OpenRaiser/Intern-Atlas huggingface dataset:
An Educational Fuzzy-based Control platform using LEGO Robots
Venue: arXiv cs.RO 2014
Year: 2014

Fuzzy controllers have gained popularity in the past few decades with successful implementations in many fields that have enabled designers to control complex systems through linguistic-based rules in contrast to traditional methods. This paper presents an educational platform based on LEGO\c{opyright} NXT to assist the learning of fuzzy logic control principles at undergraduate level by providing a simple and easy-to-follow teaching setup. The proposed fuzzy control study aims to accompany students to the learning of fuzzy control fundamentals by building hands-on robotic experiments. The proposed educational platform has been successfully applied to several undergraduate courses within the Electronics Department in the University of Guadalajara. The description of robotic experiments and the evaluation of their impact in the student performance are both provided in the paper.
Trading off Worst and Expected Cost in Decision Tree Problems and a Value Dependent Model
Venue: arXiv cs.DS 2014
Year: 2014

We study the problem of evaluating a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function. Reading the value of a variable is done at the expense of some cost, and the goal is to design a strategy (decision tree) for evaluating the function incurring as little cost as possible in the worst case or in expectation (according to a prior distribution on the possible variables assignments). Except for particular cases of the problem, in general, only the minimization of one of these two measures is addressed in the literature. However, there are instances of the problem for which the minimization of one measure leads to a strategy with a high cost with respect to the other measure (even exponentially bigger than the optimal). We provide a new construction which can guarantee a trade-off between the two criteria. More precisely, given a decision tree guaranteeing expected cost $E$ and a decision tree guaranteeing worst cost $W$ our method can guarantee for any chosen trade-off value $ρ$ to produce a decision tree whose worst cost is $(1 + ρ)W$ and whose expected cost is $(1 + \frac{1}ρ)E.$ These bounds are improved for the relevant case of uniform testing costs. Motivated by applications, we also study a variant of the problem where the cost of reading a variable depends on the variable's value. We provide an $O(\log n)$ approximation algorithm for the minimization of the worst cost measure, which is best possible under the assumption $P \neq NP$.
Distributed Versioned Object Storage -- Alternatives at the OSD layer (Poster Extended Abstract)
Venue: arXiv cs.DC 2014
Year: 2014

The ability to store multiple versions of a data item is a powerful primitive that has had a wide variety of uses: relational databases, transactional memory, version control systems, to name a few. However, each implementation uses a very particular form of versioning that is customized to the domain in question and hidden away from the user. In our going project, we are reviewing and analyzing multiple uses of versioning in distinct domains, with the goal of identifying the basic components required to provide a generic distributed multiversioning object storage service, and define how these can be customized in order to serve distinct needs. With this primitive, new services can leverage multiversioning to ease development and provide specific consistency guarantees that address particular use cases. This work presents early results that quantify the trade-offs in implementing versioning at the local storage layer.
Neural tuning size is a key factor underlying holistic face processing
Venue: arXiv cs.AI 2014
Year: 2014

Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person's life, and face recognition is crucial for daily social interaction. Faces are also unlike any other stimulus class in terms of certain physical stimulus characteristics. Furthermore, faces have been empirically found to elicit certain characteristic behavioral phenomena, which are widely held to be evidence of "holistic" processing of faces. However, little is known about the neural mechanisms underlying such holistic face processing. In other words, for the processing of faces by the primate visual system, the input and output characteristics are relatively well known, but the internal neural computations are not. The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects. In this computational modeling work, we show that a single factor - "neural tuning size" - is able to account for three key phenomena that are characteristic of face processing, namely the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole-Part Effect (WPE). Our computational proof-of-principle provides specific neural tuning properties that correspond to the poorly-understood notion of holistic face processing, and connects these neural properties to psychophysical behavior. Overall, our work provides a unified and parsimonious theoretical account for the disparate empirical data on face-specific processing, deepening the fundamental understanding of face processing.
Optimal Resource Allocation with Semi-Bandit Feedback
Venue: arXiv cs.LG 2014
Year: 2014

