OpenRaiser/Intern-Atlas huggingface dataset:
Retrieval of multimedia stimuli with semantic and emotional cues: Suggestions from a controlled study
Venue: arXiv cs.HC 2017
Year: 2017

The ability to efficiently search pictures with annotated semantics and emotion is an important problem for Human-Computer Interaction with considerable interdisciplinary significance. Accuracy and speed of the multimedia retrieval process depends on the chosen metadata annotation model. The quality of such multifaceted retrieval is opposed to the potential complexity of data setup procedures and development of multimedia annotations. Additionally, a recent study has shown that databases of emotionally annotated multimedia are still being predominately searched manually which highlights the need to study this retrieval modality. To this regard we present a study with N = 75 participants aimed to evaluate the influence of keywords and dimensional emotions in manual retrieval of pictures. The study showed that if the multimedia database is comparatively small emotional annotations are sufficient to achieve a fast retrieval despite comparatively lesser overall accuracy. In a larger dataset semantic annotations became necessary for efficient retrieval although they contributed to a slower beginning of the search process. The experiment was performed in a controlled environment with a team of psychology experts. The results were statistically consistent with validates measures of the participants' perceptual speed.
Comparing affective responses to standardized pictures and videos: A study report
Venue: arXiv cs.HC 2017
Year: 2017

Multimedia documents such as text, images, sounds or videos elicit emotional responses of different polarity and intensity in exposed human subjects. These stimuli are stored in affective multimedia databases. The problem of emotion processing is an important issue in Human-Computer Interaction and different interdisciplinary studies particularly those related to psychology and neuroscience. Accurate prediction of users' attention and emotion has many practical applications such as the development of affective computer interfaces, multifaceted search engines, video-on-demand, Internet communication and video games. To this regard we present results of a study with N=10 participants to investigate the capability of standardized affective multimedia databases in stimulation of emotion. Each participant was exposed to picture and video stimuli with previously determined semantics and emotion. During exposure participants' physiological signals were recorded and estimated for emotion in an off-line analysis. Participants reported their emotion states after each exposure session. The a posteriori and a priori emotion values were compared. The experiment showed, among other reported results, that carefully designed video sequences induce a stronger and more accurate emotional reaction than pictures. Individual participants' differences greatly influence the intensity and polarity of experienced emotion.
On the stability and convergence of a class of consensus systems with a nonlinear input
Venue: arXiv cs.SY 2017
Year: 2017

We consider a class of consensus systems driven by a nonlinear input. Such systems arise in a class of IoT applications. Our objective in this paper is to determine conditions under which a certain partially distributed system converges to a Lur'e-like scalar system, and to provide a rigorous proof of its stability. Conditions are derived for the non-uniform convergence and stability of such a system and an example is given of a speed advisory system where such a system arises in real engineering practice.
On Convergence of Emphatic Temporal-Difference Learning
Venue: arXiv cs.LG 2017
Year: 2017

We consider emphatic temporal-difference learning algorithms for policy evaluation in discounted Markov decision processes with finite spaces. Such algorithms were recently proposed by Sutton, Mahmood, and White (2015) as an improved solution to the problem of divergence of off-policy temporal-difference learning with linear function approximation. We present in this paper the first convergence proofs for two emphatic algorithms, ETD($λ$) and ELSTD($λ$). We prove, under general off-policy conditions, the convergence in $L^1$ for ELSTD($λ$) iterates, and the almost sure convergence of the approximate value functions calculated by both algorithms using a single infinitely long trajectory. Our analysis involves new techniques with applications beyond emphatic algorithms leading, for example, to the first proof that standard TD($λ$) also converges under off-policy training for $λ$ sufficiently large.
Stabilization of uncertain systems using quantized and lossy observations and uncertain control inputs
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we consider a stabilization problem of an uncertain system in a networked control setting. Due to the network, the measurements are quantized to finite-bit signals and may be randomly lost in the communication. We study uncertain autoregressive systems whose state and input parameters vary within given intervals. We derive conditions for making the plant output to be mean square stable, characterizing limitations on data rate, packet loss probabilities, and magnitudes of uncertainty. It is shown that a specific class of nonuniform quantizers can achieve stability with a lower data rate compared with the common uniform one.
CloudCV: Large Scale Distributed Computer Vision as a Cloud Service
Venue: arXiv cs.CV 2017
Year: 2017

We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.
Counting Skolem Sequences
Venue: arXiv cs.DM 2017
Year: 2017

