(\log\log n)^2)$, which is RAM-optimal for $w=Θ(\log n)$ and sufficiently long patterns.
ACO for Continuous Function Optimization: A Performance Analysis
Venue: arXiv cs.NE 2017
Year: 2017

The performance of the meta-heuristic algorithms often depends on their parameter settings. Appropriate tuning of the underlying parameters can drastically improve the performance of a meta-heuristic. The Ant Colony Optimization (ACO), a population based meta-heuristic algorithm inspired by the foraging behavior of the ants, is no different. Fundamentally, the ACO depends on the construction of new solutions, variable by variable basis using Gaussian sampling of the selected variables from an archive of solutions. A comprehensive performance analysis of the underlying parameters such as: selection strategy, distance measure metric and pheromone evaporation rate of the ACO suggests that the Roulette Wheel Selection strategy enhances the performance of the ACO due to its ability to provide non-uniformity and adequate diversity in the selection of a solution. On the other hand, the Squared Euclidean distance-measure metric offers better performance than other distance-measure metrics. It is observed from the analysis that the ACO is sensitive towards the evaporation rate. Experimental analysis between classical ACO and other meta-heuristic suggested that the performance of the well-tuned ACO surpasses its counterparts.
On User Pairing in NOMA Uplink
Venue: arXiv cs.IT 2017
Year: 2017

User pairing in Non-Orthogonal Multiple-Access (NOMA) uplink based on channel state information is investigated considering some predefined power allocation schemes. The base station divides the set of users into disjunct pairs and assigns the available resources to these pairs. The combinatorial problem of user pairing to achieve the maximum sum rate is analyzed in the large system limit for various scenarios, and some optimum and sub-optimum algorithms with a polynomial-time complexity are proposed. In the first scenario, $2M$ users and the base station have a single-antenna and communicate over $M$ subcarriers. The performance of optimum pairing is derived for $M\rightarrow \infty$ and shown to be superior to random pairing and orthogonal multiple access techniques. In the second setting, a novel NOMA scheme for a multi-antenna base station and single carrier communication is proposed. In this case, the users need not be aware of the pairing strategy. Furthermore, the proposed NOMA scheme is generalized to multi-antenna users. It is shown that random and optimum user pairing perform similarly in the large system limit, but optimum pairing is significantly better in finite dimensions. It is shown that the proposed NOMA scheme outperforms a previously proposed NOMA scheme with signal alignment.
A Location-Based Game for Two Generations: Teaching Mobile Technology to the Elderly with the Support of Young Volunteers
Venue: arXiv cs.HC 2017
Year: 2017

This paper presents a cooperative location-based game for the elderly with the use of tablets equipped with mobile application. The game was designed to tackle at once several crucial topics related to the issue of aging, namely the social inclusion, education in the field of modern technology, motivation for learning as well as physical activity. Mixed-aged teams consisting of two players: a junior and a senior took part in the game. The preliminary results suggest that the game can successfully address a number of issues including improving the elderly technical skills, increasing the elderly physical activity as well as positive intergenerational interaction. The paper describes the game setup in details and presents some initial data gathered during the gameplay.
Efficient Strategy Iteration for Mean Payoff in Markov Decision Processes
Venue: arXiv cs.PF 2017
Year: 2017

Markov decision processes (MDPs) are standard models for probabilistic systems with non-deterministic behaviours. Mean payoff (or long-run average reward) provides a mathematically elegant formalism to express performance related properties. Strategy iteration is one of the solution techniques applicable in this context. While in many other contexts it is the technique of choice due to advantages over e.g. value iteration, such as precision or possibility of domain-knowledge-aware initialization, it is rarely used for MDPs, since there it scales worse than value iteration. We provide several techniques that speed up strategy iteration by orders of magnitude for many MDPs, eliminating the performance disadvantage while preserving all its advantages.
Finite model reasoning over existential rules
Venue: arXiv cs.LO 2017
Year: 2017