We study a sequential resource allocation problem involving a fixed number of recurring jobs. At each time-step the manager should distribute available resources among the jobs in order to maximise the expected number of completed jobs. Allocating more resources to a given job increases the probability that it completes, but with a cut-off. Specifically, we assume a linear model where the probability increases linearly until it equals one, after which allocating additional resources is wasteful. We assume the difficulty of each job is unknown and present the first algorithm for this problem and prove upper and lower bounds on its regret. Despite its apparent simplicity, the problem has a rich structure: we show that an appropriate optimistic algorithm can improve its learning speed dramatically beyond the results one normally expects for similar problems as the problem becomes resource-laden.
Successive Optimization Tomlinson-Harashima Precoding Strategies for Physical-Layer Security in Wireless Networks
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose successive optimization non-linear precoding strategies for physical-layer security in wireless networks. We also investigate different precoding techniques for multi-user MIMO systems under various conditions of channel state information (CSI) between the access point and the users and the eavesdroppers. A non-linear precoding technique based on Successive Optimization Tomlinson-Harashima Precoding (SO-THP) and Simplified Generalized Matrix Inversion (S-GMI) technique is proposed along with a strategy for injecting artificial noise prior to transmission. Simulation results show that the proposed SO-THP+S-GMI precoding technique outperforms existing non-linear and linear precoding algorithms in terms of BER and secrecy rate performances.
A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition
Venue: arXiv cs.CV 2014
Year: 2014

Retrieving similar images from a large dataset based on the image content has been a very active research area and is a very challenging task. Studies have shown that retrieving similar images based on their shape is a very effective method. For this purpose a large number of methods exist in literature. The combination of more than one feature has also been investigated for this purpose and has shown promising results. In this paper a fusion based shapes recognition method has been proposed. A set of local boundary based and region based features are derived from the labeled grid based representation of the shape and are combined with a few global shape features to produce a composite shape descriptor. This composite shape descriptor is then used in a weighted ranking algorithm to find similarities among shapes from a large dataset. The experimental analysis has shown that the proposed method is powerful enough to discriminate the geometrically similar shapes from the non-similar ones.
Subword complexity and decomposition of the set of factors
Venue: arXiv cs.FL 2014
Year: 2014

In this paper we explore a new hierarchy of classes of languages and infinite words and its connection with complexity classes. Namely, we say that a language belongs to the class $L_k$ if it is a subset of the catenation of $k$ languages $S_1\cdots S_k$, where the number of words of length $n$ in each of $S_i$ is bounded by a constant. The class of infinite words whose set of factors is in $L_k$ is denoted by $W_k$. In this paper we focus on the relations between the classes $W_k$ and the subword complexity of infinite words, which is as usual defined as the number of factors of the word of length $n$. In particular, we prove that the class $W_{2}$ coincides with the class of infinite words of linear complexity. On the other hand, although the class $W_{k}$ is included in the class of words of complexity $O(n^{k-1})$, this inclusion is strict for $k> 2$.
Towards an Error Correction Memory to Enhance Technical Texts Authoring in LELIE
Venue: arXiv cs.CL 2014
Year: 2014

In this paper, we investigate and experiment the notion of error correction memory applied to error correction in technical texts. The main purpose is to induce relatively generic correction patterns associated with more contextual correction recommendations, based on previously memorized and analyzed corrections. The notion of error correction memory is developed within the framework of the LELIE project and illustrated on the case of fuzzy lexical items, which is a major problem in technical texts.
CTL+FO Verification as Constraint Solving
Venue: arXiv cs.LO 2014
Year: 2014

Expressing program correctness often requires relating program data throughout (different branches of) an execution. Such properties can be represented using CTL+FO, a logic that allows mixing temporal and first-order quantification. Verifying that a program satisfies a CTL+FO property is a challenging problem that requires both temporal and data reasoning. Temporal quantifiers require discovery of invariants and ranking functions, while first-order quantifiers demand instantiation techniques. In this paper, we present a constraint-based method for proving CTL+FO properties automatically. Our method makes the interplay between the temporal and first-order quantification explicit in a constraint encoding that combines recursion and existential quantification. By integrating this constraint encoding with an off-the-shelf solver we obtain an automatic verifier for CTL+FO.
Community-driven reviewing and validation of publications
Venue: arXiv cs.DL 2014
Year: 2014

In this report, we share our practical experience on crowdsourcing evaluation of research artifacts and reviewing of publications since 2008. We also briefly discuss encountered problems including reproducibility of experimental results and possible solutions.
Embedded Controlled Languages
Venue: arXiv cs.CL 2014
Year: 2014

Inspired by embedded programming languages, an embedded CNL (controlled natural language) is a proper fragment of an entire natural language (its host language), but it has a parser that recognizes the entire host language. This makes it possible to process out-of-CNL input and give useful feedback to users, instead of just reporting syntax errors. This extended abstract explains the main concepts of embedded CNL implementation in GF (Grammatical Framework), with examples from machine translation and some other ongoing work.
Automatic Channel Fault Detection and Diagnosis System for a Small Animal APD-Based Digital PET Scanner
Venue: arXiv cs.AI 2014
Year: 2014