We compute the number of solutions to the Skolem pairings problem, S(n), and to the Langford variant of the problem, L(n). These numbers correspond to the sequences A059106, and A014552 in Sloane's Online Encyclopedia of Integer Sequences. The exact value of these numbers were known for any positive integer n < 24 for the first sequence and for any positive integer n < 27 for the second sequence. Our first contribution is computing the exact number of solutions for both sequences for any n < 30. Particularly, we report that S(24) = 102, 388, 058, 845, 620, 672. S(25) = 1, 317, 281, 759, 888, 482, 688. S(28) = 3, 532, 373, 626, 038, 214, 732, 032. S(29) = 52, 717, 585, 747, 603, 598, 276, 736. L(27) = 111, 683, 611, 098, 764, 903, 232. L(28) = 1, 607, 383, 260, 609, 382, 393, 152. Next we present a parallel tempering algorithm for approximately counting the number of pairings. We show that the error is less than one percent for known exact numbers, and obtain approximate values for S(32) ~ 2.2x10^26 , S(33) ~ 3.6x10^27, L(31) ~ 5.3x10^24, and L(32) ~ 8.8x10^25
The method of artificial systems
Venue: arXiv cs.AI 2017
Year: 2017

This document is written with the intention to describe in detail a method and means by which a computer program can reason about the world and in so doing, increase its analogue to a living system. As the literature is rife and it is apparent we, as scientists and engineers, have not found the solution, this document will attempt the solution by grounding its intellectual arguments within tenets of human cognition in Western philosophy. The result will be a characteristic description of a method to describe an artificial system analogous to that performed for a human. The approach was the substance of my Master's thesis, explored more deeply during the course of my postdoc research. It focuses primarily on context awareness and choice set within a boundary of available epistemology, which serves to describe it. Expanded upon, such a description strives to discover agreement with Kant's critique of reason to understand how it could be applied to define the architecture of its design. The intention has never been to mimic human or biological systems, rather, to understand the profoundly fundamental rules, when leveraged correctly, results in an artificial consciousness as noumenon while in keeping with the perception of it as phenomenon.
Distance-based accessibility indices
Venue: arXiv cs.SI 2017
Year: 2017

The paper attempts to develop a suitable accessibility index for networks where each link has a value such that a smaller number is preferred like distance, cost, or travel time. A measure called distance sum is characterized by three independent properties: anonymity, an appropriately chosen independence axiom, and dominance preservation, which requires that a node not far to any other is at least as accessible. We argue for the need of eliminating the independence property in certain applications. Therefore generalized distance sum, a family of accessibility indices, will be suggested. It is linear, considers the accessibility of vertices besides their distances and depends on a parameter in order to control its deviation from distance sum. Generalized distance sum is anonymous and satisfies dominance preservation if its parameter meets a sufficient condition. Two detailed examples demonstrate its ability to reflect the vulnerability of accessibility to link disruptions.
Measuring centrality by a generalization of degree
Venue: arXiv cs.SI 2017
Year: 2017

Network analysis has emerged as a key technique in communication studies, economics, geography, history and sociology, among others. A fundamental issue is how to identify key nodes, for which purpose a number of centrality measures have been developed. This paper proposes a new parametric family of centrality measures called generalized degree. It is based on the idea that a relationship to a more interconnected node contributes to centrality in a greater extent than a connection to a less central one. Generalized degree improves on degree by redistributing its sum over the network with the consideration of the global structure. Application of the measure is supported by a set of basic properties. A sufficient condition is given for generalized degree to be rank monotonic, excluding counter-intuitive changes in the centrality ranking after certain modifications of the network. The measure has a graph interpretation and can be calculated iteratively. Generalized degree is recommended to apply besides degree since it preserves most favourable attributes of degree, but better reflects the role of the nodes in the network and has an increased ability to distinguish among their importance.
The "art of trellis decoding" is fixed-parameter tractable
Venue: arXiv cs.DS 2017
Year: 2017