Ontology-based query answering (OBQA) asks whether a Boolean conjunctive query is satisfied by all models of a logical theory consisting of a relational database paired with an ontology. The introduction of existential rules (i.e., Datalog rules extended with existential quantifiers in rule-heads) as a means to specify the ontology gave birth to Datalog+/-, a framework that has received increasing attention in the last decade, with focus also on decidability and finite controllability to support effective reasoning. Five basic decidable fragments have been singled out: linear, weakly-acyclic, guarded, sticky, and shy. Moreover, for all these fragments, except shy, the important property of finite controllability has been proved, ensuring that a query is satisfied by all models of the theory iff it is satisfied by all its finite models. In this paper we complete the picture by demonstrating that finite controllability of OBQA holds also for shy ontologies, and it therefore applies to all basic decidable Datalog+/- classes. To make the demonstration, we devise a general technique to facilitate the process of (dis)proving finite controllability of an arbitrary ontological fragment. This paper is under consideration for acceptance in TPLP.
Long-Term Memory Networks for Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

Question answering is an important and difficult task in the natural language processing domain, because many basic natural language processing tasks can be cast into a question answering task. Several deep neural network architectures have been developed recently, which employ memory and inference components to memorize and reason over text information, and generate answers to questions. However, a major drawback of many such models is that they are capable of only generating single-word answers. In addition, they require large amount of training data to generate accurate answers. In this paper, we introduce the Long-Term Memory Network (LTMN), which incorporates both an external memory module and a Long Short-Term Memory (LSTM) module to comprehend the input data and generate multi-word answers. The LTMN model can be trained end-to-end using back-propagation and requires minimal supervision. We test our model on two synthetic data sets (based on Facebook's bAbI data set) and the real-world Stanford question answering data set, and show that it can achieve state-of-the-art performance.
Cooperative Kernels: GPU Multitasking for Blocking Algorithms (Extended Version)
Venue: arXiv cs.PL 2017
Year: 2017

There is growing interest in accelerating irregular data-parallel algorithms on GPUs. These algorithms are typically blocking, so they require fair scheduling. But GPU programming models (e.g.\ OpenCL) do not mandate fair scheduling, and GPU schedulers are unfair in practice. Current approaches avoid this issue by exploiting scheduling quirks of today's GPUs in a manner that does not allow the GPU to be shared with other workloads (such as graphics rendering tasks). We propose cooperative kernels, an extension to the traditional GPU programming model geared towards writing blocking algorithms. Workgroups of a cooperative kernel are fairly scheduled, and multitasking is supported via a small set of language extensions through which the kernel and scheduler cooperate. We describe a prototype implementation of a cooperative kernel framework implemented in OpenCL 2.0 and evaluate our approach by porting a set of blocking GPU applications to cooperative kernels and examining their performance under multitasking. Our prototype exploits no vendor-specific hardware, driver or compiler support, thus our results provide a lower-bound on the efficiency with which cooperative kernels can be implemented in practice.
Capacity of Wireless Distributed Storage Systems with Broadcast Repair
Venue: arXiv cs.IT 2017
Year: 2017

In wireless distributed storage systems, storage nodes are connected by wireless channels, which are broadcast in nature. This paper exploits this unique feature to design an efficient repair mechanism, called broadcast repair, for wireless distributed storage systems in the presence of multiple-node failures. Due to the broadcast nature of wireless transmission, we advocate a new measure on repair performance called repair-transmission bandwidth. In contrast to repair bandwidth, which measures the average number of packets downloaded by a newcomer to replace a failed node, repair-transmission bandwidth measures the average number of packets transmitted by helper nodes per failed node. A fundamental study on the storage capacity of wireless distributed storage systems with broadcast repair is conducted by modeling the storage system as a multicast network and analyzing the minimum cut of the corresponding information flow graph. The fundamental tradeoff between storage efficiency and repair-transmission bandwidth is also obtained for functional repair. The performance of broadcast repair is compared both analytically and numerically with that of cooperative repair, the basic repair method for wired distributed storage systems with multiple-node failures. While cooperative repair is based on the idea of allowing newcomers to exchange packets, broadcast repair is based on the idea of allowing a helper to broadcast packets to all newcomers simultaneously. We show that broadcast repair outperforms cooperative repair, offering a better tradeoff between storage efficiency and repair-transmission bandwidth.