Fault detection and diagnosis is critical to many applications in order to ensure proper operation and performance over time. Positron emission tomography (PET) systems that require regular calibrations by qualified scanner operators are good candidates for such continuous improvements. Furthermore, for scanners employing one-to-one coupling of crystals to photodetectors to achieve enhanced spatial resolution and contrast, the calibration task is even more daunting because of the large number of independent channels involved. To cope with the additional complexity of the calibration and quality control procedures of these scanners, an intelligent system (IS) was designed to perform fault detection and diagnosis (FDD) of malfunctioning channels. The IS can be broken down into four hierarchical modules: parameter extraction, channel fault detection, fault prioritization and diagnosis. Of these modules, the first two have previously been reported and this paper focuses on fault prioritization and diagnosis. The purpose of the fault prioritization module is to help the operator to zero in on the faults that need immediate attention. The fault diagnosis module will then identify the causes of the malfunction and propose an explanation of the reasons that lead to the diagnosis. The FDD system was implemented on a LabPET avalanche photodiode (APD)-based digital PET scanner. Experiments demonstrated a FDD Sensitivity of 99.3 % (with a 95% confidence interval (CI) of: [98.7, 99.9]) for major faults. Globally, the Balanced Accuracy of the diagnosis for varying fault severities is 92 %. This suggests the IS can greatly benefit the operators in their maintenance task.
Band-Sweeping M-ary PSK (BS-M-PSK) Modulation and Transceiver Design
Venue: arXiv cs.NI 2014
Year: 2014

Channel Estimation is a major problem encountered by receiver designers for wireless communications systems. The fading channels encountered by the system are usually time variant for a mobile receiver. Besides, the frequency response of the channel is frequency selective for urban environments where the delay spread is quite large compared to the symbol duration. Estimating the channel is essential for equalizing the received data and removing the Inter-Symbol Interference (ISI) resulting from the dispersive channel. Hence, conventional transceivers insert pilot symbols of known values and detect the changes in it in order to deduce the channel response. Because these pilots carry no information, the throughput of the system is reduced. A Novel modulation scheme is presented in this work. The technique depends on using a carrier signal that has no fixed frequency, the carrier tone sweeps the band dedicated for transmission and detects the transfer function gain within the band. A carrier signal that is Frequency Modulated (FM) by a periodic ramp signal becomes Amplitude Modulated (AM) by the channel transfer function, and thus, the receiver obtains an estimate for the channel response without using pilots that decrease the systems throughput or data rate. The carrier signal itself acts as a dynamic frequency domain pilot. The technique only works for constant energy systems, and thus it is applied to PSK transceivers. Mathematical formulation, transceiver design and performance analysis of the proposed modulation technique are presented.
Lifted Tree-Reweighted Variational Inference
Venue: arXiv cs.AI 2014
Year: 2014

We analyze variational inference for highly symmetric graphical models such as those arising from first-order probabilistic models. We first show that for these graphical models, the tree-reweighted variational objective lends itself to a compact lifted formulation which can be solved much more efficiently than the standard TRW formulation for the ground graphical model. Compared to earlier work on lifted belief propagation, our formulation leads to a convex optimization problem for lifted marginal inference and provides an upper bound on the partition function. We provide two approaches for improving the lifted TRW upper bound. The first is a method for efficiently computing maximum spanning trees in highly symmetric graphs, which can be used to optimize the TRW edge appearance probabilities. The second is a method for tightening the relaxation of the marginal polytope using lifted cycle inequalities and novel exchangeable cluster consistency constraints.
Distributed Scheduling Algorithms for Crosspoint-Buffered Switches
Venue: arXiv cs.NI 2014
Year: 2014

Given the rapid increase in traffic, greater demands have been put on high-speed switching systems. Such systems have to simultaneously meet several constraints, e.g., high throughput, low delay and low complexity. This makes it challenging to design an efficient scheduling algorithm, and has consequently drawn considerable research interest. However, previous results either cannot provide a $100\%$ throughput guarantee without a speedup, or require a complex centralized scheduler. In this paper, we design a {\it distributed} $100 \%$ throughput algorithm for crosspoint buffered switches, called DISQUO, with very limited message passing. We prove that DISQUO can achieve $100\%$ throughput for any admissible Bernoulli traffic, with a low time complexity of $O(1)$ per port. To the best of our knowledge, it is the first distributed algorithm that can provide a $100\%$ throughput for a crosspoint buffered switch.
Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams
Venue: arXiv cs.CV 2014
Year: 2014