Given n subspaces of a finite-dimensional vector space over a fixed finite field $\mathbb F$, we wish to find a linear layout $V_1,V_2,\ldots,V_n$ of the subspaces such that $\dim((V_1+V_2+\cdots+V_i) \cap (V_{i+1}+\cdots+V_n))\le k$ for all i, such a linear layout is said to have width at most k. When restricted to 1-dimensional subspaces, this problem is equivalent to computing the trellis-width (or minimum trellis state-complexity) of a linear code in coding theory and computing the path-width of an $\mathbb F$-represented matroid in matroid theory. We present a fixed-parameter tractable algorithm to construct a linear layout of width at most k, if it exists, for input subspaces of a finite-dimensional vector space over $\mathbb F$. As corollaries, we obtain a fixed-parameter tractable algorithm to produce a path-decomposition of width at most k for an input $\mathbb F$-represented matroid of path-width at most k, and a fixed-parameter tractable algorithm to find a linear rank-decomposition of width at most k for an input graph of linear rank-width at most k. In both corollaries, no such algorithms were known previously. It was previously known that a fixed-parameter tractable algorithm exists for the decision version of the problem for matroid path-width, a theorem by Geelen, Gerards, and Whittle~(2002) implies that for each fixed finite field $\mathbb F$, there are finitely many forbidden $\mathbb F$-representable minors for the class of matroids of path-width at most k. An algorithm by Hliněný (2006) can detect a minor in an input $\mathbb F$-represented matroid of bounded branch-width. However, this indirect approach would not produce an actual path-decomposition. Our algorithm is the first one to construct such a path-decomposition and does not depend on the finiteness of forbidden minors.
Algorithmic and enumerative aspects of the Moser-Tardos distribution
Venue: arXiv cs.DM 2017
Year: 2017

Moser & Tardos have developed a powerful algorithmic approach (henceforth "MT") to the Lovasz Local Lemma (LLL); the basic operation done in MT and its variants is a search for "bad" events in a current configuration. In the initial stage of MT, the variables are set independently. We examine the distributions on these variables which arise during intermediate stages of MT. We show that these configurations have a more or less "random" form, building further on the "MT-distribution" concept of Haeupler et al. in understanding the (intermediate and) output distribution of MT. This has a variety of algorithmic applications; the most important is that bad events can be found relatively quickly, improving upon MT across the complexity spectrum: it makes some polynomial-time algorithms sub-linear (e.g., for Latin transversals, which are of basic combinatorial interest), gives lower-degree polynomial run-times in some settings, transforms certain super-polynomial-time algorithms into polynomial-time ones, and leads to Las Vegas algorithms for some coloring problems for which only Monte Carlo algorithms were known. We show that in certain conditions when the LLL condition is violated, a variant of the MT algorithm can still produce a distribution which avoids most of the bad events. We show in some cases this MT variant can run faster than the original MT algorithm itself, and develop the first-known criterion for the case of the asymmetric LLL. This can be used to find partial Latin transversals -- improving upon earlier bounds of Stein (1975) -- among other applications. We furthermore give applications in enumeration, showing that most applications (where we aim for all or most of the bad events to be avoided) have many more solutions than known before by proving that the MT-distribution has "large" min-entropy and hence that its support-size is large.
Effects of Relay Selection Strategies on the Spectral Efficiency of Wireless Systems with Half- and Full-duplex Nodes
Venue: arXiv cs.IT 2017
Year: 2017

This work proposes an analytical framework to study how relay selection strategies perform in half- and full-duplex deployments by combining renewal theory and stochastic geometry. Specifically, we assume that the network nodes -- operating in either half- or full-duplex mode -- are scattered according to a two-dimensional homogeneous Poisson point process to compute the relay selection cost by using a semi-Markov process. Our results show: ($i$) fixed relay outperforms the reactive option in either cases, ($ii$) the performance of both reactive and fixed relay strategies depends on the self-interference attenuation in full-duplex scenarios, evincing when they outperform the half-duplex option, and ($iii$) the reactive relay selection suffers from selecting relays at hop basis, while the fixed relay selection benefits most from the full-duplex communication.
A Brain-like Cognitive Process with Shared Methods
Venue: arXiv cs.AI 2017
Year: 2017

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.
Synthesis of Recursive ADT Transformations from Reusable Templates
Venue: arXiv cs.PL 2017
Year: 2017

Recent work has proposed a promising approach to improving scalability of program synthesis by allowing the user to supply a syntactic template that constrains the space of potential programs. Unfortunately, creating templates often requires nontrivial effort from the user, which impedes the usability of the synthesizer. We present a solution to this problem in the context of recursive transformations on algebraic data-types. Our approach relies on polymorphic synthesis constructs: a small but powerful extension to the language of syntactic templates, which makes it possible to define a program space in a concise and highly reusable manner, while at the same time retains the scalability benefits of conventional templates. This approach enables end-users to reuse predefined templates from a library for a wide variety of problems with little effort. The paper also describes a novel optimization that further improves the performance and scalability of the system. We evaluated the approach on a set of benchmarks that most notably includes desugaring functions for lambda calculus, which force the synthesizer to discover Church encodings for pairs and boolean operations.
Deep Recurrent Q-Learning for Partially Observable MDPs
Venue: arXiv cs.LG 2017
Year: 2017

Deep Reinforcement Learning has yielded proficient controllers for complex tasks. However, these controllers have limited memory and rely on being able to perceive the complete game screen at each decision point. To address these shortcomings, this article investigates the effects of adding recurrency to a Deep Q-Network (DQN) by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. The resulting \textit{Deep Recurrent Q-Network} (DRQN), although capable of seeing only a single frame at each timestep, successfully integrates information through time and replicates DQN's performance on standard Atari games and partially observed equivalents featuring flickering game screens. Additionally, when trained with partial observations and evaluated with incrementally more complete observations, DRQN's performance scales as a function of observability. Conversely, when trained with full observations and evaluated with partial observations, DRQN's performance degrades less than DQN's. Thus, given the same length of history, recurrency is a viable alternative to stacking a history of frames in the DQN's input layer and while recurrency confers no systematic advantage when learning to play the game, the recurrent net can better adapt at evaluation time if the quality of observations changes.
Contraction analysis of switched Filippov systems via regularization
Venue: arXiv cs.SY 2017
Year: 2017

We study incremental stability and convergence of switched (bimodal) Filippov systems via contraction analysis. In particular, by using results on regularization of switched dynamical systems, we derive sufficient conditions for convergence of any two trajectories of the Filippov system between each other within some region of interest. We then apply these conditions to the study of different classes of Filippov systems including piecewise smooth (PWS) systems, piecewise affine (PWA) systems and relay feedback systems. We show that contrary to previous approaches, our conditions allow the system to be studied in metrics other than the Euclidean norm. The theoretical results are illustrated by numerical simulations on a set of representative examples that confirm their effectiveness and ease of application.
Equivalence of the filament and overlap graphs of subtrees of limited trees
Venue: arXiv cs.DM 2017
Year: 2017

The overlap graphs of subtrees of a tree are equivalent to subtree filament graphs, the overlap graphs of subtrees of a star are cocomparability graphs, and the overlap graphs of subtrees of a caterpillar are interval filament graphs. In this paper, we show the equivalence of many more classes of subtree overlap and subtree filament graphs, and equate them to classes of complements of cochordal-mixed graphs. Our results generalize the previously known results mentioned above.
t factor: A metric for measuring impact on Twitter
Venue: arXiv cs.DL 2017
Year: 2017

Based on the definition of the well-known h index we propose a t factor for measuring the impact of publications (and other entities) on Twitter. The new index combines tweet and retweet data in a balanced way whereby retweets are seen as data reflecting the impact of initial tweets. The t factor is defined as follows: A unit (single publication, journal, researcher, research group etc.) has factor t if t of its Nt tweets have at least t retweets each and the other (Nt-t) tweets have <=t retweets each.
The impact of degree variability on connectivity properties of large networks
Venue: arXiv cs.SI 2017
Year: 2017

The goal of is to study how increased variability in the degree distribution impacts the global connectivity properties of a large network. We approach this question by modeling the network as a uniform random graph with a given degree sequence. We analyze the effect of the degree variability on the approximate size of the largest connected component using stochastic ordering techniques. A counterexample shows that a higher degree variability may lead to a larger connected component, contrary to basic intuition about branching processes. When certain extremal cases are ruled out, the higher degree variability is shown to decrease the limiting approximate size of the largest connected component.
Contradiction-tolerant process algebra with propositional signals
Venue: arXiv cs.LO 2017
Year: 2017

In a previous paper, an ACP-style process algebra was proposed in which propositions are used as the visible part of the state of processes and as state conditions under which processes may proceed. This process algebra, called ACPps, is built on classical propositional logic. In this paper, we present a version of ACPps built on a paraconsistent propositional logic which is essentially the same as CLuNs. There are many systems that would have to deal with self-contradictory states if no special measures were taken. For a number of these systems, it is conceivable that accepting self-contradictory states and dealing with them in a way based on a paraconsistent logic is an alternative to taking special measures. The presented version of ACPps can be suited for the description and analysis of systems that deal with self-contradictory states in a way based on the above-mentioned paraconsistent logic.