Learning object detectors requires massive amounts of labeled training samples from the specific data source of interest. This is impractical when dealing with many different sources (e.g., in camera networks), or constantly changing ones such as mobile cameras (e.g., in robotics or driving assistant systems). In this paper, we address the problem of self-learning detectors in an autonomous manner, i.e. (i) detectors continuously updating themselves to efficiently adapt to streaming data sources (contrary to transductive algorithms), (ii) without any labeled data strongly related to the target data stream (contrary to self-paced learning), and (iii) without manual intervention to set and update hyper-parameters. To that end, we propose an unsupervised, on-line, and self-tuning learning algorithm to optimize a multi-task learning convex objective. Our method uses confident but laconic oracles (high-precision but low-recall off-the-shelf generic detectors), and exploits the structure of the problem to jointly learn on-line an ensemble of instance-level trackers, from which we derive an adapted category-level object detector. Our approach is validated on real-world publicly available video object datasets.
State of Büchi Complementation
Venue: arXiv cs.FL 2014
Year: 2014

Complementation of Büchi automata has been studied for over five decades since the formalism was introduced in 1960. Known complementation constructions can be classified into Ramsey-based, determinization-based, rank-based, and slice-based approaches. Regarding the performance of these approaches, there have been several complexity analyses but very few experimental results. What especially lacks is a comparative experiment on all of the four approaches to see how they perform in practice. In this paper, we review the four approaches, propose several optimization heuristics, and perform comparative experimentation on four representative constructions that are considered the most efficient in each approach. The experimental results show that (1) the determinization-based Safra-Piterman construction outperforms the other three in producing smaller complements and finishing more tasks in the allocated time and (2) the proposed heuristics substantially improve the Safra-Piterman and the slice-based constructions.
An Efficient Synchronous Static Memory design for Embedded System
Venue: arXiv cs.AR 2014
Year: 2014

Custom memory organization are challenging task in the area of VLSI design. This study aims to design high speed and low power consumption memory for embedded system. Synchronous SRAM has been proposed and analyzed using various simulators. Xilinx simulator simulates the Synchronous SRAM memories which can perform efficient read/write capability for embedded systems. Xinix tool also provide the access time that required selecting a word and reading it. Synchronous Static RAM which has easily read /writes capability and performs scheduled read /writes operation in efficient manner.
Comparative Study of MAC Layer Protocols in Wireless Sensor Networks: A Survey
Venue: arXiv cs.NI 2014
Year: 2014

Wireless Sensor Networks (WSN) is the collection of many small size low cost, battery operated sensor nodes distributed over the targeted region to collect the information of interest. We can say these networks can be a fruitful solution for many applications such as target tracking, intrusion detection etc. Whenever we are talking about MAC layer protocols we need to give stress on energy efficiency factor.
Typed Hilbert Epsilon Operators and the Semantics of Determiner Phrases (Invited Lecture)
Venue: arXiv cs.CL 2014
Year: 2014

The semantics of determiner phrases, be they definite de- scriptions, indefinite descriptions or quantified noun phrases, is often as- sumed to be a fully solved question: common nouns are properties, and determiners are generalised quantifiers that apply to two predicates: the property corresponding to the common noun and the one corresponding to the verb phrase. We first present a criticism of this standard view. Firstly, the semantics of determiners does not follow the syntactical structure of the sentence. Secondly the standard interpretation of the indefinite article cannot ac- count for nominal sentences. Thirdly, the standard view misses the linguis- tic asymmetry between the two properties of a generalised quantifier. In the sequel, we propose a treatment of determiners and quantifiers as Hilbert terms in a richly typed system that we initially developed for lexical semantics, using a many sorted logic for semantical representations. We present this semantical framework called the Montagovian generative lexicon and show how these terms better match the syntactical structure and avoid the aforementioned problems of the standard approach. Hilbert terms rather differ from choice functions in that there is one polymorphic operator and not one operator per formula. They also open an intriguing connection between the logic for meaning assembly, the typed lambda calculus handling compositionality and the many-sorted logic for semantical representations. Furthermore epsilon terms naturally introduce type-judgements and confirm the claim that type judgment are a form of presupposition.
Performance Evaluation of Incremental K-means Clustering Algorithm
Venue: arXiv cs.IR 2014
Year: 2014

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and incremental K-means clustering using that particular database. It also evaluates that the particular point of change in the database upto which incremental K-means clustering performs much better than the existing K-means clustering. That particular point of change in the database is known as "Threshold value" or "% delta change in the database". This paper also defines the basic methodology for the incremental K-means clustering algorithm.